Application of the SCOR Model in Supply Chain Management Rolf G. Poluha
Amherst, New York
Copyright 2007 Rolf G. Poluha All rights reserved Printed in the United States of America No part of this publication may be reproduced, stored in or introduced intoa retrieval system, or transmitted, in any form, or by any means (electronic,mechanical, photocopying, recording, or otherwise), without the priorpermission of the publisher. Requests for permission should be directed
[email protected], or mailed to Permissions, Cambria Press, PO Box 350, Youngstown, New York 14174-0350. This book has been registered with the Library of Congress. Poluha, Rolf G. Application of the SCOR model in supply chain management / Rolf G. Poluha. p. cm. Includes bibliographical references and index. ISBN 978-1-934043-23-3 (alk. paper) 1. Business logistics—Management.I. Title. HD38.5.P65 2007 658.7—dc22 2007012289
For my wife, Sandra, my children, Kim, Dion and Tia,and my parents, Edeltraud and Alfred
IV. Foreword Dr. Poluha presents himself with the complex task of examining the most commonly known reference model for the Supply Chains of organizations, namely the SCOR model (Supply Chains Operations Reference Model) by the Supply-Chain Council (SCC), within the framework of an empirical examination of its value for Supply Chain analyses in and for the purpose of practical applications. In recent years, the SCOR model has achieved ever-growing importance, most importantly in the North American field of business, but also increasingly in
Asia and Europe. The origins and aims of the model are just as comprehensively discussed as its strengths and weaknesses. In addition to this, impressive examples of application from business practices are also represented. It is surprising that hardly any scientific studies are available with reference to the model and its application. In actual fact, its reference to realization and its efficiency are simply taken to be a given quantity. Dr. Poluha’s work wishes to accomplish an exploratory contribution to the scientific examination of the model. For this purpose, roughly 80 empirically gained sets of data from
companies in Europe, North America and Asia are evaluated and interpreted by means of statistics. The analysis is performed by means of special performance indicators, which form a basis for the structure of the model, and are discussed in detail. During the statistical evaluation, the method of procedure orientates itself upon a sequence, which is logically produced and incorporates statisticallydescriptive descriptions, inferencialstatistical evaluations, interpretative attempts at the explanation of nonconfirmed results, as well as comprehensive thoughts upon an aggregated level. In addition to this, an
attempt is made to submit the model to examination by means of a procedure for structural analysis. A concrete example of a theorybased empirical research project is suggested as a topic of possible and subsequent research. Hypotheses represent the basis for deliberations, which are founded upon model-specific performance indicators and are deduced from a distinctly and clearly organized depiction of the SCOR model. Conclusions to the model are drawn and potentials for improvement are extricated by the comparison of work-theses and results of the empirical examination. Innovative initiatives for
the configuration and possibilities for utilization of the SCOR model are presented and consequently discussed. The restrictions of the presently available SCOR model are elaborated, wherein a central role is played by the missing dimensions of the configuration of organization and human resources. The work offers an exploratory and interim result towards the scientific research into the SCOR model and its application. The author has benefited from his extensive experience in the consultation practice, and his ability to call upon the use of relevant data and material. Continuous studies can expand upon the results and are urgently needed
in order to scrutinize and extend the accumulated knowledge, as well as to throw light upon any outcome of the findings that may appear inconclusive. In this manner, success can be achieved in generating incentives for the maintenance and further development of the SCOR model. Prof. Dr. Dietrich Seibt University of Cologne, Cologne, Germany, April 2007
V. Preface This book is designed to provide an overview of the SCOR model in its present form, as well as the operational possibilities for analysis and measurement of the performance potential of Supply Chains. Subsequent to this, the examination design and the results of an empirical study are introduced, which were designed to make the structure of the SCOR model operational, and to subject it to a test with regards to its solidity and proximity to truth.2 In roughly the last ten years, the meaning of logistic processes in
companies has strongly increased. Whilst before, logistics still predominantly represented a vertical company function, the functional encroachment and integrated view of a supply chain have stepped into the foreground. This reflects itself, for example, in the creation of a new political economic discipline, Supply Chain Management, and the increasing anchoring of this discipline within companies. The work at hand moves the Supply Chain into the focal point. For the purpose of structuring the related Supply Chain Processes, the so-called Supply Chain Operations Reference Model
(SCOR) model is utilized and thoroughly reflected upon with regards to the possibilities pertaining to its explanation and description. The selected research goals can be summarized as follows: According to investigation conducted by the author, no academic studies have as yet been performed to specifically analyze the SCOR model structure; The SCOR model structure is presumed to be correct and the model is being used for application in projects, or for subsequent studies; The model is increasingly “popular” and used in practice,
primarily in North America and Asia, with Europe still lagging behind; There is an assumption of “correctness” simply because it is applied. However, there is no objective external assessment for the validity of the model or its internal measures; This leaves the model’s user at some risk that despite wide use, the model itself may be, at least partially, incorrect. Due to the represented situation, the book tries to find answers to the following research questions (scientific
motivation): How could the consistency, i.e., assumed alignment of the model’s performance measurements, be tested? How could the SCOR model be made operational for statistical analysis? What would be the implications of “inconsistency” (i.e., lack of assumed alignment of the model’s performance measurements)? Is “inconsistency” a SCOR problem, or one that it inherits and brings into clearer view due to its framework?
The author by no means claims to perform a generally valid and final examination of the SCOR model. It is obvious that such a goal would go beyond the scope of a single and initial research effort, if it would be possible at all. The intention is rather to perform an exploratory contribution to research, an exploratory attempt to gain initial and consequently provisional results concerning the questions and research goals listed above, and furthermore, to initiate and support subsequent research that can build upon those results.
VI. Acknowledgements The work at hand has been created in the context of an in-service dissertation project over a period of time, primarily between 2001 and 2005, at the Department of Information Systems and Information Management at the University of Cologne, Germany by Prof. Dr. Dietrich Seibt, who is presently leading the Research Department for Information Systems and Learning Processes. Since then, the content has been continuously updated and comprises the status quo of practical knowledge and academic research as at the beginning of 2007.
As the author, I have been working as a consultant and project manager with Bearing Point (formerly KPMG Consulting) and SAP AG on projects in Europe and the United States during the creation of this work. In the course of these projects, I had ample opportunity to acquire intensive and extended insights into the topics of Supply Chain Management and SCOR. The resulting experiences are reflected in the work in many ways. Special thanks for the initiation, development and execution of the work are due to my doctoral advisor, Prof. Dr. Dietrich Seibt. He literally took and fulfilled the role and acted as a real
advisor. In this sense, he always gave me the appropriate and necessary impulses at the right time with his suggestions, encouragement and constructive criticism. The second opinion was kindly adopted by Prof. Dr. Detlef Schoder, director of the Seminar for Information Systems and Information Management at the University of Cologne. The chair of the debate was Prof. Dr. Ulrich Thonemann, director of the Seminar for Supply Chain Management and Management Science at the University of Cologne. Further thanks go to Prof. Richard Welke, director of the Center for
Process Innovation at J. Mack Robinson College of Business at the Georgia State University. Based on his experiences with normative models in general, and the SCOR model in particular, he provided me with helpful references and suggestions, which have been incorporated into the work. I would also like to thank the libraries of the University of Stuttgart, Germany, North Carolina State University, Georgia Institute of Technology, and Georgia State University. They have granted me generous access to their archives, which allowed me to consider a broad range of literature from authors from both the
United States and Europe. Furthermore, Mr. Derek Hardy for his outstanding support in translating this work from German into English language. And finally, Dr. Paul Richardson and Ms.Toni Tan of Cambria Press and their team for the excellent support in publishing this book. As the motto for the book, I would like to use the guiding theme of a scientist and researcher, Karl R. Popper, whose work and insights have accompanied and inspired me over the years: Not from the beginning did the Gods reveal everything to the Mortals. But over time they
will find, seeking, the Better.3 In this sense, I wish the seeking reader that this work will help to gain some new and interesting insights. And I hope that it can initiate and contribute to extend existing knowledge and expertise. Dr. Rolf G. Poluha Atlanta, Georgia, April 2007
VII. Abbreviations Third-Party Logistics Service Provider α-error Alpha Error (Type I Error) β-error Beta-Error (Type II Error) abbrev abbreviated ABC Activity-based costing Aktiengesellschaft (Public AG Company) AGFI Adjusted-Goodness-of-Fit-Index approx. approximately ASC Adaptive Supply Chain AMOS Analysis of Moment Structures am. above-mentioned 3PL
AMR Advanced Manufacturing Research AP Asia Pacific APS Advanced Planning Systems Architecture of integrated ARIS Information Systems bm. below-mentioned bn Billion B2B Business-to-Business B2C Business-to-Consumer BC before Christ Business Information Analysis BIAIT and Integration Technique Business Information Control BICS Study
BKM Best Known Method BP Business Process BPA Business Process Analysis Bravais-Pearson Correlation BPC Coefficient BPM Business Process Management BPO Business Process Optimization BPR Business Process Reengineering BRE Business Rules Engine Balanced Scorecard BSCol Collaborative BSC Balanced Scorecard ca. circa CCC Customer-Chain Council Customer-Chain Operations
CCOR Reference Model CEO Chief Executive Officer CFO Chief Financial Officer Chap. Chapter CLM Council of Logistics Management Co. Company COGS Cost of goods sold Col. Column Corp. Corporation cp. compare CPA Certified Public Accountant Collaborative Planning, CPFR Forecasting and Replenishment CPG Consumer Packaged Goods CRM Customer Relationship
Management CSCO Chief Supply Chain Officer CSF Critical Success Factor Die Betriebswirtschaft (The DBW Business Studies) DCC Design-Chain Council Design-Chain Operations DCOR Reference Model Desc. Description df degree of freedom Diag. Diagram Diags. Diagrams Dola Day of last access DoD US Department of Defense DOS Days on stock
DP Data processing DLA US Defense Logistics Agency E-Business Electronic Business E-Commerce Electronic Commerce E- Electronic Customer Relationship CRM Management EElectronic Supply Chain SCM Management EBN Electronic Buyers News ed. editor EDI Electronic Data Interchange EMEA Europe, Middle East and Africa ERP Enterprise Resource Planning e.g. exempli gratia (for example) et al. et alii (and others)
etc. et cetera EUR Euro f degree of freedom FG Finished goods Fig. Figure Forschungsinstitut fuer fir Rationalisierung (Research Institute for Rationalization) FTE Full-Time-Equivalent FTF Face-to-face G2B Government-to-Business G2C Government-to-Consumer G2G Government-to-Government GFI Goodness-of-fit-Index Gesellschaft mit beschränkter
GmbH Haftung (Limited Liability Company) GPF Geographic Product Flow Handelsgesetzbuch (German HGB Commercial Code) HP Hewlett-Packard HRM Human Resources Management HW Hardware Handwörterbuch der HWB Betriebswirtschaft (Dictionary of Business Studies) i.e. id est (that is) ibid. ibidem (at the same place) Information and Communication ICT Technology I-C Intra-Competency
I- Inter-Competency/Performance CP Attribute I-P Intra-Performance Attribute Inc. Incorporated Integrated Supply Chain ISCM Management Program iSNG Intel Supply Network Group IS Information System IT Information Technology Kölner Integrationsmodell KIM (Cologne Integration Model) KPI Key Performance Indicator KRP Kostenrechnungspraxis LA Latin America LISREL Linear Structural Relationships
LLC Limited Liability Company Ltd. Limited Company m Million M SCOR Model group Max Maximum MCC Micro Compact Car AG MES Manufacturing Execution Systems Min Minimum MIS Management Information System Massachusetts Institute of MIT Technology MPS Master Production Schedule Maintenance, Repair and MRO Operating Equipment n number (as part)
N number (total) NA North America Network Decision Support NDST Technology No. Number Nos. Numbers non-signif. non-significant NPV Net Present Value ny no year specified NYSE New York Stock Exchange ODM Original Design Manufacturer OEM Original Equipment Manufacturer US Office of the Secretary of OSD Defense p. page
pp. pages P Probability Para. Paragraph Paras. Paragraphs P(α) Probability of Error PC Personal Computer PMProduct-MomentCorrelation Correlation PMG Performance Measurement Group Pittiglio, Rabin, Todd & PRTM McGrath q.v. quod vide (which see) Bravais Pearson Correlation R Coefficient R&D Research and Development
re. regarding ROA Return on Assets ROE Return on Equity ROI Return on Investment ROS Return on Sales s Standard deviation SA Société Anonym SAS Société par actions simplifiée SC Supply Chain SCC Supply-Chain Council Supply Chain Design SCDM Management SCM Supply Chain Management Supply Chain Operations SCOR Reference Model
SCORcard SCOR-based Supply Chain Scorecard Sect. Section SEM Structural Equation Model SFA Sales Force Automation [sic!] just as that (“same in copy”) SIG Special Interest Group signif. significant Singapore Institute of SIMTech Manufacturing Technology SKU Stock Keeping Unit SN Supply Network SNG Supply Network Group Statistical Package for Social SPSS Sciences ?(former desc.)
SPSS Statistical Product and Service Solutions (current desc.) Supplier Relationship SRM Management SV Shareholder Value SW Software Tbl. Table Transmission Control TCP/IP Protocol/Internet Protocol TQM Total Quality Management UK United Kingdom UoM Unit of Measure US United States USA United States of America USD US-Dollar
V Variation range VAR Value-added reseller VC Value Chain VCG Value Chain Group Value Chain Operations VCOR Reference Model Ver. Version VMI Vendor Managed Inventory Vol. Volume vs. versus WFM Workflow Management WIP Work in process Wirtschaftswissenschaftliches WiSt Studium (Studies of Economic Science)
X Arithmetic mean Zeitschrift für Betriebswirtschaft ZfB (Journal of Business Studies) Zeitschrift für Zfbf betriebswirtschaftliche Forschung (Journal of Business Research) 1. Chosen conventions with regards to the structure: –On the topmost level are the chapters (abbrev. as chap.): Example: Chapter A (represented in bold and large writing). –Beneath these follow the respective sections (abbrev. as sect.): Example: 1.1 (represented in bold and normal-sized writing). –Allocated to a section, there may be paragraphs (abbrev. as para.): Example: 1.1.1 (represented in bold, normal-sized writing and italics). If need be, there are at least two paragraphs allocated to one section. –Sub-paragraphs may follow beneath a paragraph:
Example: 1.1.1.1 (represented in bold, normal sized writing, italics and indented). This level represents the lowest possible level of structure, referred to, analogue to the previous level and also simplifi ed with the term para., (for example: see for this purpose para. 1.1.1.1) 2. With regards to the proximity to truth , reference is made to the criteria of truth in the sense of conformity of theoretical statements and the political economic or respective corporate reality (correspondence theory). With this, a continued comparison of theoretical statement and observed reality is assumed in a hermeneutic sense. The SCOR model makes allowances for the fact that it is, as it were, an evolutionary model, which is adapted to the (changed) reality in regular cycles (for more information concerning the correspondence theory, cp. for example (Neale, 2002) Facing Facts). 3. Cp. (Popper, 1989) Logik der Forschung (The Logic of Scientific Discovery), p. XXVI. The quote is originally from Xenophanes (570 – 474
BC), founder of the so-called Eleatic School of Philosophy, cp. (Encyclopedia of Philosophy, 2004) Xenophanes. For Karl Popper and his work cp. for instance (Magee, 1986, Karl Popper; Geier, 1994, Karl Popper; Popper, 1 9 9 5 , Objektive Erkenntnis (Objective Knowledge))
Chapter One
Objectives, methodology, approach and definition of terms “Using SCOR has become a way of life for the company, including getting the top executives together to make across-the-board decisions. (…) It costs nothing. All SCOR is is [sic!] a tool that tells you what the possible metrics are you can use to
determine how your business is doing. (…) SCOR isn’t magic. It’s a good, simple management tool, and I don’t know why everyone doesn’t use it.”1
1.1 Foundations and Objectives of the Work 1.1.1 Arrival at and objectives of the research The last ten or so years have seen a pronounced change in the level of importance that companies attach to logistical processes.2 Whereas logistics have, traditionally, been seen largely as a vertical function of organizations, the
more recent comprehensive functional and integrated view has become more widespread, particularly in the guise and the framework of a Supply Chain.3 This process may be discerned, for example, in the creation of a new management discipline, Supply Chain Management, and the increasing emphasis on this discipline within companies.4 In conjunction with this change of focus, in recent times a growing number of companies have introduced a new position, namely that of Chief Supply Chain Officer (CSCO) or Supply Chain President, who often reports directly to the Chief Executive Officer (CEO).5
This work moves the Supply Chain into the center of the discussion. The Supply Chain can, in the first instance, be represented by means of the physical flow of material. It can also be used to illustrate the underlying organization or organizational segment: procurement, for example, or sales. Finally, the representation is possible by use of Supply Chain processes: the purchasing process, for example, or the sales process. In the course of this study, the primary focus will be on the range of processes within an organization’s Supply Chain. For the purpose of the structuring of these Supply Chain processes, the so-
c a l l e d Supply Chain Operations Reference Model (SCOR) is utilized and assessed with regard to the possibilities pertaining to its clarification and description. The SCOR model6 was developed by an independent, nonprofit-orientated association,7 namely the Supply-Chain Council (SCC). The Supply-Chain Council was founded in 1996 by the business consultancy Pittiglio, Rabin, Todd & McGrath (PRTM) and Advanced Manufacturing Research (AMR), and originally consisted of 69 voluntary members.8 Membership is open to all companies and organizations that are interested in the application and further
development of modern and qualified systems and practices for the management of the Supply Chain.9 This study addresses primarily the following questions: How could the SCOR model, on the basis of model-immanent performance indicators, be transposed upon a theses model and thereby operationalized? How could this theses-transposed illustration of the SCOR model be submitted to an explorative examination based upon empirical data?
Since its inception, the SCOR model has been the subject of academic research.10 However, these studies have traditionally been based on a given structure of the model, and in turn been concerned with its applicatory uses.11 At the time of writing, however, no study has focused solely on the model structure itself. Thus, the author has chosen to address the SCOR model in theses format and test it by means of its “operational qualities.” In this sense, the study tests the structure of the model and asks whether it can be considered to be “suitable and correct” (entirely or partly). This is
particularly pertinent as, in the years since the “breakthrough” of the model’s inception, it has been greatly diffused throughout North America and Asia, although in this respect Europe currently lags slightly behind. This continuously expanding field makes such a study more necessary than ever. Having said that, the work in no way wishes to lay claim to performing a universally valid examination of the SCOR model. It is not, moreover, an attempt to examine the SCOR model as such, but to strive for a respectively compiled illustration, or model operationalization, in order to make an exploratory contribution to research. It
is, therefore, not primarily about the examination, verification, or falsification of theses, but about an exploratory attempt at gaining initial – and consequently provisional – results. A step-by-step accumulation of knowledge stands in the foreground that must, under all circumstances and even after conclusion of the work, be continued through further focused examinations built upon its findings.12 Within these parameters, however, and by use of the answers to the abovementioned research questions, it is nonetheless possible to identify potential areas for improvement, and also to make recommendations accordingly. Such
recommendations are made on the understanding that the accumulated knowledge is relative to the developed illustration of the SCOR model, whereby factors like a possible “mis-match” between theoretical model and empirical reality or the quality of the applied data can play a role.13 In addition to this, it must be borne in mind during transposition of conclusions onto the actual SCOR model that such a practice can only be carried out within certain given limitations, which will be introduced below.14
1.1.2 Methodical approach to the work The core of this study consists of an
examination15 of the SCOR model in respect of the Supply Chains of selected companies. For this purpose empirical results, accumulated via the use of quantitative questionnaires, are used. To preserve the anonymity of the clients, the results of the empirical study are presented on a neutral basis, without mentioning the names of the respective companies. However, the characteristics relevant to these companies (indications of their industry affiliations, for example) are represented, as these are germane to the study. The exploratory examination of the SCOR model is based upon the analysis
of the scientific and application-related research already conducted in the field. It is also based on the authors extensive practical experience gained during a tenure for years as a business consultant, particularly with respect to the field of Supply Chain Management, and specifically the SCOR model. The examination of the SCOR model involves a comparison of the theses developed during the course of the work and their results, accumulated by empirical examination. An evaluation, to include recommendations for the improvement of the model, appears at the end of the work. In the first stage, the study applies
scientific statistical methods in order to test the conformity of the developed model. The findings of the empirical study then provide the basis for recommendations that follow for the improvement of the SCOR model, or for Supply Chain analysis. The conclusion provides modern concepts and tools for Supply Chain formation, as well as suggestions for further research in the fields of Supply Chain Management and SCOR. The theses that are outlined, although not yet verified, within the work refer to the connections between the interval-scaled model parameters of the SCOR model.16 The verification of
the connections advocated in the hypotheses will succeed these by means of additional descriptive-statistical clarifications. Although the theses themselves are, at this stage of research in the field, often not completely verified, special attention is paid to those companies that differ from a given basic tendency in order that such deviational cases can be clarified.17 The findings are thus articulated in accordance with the submitted theses. In the findings, the material data or the correlation of the individual variables (according to the thesis in question) are primarily verified on an empirical basis.
Diag. 1-1: Research-logical course of the work19
According to Friedrichs,18 the researchlogical course of the empirical examination orientates itself upon connections to discovery, reasoning and evaluation. Interpretations – unless relevant to the statistical durability of
the hypotheses – or appraisals with regards to concrete procedural recommendations play no role as far as the findings are concerned. On the contrary, such respective conclusions are drawn later in the study. The following diagram represents, in graphical form, the methodical approach that shapes the structure of this work.
1.2 Integration of the Subject Matter into the Scientific and Empirical Discussion The methods by which companies plan, purchase, produce, and sell their
products substantially influence their position within the market. In the present-day business environment, transparency, efficiency and speed are the key factors in determining a company’s success or failure. Efficient monitoring of the procedures and processes is seen as vital if the company is to derive advantages (which apply to all aspects of its business) from using profit-effective acquisition potentials, from the reduction and outsourcing of stock, and from the improvement in customer relations arising out of a better delivery service. The continuous process of globalization in the procurement and
distribution markets, when coupled with the modern trend towards a more worldwide distribution of production locations, demands not only that a business plans and optimizes its valuegenerating processes20 and business logistic networks as a whole, but also that it develops greater levels of effective customer management. This represents a great challenge for those responsible for such areas of a company’s life because they must both realize operational improvements whilst simultaneously minimizing costs, without letting customer service suffer in the process. As there are obviously conflicting objectives in this case, the implementation methods must be
perfectly balanced and all relevant aspects included into any deliberations on these issues.21 As a result of increased globalization, many companies are confronted with the challenge of having to plan and monitor their material and information flows continuously and efficiently – from procurement, through production, and up to marketing. It is often the case, however, that marketing plans are noted for their inexactness and subsequent lack of verification as to their implementation ability, and that as a result companies are increasingly forced into overstocking and costintensive bottleneck monitoring.22
Production and procurement can often not react flexibly enough to fluctuations in demand, resulting in an increasing inability to meet scheduled delivery times and, often, the accumulation of cost-intensive stockpiles of goods or resources. It is now acknowledged by companies involved in such fields that success or failure is regulated by the “weakest link in the Supply Chain.” Gutenberg’s Balancing Law of Planning (Ausgleichsgesetz der Planung)23 is of great importance in this capacity. Although originally formulated around the internal structure of a business, this law can also be applied to the complete
supply chain and therefore necessitates increased cooperation between those companies involved in order to displace the supply chain bottlenecks. Consequently, companies see themselves facing the following questions:24 By which means, keeping in mind deadlines, cost and level of service,25 can a constant balance be created between the supply-side (stock, production and transport capacity, etc.), and the demandside? In which way, and at which point in time, must the supply-side be respectively enlarged or reduced?
Leading companies proactively occupy themselves with these questions and integrate their partners more closely into the planning process. Their aim is to constantly increase the continuity and transparency of all the business processes and to simultaneously recognize and remedy bottlenecks and missed deadlines. The main challenge facing a company in this process is to secure an economically favorable and flexible integration of the business partners’ data (suppliers, logistic services, sales branches etc.) into their own marketing, procurement, production, distribution and transport planning, and thereby create unified and
consistent plans.26 These topics and requirements are at present being intensively discussed in economic science as well as in business practice. One question that has become associated with this debate is that of whether Supply Chain Management (SCM) is just a fashion or is poised to developed further into a recognized management concept in its own right.27 This study cannot offer a definitive answer to such a question at this time: a number of scientific contributions already exist for that purpose.28 Rather, this study stands as an empirical contribution that answers the question
how companies’ Supply Chains can be analyzed and optimized through an ongoing process of study.
1.3 Representation of the Supply Chain as a Business Reference System In order to be able to observe the Supply Chain (SC)29 more closely, it is necessary to establish a definition of the term. In literature a multitude of definitions can be found which are described in this chapter. As the term was mainly developed and disseminated in the USA, its characterization is strongly influenced by authors from the Anglo-Saxon speaking regions.
An understanding of the Supply Chain is important for those involved in the implementation of procedural improvements to the Supply Chain. The Supply Chain can be widely or narrowly defined, depending upon the perspective. At present, the tendency is for a wider definition, as seen at the conference carried out by the Council of Logistics Management (CLM)30 in the year 2002 and integrated into the definition advocated by the CLM. In accordance with this the Supply Chain can now be described as the total of all activities, procedures, etc., that are applied to a product from beginning to end.31
In this sense the Supply Chain may be seen as beginning, for example, in the mining of ore, the extraction of raw materials from the ground, or the planting of seeds. The chain continues through a multitude of transformations and distributions, which deliver the product to the end user. It ends with the conclusive disposal of the product and its residues. In line with this understanding, the Supply Chain represents more than just the physical movement of the goods: it also takes into account movements of information, finance, and knowledge. It can therefore be taken from this that the Supply Chain comprises all
procedures within the product life cycle,32 including the physical, informative, financial and knowledgebased procedures for the movement of products and services (from the supplier right up to end consumers).33 On the process side, a Supply Chain consists of all organizations included in the design, production and delivery of a product to the market.34
1.3.1 Definition of the Supply Chain The definitions that can be found in academic and business-orientated literature include the whole span of perspectives – from the very narrow to
the very wide demarcation of the term. Although the Supply Chain’s spectrum has expanded in the past years, more narrowly demarcated or emphasized definitions can still be found today. An overview of the various approaches to the definition of the Supply Chain follows. An appreciation of the differences involved in this field is a necessary precondition for the full comprehension of Supply Chain Management. A key decision in this field is that of the side from which the Supply Chain is viewed, i.e., from the customer or the supplier side. In the supplier-centric approach, the Supply Chain represents a
network of suppliers which manufacture goods. These goods are traded amongst each other as well as with additional parties. The goods originate with the supplier and arrive finally with the target customer. Between these start and end points, they often run through distributors and processing companies.35 This supply-side view is countered by the customer-centric approach, which assumes that a Supply Chain consists of all necessary stages that are – directly or indirectly – involved in fulfilling a customer requisition. In this specific case, the focus is upon the transportation businesses, warehouses, dealers and the customers in question.36
The combination of both approaches leads to a superior definition, whereby the Supply Chain is seen as an agreement between companies in order to provide the market with products and services.37 Furthermore, this comprehensive point of view can be raised to a global level and placed in the context of a global association of organizations. In this sense a Supply Chain represents a global network of organizations, working together to improve the flow of material and information between supplier and customer. The operational objectives are the lowest possible cost and the highest possible speed. The ultimate objective is the satisfaction of
customer needs. The information flow runs, as it were, forward-facing (i.e., from customer to supplier); the material flow, on the other hand, runs backwardfacing (i.e., from supplier to customer). Furthermore, information flows from customers to dealers, manufacturers, logistic services and raw material suppliers. Material flows from raw material suppliers or component suppliers to customers. The common trait of both the material and information flows is that the process amongst the Supply Chain partners should be coordinated, and this also implies that some degree of forward and backward coordination isrequired.38
To take this line of thought further, the approach can also be differentiated from the supply and demand aspect. A Supply Chain has the purpose of transferring products and services from the suppliers up to the consumer (for example organizations, stores, individual people). The actions within the Supply Chain change depending upon the product and type of demand, but it is possible to identify a number of generally valid value-creating processes as follows:39 Make:Manufacture of material or building components, etc. Combine:Assemble, package, etc.
Move:Distribute, collect, etc. Store:Stock, trade, etc. Customize:Install, configure, etc. The demand-side Supply Chain can be described as the demand chain focusing upon market demand towards suppliers. It becomes clear during the explicit consideration of the demand that a respective Supply Chain is customer driven.40 The term pull concept is occasionally used to describe this process of government by a form of “demand vacuum.”41 In exactly the same way as a supplier can have a multitude of Supply
Chains to monitor, the supplier’s customer also has limited demand chains that can be individually analyzed. The demand chain translates a customer’s objective into information that the supplier can use as an instruction to act upon and is, in this sense, determined by a decision process. The four general stages by which the decision process is characterized begins with the definition of the purpose (define purpose). At the second stage planning takes place (plan), for example in the form of a category plan. The third stage comprises the management of consumption and requirements (manage consumption and requirements); for example, within the field of stock management. Buying
transactions (purchase transactions) are at the center of the last stage; for example, a call-off order as part of a master agreement.42 An alternative approach is the organization-orientated view of the Supply Chain. In accordance with this, the Supply Chain represents an alignment of processes within a company, as well as with other companies (inter- and intra-business processes), which produce goods and services and deliver them to the customer. It comprises actions such as, for instance, procurement of material, production planning and distribution. Purchasing, production, inventory
management, stock-keeping and transport are usually regarded as part of the SC organization. Marketing, sales, financial areas and strategic planning, on the other hand, are not considered to be a part of the SC organization. Product development, marketing plans, order registration, customer service and company accounting are not clearly assigned. They clearly belong to the SC processes, but are only seldom part of the SC organization.43 The combination of the process and organization-orientated points of view can be summarized as follows: “The supply chain includes the
organizations and processes for the acquisition, storage, and sale of raw materials, intermediate products, and finished products. Supply chain product flow is linked by physical, monetary, and information flows.”44 The present view of a Supply Chain is largely one-dimensional, but a further differentiation can take place with the aid of its layered arrangement of various levels. In accordance with this view, a Supply Chain is an alignment of suppliers and customers, beginning at one end with raw material and ending at the other with the delivery of a
completed product to an end customer. The Supply Chain can be dissected into several layers. A single-level Supply Chain purely illustrates the direct customer and supplier, whereas a multi-level Supply Chain can reach as far as the raw materials on the one hand and the disposal of worn-out finished goods on the other. The complexity increases proportionately with the increase in the number of levels. Most companies, therefore, have neither the means nor the resources to monitor the Supply Chain network and because of this restrict themselves to one or two levels. In addition to the levels, the
components flowing through the Supply Chain must be considered and illustrated: Goods and services in one direction, payments in the other, and information in both directions.45 The interpretation of a bidirectional information flow advocated here represents the reality far better than the multi-directional flow of information described above. Actual concepts, like that of Collaborative Planning, Forecasting and Replenishment (CPFR), for example, build upon a flow of information in both directions.46 A further criterion that can be included into the Supply Chain’s description is the decision aspect. A
multitude of decisions must be reached within a Supply Chain showing a large number of SC partners. These decisions refer, for example, to investments, strategies for coordination and cooperation with partners, customer service, and profit-maximizing strategies. Some of these decisions have far-reaching influences upon the Supply Chain and are of a complex nature because with increasing market dynamics, a constantly higher level of uncertainty exists regarding the effects, and a multitude of variables must be taken into account. The Supply Chain resulting from this can be described as a market-driven Supply Chain.47
The inclusion of the respective company’s functional areas and the main activities connected therewith lead to a functional description of the Supply Chain. The following five main activities can be identified with regards to a company’s functional areas:48 Purchasing: includes the tasks of purchasing raw materials, components, resources and services. Manufacturing: refers to the manufacture of products or services in addition to resource maintenance and repair, as well as the training of co-workers. It may be
summarized therefore as the implementation of all tasks necessary for production. Movement: consists of the transportation of materials and personnel inside and outside the Supply Chain. Storage: refers to those products which find themselves being processed (work in process, WIP) in addition to raw materials, whilst these await transportation or reformation, and the finished goods before these are sent to the customer. Sale: comprises all marketorientated activities, including marketing and sales.
The step from a static to a dynamic view of the Supply Chain is achieved by including activities pertinent to the functional area. Up to now, the previously mentioned flows of material, payments and information have been regarded as linear and coupled. Due to the introduction of the internet49 and the acceleration of information flows associated with it, these flows have (to a certain degree) become uncoupled from one another. Information now flows in a predominantly independent manner from the respective flows of material and payments. As a result of this, Supply Chains in
the traditional sense have further developed themselves into networked Supply Chains, which network the SC partners together with the best suited components, technologies and customer services. SC networks are additionally dynamic in nature and make it possible for SC partners to be included or excluded according to certain criteria; for example, technological advantages, product life cycles and customer preferences.50 These dynamic Supply Chains promote, amongst other things, the development of new business strategies. Within this framework, focus is placed upon new methods of customer
integration, outsourcing of business functions, cooperation with customers and suppliers51 and inventory 52 management. By these means traditional linear Supply Chains are converted into dynamic SC networks.53 A further integral SC element is represented by its value generative character (value-add).54 According to this, the Supply Chain is a network of organizations, which are associated with each other in a forwardand backward-facing manner, in order to generate value within diverse processes and activities. This value is reflected in products and services which are delivered to the end consumer.55
Normann and Ramirez describe the connection between value generation and the respective business and Supply Chain strategy as follows: “Strategy is the art of creating value. It provides the intellectual framework, conceptual models, and governing ideas that allow a company’s managers to identify opportunities for bringing value to customers and for delivering that value at a profit. In this respect, strategy is the way a company defines its business (…).”56
With consideration given to value generation, aspects of ?information technology can finally be integrated into the operationalization of a Supply Chain. The result is a so-called Value Chain (VC). Accordingly, a Value Chain represents a business outline that uses digital SC concepts to ensure not only customer satisfaction,57 but also profitability.58 The VC focuses mainly upon the competitive factors of time and flexibility59 and has the primary objective of being able to react quickly and flexibly to changing customer requirements. The special characteristics of a
value chain pronounce a distinct difference from a traditional business outline and may be described as follows:60 Customer-aligned Collaborative and systematic Agile and scaleable Fast material, payment and information flows (fast flow) Upheld by Information Technology (IT) (digital). A Value Chain is hence positioned above the concept of the Supply Chain. It assumes the reality of a Supply Chain and focuses explicitly upon the
generation of value for all involved parties (the company itself, customers and suppliers). It still represents, to a greater extent, a static system, but the (bilateral) information flow is often supported by modern IT systems.61 The approaches outlined above assume physical partners to be participants in the Supply Chain. Virtual value nets 62 originate due to the increasing usage of virtualization. The linear, physical Value Chain model has adjusted itself accordingly. This reformation reaches above the physical boundaries of a marketplace and into the global and fast-developing digital economy. With the introduction of the
internet and the increasing role of technology as a catalyst of new strategies, companies find themselves confronted with new strategic requirements and management 63 problems. The real-time information exchange and the interactive performance potential of the internet have changed the business environment to the point where it is now not only customers, but also other companies that have access to alternative products and services. New distribution channels are establishing themselves and leading to opportunities for optimizing value generation and simultaneously enabling interactions to
become more transparent. The winners in these virtual value chains will be those who have faster access to information and resources, and can at the same time extract from this the suitable competitive and SC strategies.64 Because of this, the traditional physical alliance has developed itself into a virtual alliance, in which there are an increased number of possible SC partners who exchange information. The virtual Value Chain represents an alignment of market partners who work together as a unit, whereby each of them contributes, so to speak, a component of the value. The value-donating activities extend outwards from the supply-side in
the form of the raw materials, incoming logistics and production procedures, right up to the demand-side in the form of outgoing logistics, marketing and sales.65 Michael Dell, founder of the Dell company,66 describes a virtually integrated organization as an organization that is not networked by physical objects of wealth, but by information67 – or, alternatively expressed, by information technology (IT). The Supply Chain is, therefore, a component of a superior Electronic Business (E-Business) concept. This
association is illustrated by definition developed by Seibt:
the
An organization practices Electronic Business, if several or all business processes within the organization between itself and its business partners between itself and a third party (e.g., authorities) are totally or partially realized via the assignment of electronic communication networks and are supported by
Information Communication (ICT) systems.68
and Technology
In the interests of clarity, it is important to differentiate here between E-Business and the related concept of Electronic Commerce (E-Commerce) that generally denotes the electronic execution of business transactions.69 The part of the E-Business concept relevant to the Supply Chain is also often referred to as Electronic Supply Chain Management (E-SCM).70 Ross describes E-SCM as the tactical and strategic components of the business strategy that aim to combine the
common production capacities and resources of overlapping SC systems by means of internet technology, with the objective of creating customer 71 advantages. The main difference from the “traditional” SCM or respective value chain management is thus seen in the fact that information technology is applied in the process in order to support the optimal completion of the flow of goods and information.72
1.3.2 Categories of Supply Chains The definitions included above focus on various Supply Chain features or characteristics. Building upon these, however, a variety of extra
categorizations should also be included in the equation, and these are summarized below. One possibility lies in the question of whether the Supply Chain is chiefly aimed at the product or the end customer. Ayers suggests the following differentiation in this context:73 Product-centric Supply Chains are Supply Chains tailored in accordance with special products. One or more product offers can result from this, which constitute a separate Supply Chain. Customer-centric Supply Chains are Supply Chains tailored in
accordance with special market segments. One or more Supply Chains may result from this, which are organized around market segments. A further difference can be identified by looking closely at the business strategy, and requirements associated with it:74 Arm’s length, open competition : competitive offers and tender action. The emphasis here is upon intense trading. Commodity trading: independent marketing, forced by the necessity
of the business agreement. The emphasis here is upon monitoring the deviational range of commodity goods. Partnering for customer delight: openness, trust and splitting of the work to be carried out. The emphasis here is upon supplier performance extended towards the customer (forward-facing in the Supply Chain) and the value aspect extended from the customer towards the supplier (backwardsfacing in the Supply Chain). From suppliers’ suppliers to customer’s customers : here there is a linkage of all market participants within a horizontal
Supply Chain. The emphasis is upon seamless delivery, optimization and integration. Lean supply chains and systems integration: these are associated with cost minimization and reformation of the cost structure by stages. Their emphasis is upon efficient cooperation, but not, however, upon economizing, which could lead to resource bottlenecks. Competing constellations of linked companies: here, market leaders form an alliance with the best market partners. The emphasis is upon performance potential, capabilities and organizationcultural combination ability.
Interlocking network supply between competitors: these consolidate the step-by-step completion of transactions. The emphasis is upon unification where a minor competitive advantage75 exists, with the aim of using synergies. Asset control supply – dominate or die: this approach is used to gain control over the assets and target their application. The emphasis is upon the correct usage of competitive instruments at tender action stage. Virtual supply – no production, only customers: here, fixed costs are kept low by outsourcing of
production.76 The emphasis is upon marketing and distribution capabilities. In focusing upon the primary Supply Chain area or (to use another title) the corporate-policy point of view, it is possible to categorize further and differentiate on the grounds of strategy, function, logistics-transportation, and information management points of view. T h e strategic view considers the Supply Chain design to be the most important element of the competitive strategy. As a part of this, the Supply Chain represents an alignment of resources which are used to support the
product’s position in the market with regard to the combination of end customers, price calculation and sales measures. The purpose of such a process is the improvement of profit margin upon product turnover. In the functional view, the Supply Chain consists of the individual organizations that are required in order to purchase, transform and sell materials. The focal point is occupied by the material: its procurement, transportation, and other costs are important. The aim is to lower cost in the functional areas relevant to success.77
T h e logistics-transportation view assumes that the Supply Chain represents the physical course of a product through a number of operating plants and facilities which are connected by means of a transport association. These facilities and installations include factories, warehouses, sales centers, vehicle pools and distribution centers, and the view seeks to bring about the minimization of logistic cost. In the information management view, the information flow between the various parties represents the integration factor. In this sense, an integrated Supply Chain possesses a communal basis of information, as well as mechanisms with
which to exchange this information amongst the participants. Accordingly, the aim of this view is a reduction of the information process cost.78 This study follows the latter categorization. This type of linkage is anchored most strongly into the SCOR model, which will be dealt with later. But for the purposes of this study, it must be decided what the importance of the Supply Chain is with regard to the competition between companies. The unequivocal – and simultaneously most “radical” – answer, with which the author agrees, is as follows: “The leading-edge companies
(…) have realized that the real competition is not company against company, but rather supply chain against supply chain.”79 There is not much more to add to this quotation, although it does serve as a powerful example of the way in which the term has brought forth and seen the rapid development of a new discipline in the last decade: management of the Supply Chain or Supply Chain Management, respectively. 80 This will be the focus of the study in the pages which follow.
1.4 Overview of the Present
Status ?of Supply Chain Management ?in Literature The role of Supply Chain Management (SCM)81 within an organization has changed considerably over the roughly last three decades. In the 1970s, when the area was better known as logistics, it was largely restricted to the integration of storage and transportation policies within a company. In addition to this, the high interest rates (often in the two-digit region) that most countries experienced during that decade forced companies to be particularly vigilant when it came to the investment of their capital. At this time, leading logisticians were primarily concerned with reducing their stocks.
Their focus was mainly upon how the business could implement internal changes, which would lower the inventory and logistic costs. Even attempts to reduce production and delivery cycle time and as a result of this, safety stock, were carried out internally because cycle times were mainly considered to be incoming information for the forecasting and procurement process. In the 1980s, the focus shifted towards restructuring the cost structures within the Supply Chain. Attention was diverted to integration of Supply Chain procedures in order to reduce SC business cost and assets for the Supply
Chain. Around the end of the 1980s, SCM then changed its focus from cost reduction towards the improvement of customer service. The advantages sought by means of an improvement of the Supply Chain’s performance included higher turnover and higher profitability, due to a greater share in the market, and pricing advantages over the competition which manifested themselves in higher margins. The level of interest in improving customer service was an equally important feature of businesses during the 1990s. In the same way, business growth – which had been considered within many companies to be the
responsibility of product development, marketing and sales – was adopted as an SCM objective. In the present decade, the field of SCM has seen further changes, namely the development of Strategic Supply Chain Management. As opposed to the traditional point of view, in which it was only a partial definition of objectives, SCM has achieved a strategic function which immediately contributes to the organization’s success and has simultaneously become an immanent component of business strategy. It is increasingly the shared view that SCM not only determines the business strategy of many companies, but
also makes their successful trading possible. Alternatively expressed, SCM is simultaneously conditional for successful business strategy and a determining factor of business strategy designation.82 In addition to these factors SCM was, above all, strongly concentrated on improvements with regard to the supplysided processes. Thinking in this area tended to overlook, however, the fact that companies who wished to monitor their Supply Chain in an optimal way could only achieve this goal if they were able to recognize the fundamental connection between supply and demand – and the resulting effects of this upon
the SC strategy. In many cases, however, companies scrutinized their supply-sided possibilities, but neglected the demand factor. The relationship between the supply and demand side lies in the fact that demand determines the Supply Chain’s aim and therefore has a determinative character, whilst the supply-orientated performance potential supports the fulfillment of the demand. Now that this link has been affirmed, companies must find new means of creating the coordinated monitoring of supply and demand chains. SCM represents a central component of these efforts.83 The ability of a business to
reconcile supply and demand is a function of its capability to react, or alternatively expressed, its capability to answer to market signals in a timely manner. This flexibility, on the other hand, is mainly influenced by the company’s working capital and operating capital expenses. Organizations have often fought to adapt supply and demand in this manner because during this process, the focus falls upon improvement in forecast accuracy, production, and inventory optimization and the reduction of cycle times.84 Consideration should thereby be given to the fact that, although useful,
these measures do not offer a unified solution. Companies must therefore also consider such measures which include labor and capital equipment costs, and they must find new ways to adjust the incentive systems, not only internally, but also within the extended Supply Chain (i.e., with reference to the SC partners).85
1.4.1 Evolution of Supply Chain Management Long before the term Supply Chain was created and the new discipline of formation and optimization of this Supply Chain—Supply Chain Management—emerged, people were speaking of a so-called logistic chain.
This logistic chain stood in the center of a discipline described as logistics (and nowadays is partially still described as such). Hugos submits the following description: “The term ‘supply chain management’ arose in the 1980s and came into widespread use in the 1990s. Prior to that time, businesses used terms such as ‘logistics’ and ‘operations management’ instead.”86 To this end and for the purpose of demarcation, it is useful to introduce several definitions of logistics at this
point. In the classic terminology of the Council of Logistics Management (CLM),87 logistics are described as the processes used to plan, implement and control the efficient flow of material, beginning with storage of raw materials, through work in process (WIP), to finished products and services, as well as the respective information from the outlet to the point of consumption. This field includes incoming and outgoing goods as well as internal and external material movements. The ultimate purpose is to be able to fulfill customer requirements.88 Logistics can also be seen from the organizational aspect, as representative
of an objective-orientated logic which exists to monitor the processes of planning, allocation and control of financial resources - processes which are reserved for the physical distribution, production support and purchase transactions.89 Other definitions focus upon the issue of conceptual integration by which logistics include the creation of relationships to time, space, amount, shape and possession, not only within one company, but also in conjunction with other companies. The tools used to arrive at a logistical target are strategic management and infrastructure and resource management. The aim is to
create products and services that satisfy customer needs. Within this, logistics are involved at all levels of planning and implementation on strategic, operational and tactical levels.90 Logistics management also inevitably presents limitations and dependencies. Accordingly, logistical activities usually consist of incoming and outgoing logistics, vehicle-pool or fleet management, respectively, stockkeeping, material movement, order registration and completion, logistic network design, inventory management, supply and demand planning, and the coordination and monitoring of logistic service providers.91 Only in a limited
way do such activities cover issues of procurement and purchasing, installation and packaging, and customer service.92 From such definitions, it is but a short step to move to management of the Supply Chain (i.e., Supply Chain Management) which is identifiable on account of its integrative character. Therefore, Supply Chain Management (SCM) includes not only logistics but also, above and beyond this, business areas such as purchasing, marketing and information technology. The major purpose of this field is to improve Supply Chain efficiency.93 Expressed another way, SCM can
be defined as the integrated planning and monitoring of processes in the value chain. The representative objective in this case is the optimal satisfaction of customer needs. In this sense, logistics management represents a component of SCM. This component has the task of planning, implementing and controlling the efficiency and effectivity of the forward- and backward-facing flows of goods, services and appropriate information, with the intention of fulfilling customer requirements.94 As a result of this, SCM represents an integrative functional area whose priority is the responsibility for the connection of main business functions
and processes within an organization and also of other firms included in the Supply Chain. These connections are intended to help the business arrive at a consistent and achieveable business model that comprises the logistics management functions in addition to production flows and has the task of ensuring the coordination of the SC processes with the functional areas of marketing, sales, product design, finances and information technology.95 SCM also comprises the planning and monitoring of all logistics management activities. Beyond this, however, it comprises the coordination of and cooperation with the business
partners within the Supply Chain, such as suppliers, distributors, logistic service providers and customers. In the main, SCM integrates the management of supply and demand – Supply and Demand Management – within one business, and also throughout various other firms.96 A clear definition of the term SCM is, however, nowhere near as simple as one would imagine due to the diffusion of the term in modern usage. In actual fact, the term SCM is associated with various meanings. In the widest sense, it encompasses all logistical activities, customer-supplier relationships, development and introduction of new
products, inventory management and facilities. The concept allows itself to be applied, in analogue, to the area of service provision. Many practitioners define SCM more closely and restrict the definition to activities within one company’s Supply Chain. By this they inevitably reduce the application area of improvement measures to their own business and the internal Supply Chain, without the inclusion of external Supply Chains.97
1.4.2 Definition of the term Supply Chain Management At this point, it is useful to define Supply Chain Management in order to outline the applicable boundaries of this study.98
Originating from the classical planning and control approach, Supply Chain Management represents an expansion of the existing approach into a companyspanning planning and control strategy. This is also inherently connected with the Advanced Planning System (APS), 99 which also explicitly includes an information technology (IT) support aspect. If the time dimension or the planning horizon is included, SCM can be defined as the coordination of the strategic and long-term orientated cooperation between all participants within the whole SC network.100 This includes the purchasing area as well as
the production area, and extends into the fields of product and process innovation where the purpose is to develop and manufacture products. Each SC participant is active in the area for which he possesses core competences.101 The choice of further SC partners is mainly made from the aspect as to which potential is present for the realization of shorter lead times.102 SCM may be described as the process of planning, introducing and controlling an efficient and effective flow of goods, services and relevant information, from the starting point of the Supply Chain right up to the point of
consumption. The focus of such a process is the satisfaction of customer requirements.103 By a further differentiation of the process-related point of view, SCM can also be seen as the design, maintenance, and application of SC processes for the satisfaction of end customers needs. In this sense, it covers Supply Chain formation as well as the consequent operation and maintenance. New tasks ensue for the involved executives, because traditional tasks have to be completed in a new way. Principally, the introduction of an (explicit) SCM discipline has, as a consequence, an extension of the range of tasks and responsibilities of coworkers.104
Apart from this, the business process-related definition can be extended to the point where SCM represents the integration of business processes from the end customer right up to the suppliers. This integration provides the products, services and information that generate value for the customer. Having said that, SCM leads to a change in the existing Supply Chain and generates customer benefits by means of the targeted usage of information associated with the Supply Chain.105 The organizational processes within the Supply Chain must also be planned, monitored and controlled, a task that requires a generally accepted
system of objectives.106 Extending from the (physical) goods flow, the Supply Chain subsumes all those activities associated with the flow and transformation of goods, starting with raw material right up to the end consumer, as well as the associated information flows. SCM therefore represents the integration of these activities by means of improved relationships with the SC partners, in order to gain a permanent competitive advantage.107 The definition also reminds us that SCM arises from a constantly selfdeveloping management philosophy. In
the framework of this philosophy, the objective is to combine the common production competences and production resources of the business functions that lie not only within the organization, but also with the external allied SC partners. The aim is to create a highly competitive SC system, furnished with customer benefits which targets the development of innovative solutions and the synchronization of the product, service and information flows. The ultimate goal is the generation of maximum value for the customer.108 If one continues this almost dialectic development of SCM, the further developments of earlier
management concepts, such as Lean Manufacturing,109 may be seen as precursors of the practice. In such concepts the application area is extended into the sphere of distribution.110 In this sense, the aim of SCM is to improve the efficiency of the product delivery process, starting with material suppliers right up to the end customer, in order to deliver the correct product at the correct time, with the minimum of completion effort and safety stock.111 The focus of improvement measures lies in the areas of coordination, distribution, production and purchasing – spread over organizational units and various firms.112
Seen from the functional side, SCM may be defined as the systematic, strategic coordination of traditional business functions and of the tactical measures beyond these business functions. This means that it includes the functions within the respective business, as well as throughout various firms which are integrated into the Supply Chain. The practice aims for long-term improvement of the performance capacity of the individual firms in addition to the Supply Chain as a whole.113 Seen from a behavioral angle, SCM can be defined as those activities carried out in order to influence the Supply
Chain’s behavior. In this form, SCM represents the coordination of production, inventory stocks, locations and transportation amongst the SC participants, in order to ensure the best relationship between performance capacity (capability) on the one side, and efficiency on the other. 114 Both objective criteria – performance capability and efficiency – will be more closely examined later, as they represent, so to speak, the two pillars of the SCOR model – or more specifically the two sides of the equation with regard to the performance indicators which form the basis of the model.115 A further possible differentiation
can be undertaken by looking closely at the two sides of the Supply Chain, i.e., monitoring the supplier-side (suppliercentric supply chain management) on the one hand, and the customer-side (customer-centric supply chain management) on the other. In accordance with this, the distinction of the supplier-centric approach exists in the fact that the business and its suppliers, distributors and customers, – i.e., all the business associations in the further sense – cooperate in order to provide the market with a common respective product or service, for which the customer is prepared to pay the required amount. The group of firms recruited from the respective partners or
participants functions like an expanded business116 and ensures the optimal use of shared resources (manpower, procedures, technologies and performance measurement), in order to attain synergies. The results are products and services which combine high quality with value for money and can be quickly delivered to the market.117 The definition of the customercentric approach purely requires that the conventional definition be expanded as follows (emphasized): The business and its suppliers, distributors and customers – i.e., all Supply Chain parties
in the further sense – work together in order to provide the market with a common product or service respectively, for which the customer is prepared to pay the required amount throughout the total life cycle of the product. The group of firms recruited from the SC partners or participants respectively, functions so to speak as an expanded business and ensures the optimal usage of shared resources in order to attain synergy. The results are products and services of high quality that can be quickly
delivered onto the market and ensure customer 118 satisfaction. For the SCM’s focus upon the customer side, the terms demand-supply chain management or demand management can occasionally be used. The primary purpose of this concept is the generation of value for the customer, with simultaneous performance capability improvement regarding asset performance and cost-efficiency. 119 The SCM’s primary objective is the enhancement of the marketing of goods and services to the respective end customer or end consumer whilst simultaneously lowering inventory
stocks and minimizing costs.120 Conflicting objectives – so-called trade-offs – inevitably arise from this, because the underlying competitive factors (cost, time, quality and flexibility) compete with one another. As a result, SCM seeks to optimize the efficiency of the companies involved and harmonize the conflicting objectives (under the provisions of the priorities according to each chosen competitive strategy).121
1.4.3 Value-based Supply Chain strategies In recent years the number of companies following a so-called Value Chain strategy has risen markedly. This
tendency has been mainly promoted by firms who use highly developed information technologies to improve their capability in the field of SCM. A decisive factor to business success is their competence in being able to offer innovative products of the highest possible quality, at marketable prices and faster than the competition.122 The SCM ompetence’s 123 objective, using respective SC processes, is to improve service of customer requirements, make better decisions, and enhance business performance to secure a competitive advantage.124 The consequence is that a multitude of organizations have drafted strategies which focus upon the relevant processes for the fulfillment of demand
(demand fulfillment process). Such strategies are ultimately supposed to contribute to optimizing order cycle times, financial flows (cash flow),125 Return on Equity (RoE), market share and profitability. In this sense, they represent the basis of the SC strategy.126 SCM represents a mutually dependent organizational structure which connects functions, firms and countries with one another, synchronizes goods movement with demand rate and propagates the value generated on the global market. For each product there is a Supply Chain, and for each Supply Chain a competitor. These chains are developed by large corporations –
typically distinguished wholesale chains and Original Equipment Manufacturers (OEMs)127 – who have the necessary vision and enforcement potential to advance their SC partners’ performance capability, exchange data and work in an alliance, in order to ensure a superior market position and the improved efficiency of the business.128 The development of the valueoriented SC approaches results from the recognition that the isolated optimization of individual parts of the Supply Chain does not lead to an overall costfavorable solution. Goldrath summarizes this in the recognition that the sum of local optima is not equal to the global
optimum.129 It is therefore necessary to view the alignment of events within the Supply Chain as a whole (holistic), starting with the customer requisition, as far back as the purchase order to the raw material supplier, as well as forwardfacing through all businesses included in the manufacture and delivery of the product to the end customer. Focusing on the Supply Chain as a whole represents the first stage; focusing on the product the second; and the inclusion of the value-generating flows in the sense of a value-oriented, SC-focused process organization, as opposed to the traditional performance measurement that was built-up on structural organizations,130 represents the third
stage. The assignment of a “value stream” is thereby possible, which illustrates the present-day business processes more effectively than would be the case within the framework of the conventional Supply Chain.131
1.4.4 De-integrated Supply Chain strategies De-integrated Supply Chain strategies132 are a diametric oppositional approach in connection with SC strategies, because the latter specifically shifts the importance of integration into the focal point. Within the development framework of the so-called SMART automobile,133 a feasibility study was
carried out in the first instance. The Supply Chain developed in this context and at that point in time, mid 1990s, represented a completely new approach. In this way, for example, new models were created for supplier inclusion and production outsourcing, which were distinguished by pre-installation at the supplier’s location, integration of suppliers in the design and final assembly, and the proportional ownership-splitting of production locations. Additional questions arose, for example, from the fact that the initializing company only contributed roughly 15 percent of the value-add
within the Supply Chain. The concrete question resulting from this was how a Supply Chain, within which the central business only provides a relatively minor contribution in value, could be monitored.134 The de-integrated Supply Chain developed within the framework of the feasibility study represented the basis for the introduction of so-called customer-specific series production (mass customization).135 Campbell and Wilson describe the approach of a deintegrated Supply Chain with the term strategic network and define this as an opposite pole to the previously represented, value-orientated SC approaches (value concepts). In accordance with this, the value-
orientated approaches within business systems that simultaneously postulate a close cooperation and the retention of independent firms are the most effective. Four characteristic features of a business system allow themselves to be identified, which are advantageous to the development of strategic networks:136 Some critical SC activities must show advantages if they are to be implemented in a de-integrated form. This can be determined by differences regarding market entry barriers and competitive advantages. Specialized investments lead to higher efficiencies. These can be represented
in the form of capital investments or investment in the workforce. The adaptation speed (speed of responsiveness) is of fundamental importance. Innovation presupposes the comprehension of the SC system as a whole.
1.5 Methods of Analysis and Measurement of the Performance Potential of the Supply Chain 1.5.1 Description of Supply Chain Processes A process can be defined as a line of
sequential activities and actions which lead, over time, to a result. Processes may be further subdivided into partial processes. Furthermore, a differentiation can be made between key processes, which include main or partial processes and immediately contribute to purposefulfillment in the business core, and support processes, which represent associated activities in support of the key processes.137 The following listing shows typical key processes in production businesses:138 Product design Development Order acquisition
Production planning Procurement Production Distribution and disposal. Often the key processes named are also distributed amongst several companies if a respective division of the work is predetermined. The key processes are integrated into the previously mentioned product life cycle.139 Fundamentally, two varying primary approaches for the respective illustration or description of Supply Chains can be differentiated: the Process Chain Approach and the Supply Chain Operations Reference Model (SCOR).
The SCOR model extends itself over the complete Supply Chain, beginning with the procurement process (source of supply), up to the point of consumption. It is an ideal industryspanning approach, in which the procedures within a Supply Chain are agreed upon by the partners.140 As the SCOR model will be dealt with more explicitly later, the alternative approach will be explained in more detail first. The process chain approach , also referred to as the Process Chain Model, forms a businesses Supply Chain seen from a purely process-oriented perspective. The result is a type of
process-focused Supply Chain, for which the description Process Chain can be found.141 The process chain model enables a visualization and analysis, in addition to process organization within the Supply Chain. With this, every process within the Supply Chain, which is reflected in the form of process chain elements, can be represented by means of the following parameters:142 Input Output Resources Structures Control
A process chain element is associated with the business environment via the input, which describes the “load” under which the Supply Chain stands, and also refers to the output. According to process chain design, the respective process chain element transforms a given input into a given output. The process, which underlies the design, is described by process chain elements upon a lower, i.e., more detailed, level.143
1.5.2 Quantities/Timesframework in the context of the Supply Chain
The comparison between input and output quantities allows conclusions to be drawn both with regard to the productivity144 of the process chain and to its effectiveness and efficiency. 145 The approach also aims to make the necessary information available for the implementation of model-supported, quantitative Supply Chain performance analyses. This quantities/timesframework is vitally important in the field of indicator measurement and its necessary factors. Cost accounting is an important part of the successful business operations. Before costing can be undertaken, a number of decisions have to be made for
example with reference to the timing of the cost accounting. Pre- and postcosting can be differentiated with regards to the timing:146 Pre-costing is necessary if a product is newly introduced onto the market. Exact cost data are therefore not known, but can be estimated on the basis of factors like presumed purchase prices or preparation periods. Pre-costing thus enables an initial price determination. Post-costing is mostly undertaken upon expiry of an accounting period and namely on the basis of actual
cost data. Deviations identified in this process can then be used to influence price corrections. Costs represent the assessed consumption of production factors for the provision and marketing of business performance, as well as the maintenance of operational preparedness. Alternatively expressed, quantities and times are multiplied, or respectively appraised, by prices or rates, leading to a cost figure.147 Costing, also described a s cost accounting, is concerned with the distribution of costs to the individual product or performance. It therefore identifies the personal cost and with that
creates the basis for price politics through the identification of lowest price limits.148 Business leaders are usually most concerned with the measurement of Supply Chain costs (SC costs). This refers to an area that often comprises a complex alignment of activities, and an exact measurement is often difficult. There are two primary stages which must be successfully implemented in order to ensure the exact measurement of the SC costs:149 First, the cost structure of the Supply Chain must be located as
close to the reality of the situation as possible. Further, the system for the measurement and reporting of these costs must be well designed. In addition to the specific SC costs, the most generally used components that can be accurately measured (in postcosting) or estimated (in pre-costing) are quantities and times. The quantities/times-framework can consequently serve to determine the merits or demerits of a certain alternative, without requiring large amounts of time devoted to the evaluation of prices and/or calculatory rates. This is particularly important
when it can be assumed that no substantial differences are to be expected with regards to quantities and times. Stemmler combines the meaning of the quantities/times-framework constituted by these diverse aspects and within the context of the Supply Chain as follows: There is no doubt that a successful business depends on accurate and timely delivery of goods or services to its customers. Supply chain management aims at minimizing mass and time. Needless to say, an efficiently managed supply chain requires
measurement of the costs150 associated with the physical movement of goods and related information flows.151 The quantities/times-framework and the costs connected with it will be referred to in the context of performance indicators described below.
1.5.3 Special Performance Indicators of the Supply Chain Indicators for the purpose of evaluating an organization’s performance capability (performance indicators) should cover the financial area as well as the
operational procedures, as the objective is to attain customer satisfaction at low costs and to ensure long-term competitive capability. In this sense, performance indicators are not only intended to contribute to the continual improvement of the Supply Chain’s performance, but also to further refine a competitive business strategy. To be most effective, the performance indicators should be easy to define, simple to apply and easy to comprehend, in order to enable the executives who use them to react speedily and suitably with adequate measures. The performance of the operating procedures is a substantial premise for
(external) customer satisfaction. The financial performance potential, on the other hand, reflects the company’s (internal) profitability and its ability to be competitive in the long-term. In the short-term period, the estimation of the financial performance potential consists of the measurement of incremental cost per unit152 for every activity and every project, in addition to the measurement of non-value generating expenditure.153 In the mid and long-term, a reliable estimate is problematic. This fact can be attributed to a number of causes; for example, the consideration of costs for Research and Development (R&D), since R&D costs cannot be split and applied to each individual product.
During all this, the business executives must consider that capital investors are focused upon maximizing the capital productivity of the invested capital, and that such a focus favors maximization of the profit margin and capital turnover. Finally, they must allow sufficient financial clearance whilst making strategic decisions or, in other words, ensure the business has a sufficient cash flow.154 Business Performance Management seeks to ensure, within the framework of business leadership, that the focus is upon achievement of the defined strategic and company
objectives. To this end performance is measured and monitored by means of performance indicators. In this case, however, not all measurement procedures and indicators lead to their objectives. Many organizations are barely in a position to cope with the amount of data, which is either irrelevant, too explicit, badly classified and of low value for decision making, or on the other hand can be difficult to obtain. A glut of information can, in fact, have a detrimental effect. Several of the indicators defined above may only have a nominal relationship to an organization’s goals. They are, therefore, not relevant to the
achievement of objectives. Other indicators can be misinterpreted, because their meaning is unclear or ambiguous, resulting in wrong decisions with far-reaching consequences. This leads to a management reporting system that is characterized by Key 155 Performance Indicators (KPIs) and their application – for example, in the framework of a Balanced Scorecard , which will be dealt with more closely in the next section. The key performance indicators must be seen in conjunction with the so-called Critical Success Factors, (CSF): Critical success factors serve the purpose of identifying the substantial factors for the organization’s success.156 These more qualitative
critical success factors are measured and quantified by the key performance indicators.157 Various studies have shown that companies which objectively control and monitor their performance by indicators are more successful than those which do not do this at all.158 If business executives are informed of the performance indicators and the factors that influence them and lead to results, they can make better and more effective decisions. Control of the performance indicators must, therefore, be directed towards the targets, problem areas and decisive factors, in other words: the critical success factors. The resulting
advantages allow themselves to be collected as follows:159 Better achievement of objectives Better and quicker decision making All staff are aligned to common goals Managers and staff have greater confidence and motivation. The problems immanent to the general performance indicators have lead to the development of special performance measures and metrics, used to support companies in specific areas such as SCM. Novack et al. have developed a questionnaire for
performance measurement in the logistics field (logistics service performance). In this a differentiation is made between ten so-called logistics activities and five logistics service outputs. The logistic activities contain the Supply Chain’s partial processes:160 Purchasing Inbound transportation Packaging Inventory management Warehousing Manufacturing Intra-company transportation Order processing Outbound transportation
Logistics planning
design
and
strategic
The logistics service outputs measure the performance of the aforementioned activities and therefore represent performance indicators. These include:161 Product availability Order cycle time Logistics operations responsiveness Logistics system information Post sale customer support.
Another possibility for improved control may be found in the differentiation by process performance indicators (process measurements) and the method used to measure the indicators (metric measurements). The process performance indicators include, in the first instance, customer satisfaction. This can be measured by the collection and evaluation of customer complaints, thus enabling the customer to be included in product- and procedureorientated performance evaluations.162 A further indicator is the quality of customer deliveries. This focuses upon a product’s successful delivery to a customer, fulfillment of his expectations
and the extent to which the product is useful to him. These customer expectations include, as a rule, perfect order rates as well as the delivery of the product to the correct location, in good condition and at the correct time. Finally, there is also the time between order submission and respective delivery and payment (orderto-deliver/cash cycle time). This represents that part of the cycle, which covers the period from the submission of the order up to delivery, and measures the amount of time which passes between placing the order on the customer side and the receipt of the delivery/payment.163
A procedure that utilizes the range of such cost measurement elements is that of Activity-Based Costing (ABC).164 Aside from the classical allocation of cost by areas of expenditure, Activitybased Costing has been particularly popular and important in the field of logistic services in recent years. Due to the fact that performances within a Supply Chain often involve overlapping and cross-sectional tasks, the formation of a cost distribution by areas of expenditure is often difficult. Additionally, cost distribution transparency on an internal and higher operational level is often not possible.165
It is therefore necessary to identify those factors that can influence costs within the framework of process cost identification. These influencing factors are described as cost drivers. Those drivers that are themselves shaped by quantity (amount) and those that are dependent on performance are differentiated. The aim is to identify the cost per process implementation. The relevant basis data is collected from the study of the individual activities of the process.166
1.5.4 Measurement of Performance Indicators:
Balanced Scorecard and Supply Chain Scorecard In order to illustrate how the aforementioned performance indicators are respectively measured or can be made concrete, it is useful to give an example and explain the procedure applied in it. The following example is supposed to show how performance indicators in the logistics area can be classified and identified.167 For this purpose, reference will then be made to the previously mentioned study by Novack et al. concerning the measurement of logistics service performance.168 The method of procedure for
measuring the performance of the defined indicators was carried out in three stages by means of a questionnaire.169 The rate of return was approximately 1,600 executives from the field of logistics. The companies were distributed over a multitude of industry sectors, whereby the majority – roughly a quarter – came from the Food and Beverage industry. The first stage consisted of the identification of logistics activity costs and performance. The purpose of this part of the questionnaire was to find out which percentage of the companies questioned measured the costs connected with performance and logistics
activities. During this the precondition was taken as a basis that fundamental measurement of costs and services represents a necessity for quantification of the logistic value. The second stage consisted of the identification of relative cost and relative value creation. The people questioned were asked in the survey to prioritize the ten logistic activity areas with regards to their percentage of the logistics activity costs as part of the total of the firm’s expenditure, and the relative value generation of each activity in their respective company. A ranking of 1 was allocated to the highest relative logistic activities cost and value
generating percentage, a ranking of 2 to the second highest and so on, up to a ranking of 10 for the lowest percentage. This was done for two reasons: firstly, it would determine whether a relationship exists between what a company measures and the relative logistic activity costs and value generating percentages which are actually measured. Secondly, it would help identify whether a relationship exists between the cost of an activity and the value generation for a business that results from it. In the third and final stage, the logistics service performance was identified. This part contained two
separate questions. First, the people questioned were asked to specify whether they measure the five logistic service outputs (product availability, order cycle time, logistics operations responsiveness, logistics system information and post sale customer service) at all. They were then asked to allocate a ranking for the five outputs with 1 for is most important and 5 for is least important. One of the results identified within the framework of the study was that logistics activities are more important from the company’s point of view, whilst logistics service outputs are more important for the customer.170
The problems that the study brought to light were the trigger for a study of the the me Performance Measurement in Businesses of the Future that was carried out at the beginning of the 1990s at the Nolan Norton Institute,171 which at that time was the research branch of the consultancy company KPMG.172 The study confirmed that in addition to the problems of redundant effort and lack of comparability, conventional approaches for performance measurement restricted themselves too strongly to monetary measures, and therefore the valuegenerating and future-directional processes, such as SC processes, received only limited consideration.173
Apart from this the study represented a milestone for the modification of business performance measurement by the development of a balanced evaluation list, the so-called Balanced Scorecard (BSC) . The further development lay in, above all, not only optimizing existing processes, but also including new processes, structures and procedures. By these means the method gains in innovative strength.174 The concept of the BSC was introduced by Norton and Kaplan with the intention of contributing to the development of business objectives, building upon the support of the definition of strategic initiatives in order
to achieve these objectives, and finally making possible the measurement of results over the course of time. The method of the BSC was not completely new at the point in time of its development (the early 1990s), because companies were already using a number of indicators – financial as well as nonfinancial, tactical and operational – but the application of such a structured concept, leading to an accurate measurement of the company’s performance against its set objectives, was relatively new. 175 The BSC then became the preferred measurement tool of the largest consultancy companies.176 The method has also become an aid
for the evaluation of value-generating strategies, in order to monitor the success of value-orientated processes and to monitor whether the involved interest groups (stakeholders) receive the value they expect – for example, with regards to the Return on Investment (ROI).177 BSC achieves both the balance and the visualization of indicators by means of an evaluation list (scorecard). The balance aims at the equality between the following components: strategic vs. operational indicators, monetary vs. non-monetary measures, long-term vs. short-term positions, cost drivers vs. performance drivers, hard vs. soft factors, internal vs. external processes, past vs. future
performances.178 During the visualization of the indicators by means of an evaluation list, the company’s vision, determined by business executives, remains the focus of observation.179 This vision must be operationalized by strategies as well as activities. The business vision, strategies and activities are usually observed from four perspectives:180 Financial, which covers the capital backflow and value generation. Customer, characterized by customer satisfaction, customer retention, market share.
Business process, containing quality, reaction time, cost and introduction of new products. Learning and growth, which includes satisfaction of co-workers and availability of information systems. Each of these perspectives within the BSC framework is itself determined by four expressions: objectives, measures, targets and initiatives.181 The BSC guarantees, as it were, a balanced view of the chosen financial and nonfinancial indicators that are necessary in order to drive the strategic plan and monitor the company’s performance. In
most cases, the indicators are converted by means of databases (data 182 warehouses) and spreadsheet analysis. These were often problematic, however, as they were often focused not exclusively on those processes that were critical to the business success, but on the entire spectrum of processes and systems. Equally, the collection, aggregation and analysis of the (correct) data, was often presented in a manner that made sustained analysis difficult (and sometimes even impossible), because the necessary data was not always available. BSC has developed itself into one of the most far reaching and recognized
methods of definition and monitoring of business strategy. According to an exemplary study on the theme of performance monitoring, almost half (46 percent) of the people questioned stated that their companies were implementing the BSC method or planned to use it in the future. It was remarkable that the diffusion was greater in the industrial and marketing sector than in the service sector. The application of the BSC in such cases was mainly done with the support of the Chief Executive Officer (CEO) or the Chief Finance Officer (CFO).183 The exact characteristic of the Scorecard depends, in the main, upon the
business area under examination. For the SC field, a special Supply Chain evaluation list, a so-called Supply Chain Scorecard , was developed.184 The particular indicators necessary to measure the supply chain performance vary, depending upon customer type, product line, industrial sector, in addition to other factors. Because the Supply Chain ultimately targets the end customer, the point of view of the end recipient must be included during the development of a SC Scorecard and the identification of the particular metrics. That consequently includes aspects that are relevant to the capabilities of the Supply Chain: those which satisfy the end customer’s requirements in the most
cost-effective manner.185 Because development of a Scorecard specially directed at the Supply Chain conditionally requires SC partners to reveal business objectives and data, the implementation is not practical if no trust exists amongst the firms cooperating within the Supply Chain. Therefore, a SC Scorecard shared by all parties within the Supply Chain requires a considerable degree of trust. Simultaneously, however, the communal development of a Scorecard and the sharing of data associated with it can serve to strengthen the mutual trust and the sense of partnership. Despite this, the introduction of a Scorecard
directed at the whole Supply Chain, although theoretically desirable, is relatively difficult to attain in practice.186 Next to the Scorecard, the previously mentioned SCOR model represents another unified approach, especially for the measurement of Supply Chain performance. Because the approach is extended to cover the whole Supply Chain, the procedures are configurable and the possibility of illustrating various alternatives of the same process exists. Through this, a “standardized” language evolves, so to speak, for internal, intra-company, and integrated communication, respectively,
which in turn is a substantial condition for the performance comparison between the SC partners. The performance of each of the processes in standardized Supply Chains is measured with the aid of specialized performance indicators.187 The indicators used within the SCOR model’s framework will be explored in more detail in subsequent chapters.
1.6 Focus of the Work on the SCOR Model In 1996, the so-called Supply-Chain Council (SCC) was founded in the United States.188 With the Supply Chain Operations Reference (SCOR) model,
this organization created a support for the standardization or respective “normalizing” of Supply Chains within an organization, as well as between the business itself and other organizations. The primary objective of the SCC is to promote a common understanding of the processes and activities in the various businesses which participate in a Supply Chain network. The process categories in the SCOR model are differentiated with the aid of the dimensions production concept and orientation of product structure . The expression discrete corresponds in this instance to the orientation within the installation, i.e., a convergent production structure, whilst the expression process
corresponds to orientation within the process, i.e., a divergent production structure.189 The main task of the previously represented Supply Chain Management (SCM) concept is the continuous synchronization of value generation within the whole Supply Chain network and subsequent harmonizing with consumer demand. The SCOR modelbased Supply Chain uses the SCOR monitoring processes as its foundation within all businesses involved both on the supplier and customer sides.190 All conditions required to fulfill the process stages are upheld and mutually
agreed, as a whole, by the allied participants. The planning and control methods required to make this possible are consequently identical to the methods used within firms for internal planning and control. Further measures include procedures necessary to access data between companies, especially data pertinent to inventory and capacity. 191 A great advantage of the SCOR model lies in its definition of a common language for communication between the various business-internal functions and the business-external Supply Chain partners. Only within a common comprehension of the relevant processes is the formation of customer-supplier relationships possible.192
The definition of indicators for the Supply Chain performance in the SCOR model creates the prerequisite for its continual evaluation and optimization. Furthermore, the comparison of SC performances is only possible with the aid of a special comparative procedure, called Benchmarking,193 based upon such indicators.194 The increasing diffusion area of SCOR model acceptance in the USA since the late 1990s, seen alongside the rapidly climbing number of SCC members, is an indication that a de facto standard for Supply Chain analysis is developing. With the reinforced efforts of the SCC to create a user basis in Europe via the
foundation of a European Chapter, the SCOR model will, on these indications, continue to be diffused throughout Europe.195 Welke grasps the standardization aspect and describes SCOR as a normative model. A normative model consists of a predefined set of alternatives, and Welke describes how an object of the model “should be seen and behave.” The value of normative models lies primarily in the following areas:196 Simplification of modeling – constrained choice vs. green field,
by means of a higher degree of abstraction. Making model exchange possible throughout business units and organizations by means of standardization. Description of common problems and metrics by means of standardization. Exchange of benchmarking and best practices197 by means of standardization. Normative models do not represent anything fundamentally new and have been around since the 1980s. Their origin stems back to the so-called
Business Information Analysis and Integration Technique (BIAIT) according to Burnstine and Soknacki. Whilst the growth in the computer industry at this time was way above average, this growth was not accompanied by a comparable improvement in communication between executives in the companies applying it and their IT managers with regards to the effective assignment of the new computer-supported technology. It became obvious that there was a real need to find ways of making clear to executives the importance of IT applications. BIAIT was developed to solve these communication and 198 estimation problems.
In addition to BIAIT there were other, earlier, approaches to developing normative business process models: for example, the so-called Kölner Integrationsmodell (KIM) (roughly meaning Cologne Integration Model), developed by Grochla; the model of the c o m p l e t e InformationssystemArchitektur (ISA) (roughly meaning Information System Architecture ) by Krcmar; and the Architektur integrierter Informationssysteme (ARIS) (roughly me a ni ng Architecture of integrated Information Systems) developed by Scheer.199 Normative
models
are
predecessors of the SCOR model, in the sense that they are particularly focused upon companies’ Supply Chains. Instead of the term normative models, one often finds the term reference model for SCOR. This will be dealt with more closely in Chapter 2.200 The normative models presently available allow themselves to be systemized according to the following three categories:201 The modeling viewpoint, i.e., to do with a structural or behavioral model The E-Business-field, i.e., to do with Business-to-Business (B2B), Business-to-Consumer (B2C) or
Government-to-Consumer (G2C)202 The commercial branch, i.e., to do with businesses from industry, marketing or the public sector. This study focuses upon industrial companies, because the author has practical experience in this commercial field. Furthermore, it focused upon the E-Business field (B2B), because it can be assumed that the greatest scope for competitive success resides there.203 Apart from that, this field has by far the greatest influence upon the formation and monitoring of Supply Chains, or, alternatively expressed, the massive growth in the B2B field has, without a doubt, highlighted the importance of
SCM.204 With reference to the models available to industry for B2B, there is presently only one model that can be taken seriously, namely the SCOR model. Further models for E-Business are described in literature, but have not been comprehensively documented, or do not represent a normative model in the sense introduced here.205 For these reasons, this work will focus upon the SCOR model, which has successfully entered into use in the public sector area and gained in importance over its time there. This does not, however, refer to the previously
illustrated E-Business area Governmentto-Consumer (G2C), but rather more to the Government-to-Government (G2G) and Government-to-Business (G2B) areas. The aforesaid arrangement can therefore be expanded by these business areas in which the SCOR model presently takes a primary position, as will be made clear.206
1.7 Analysis of Supply Chain Processes by Use of the SCOR Model Razvi explains the essential character of Supply Chain Planning and Analysis as follows:
“Business today has evolved so that competition is between whole supply chains rather than individual companies. Selecting a few targeted key performance indicators can help a company to concentrate on its supply chain goals. Choosing the wrong indicators, on the other hand, could lead to a decline in supply chain performance. In addition, analyzing the supply chain based solely on individual events can have the opposite effect, causing turbulence in the supply chain.”207
The statement is made with reference to the degree of importance attributed to the analysis of the Supply Chain and its performance. Hugos defines five so-called supply chain drivers, which dominate the SC’s performance potential. Each of these drivers has two competences (or respectively, and more precisely, Supply Chain competences, as in the submitted context):208 The performance capability and the efficiency. The five drivers and the two expressions are connected in the following way:209 Production: This driver can be
arranged highly capable from a performance point of view, for example, by the construction of additional factories showing surplus capacities and use of flexible manufacturing procedures, in order to produce a large assortment of products. If efficiency is sought, a firm can build factories with low surplus capacity and optimize the factories for the manufacture of a limited assortment. Inventory: Performance capability can be achieved here by holding a high amount of inventory stock for a large assortment of products. Efficiency, with regards to
inventory management, would demand reduction of inventory amounts for all products – especially those which show a low turnover rate. Location: A location approach that emphasizes performance potential would be seen, for example, where a computer firm opens many branches to be physically close to the customer base. Efficiency can be achieved by serving all the customers from only a few locations and thereby centralizing activities. Transportation: Performance capability can be achieved by a transportation mode that is fast and
flexible. Efficiency can be achieved by transporting products in collective deliveries (batches) and by fewer deliveries. Information: The influence of this driver grows continuously, whilst the technology for collection and distribution of information spreads increasingly, is simpler to use and becomes less expensive. A high performance potential can be achieved by companies with the accurate and up-to-date collection of data, whereby that task is represented by the four drivers previously mentioned. If the objective is greater efficiency, it can be achieved by the collection
of a smaller amount of data, resulting in a reduction in the associated and required activities. The following illustration combines these connections once again in a graphical manner: Diag. 1-2: Connection between Supply Chain drivers and Supply Chain competences.210
Both of the Supply Chain competences named above – capability and efficiency – will be examined more closely in due course. Both are subsumed under the term Performance in this study and measured by the illustrated KPIs.
1.7.1 Efficiency of the Supply Chain Effectivity is defined in business science as the degree of objective achievement and is consequently a proportionate measure for work performance (output). It is therefore about doing the right things.211 Efficiency as a possible subobjective of effectivity represents a relationship between input-variables and output-variables and can therefore serve as a yardstick for resource efficiency. 212 Thus, it is about doing the things right. Effectivity implies, for example, an attractive price-performance ratio for
the customer, competitive advantages in the usage-related quality elements, a point of entry into the market which conforms to the objective, or a means of marketing products in accordance with or in excess of the planning level. Activities are considered efficient if they accompany relatively low cost, a relatively short development period or the use of synergy effects. Together, effectivity and efficiency influence commercial success.213 In a commercial business, efficiency is therefore respectively determined or reflected mainly by costs. In connection to the costs within the Supply Chain, one also speaks of Supply
Chain costs. SC costs can be recorded within the framework of a target costing in supply chains.214 Target costing in Supply Chains expands the marginal costing approach to cover the whole Supply Chain. The scope of conventional cost control is a single firm. The fundamental idea of cost control within a Supply Chain is to extend the cost control approach to cover the whole Supply Chain, which requires an approach that goes above and beyond organizational 215 boundaries. The inter-organizational cost control resulting from this represents an approach that seeks to monitor costs and
the profits resulting from them. Synergies are thereby used, which exist throughout several firms in a Supply Chain. Traditional cost controlling systems are only partially successful in ensuring an exact analysis of the costs beyond the domain of production. As opposed to this, activity-based costing systems216 assist organizations to allocate costs associated with supplier and customer relations more closely. This supplier- and customer-orientated cost information enables firms to identify opportunities to increase cost efficiency of their market relations within the Supply Chain.217 SCM has substantially contributed
to lowering operational inventory buffers and costs pertaining to manufacturers, wholesalers and retailers. Firms, for example, which participated in an initiative by the Massachusetts Institute of Technology (MIT)218 under the name Integrated Supply Chain Management Program (ISCM)219 reported a significant improvement with regards to their Supply Chains. According to this, firms reduced inventory buffers by half, increased on-time deliveries by 40 percent, and reduced the percentage of non-deliverable products to a fraction, simultaneously doubling the rate of inventory turnover.220
The business consultancy Pittiglio, Rabin, Todd & McGrath (PRTM) 221 discovered in a study intended to compare Supply Chain performance (Supply Chain Benchmarking Study) that leading Supply Chain companies invest on average between three percent and seven percent less of their profit for the management of their Supply Chain as their competitors. This degree of cost effectivity directly improves the percentage in trading margin or creates an opportunity of permanently lowering prices. As an example, leading companies within the food industry report about five percent lower SC costs than their competitors. In that industry, an increase of five percent in the margin
(or a consequent permanent decrease in price, if the savings are – at least partially – passed on to the end consumer) is of great importance. Having said that, the trading margin for the typical retailer comprises less than a half of cost savings achievable by leading businesses within the SCM area.222 Stock-keeping223 is a substantial component of Supply Chain costs, which under certain circumstances can quickly form a high percentage of the total inventory value. In the computer industry, for example, a rough estimate of the annual stock-keeping costs can be identified by the combination of the
capital costs (10 percent) and price erosion (25 percent), which equate to 35 percent of the net asset value. A good measure for orientation can be identified by the calculation of stock-keeping costs over a 10 day period. The formula for this is as follows: 10 days multiplied by 35 percent, divided by 365 days, equals 1 percent. This means that a respective reduction or increase of the storage duration of 10 days leads to a respective one percent improvement or deterioration of the backflow of goods.224 For many firms, stock-keeping costs have been a substantial impulse for implementing value innovations in the
sense of a new offer, which gives customers a significant increase in added value. Stock-keeping costs consist of the following components:225 Obsolescence, i.e., price erosion, wear-and-tear. Lost sales Personnel costs, i.e., work induced by stock Fixed assets, i.e., storage space and accessories Insurance Administration, i.e., stock checking and costs for information technology Capital costs, i.e., raw materials,
finished products and goods which find themselves in production. Stock-keeping costs have been identified as one of the main cost drivers and have a substantial influence upon a company’s profitability. The primary factor in positively influencing stockkeeping costs is, therefore, stockkeeping management, which represents an integral component within Supply Chain monitoring.226
1.7.2 Performance Capability of the Supply Chain According to Bovet and Martha, the
capability in the Supply Chain context includes reliability and flexibility. They describe this connection as follows: “Reliability is an important dimension of world class service. Reliability means predictable, on-time delivery of perfect orders, as expected by the customer. A perfect order is one that is shipped on time and complete, but more important, one received at the customer’s desired location within a precise time window, in excellent condition, and ready to use. It also includes the flexibility to respond to
last-minute changes by the customer at equally high service level.”227 The associated improvements directly or indirectly influence a Supply Chain’s performance capability. The measurement of these indicators plays, as already explained in section 1.5, a central role within the framework of change implementation. It constitutes a kind of “strength effect” and drives the activities onwards. The implementation of effective measurement procedures brings with it permanent challenges for business executives. It requires not only the restructuring of existing performance measurement procedures, but also the
establishment of a structured process for monitoring the Supply Chain.228 Evans and Danks extract the process of value creation by means of strategic Supply Chain monitoring (value creation through strategic supply chain management) straight from the value that is achieved for the company’s shareholders (Shareholder Value, SV).229 Building upon this, they define the so-called Shareholder Value Approach.230 The approach focuses upon the businesses’ value or, respectively, the advantages of this value for the shareholders of a business. The desire to positively influence the SV has in many cases represented the
starting point for Supply Chain improvement. The Supply Chain can immediately influence the profitability as well as invested capital via the following determinants:231 Profitability:232 On the one hand, it includes revenue in the sense of a high market share, larger trading margins and higher product availability. On the other hand, it contains cost with the objective of lower costs for sales, transportation, stock-keeping, material movement and distribution planning. Invested capital:233 This
comprises, on the one hand, working capital with the objective of lower stocks of raw material and finished products, in addition to shorter payback cycles. On the other hand, it comprises fixed capital with the primary intention of binding less capital into capital goods (for example, vehicle pool, warehouses, accessories for the movement of materials). Repeated references will be made to the determinants for Supply Chain performance in the chapters that follow, because they also find a role within the framework of the SCOR model. During this study recourse will often be made to
the division of performance potential into performance capability on the one hand – as a respectively external or customer-related component – and efficiency on the other hand – as a respectively internal or business-related component.234
Chapter Two
The Supply Chain Operations Reference Model (SCOR model) of the Supply-Chain Council This chapter focuses primarily upon the context of discovery as defined by Friedrichs within the framework of a research-logical course. Under context of discovery,
the motive that leads to a research project is understood. The motives are different in their starting points, leading to an examination. They all refer, however, to social 235 problems.
2.1 Origin and Objectives of the SCOR Model 2.1.1 Intention of the SCOR model The Supply-Chain Council (SCC)236 was founded with the aim of creating an “ideal” model of the Supply Chain. For this purpose, the Supply Chain
Operations Reference Model (SCOR model) was defined as a standardized process reference model of the Supply Chain, and has been continuously enhanced. With the SCOR model providing a unified description, it is therefore possible to consider analysis and evaluation of Supply Chains not only between one company and another, but also across sectors of wider industry. The SCOR model is used in three exercises:237 1. To evaluate and compare Supply Chains’ performance potential 2. To analyze and, if necessary, optimize integrated Supply Chains throughout the partners within the logistic chain.
3. To determine suitable places for the assignment of software and its functionality within the Supply Chain. The initial concept of the Supply Chain is that every production and logistics network can be described using five fundamental base processes.238 With each of the four main execution processes – source, make, deliver and return – materials or products are used or transported. By joining these processes into a chain it is possible to define customer-supplier relations and factor in the fifth base process, that of planning. If one combines all the main processes the result is a complete model of the production and logistics network.
The description of these fundamental processes within the Supply Chain is a substantial component of the SCOR model.239 Another factor here is the definition of metrics for the evaluation of the processes’ performance within the Supply Chain. This definition is important as it can be used to form the basis of a performance comparison (Benchmarking) either with other companies or other Supply Chains in the same industry. For the main processes the SCC members compiled the best known methods for achievement of high performance, the so-called Best 240 Practices, and integrated them into
the model. Finally, the SCC also added software system requirements into the model, which are helpful with the realization of these practices.241 Collectively the intention of the SCOR model can therefore be described as follows: “The Supply-Chain Council has published a SCOR model that describes the (supply chain) at multiple levels of detail, identifies best practices, and defines 242 associated KPIs for each process. Organizations are
beginning to leverage SCOR standards to drive consensus on terminology, processes, and expectations among 243 trading partners.”
2.1.2 Descent of the SCOR model The SCOR model was developed and promoted by the SCC as a pan-industry standard for Supply Chain monitoring. The SCC was founded in 1996 by the Business Consultancy agency Pittiglio, Rabin, Todd & McGrath (PRTM) 244 and Advanced Manufacturing Research (AMR),245 and originally included 69 voluntary member firms. Of equal
importance for the SCOR model’s diffusion are the respective inputs of manufacturers and implementers of system technologies, researchers and scientists, and governmental organizations. All of these groups participate in the SCC’s activities and in the development and enhancement of the model. By the beginning of 2006, the SCC had more than 1,000 members worldwide and had branches in North America, Europe, Japan, Australia/New Zealand, Southeast Asia and South Africa.246 The SCC is greatly interested in promoting the widest possible diffusion of the SCOR model with a view to
building better customersupplier relationships. It is also interested in the improvement of its members’ software systems through the use of mutual metrics and terms. Apart from this, the goal is to quickly recognize and adopt best practices, regardless of their origins.247 Whilst much of the model-based content has been used by practitioners for years, the model offers a special framework within which business processes, performance indicators, best practices and system technologies can be linked with one another. The result is a unified structure for both supporting communication amongst the Supply
Chain partners and increasing the effectivity and efficiency of Supply Chain monitoring and other activities.248 Member firms pay a small yearly subscription in support of the SCC’s functions. All who use the SCOR model are asked to make reference to the SCC in documents or representations applying to the model, in addition to all cases of its application. Additionally, members are urged to regularly visit the SCC’s internet page249 and make themselves familiar with the latest information available in order to ensure that they are using the latest version of SCOR.250 The SCOR model represents, in a transposed sense, the SCC’s consensus with respect
to the management of the Supply Chain.251
2.1.3 Structure and processes of the SCOR model The five basic management processes or chevrons, which form the SCOR model’s basis, are Plan, Source, Make, Deliver and Return. In addition to these five main processes, which form the organizational structure of the SCOR model, the following three process types can be differentiated:252 Planning: A planning element is a
process that adjusts the expected resource need to the expected demand conditions. Planning processes balance out the aggregate demand over a certain planning horizon. Planning processes usually take place at regular intervals and can contribute to Supply Chain reaction times. This type of process is referenced to the above-named main process Plan. Execution: Execution processes are triggered by planned or actual demand, which changes the condition of a product. They include dispatching and sequencing, changes in materials and services and product movement. This type of
process therefore incorporates the aforementioned main processes Source, Make, Deliver and Return. Enable, formally known as Infrastructure: Enabling processes are responsible for the preparation, maintenance and monitoring of information or relationships, upon which the previously-mentioned planning and execution processes depend. The following illustration collates the model’s structure in graphical form. Diag. 2-1: Arrangement of the SCOR model around five main Business Management Processes.253
In accordance with the illustration, the model includes an organization’s own Supply Chain and its respective five basic processes. Beyond this, however, it can also span the customers’ Supply Chains on the one hand, as well as those of the suppliers on the other. To take the process a stage further, the supplier’s suppliers and customer’s customers can be included. In this sense the model contains all interactions with customers, from order entry up to the
paid invoice. Furthermore, it comprises all products, i.e., physical, material and services, from the supplier’s suppliers right up to the customer’s customers, inclusive of equipment, accessories, spare parts, and software. Finally, it takes into account all interactions with the market – beginning with the understanding of demand as a whole, right up to completion of the order.254 The model’s notation is predetermined and follows consistent conventions in the processes’ descriptions: The letter P elements
stands
for Plan
The letter S represents Source elements The letter M stands for Make elements The letter D represents Deliver elements The letter R stands for Return elements. These main processes can also take the form of enabling processes. In that case the respective process is prefixed with an E, which indicates that the resulting process represents an Enable element. Example: EP represents an enabling element within the planning process.255 Within the main processes
there is also a universally valid structure, whereby the model focuses, so to speak, upon the product environment. This is shown in the following example, which represents the manufacture or Make process:256 Make-to-Stock – M1 Make-to-Order – M2 Engineer-to-Order – M3 Retail Product – M4. The assignment is formed respectively with reference to the procurement process Source:257
Source Make-to-Stock Product – S1 Source Make-to-Order Product – S2 Source Engineer-to-Order Product – S3. An analogue also applies for the process Deliver:258 Deliver Make-to-Stock Product – D1 Deliver Make-to-Order Product – D2 Deliver Engineer-to-Order Product – D3.
T h e Return process inevitably deviates from this and is distinguished by the following sub-processes:259 Return Defective Product – R1 Return Maintenance, Repair or Overhaul – R2 Return Excess Product – R3. The respective enabling elements are also described within each section of the planning and procedure processes. In this case, the format shown above is also applicable in the description and graphical illustration.260
The following illustration, taken unchanged from the model description released by the Supply-Chain Council (SCC), gives a collective overview of the associations and underlines once again the fact that the model spans all processes from the supplier right up to the customer. Additionally, in the case of SCOR we are dealing with a hierarchical model with several levels. The company’s Supply Chain itself represents the starting level (level 1). The main process level following this, i.e., Plan – P, represents the second level. This is shown by a single number and is followed by the target item of the
main process, i.e., P1 – Plan Supply Chain. The exact number can be extracted from the relevant position within the model’s structure. Further down from that is the third level, where the respective concrete process stages are located, i.e., P1.1 – Identify, Prioritize, and Aggregate Supply Chain Requirements.262 The following illustration, also taken unchanged from the SCC’s model description, represents the associations in graphical form using the process Plan. Reference is made here to the planning process contained in Diag. 2-2, namely P1 – Plan Supply Chain, which is allocated to the second level and its
sub-processes represented on the following third level. In this sense analogue representations exist within the model description for all main processes and their relevant sub-processes contained in Diag. 2-2. Further levels, i.e., those below the third level are, however, not included in the model, because they are of industry-specific character and would therefore contradict the basic concept of SCOR – that it represents an industry-spanning model.263 Diag. 2-2: SCOR model structure261
The processes from the fourth level onwards prove themselves to be so industry-specific and upon increasing levels even company-specific that standardization is no longer realistically
possible. The fourth, and all following, levels represent the object of implementation projects, whereby the fourth level refers to task, the fifth level to activities and the sixth level refers to instructions.264 The SCC’s SCOR model documentation contains seven basic sections: An Introduction, a section for each process of the second level (Plan, Source, Make, Deliver, Return), as well as a Glossary. Diag. 2-3: SCOR process stages by example of the process Planning (Plan)265
For reasons due to Supply Chain modelling, the basic process Return is listed in connection with two further basic processes: Source and Deliver.
The process of returning to suppliers, i.e., the return of raw materials, is documented as Source Return activity. The process that connects an organization to its customers, i.e., refers to the receipt of returned finished goods, is documented as the Deliver Return activity. This stems back to the SCOR Supply Chain’s fundamental thought represented in Diag. 2-1, whereby the model incorporates everything from the supplier right up to the customer.266 The planning and execution processes represent the center of the documentation, whilst the glossary contains a list of those standard process and metric terms used within the
document. The sections that occupy themselves with the types of planning and procedural processes are organized in the form of a unified structure: At the beginning of each section is an illustration, which contains a visual representation of the respective process element, the relationships of such elements to one another, and any relevant incoming and outgoing information (for an example see Diag. 23 illustrated above). Tables with text follow the illustration, and comprise the following information in the order mentioned:267 Standard name of the process
element, i.e., Process category – Plan Supply Chain Notation of the process element, for example Process number – P1 Standard definition of the process element. Example: Process category definition with the following description: The development and implementation of procedures for resource allocation over a given time span, in order to complete certain Supply Chain requirements. Performance Attributes associated with the process element. Example: Performances attribute Reliability, metric Delivery performance.268 Best practices for each respective
special process. In this case such best practices are examples, but not a complete listing. This section also includes special characteristics or respective possible features that can contribute to an increase in performance. An exemplary best practice is that the SC process should possess a higher degree of integration, starting with collection of customer data right up to receipt of the customer order and throughout production, right up to purchase requisitions upon suppliers. A possible arrangement to this end could take the form of an integrated SC planning system269 with interfaces to all supply and
demand sources by means of ITbased systems. In a similar manner to the process elements, the performance attributes and metrics are built-up hierarchically. Although not explicitly represented in the model, they are typically assigned to the first level of the respective planning process (i.e., P1 – Plan Supply Chain). From there, and following the hierarchy, they become decomposed and assigned to the respective planning, execution and enabling elements.270 This will be dealt with more closely below.
2.1.4 Performance attributes
and Level 1 Metrics Level 1 Metrics are primarily forms of measured data at a higher level, which can extend themselves through several SCOR processes. These metrics do not inevitably and explicitly refer to one of the SCOR basic processes of the first level (Plan, Source, Make, Deliver, and Return).271 The metrics can rather be seen in conjunction with the performance attributes. In the present version of the SCOR model (Version 8.0 as at beginning of 2007) the following five performance attributes are used: Reliability, Responsiveness, Flexibility, Costs, and Asset Management. Each
of
these
performance
attributes directly refers to the Supply Chain, which is why the prefix “Supply Chain” can be added (for example, “Supply Chain Reliability,” etc.). 272 The illustration below which is taken directly from the model description by the SCC gives an overview of the Performance Attributes used within the SCOR model. In order to operationalize the performance attributes, they must be connected in a further stage with the Metrics Level 1. For example, the metric fo r order fulfillment lead time can be coupled with the performance attribute responsiveness. The performance attributes are characteristics of a given Supply Chain
for analysis and comparison with other Supply Chains with competing strategies. Without such characteristics it would be extremely difficult, for example, to compare an organization which follows a low-cost strategy to one whose objective is the highest possible level of delivery reliability.273 As described above, the performance attributes are connected to the metrics of the first level. The latter represent measures that enable an organization to calculate how successful it is with regards to the achievement (or not) of its desired position within the competitive market place. Although the performance attributes are critical for
the application of the model, formal definitions were only integrated into later versions. For example, standard performance attributes were introduced into Version 4.0 of the model.275 In Version 5.0, the process descriptions which are assigned to the activities of the second and third levels were adjusted in order to ensure that the metrics used actually measure their intended objects. These modifications are two examples which show how the SCOR model originated through an iterative process and, even now, is constantly revising itself. This undoubtedly represents one of the great strengths of the model.
Diag. 2-4: Connection between SCOR performance attributes and metrics of the first level.274
The metrics used are of a hierarchical nature similar to that of the process elements. The metrics of the first level result from aggregate calculations, which in turn are based upon the metrics of the levels below
them (level 2 and so on). For example, the delivery performance is calculated as the total amount of products delivered punctually and completely. Beyond this, metrics are also assigned on a lower level in order to diagnose deviations between the performance and the plan. It can therefore be thoroughly advantageous for an organization to examine the correlation between the requested delivery date (request date) and the approved delivery date (commit date).276
2.1.5 Changes in SCOR Version 6.0 As previously explained, the SCOR model originated through a type of
evolutionary process and went through several revisions from version to version. In due course, SCOR Version 6.0 finds itself applied mainly with respect to the associated performance indicators. The following lines deal with its main differences from the previous version, an understanding of which is necessary in order to be able to judge the model’s evolution. Version 6.0 of the SCOR model represents the sixth substantial revision since the introduction of SCOR. Model revisions are normally implemented when the members of the SCC deem that changes are necessary in order to promote the continued effective
usage of the model. When the committee responsible for the metrics announced that the metrics of the first level did not consistently correspond with the main processes on the first level it became necessary to prepare a revised model. The shortcomings were mainly corrected in Version 6.0, but minor changes were announced for the later Version 7.0.277 In Version 6.0 changes were implemented in three primary areas: Retail processes, Return processes and Electronic Business (E-Business). The delivery processes were extended with regards to the sales processes and expanded by a new process element, D4 – Deliver Retail Product. This
extension makes allowance for special features with reference to the activities and their sequence which are associated with the delivery (normally to the end consumer). Within the delivery return process, the process element R 2 – Return of Maintenance, Repair and Overhaul Product has been reconfigured after over one year of assignment in order to better reflect the processes in practice. The processes associated with the return of the said products (SR2, MR2), have been brought up-to-date for better use and the definitions associated with them have been improved accordingly. In this version of the model, only the SR2 and
the DR2 elements have been revised. It was envisaged that revisions to cover the SR1-, DR1-, SR3- and DR3processes would feature in one of the next versions.278 Regarding the concept of Electronic Business (E-Business),279 best practices were included in the manufacturing process. This represented a continuation of the inclusion of best practices, which was introduced in Version 5.0. During the examination of the effects of new technologies upon Supply Chain monitoring, the SCC came to the conclusion that although the applied technologies have altered, the fundamental processes associated with
the Supply Chain have remained unchanged. The assigned best practices have, however, changed substantially due to the influence of new technologies. In its overhauling of the best practices and relevant technological descriptions, then, the updated version of the SCOR model represented the formal recognition of tried E-Business methods and E-Business technologies by the SCC.280
2.1.6 Changes in SCOR Version 7.0 In SCOR Version 7.0, which has been published by the Supply-Chain Council in March 2005, changes were
congregated in two areas.281 Firstly, the application of the performance indicators has been simplified. For this purpose the first level performance metrics were reconfigured and their fundamental structure carved out more elaborately. The number of first level metrics was reduced from thirteen to nine. This did not, however, mean that the respective metrics have gone altogether. They have, rather, been allocated to the performance measure level below it. A consequence of this restructuring was that the processes within the SCOR process elements Deliver had to be adjusted. The respective processes on the third level
were therefore extended in order to guarantee a better alignment with the cycle time and the cost-specific performance metrics.282 Beyond this, a new section added to the performance indicators has been attached as an additional appendix, which described the individual metrics and their calculation in detail. Additionally the explanation of the metric’s influence has been substantially expanded. Only the metrics of the first level are contained in the Appendix to Version 7. The question of whether or not future SCOR model versions would also include similar detailed descriptions for all performance
indicators was under construction at this time.283 The second area in which changes have been made was that of the best practices. Here a number of new procedures were added. In particular, twelve new best practices were included, of which four were already contained within SCOR Version 6.1, but not explained in detail. The new procedures were listed in the Appendix, and discussed and dealt with in detail there. Whilst the Appendix to SCOR Version 7 contained those best practices which were newly included or changed, future versions of the model would likely include the list (and associated
explanations) within the model itself.284
2.1.7 Changes in SCOR Version 8.0 Version 8.0 represents the most up-todate version of the SCOR model.285 It features a number of fundamental revisions, whereby the processes of the first, second and third level remain unchanged from the previous Version 7.0. The main changes are in the areas of performance indicators, best practices, illustration of inputs and outputs, workflow diagrams and the SCOR database.286 With reference to the performance indicators, an additional Level 1 Metric
– namely Return on Working Capital – can be found within the performance a t t r i b u t e Assets. Several further performance indicators have been simplified. The second level processes now exclusively have performance measures of the first level assigned to them. Apart from this an additional cost measure for each individual process has been integrated. The adoption of a cycle time measure for each process was similarly pursued in Version 7.0. By these means, it is possible to aggregate those two measures to the first level performance metrics. In order to illustrate the aggregation possibilities and fundamental hierarchy, a completely new appendix for the performance
indicators (Metrics Appendix) has been included in the SCOR model’s documentation.287 The Best Practices Appendix was also revised with the objective of creating a clear and consistent point of reference. Changes to several definitions were brought up to date, and their assignment to the corresponding processes was changed accordingly. The best practices no longer contain the column Feature, as this is a remnant of the time when the model description still contained associated software characteristics, which are no longer identified by the Supply-Chain Council. The respective column has, where
necessary, been definition.288
replaced
by
a
The revised model description also includes the new Inputs and Outputs, along with their definition. These were not respectively present, described or referred to in any of the previous versions of the SCOR model. This element was created by a group within the SCC that assigned the ISA-95 standard289 by the ISA organization290 in conjunction with SCOR. The corresponding definitions were inserted in order to fulfill the requirements on the SCOR side as well as those of ISA95.291
Due to the first-time adjustment of the SCOR model to a form of illustration that is compatible with Business Process Management (BPM),292 the Workflow Graphics are markedly different from the earlier ones, which were bound to the Microsoft Word format. They now also include the work or tasks to be carried out (deliverables), i.e., those elements that move from one process to, or respectively into, the next (input) and back out of this process (output). As a result of these complex work procedures and the fact that the illustration is in a special software program replacing Microsoft Word, the font size in the work procedure diagrams is very small. The diagrams are therefore available on
the Supply-Chain Council’s internet page293 in a Hyper Text Markup Language (HTML)-format with an enlargement function.294 It is envisaged that the next SCOR version – probably Version 8.1 – will illustrate and offer the data stored in BPM-compatible format within the SCOR database in a format that is independent of the manufacturer. At present the SCC is working on this suggestion with the respective system suppliers. In addition to this, a software license program is being considered with regards to the release of future SCOR model versions in an electronic format.295
In this study references to the SCOR Model are to Version 8.0. The only exception to this is the issue of performance indicators, where Version 6.0 will be used. This is because Version 6.0 was the up-to-date model at the time of the theses model’s compilation and as a result of this, the research is based upon the performance terms within Version 6.0. The validity of the evaluations is, however, not influenced by this because, as mentioned above, no performance indicators were removed or added. The only change has been in the position of the performance indicators within the model’s hierarchy.
2.2 Limitations of the Practical Areas of the SCOR Model’s Application as Descriptive Model for the Analysis of Companies’ Supply Chains As already noted, the SCOR model was developed to describe business activities relevant to the Supply Chain, which are linked to all phases that are run through in order to satisfy customer requirements. The model is characterized by five basic processes. By illustrating Supply Chains using these process building blocks and
generally valid definitions, the model can serve to describe Supply Chains both of a very simple or very complicated nature.296 In describing, as it were, the “depth” and “width” of any chosen Supply Chain, the model has been able to contribute to delivering a basis for SC improvements for global as well as location-specific projects.297 As shown in the next illustration, the SCOR model represents a so-called Business Process Reference Model . Reference models are considered to be normative models as represented in Chapter 1.298 With SCOR, we are dealing with a special model, which connects process elements, performance
indicators, best practices and the specialties relevant to the implementation of Supply Chain activities in a very distinctive way. The singularity and effectivity of the model and its successful application are mainly based upon the concentrated and regulated assignment of these four elements.299 Reference models are principally used to systematize business processes and to represent them in a unified manner. The SCOR model builds upon the input, throughput and output scheme that is used within the process monitoring framework. The model is used to represent the processes on
various levels and to determine their formulation in stages.301 The SCC defines the term reference model as follows: “Process reference models integrate the well-known concepts of business process reengineering, benchmarking, and process measurement into a cross-functional 302 framework.” Reference models are based upon workflows and the monitoring of these workflows (Workflow Management ).303 They identify the interfaces within the structure of the work procedure which
enable products to interact on a variety of levels. All systems for monitoring work procedures contain a number of universally valid building blocks, which influence each other within a defined set of scenarios and work together. Various products typically show a difference in levels of performance within the universally valid building blocks. Diag. 2-5: SCOR as a hierarchical model300
To achieve interoperability between the various work procedures, it is necessary to determine a standardized number of interfaces and formats for the exchange of information. This can take
place by the assembly of unambiguous interactive scenarios with reference to these interfaces. The interactive scenarios in turn serve to identify various levels with functional concurrences, which are in line with the product range found on the market.304 In addition to this, a reference model represents a Supply Chain model that can support the introduction of application systems.305 The advantages of a reference model result, in this context, from the ability to enable the detailing of several observation levels and methods of questioning. Firstly, this includes the description of process conditions and process results, i.e., the
answering of the questions as to which data, information and resources are used and which objects are being processed.306 Secondly, it contains the description of the associated procedure from a process point of view, i.e., the answering of the questions as to which partial processes and results pilot the process and which organizational areas are involved.307 An important point in this context is that the model explicitly describes processes and not functions. Expressed another way, the model concentrates upon the activities involved rather than the people or organizational units that carry out these activities.308 The relevant
process is shown in the following illustration. Process Decomposition Models, whose intention deviates considerably from the formerly mentioned process reference models, must be clearly differentiated. The SCOR model provides the service of a language for the communication between SC partners. Process decomposition models are, on the other hand, designed to observe a special configuration of process elements. Therefore they are missing the integrative character – with regards to the business-internal as well as the business-integrated Supply Chain.310
Electronic Business (E-Business) has risen in importance as a new application domain in conjunction with reference models in recent years.311 According to Fettke and Loos, reference models for E-Business are those which support the formation of E-Business systems.312 Following this, the SCOR model can also be understood as an EBusiness reference model, because its application can determine a comprehensive application of information technologies. Diag. 2-6: SCOR as an activity-orientated reference model for business processes309
2.3 Strengths and Weaknesses of the SCOR Model Based Upon the Present Discussion 2.3.1 Strengths and potentials of the model One of the great strengths of the SCOR model is its capacity to predict both duration and costs, particularly when it
is implemented within the framework of a Supply Chain analysis project (described in the following course as SCOR project in short). SCOR projects are often formulated with the following measures in mind:313 The improvement of a company’s stock market value The increase of profits and margins The increase of the available financial means by implementation of investments (i.e., IT investments) The reduction of costs The optimization of Enterprise Resource Planning (ERP).314
Handfield and Nichols provide a good summary of the more qualitative advantages in connection with the use of the model: “The major benefit of SCOR is that it gives interorganizational supply chain partners a basis for integration by providing them, often for the first time, with something tangible to talk about and work with.”315 In addition to qualitative improvements, such as improved communication between the operational areas, the model can also be used to
achieve the following quantitative results:316
(exemplary)
The improvement of operating results of an average of three percent in the initial project phase by means of cost reduction and improvement in customer service. An increase (of between twofold and sixfold) in profitability317 with regards to project investment costs within the first twelve months. This is often in conjunction with improvements that compensate for costs inside the first six months. A reduction of expenses for information technology (IT) through
minimizing system customizations and making better use of available standard functionality.
The continual actualization of the project’s portfolio 318 by continuous conversion of Supply Chain improvements with the objective of increasing annual profits by one to three percent. Hughes et al. specify the following typical and respective areas for potential improvement and optimization when SCOR is applied within a framework of initiatives to improve the Supply
Chain’s performance (Supply Chain improvement initiatives):319 Raw materials purchase costs: 25 percent Cost of distribution: 35 percent Total resource deployed: 50 percent Manufacturing space: 50 percent Investment in tooling: 50 percent Order cycle time: 60 percent New product development cycle: 60 percent Inventory: 70 percent Paperwork and documentation: 80 percent Quality defects: 100 percent.320
Stephens points out the following advantages may be achieved when SCOR is applied to integration measures (those quantified benefits which may be attained by integrating the Supply Chain). He refers to these in the context of a 1997 comparative study by the SCC:321 Delivery performance improvement: 16 to 28 percent Inventory cost reduction: 25 to 60 percent Reduction in order fulfillment cycle time: 30 to 50 percent Improvement to forecast accuracy: 25 to 80 percent
Increase in overall productivity: to 16 percent Lower supply chain costs: 25 to percent Improvement of fill rates: 20 to percent Improved capacity realization: to 20 percent.
10 50 30 10
An additional, although not immediately quantifiable, advantage to the application of the SCOR model may be found in its nature as something independent of a particular industry. 322 The freedom that this entails means that, amongst other things, it is possible to arrive at a comparison of processes in
companies from various industry affiliations and formulate an optimized process as a result.323 A report compiled by the company Intel describes the advantages that resulted from the implementation of a SCOR initiative. The advantages portrayed are mainly qualitative in nature. The project team originally responsible for the SCOR project strongly promoted the SCOR model’s diffusion throughout all areas of Intel’s Supply Chain. The subsequent report is evidence that after reflecting on the experiences recorded the team was convinced of the model’s performance capabilities and advantages. An 324
additional advantage gained, although harder to quantify, was the increase in knowledge on the part of the project team’s members with reference to business processes, Supply Chain processes, and relationships and associations within the Supply Chain. The application of the SCOR model is also seen as a positive end in itself as part of a process to internalize and comprehend the fundamental connections in the chain in a generally valid language and within a continual structure. The report stresses that the inclusion of representatives from different business areas in the model was a great advantage. As a result of this approach,
the risk that a one-side point of view would result was reduced.325 In the report’s conclusion the central knowledge database (repository), which originated out of the project was identified as a significant advantage for the business. Today this represents a substantial component of Knowledge Management326 within the framework of Intel’s Supply Chain. In addition to this, the part of the repository used for all SCOR projects was also applied to overlapping projects – for example, in the form of an initiative for business process modeling within the framework of the Enterprise Resource Planning (ERP) system.327
The companies SAP328 and PRTM329 began working together in the year 2000 on a SCOR-based, standardized program. This program can be assigned in order to compare organizations’ Supply Chain performance capability to the competition (standardized Benchmarking Program based on SCOR). Participants were able to compare their results to the results of the competition based upon the SCOR model.330 The BASF Company331 also participated in such a comparison study as carried out by SAP and PRTM. The
corporation stated that the project had contributed extremely positively to the analysis and definition of many areas for possible improvements. The study demonstrated that the application of the SCOR model holds the potential for substantial improvements in the fields of Supply Chain solutions.332 The views of leading IT research companies, like the Meta Group,333 point in the same direction. SCOR-based Supply Chain performance comparison is considered to be a useful way of supplying businesses with valuable information in order to analyze and optimize their processes. This is particularly important when the metrics
allow the Supply Chain’s performance potential to be compared to that of the company’s competitors. In association with this, the particular advantages of this model over the Supply Chain Scorecard introduced in Chapter 1 are evident, as the latter is classified as more one-dimensional and thus insufficiently integrated. As a result, the advantages and strengths of the metrics used within the SCOR model’s framework are clear.334
2.3.2 Weaknesses and limitations of the model The SCOR model is still in an evolutionary condition, and remains subject to changes. On the one hand, this
gives it a certain strength, because it guarantees the model’s continuous expansion to include up-to-date themes.335 This could involve a whole monitoring process, such as the introduction of the Return process as shown by Version 4.0 (even if not all activities in this field are contained within Version 6.0). On the other hand, this is accompanied by a particular degree of uncertainty as elements of the model valid today may – under certain circumstances – be changed in the future and thereby may lose at least part of their present validity. As in the earlier illustration 2-5, the SCOR model’s hierarchy presently
comprises three levels in order to support Supply Chains of varying complexity throughout various industries. The SCC has clarified that it does not intend to extend the model into further levels and describe how a certain organization should execute their business or adjust their present IT systems and information flows to suite market requirements. Such a statement results from the SCC’s conception of SCOR as a descriptive and formative model. It does not, however, mean that the model’s application excludes the possibility of a subsequent Supply Chain optimization building on its findings. Indeed, it even makes explicit suggestions in that direction, for
example, in the form of best practices. The model’s status does, however, mean that each organization that applies it with a view to securing improvements in the Supply Chain must expand the model – namely by the inclusion of a fourth level which illustrates the tasks. This necessitates the inclusion of organization-specific processes, systems and practices. This organization-specific extension is not supported by the SCOR model, at least not in the present version. There are, however, approaches regarding the improvement of the model’s assignment possibilities in this field, and these will be dealt with in Chapter 5.336
Furthermore, the model does not attempt to describe every business process or every activity within the Supply Chain. These deliberately excluded components are: Marketing and sales (i.e., creation of demand), research and technology development, product development and some areas of postdelivery customer service. The model also includes several functional areas as preconditions without addressing them specifically: Human Resources, Training, Quality Assurance, Information Technology, and Administration (as long as the latter does not refer to the Supply Chain’s monitoring). The present SCC position is that the respective horizontal
activities are implicitly contained within the model, and that there are particular organizations that specialize in these areas. The SCC leaves it to such organizations to offer qualified support in these fields.337
2.3.3 Critical success factors during application of the SCOR model The critical factors for success during the SCOR model’s assignment arise from the model’s possibilities and limitations, and the interdependencies between them. In this way, the model undoubtedly serves to standardize the Supply Chain’s procedures in an manner
that can span different industries. The participating companies and organizations speak, as it were, a single language in which they define their metrics identically.338 If an organization chooses to follow the SCOR model, it is important that it transposes the universally valid and formulated concept upon the specific competitive situation. As a result, it is obliged to openly address the actual work processes within the organization, and this presupposes a good knowledge of these processes. Harmon characterizes the conditions as follows: “The use of a framework-
based business process methodology is only possible in cases where a high level analysis of the processes to be analyzed already exists, and where measures of process success have already been standardized. Obviously, it will help if the standardization is done by a large, neutral standards group, like the Supply-Chain Council, since that will assure that the processes and measures are really well thought out and that individual practitioners will more readily buy into the common framework.”339
The parties involved in the Supply Chain can benefit from best practices with regards to the Supply Chain’s monitoring, and as a result of these practices the compatibility within the company-spanning Supply Chain (i.e., with the inclusion of all parties) increases. This also applies to the synchronization of the respective hardware (HW) and software (SW) solutions – a criterion not to be underestimated nowadays as these can represent a substantial cost factor and must be seen unconditionally in the context of their related advantages.340 Due to the greater complexity this entails, the synchronization is more
difficult than if companies were to concentrate purely upon their own areas. To carry this forward successfully, companies must have sufficient funds at a relatively early phase of project implementation. On the positive side, however, synergies arising from this process can yield advantages from the very beginning of the implementation phase. The SCOR model has a high degree of abstraction due to its comprehensive approach.341 It is therefore almost impossible to apply with an unstable basis of cooperation between the participating Supply Chain parties as it presupposes a certain degree of
continuity. If the approach is persistently applied, the dependency between the unified partners increases, and as a result the companies involved lose sovereignty. Whether that represents an advantage or disadvantage depends strongly upon the respective business strategy. In addition to this, the narrow supplier-customer connection finally and inevitably leads to the release of sensitive information at the interfaces, whereby critical knowledge can also flow.342 The business must decide if the benefits of this outweigh potential disadvantages. It is the author’s view that the critical success factors named do not in
themselves primarily represent a SCOR model problem, but moreover a problem of Supply Chain Management in principle. The reason for this can be seen in the fact that SCOR illustrates an organization’s Supply Chain, therefore representing a descriptive model. It doesn’t lay claim to immediately structuring the Supply Chain, i.e., to being a formative model.343 Nevertheless, it should contribute by respective recommendations to Supply Chain improvements, but this takes place due to the description and consequent extraction of action points. Brought to a precise denominator: The SCOR model itself does not form,
but contributes to a (better) formation or structure of the Supply Chain. Upon closer examination, this train of thought does not represent a disadvantage, but moreover an advantage: It is the high degree of abstraction that in fact allows the model to fulfil the requirements of a normative model or reference model respectively, as stated above. It is, however, important to take into consideration the limitations to the model’s successful assignment.
2.4 Practical areas of application of the SCOR model This section will examine the assignment
and application of the SCOR model. During this a differentiation will be made between the two following cases: Companies that have applied SCOR within the framework of a business initiative. External consultancies, which enlist the SCOR model with customers for the purpose of analysis and necessary building upon it for Supply Chain optimization.
2.4.1 Examples for the application of SCOR ?in the framework of a business
initiative In conjunction with this, the author came upon two present examples from the High Tech industry, Hewlett-Packard (HP) and Intel. In addition to this, SCOR is presently an intensely discussed topic within the logistics field of the Department of Defense (DoD) in the United States. This last case study is particularly noteworthy on account of the scope and the complexity of the respective Supply Chain. 2.4.1.1 Application of SCOR at Hewlett-Packard (HP) Hewlett-Packard’s344 Business Process Management Group (BPM) has developed a reference system for
product design and for the business areas responsible for customer relations which is built-on the SCOR model. The group has also added a reference system for demand generation (Marketing). These reference systems have been successfully used in numerous projects, and HP has handed them over to the SCC’s newly formed Special Interest Groups (SIGs) – the DesignChain Council (DCC) and CustomerChain Council (CCC),345 so that they can be adopted as open industrystandards for business process 346 monitoring and improvement. A further example of the SCOR
model’s application took place within the framework of the merger347 of HP with Compaq in 2002.348 HP considers this application to be a good example of a so-called approach to a Second Generation Business Process Change. When the merger was first announced, teams were formed by HP and Compaq to plan how Supply Chain combination could be carried out. At this time, both Compaq and HP had dozens of Supply Chains distributed all around the world. The Supply Chains had been developed at different times and were based upon differing software systems. Next to the project team responsible for the Supply Chains, there were also teams that were responsible for analyzing the software
systems which were to be applied for the new, combined functional areas of sales, marketing and development of new products, as well as the supporting functions of a financial, accounting, personnel and IT nature.349 Most of the teams began with an inventory of the systems already present. Upon completion of this they moved on to a discussion of the advantages of the various applications with a view to reaching an informed decision as to which ones were the best suited for the future. At that time it was known that complex Supply Chains can be characterized by means of SCOR diagrams of the second level (Level 2
Thread Diagrams)350 and that SCOR provides precise formulas for business metrics, which can be used upon data from the past in order to measure the achievement of success in every process on the second level. Based upon previous examples of a so-called First Generation Business Process Change, 351 there was widespread skepticism as to whether the project’s implementation – the analysis of HP–s and Compaq–s main processes, the optimization of the communal future processes and the allocation of metrics – was realistic within the given time frame. Firstly, SCOR processes of the second level, and then of the third level
were analyzed in order to determine mutuality between the present Supply Chains. Data from earlier studies was also used to identify the success of every process. In several cases two differing processes of HP and Compaq were functionally similar, but there was considerable variation in their performance potential based upon the relevant SCOR metrics.352 In other cases the processes were compatible as far as their performance potential was concerned, but one of the two showed a better functionality, which was assessed by means of the third level processes. In addition to this, the SCOR model’s application enabled the team to
identify the Supply Chain processes with the greatest efficiency and, building upon this, to choose the most suitable software applications for process support. The project’s success rested mainly upon the presence of a reference system which allowed it, in a relatively short time and in a consistent way, to analyze and optimize the Supply Chain processes, as well as to apply metrics in order to assess the effectivity and efficiency of every process (and associated sub-processes). In retrospect, then, HP was able to state that the success of the project was primarily attributable to the SCOR model’s assignment.
In the months following the merger, HP’s IT BPM Team concentrated upon considering how SCOR’s reference system could be expanded to include areas within the business that were not contained within the present version.353 Within the framework of several development and refinement cycles (iterations) and in conjunction with business partners, the model was validated on the basis of regular operational occurrences. The reference system resulting from this was an exact reflection of several of HP’s business areas and proved itself to be adequately and universally valid in its ability to analyze and describe the business processes in any number of HP’s
business areas.354 Welke summarizes such a means of approach as an expansion of the normative model’s possibilities (broadening the normative model set)355 and goes on as follows: “The full-enabling process set has been used by HP to manage the Merger and Acquisition process with Compaq. It has been the basis for the extension to an open standard, SCOR-style, normative model.”356 With this process the conversion from a universal, normative model into a SCOR-based model was effectively
completed. 2.4.1.2 Application of SCOR at Intel Intel357 describes the method and the success of the SCOR model’s application under the heading Experience with SCOR at Intel. It is thereby particularly remarkable that a personal method was developed, the socalled Intel SCOR Best Known Method (BKM). The BKM Project for Supply Chain analysis and optimization at Intel began with focused preparations. Before a team of employees from various functional areas was formed, the business information necessary to define a clear problem statement was collected by a core team. After the function-
spanning team had been formed, the whole team participated in a number of so-called Face-to-Face (FTF) work meetings to define the project milestones according to the SCOR model.358 The Intel-specific method developed from this means of implementation, BKM, recommended in effect that the core team completed the assigned tasks and collected detailed information in small workgroups (for example, based on metrics, benchmarks and financial information) and presented them during the next FTF work meeting. Additionally, SCOR-based simulations were used early on in the course of the project to analyze alternative
configurations and to later confirm the effects of the suggested changes. In some cases Intel’s Supply Chain was not as effective or reactive as would have been necessary for a growing business with a high business volume. It became evident that the company’s traditional business model was often unable to support requirements that arose in particular business areas. Furthermore, some of the established “workarounds” were not efficient or robust enough. Intel had started the first SCOR project with the objective of identifying improvement potential in the customer service and efficiency areas, purely with reference
to its own Supply Chain. The project was intended to test the SCOR model’s usage and supporting tools within the organization, and compile and establish guidelines for its application.359 The desired results of the project lay in the following areas: Documentation of the Supply Chain and the measures for improving the Supply Chain’s processes.
Identification of short-term improvements. Ensuring support on the part of the business areas and executives, and
identification of the persons responsible for long-term 360 improvements. Additional results contained a summary of project results and the learning progress achieved by the SCOR model’s usage as well as the principles compiled for SCOR application in future initiatives. Beyond this, a method was developed, due to the adoption of SCOR, which should, in the future enable the comparison of SCOR-based metrics to those of competitors, i.e., a SCOR-based performance comparison (benchmarking).361
The company-wide application of SCOR BKM within all business areas was supported and promoted by the Intel Supply Network Group (ISNG). The greatest challenge in conjunction with this resides in the requirement for circumferential training during simultaneous acceleration of the method’s further development. In order to support the latter requirement, the Intel IT research group cooperated with t h e Network Decision Support Technology (NDST) group – a group within ISNG that was responsible for simulations. The objective was to develop a SCOR-based Supply Chain simulation, and examine alternative designs of Supply Chain networks (so-
called Supply Networks, SN), as well as the effects of high-priority system solutions. BKM has meanwhile been expanded by the addition of a SCORbased planning process with regards to the Supply Network. This procedure was checked within an extended Supply Chain, which illustrated the electronic devices and component group’s Supply Chain model along with a European Original Equipment Manufacturer (OEM). The focus of this study was the possible replanning of demand response. During this, it was possible to study the effects of the balancing of demand requirements forward-facing, as it were,
within the Supply Chain (i.e., from delivery process up to procurement process), and to study effects of the replanning process upon the Supply Chain performance and costs.362 In addition to this, increased focus on the planning process also made it possible to effectively make inventory items available for order fulfilment. Within BKM, SCOR’s planning process has been specially adapted to Intel’s business requirements and its marketing partners’ companies within the expanded Supply Chain. New possibilities for collaborative planning with suppliers, as well as the sharing of demand information, have resulted from this.363
Finally, the SCOR-based simulation was integrated into tools for the application of diverse forecasting methods and the modelling of demand creation. In this context the so-called Postponement Strategies364 could be modelled, which enables examination of the effectivity of a product’s final assembly at various locations.365 In the recent past Intel has gone beyond the measures for the application and enhancements of SCOR spoken of above and has engaged in a SCORbased model that exists under the name Value Chain Operations Reference Model (VCOR).366 The model’s
promotion takes place under the auspices of an organization called the Value Chain Group (VCG) .367 VCOR builds, as it were, upon the SCOR model, but it is characterized by two main differences or variations:368 Firstly, the plan process is extended by two further so-called Macro Processes: Govern and Execute. All further processes come below these three macro processes. Secondly, the model is constituted by the following eight Process Classifications instead of the five classical SCOR main processes:
Market, Research, Develop, Acquire, Build, Sell, Fulfill and Support. Intel provides the following description for the usage and assignment of VCOR sought, and the commitment associated with it: “A general consensus has developed among partners developing essential collaboration models for product design for supply chain that the long-term value proposition is to focus on a Value Chain Operations Reference model (VCOR).
Defining business semantics in terms of the common vocabulary of VCOR aggregates business applications and business processes to a higher level of abstraction. In this way, value chain integration enables coordination across departmental, organizational, and enterprise boundaries from an overall business level perspective. The benefit is that it facilitates servicecomposed processes and, thereby, brings serviceoriented relevance to a complex IT landscape in
which ongoing, flexible adaptation is necessary.”369 The further development of VCOR on the basis of SCOR can therefore be seen as an attempt to move still further from normative models towards those enriched with Value Chain-specific aspects.370 2.4.1.3 Application of SCOR by the US Department of Defense (DoD) The US Department of Defense (DoD) maintains, according to value, the largest worldwide Supply Chain. Within the organization, the Supply Chain Integration Office of the Secretary of Defense (OSD) – Logistics and
Material Readiness is responsible for Supply Chain operation.371 The annual logistic expenses in 2004 were more than $80 billion (US),372 which were administered by more than a million logistics employees. Several years ago, the DoD began with the SCOR model’s introduction in order to lower Supply Chain cost and improve customer satisfaction. The ultimate objective was to implement an integrated Supply Chain.373 The DoD assumes that SCOR is a universally valid platform and language for cooperation with private firms and the respective defense organizations, used in order to mutually develop and
assign best practices and evaluate Supply Chain efficiency. At the end of 2004, the DoD had introduced the SCOR model throughout many areas of its business. The model therefore represents an immanent component of the strategy for Supply Chain monitoring, and its leadership of the logistic area drives future SCOR developments onwards within the Supply-Chain Council.374 The Supply Chain model developed by the DoD has the purpose of organizing relevant information and making it available (Knowledge Exchange). The SCOR model version developed by the SCC and widely diffused throughout industries thereby
developed itself into an analytic tool for Supply Chain monitoring within the public sector. 375 This refers back to the previously mentioned expansion of its spectrum of usage to cover E-Business areas Government-to-Government (G2G) and Government-to-Business (G2B).376 For the purpose of knowledge exchange, the terminology was adapted to the DoD’s SCOR model defined by the OSD, which is distinguished by the following monitoring processes: Source, Make/Repair, Deliver, Reutilize/ Dispose. Under the monitoring processes lie functional areas, which serve to categorize Supply Chain
information for the knowledge exchange. In the case of the functional areas we are dealing with Material Requirements Determination, i.e., Purchasing, Material Management, Repair/Maintenance, Material Distribution, Transportation and Material Disposition. The result is a SCOR-based Supply Chain, which takes the special requirements of the DoD into consideration.377 The following illustration summarizes the explanations in graphical form. The above Supply Chain model for knowledge exchange takes into consideration the two fundamental
perspectives from which a Supply Chain can be observed—on the one side, an internal Supply Chain, which subsumes all the monitoring processes and functional areas of an organization in order to fulfil customer orders; on the other side, an external Supply Chain, which is defined as a group of independent organizations cooperating within special channels in order to deliver a product or a service.379 The monitoring of the DoD’s Supply Chain (DoD Supply Chain Management) is based upon an integrated procedure that begins with the planning of the requirements on the customer-side with reference to material
and services and ends with the delivery of material to the customer. Included in this process are the return of material and the bidirectional information flows throughout suppliers, logistic functions and customers.380 Diag. 2-7: DoD model of Supply Chain Management378
Beyond this, Supply Chain monitoring requires a complete set of related process cycles (including planning,
procurement, repair and delivery), which are communally optimized to ensure that material and service requirements are efficiently planned and executed in order to satisfy customer needs. The monitoring of the DoD’s Supply Chain focuses firstly upon fulfilling customer requirements and only secondly upon doing this at the lowest process costs.381
2.4.2 Examples for the application of the SCOR model by external consultancies Research with regards to application of the SCOR model by external
consultancies has following results:
produced
the
There are a number of smaller business consultancies, for example SCE Limited based at Stillwater, Minnesota or mi services group with its headquarters in Wayne, Philadelphia, which have chosen to specialize in the application of the SCOR model. SCE Limited roughly describes itself as a “center of excellence in SCOR 382 application.” Companies of this type usually have less than 500 employees. There are also medium-sized
business consultancies, usually with around between 500 and 1,000 employees, that see SCOR as a substantial component of their advisory portfolio. The best known example is the consultancy Pittiglio, Rabin, Todd & McGrath (PRTM), which was immediately included in the creation of the SCOR model and is still a leader in the field today. It also formed a branch which specializes in Supply Chain Benchmarking, the Performance Measurement Group (PMG). There are then business consultancies with more than 1,000 employees, that see SCOR as an
immanent component of their advisory portfolio in the field of Supply Chain Management. In this case, we are dealing primarily with business consultancies that were initially offshoots of the so-called Big Four (or respectively the former Big Five) certified public accountants.383 According to the author’s knowledge, the firm BearingPoint (formerly KPMG Consulting) with its headquarter in McLean, Virginia, was the only example of a business of this category at the time of the submitted work’s origin, which specifically assigns the SCOR model within the framework of its consultancy
activities.
There are also software suppliers with their own advisory field in which SCOR becomes assigned, either in the field of SCM solutions or in the field of reporting. Examples of these are the firms BusinessObjects headquartered in San Jose, California,384 and SAP with its headquarter in Walldorf. Finally, there are institutes, the majority of which are associated with universities, which have set themselves the target of the further development and application of SCOR. One example of this is the
Supply Chain Management Centre of the Singapore Institute of Manufacturing Technology (SIMTech) (formerly Supply Chain Management Centre of the Gintic Institute of Manufacturing Technology). For each of the five cases listed, one of the named companies will be more closely examined in the following pages. 2.4.2.1 mi services group The tool developed by mi services group385 and based upon the SCOR model, the so-called SCORWizard, serves the purpose of automating the
SCOR model’s application. Such a tool must be clearly differentiated from those applications which have, amongst other things, the Supply Chain’s design or formation respectively as their 386 objective. In the present case we are dealing purely with a tool to support SCOR’s application as a descriptive model – not a forming model – of the Supply Chain.387 The SCORWizard consists of two components: 1. Balanced Strategic Measurement: • Adjustment of strategic objectives to Supply Chain objectives • Creation of a Balanced SCOR eCard388 • Comparison of performance with
competitors (Benchmarking) • Determination of performance objectives. 2. End-to-End Visualization: • Clear allocation of the extended Supply Chain’s physical scope •
Configuration of all process elements, determination of roles and relationships389 • Compilation of a reference system for a detailed analysis. Whilst the SCOR model delivers the general reference system, the SCORWizard automates several stages
and should add a further degree of detail without negatively influencing SCOR’s strengths. We are therefore dealing with the assignment of one of the tools originally supporting the SCOR model. The advantages named are to be characterized as follows: Simple addition, removal or change of best practices and metrics within the SCOR reference system. Creation of a knowledge base for SCOR processes Simple compilation of examples for Supply Chain illustration which are consistent throughout business areas Simple determination of the
optimized business processes and the supporting system technology. By these means, the redefinition of the Supply Chain Strategy is supported and a reference system created for the improvements along the Supply Chain. The application is therefore recommended for executives as well as employees within the operational business.390 2.4.2.2 PRTM PRTM391 was one of the first business consultancies that occupied itself with the comparison of a Supply Chain’s performance potential (Supply Chain
Benchmarking). As a result of this, a comprehensive database of Supply Chain benchmarking information was collected, whereby the fundamental metrics are based upon the SCOR model. The PRTM’s own Performance Measurement Group (PMG),392 which was founded for this specific purpose, describes itself as a leading supplier of data for Supply Chain performance comparison. The spectrum of services offered stretches from individually tailored performance comparisons up to fast, efficient diagnoses on the basis of the PMG’s Supply Chain database. In all cases, the performance comparison is
seen as a tool to provide the initial incentive and focus for Supply Chain improvements. During this, it is assumed that in the course of project implementation not only possible improvements, but also necessary initiatives for improvement can be defined. The services offered 393 individually cover: Data collection, identification of gaps and implementation planning Access to the Supply Chain’s metric database A circumferential set of data for the comparison of Supply Chains (benchmarking) which are
completely consistent with the SCOR model A specially developed model, the so-called Supply Chain Maturity Model that contains a multitude of procedures in order to estimate the company’s development condition. Highly-automated analysis tools. We are also dealing here with an automation of the basic SCOR model’s stages, as illustrated by the first example, with sporadic enhancements in selected areas. 2.4.2.3 BearingPoint BearingPoint (formerly KPMG Consulting)394 assigned the SCOR model
in the area of Supply Chain Strategy as a reliable basis for Supply Chain illustration. The methodology used within the framework of advisory projects for Supply Chain transformation and the supporting tools were completely adapted to the SCOR model.395 The five main processes of the SCOR model were therefore analyzed and evaluated by means of four key dimensions:396 Market/value chain integration Process design? Organizational design Technology design and infrastructure.
In comparison to the previously cited examples, the consultancy did not attempt to automate or extend the SCOR model. Moreover, it assigned the model to consequently develop consistent procedures.397 Additionally, BearingPoint has, in association with this, developed a personal tool (KPI Benchmarking Questionnaire) based upon quantitative questionnaires, which will still be dealt with in detail in Chapter 3, as it represents the basis of the empirical examination.398 2.4.2.4 SAP SAP399 in conjunction with PRTM conducted a SCOR-based Supply Chain
study in the years 2002 and 2003. The study evaluated and followed the Supply Chain performance of more than 100 global SAP customers in order to see what influence the processes and systems for Supply Chain planning have upon companies’ performance capability. During this, two types of metrics were differentiated:400 Internal-facing indicators: Inventory days of supply, inventory costs and cash-to-cash cycle time,401 etc. Customer-facing indicators: Order fulfillment according to customer request, on-time delivery, order
cycle times, etc. In addition to this, the study occupied itself with the degree of maturity of the planning procedure’s development position with respect to the Supply Chain, as well as that of the supporting systems (maturity of Supply Chain planning practices). It was presumed that “mature” planning procedures and systems are characterized not only by the enabling of integration within the expanded business, but also with external business partners. The study arrived at several revealing insights:402
The most significant cost reductions were identified in the area of inventory days of supply. Companies could therefore save 63 percent costs, or respectively gain a 1.7 percent improvement in profitability, with a high degree of maturity.403 In association with this, the obsolescence of stock was a highly important factor. The companies with the highest degree of maturity were able to lower the drop in inventory value by up to 84 percent. In addition, the inventory obsolescence could be reduced from 0.9 percent of the profit down to 0.3 percent by the assignment of
leading planning procedures and systems for the development of new products. It became apparent that companies with a high degree of maturity showed a 17 percent improvement in on-time order fulfillment rate and a 7 percent improvement in deliveries. Based upon the value of experience it was assumed that a 17 percent improvement in ?on-time order fulfillment rate represented an increase of 3.4 percent in profit.
Beyond this it became apparent that these firms showed a 45 percent shorter order cycle time. A
decrease of 45 percent can, according to experience, lead to a 45 percent reduction in inventory stock. Apart from the aforementioned cases, conclusions were also reached which do not allow themselves to be extracted from the collective results by means of scientific methods, but would rather seem to have a qualitative character. It was therefore stated that companies which use the SAP planning system had shown particularly positive results with regards to internal and external metrics.404 In addition to this there were indications that customers
who use the SAP application program achieved a net profit roughly three quarters higher than other companies achieved, an average margin of 14 percent as opposed to 8 percent.405 2.4.2.5 Singapore Institute of Manufacturing Technology (SIMTech) The Supply Chain Management Centre of the Singapore Institute of Manufacturing Technology (SIMTech) 406 carries out an annual Supply Chain Benchmarking Study, based upon the SCOR model, for the Southeast-Asian region.407 The participating companies come from Indonesia, Malaysia, the Philippines, Singapore and Thailand. The project was started in the year 2000, and six
reports have been submitted to date. The study is based completely upon the SCOR model and its associated metrics. The objective is to give the participating firms a clear picture to as to their position with regards to their competitors, and to what extent they must improve themselves in order to belong to the best in their peer group (Best in class). The targeted group is restricted in this case to the industrial sector. Furthermore, a better data collection is sought in order to compare the performance potential of companies and their Supply Chains in the Asian region. Given the various business environments and regional features, the collection of
such comparable data is seen as a particularly important requirement. In order to guarantee the comparability of the collected data in the Asian region, as well as with competitors in North America and Europe, it was decided to use the SCOR model in this respect. This was reached, on the one hand, because the SCOR model is seen as a comprehensive methodology for Supply Chain analysis that has been successfully able to prove its application ability and the advantages thereof. One the other hand, the SCOR model’s wide diffusion and the accessibility of comparable data associated with it, primarily in North
America, were also decisive. The initiators of the study believe that it will, over an increasing period and with the associated growth in collected data, develop into an increasingly important source of information for the respective companies within the region.408
Chapter Three
Empirical study based upon a quantitative questionnaire This chapter addresses the context of justification within the framework of the research-logical course. According to Friedrichs, this context may be characterized as follows: Under context of justification, the methodological steps must be understood with whose aid
the problem is to be examined. It is a methodological procedure by which the individual stages are interdependent. The goal is an exact as is possible, verifiable, and objective examination of the 409 hypotheses. A hypothesis or thesis 410 in this context refers to a presumption of the connection between at least two variables. An empirical theory is a system of logical, contradiction-free statements in the form of hypotheses with respect to the object to be examined, along with the associated definitions of
the terms used. Therefore, several hypotheses (or a system of hypotheses) must accompany a theory. 411 This study seeks to make an initial contribution to such a field. It has adopted an exploratory approach412 and reached some initial findings. If these are to be useful in the broader context, however, further research is needed to explore other aspects of the field.413 The fundamental central presumption of this study, which still remains to be tested, is as follows:414 The SCOR model is grouped around a main axis with a
customer-facing competence on the one side, and an internal-facing competence on the other.415
Both the SC competences are each also assigned Performance A t t r i b u t e s : Reliability and Responsiveness as well as Flexibility on the customerorientated side, Cost and Assets on the business-internal side. It is presumed that the Performance Metrics assigned to the Performance Attributes within one of the two SC competences are consistent with one another, i.e., point in the same direction. The
performance metrics assigned to the performance attributes between the two competences mutually complete each other, i.e., they guarantee a balance between the various objectives. It must be noted that besides the model’s depiction, which was developed for the purpose of examination within the framework of the work at hand and is reflected in the aforesaid central presumption, a multitude of further illustrational alternatives theoretically exist. Reference to the SCOR model in a general sense must really be seen against
this background.416 In order to examine the central presumption, hypotheses – or more exactly: a system of hypotheses – are formed and examined with the aid of statistical procedures. During this process, a hypothesis-investigative approach is adopted.417 Some of the terms necessary for this have been explained in Chapters 1 and 2, and others will be introduced before the description of the empirical examination.
3.1 Objectives of the Empirical Examination At the close of Chapter 2 it was noted that the SCOR model’s diffusion and application has increased considerably
in the past three to four years, mainly in the American and Asian regions. In spite of this increased usage, however, there has not been a detailed study that has subjected the SCOR model’s basic structure or its fundamental assumptions to a scientific examination. For the most part, only generally held indications exist, and these are based upon the value of experience. This may be due to the fact that, despite constantly increasing membership numbers and enhancements, the SCOR model is not yet fully accepted in practice.418 The examination at the center of this study is therefore intended to serve as a scientific contribution to research
into the structure of the SCOR model.419 The data or correlation of the individual variables (in accordance with the theses) will be primarily examined on an empirical basis within the findings. It is not envisaged that interpretations – unless referring to the statistical durability of the theses – or appraisals regarding concrete recommendations for action will feature in the findings.420 The respective conclusions are moreover to be reached in the further course of the work within the context of realization, in order to accommodate for the Freedom of Value Judgment Posit (Werturteilsfreiheits-Postulat) contained within research science (see chap. 5).421
A Balanced SCOR-based Supply Chain Scorecard serves as an instrument for the examination, for which the term SCORcard will hereafter be used. Bolstorff and Rosenbaum introduced the term SCORcard as Fowler’s SCORcard Matrix in connection with a practical example within an American corporation called Fowlers Inc.422 The construction of this SCORcard will be explained in the next section.
3.1.1 Concretizing of aspects and formation of theses The connection between the SCOR model’s fundamental performance-
respective terms needs to be explored in order to arrive at a form of SCORcard that can function as a basis for empirical examination.423 For this purpose, the relevant forms of data and their connection must be defined beforehand. 3.1.1.1 Overview of the performance terms relevant to the examination The following illustration represents the five Performance Attributes used within the SCOR model in addition to their definition and the associated thirteen Level 1 Metrics. The Level 1 Metrics illustrated in diag. 3-1 are in turn defined as follows:425
To (1): Delivery Performance [to commit date]: P.rcentage of orders delivered upon or before the agreed date. To (2): Fill Rate: P.rcentage of deliveries from the warehouse within 24 hours of receipt of order. In the case of services, the percentages of services are meant which were completed within 24 hours. Diag. 3-1: Assignment of the SCOR performance attributes to the associated level 1 metrics 424
To (3): Perfect Order Fulfillment: A perfect order is defined as an order which fulfills all the following requirements: Delivered complete with all order lines included. Delivered to the point in time requested by the customer whereby the customer’s definition of on-time
delivery is applied.426 The order documents (inventory list, bill of lading, invoice, etc.) are complete and accurate. The delivery is in perfect and damage-free condition. The installation, as far as applicable, has been faultlessly performed, is in accordance with the configuration requirements and ready to be taken over. To (4): Order Fulfillment Lead Times: The average actual order cycle time which is consistently and reproductively achieved. The process comprises the following stages: Respective customer signature or approval up to order confirmation,
from there to the completion of order receipt and entry, from there to the beginning of production and/or issue of purchase orders, then on to the shipment-readiness of the order, from there to product release, and finally to installation, if necessary. To (5): Supply Chain Response Time: Indicates how fast a business can adjust itself to changes to the market. To (6): Production Flexibility: Production flexibility becomes apparent in two variations: • Upside flexibility: The number of days necessary in order to ensure
an unplanned, permanent 20 percent increase in production. • Downside flexibility: The number of days required in order to react to an x-percent enduring reduction in order volume, which takes place 30 days prior to the planned delivery date without building up stock of material or raising a contractual penalty. To (7): Total SCM Cost: The sum of all costs incurred by a business due to the development of an integrated Supply Chain. Comprises all Supply Chain related costs for the Management Information System (MIS),427 finances, planning,
stock of material, material procurement and order management. To (8): Cost of Goods Sold, COGS: Direct as well as indirect costs incurred by a business in order to manufacture finished goods.428 Represents the margin as a percentage of total income. To (9): Value-Added Productivity: Calculated as total product sales minus total material purchases. To (10): Warranty Cost or Returns Processing Cost: The number of return deliveries within the guarantee period. Guarantee is an (explicit or implicit)
insurance that a particular event with reference to a component of a contract is actually correct or adjusted properly. Guarantee costs comprise material and labor costs plus the costs for the examination of a defect. To (11): Cash-to-Cash Cycle Time: The time necessary for a certain amount of money to flow back into the business after being spent upon material procurement. The value represents one of the main metrics used to identify how efficient a business monitors the financial flow between customers and suppliers. To (12): Inventory Days of Supply:
The number of days necessary in order to manufacture and sell goods. Hence, represents the time necessary to convert an investment in stock of inventory into goods sold. To (13): Asset Turns: The relationship between annual sales and total asset value. Diag. 3-1 served as an illustration of the relationship between the Performance Attributes and the Level 1 Metrics. Following on from that, it is necessary to highlight the association with the previously represented Supply Chain competences – the customercentric SC Capability on the one hand
and the business-related Efficiency on the other. 429 This correlation, for example, corresponds to the opinion held by Geimer and Becker, whereby the SCOR model’s measures are grouped around four main performance attributes: customer service, flexibility, costs, and assets. Whilst the first two areas are customer-orientated, the other two put internal business priorities into the foreground.430 Sürie and Wagner go a stage further and describe the given performance attributes as universally valid, applicable and relevant for every Supply Chain within the framework of a Supply Chain Scorecard – regardless of the
fundamental model. They assume that although every Supply Chain is unique and requires a special approach, there are still several characteristics which are applicable in the majority of cases. Because they focus upon differing aspects of the Supply Chain, they can basically be assigned to the categories already named in accordance with the performance attributes (reliability, responsiveness, flexibility, costs and assets).431 This approach is to be followed in the further course, whereby both the performance attributes Reliability and Responsiveness are sensibly combined within the performance attribute
Customer service. Bovet and Martha hold a similar viewpoint whereby – as already more closely explained – the Supply Chain’s Capability is compared with Efficiency.432 In other SCOR model illustrations, the SC performance capability is also described as a customer-orientated component and the efficiency as a business-related 433 component. The Level 1 Metrics illustrated in diag. 3-1 must, in the next stage, be assigned to Performance Measures, which are associated with a more narrowly arranged spectrum of subprocesses.434 Of the two hundred or so Performance Metrics referred to in
SCOR Version 6, this study focuses on about sixty.435 The basis for this was the quantitative survey (KPI Benchmarking questionnaire)436 used within the framework of the empirical examination, which was developed in conjunction with an internal business consultancy project.437 The influx of experience into this resulted in the focus upon roughly 60 primary performance metrics, which were enlisted again for use within the framework of the empirical examination. The main intention during this was to avoid redundancies and enable concentration upon the most relevant metrics. At the same time, such an approach ensured the representative coverage of the most decisive
influencing factors (cost, quality, time and productivity).438 As the performance metrics can be used to formulate and design measures for improvement, they may conceivably be referred to as tools for Diagnostics.439 These diagnostic instruments are, for instance, supposed to help to diagnose the deviation of the delivered orders from the original plan.440 3.1.1.2 Clarification of the performance terms There are significant gaps in the existing literature as regards the usage of certain words in reference to the process.441 In
response to this, a suggestion by Seibt was adopted and a valid nomenclature was determined for the further course of this study. 442 This is constituted hierarchically as listed below, whereby Ossola-Haring’s recommendations were followed with reference to metrics used for business management:443 The two SC competences, performance capability and effi ciency, each have four Performance Attributes assigned to them: • Customer service • Flexibility • Cost • Assets.
The performance attributes represent aggregated constructions and therefore do not allow themselves to be directly measured or calculated.444 The thirteen Level 1 Metrics: • Delivery performance [to commit date] • Fill rate • and so on. The performance metrics represent so-called relative figures or ratios, i.e., they relate two (or more) absolute figures to each other.445 3.Performance Measures are assigned to the Level 1 Metrics. Each Level 1
Metric has several performance measures assigned to it. The performance measures represent socalled absolute figures, i.e., they stem from individual numbers, sums or differences.446 4.The collective term >Key Performance Indicator (KPI)> is used for the aforementioned terms which were introduced in Chapter 1.447 The application of the performance terms subsumed beneath these KPIs within the framework of a SCOR-based quantitative analysis of the Supply Chain (KPI Benchmarking) is at the heart of further observation, the focus of which is mainly upon a process performance comparison – or
more precisely: a comparison of Supply Chain processes – in the sense of process benchmarking.448 The exact means by which the respective performance indicators relate to each other can be taken from section 3 of the Appendix, whereby the hierarchy runs from left to right – originating from the performance attributes on the left of the table, through the performance metrics, right up to the performance measures.
3.1.2 Establishment of hypotheses and SCOR model groups
The theses to be developed within the study refer, as a rule, to the correlation of interval-scaled SCOR model parameters. The theses may usefully be divided into three SCOR model groups in keeping with the central presumption formulated at the beginning of the chapter: Within a SCOR Performance Attribute: Metrics and associated Performance Measures are being examined, which are allocated to a particular Performance Attribute. For this purpose, the respective term or a b b r e v i a t i o n Intra-Performance Attribute (I-P) will be used. Between SCOR Performance Attributes,
“on one side of the equation ”: The focus here is upon metrics and associated Performance Measures within one of the two SC competences, i.e., only one side of the equation will be observed at a time. For this purpose, the respective term or abbreviation Intra-Competence (IC) will be used.449 Between SCOR Performance Attributes, however “on different sides of the equation”: Metrics and associated Performance Measures between the two SC competences, i.e., on both sides of the equation, will be observed (and with that inevitably between various
Performance Attributes). purpose, the respective a b b r e v i a t i Competence/Performance (I-CP) will be used.
For this term or o n InterAttribute
As a matter of priority, an argumentative establishment of the three SCOR model groups will be undertaken. The theses that will be examined later will be deduced from those definitions. 3.1.2.1 Intra-Performance Attribute (IP) The associated work theses refer exclusively to indicators within one performance attribute, for example within flexibility or costs. In this case,
the theses must be positively correlated and “synchronous.” For example, a high or low value for one variable means a simultaneous high or low value for the other variable. The interdependency between delivery performance and fill rate is an example of this. The circumstances in question become more apparent by use of an example from the computer industry, which shows the connection between inventory and operating margin. In this way, a rough estimate of the annual inventory costs for the PC business can be reached through the aggregation of investment cost (10 percent) and the price erosion (25 percent), which
together represent a total of 35 percent of the inventory value. A good approach is to calculate the inventory management costs for a period of ten days: 10 days multiplied by 35 percent and divided by 365 days makes 1 percent. This means that the reduction or increase of 10 days of inventory (days on stock, DOS) influences the profitability by one percent.450 One of the Level 1 metrics allocated to the performance attribute costs is that of total Supply Chain Management costs, and it is immediately affected by the process described above. The inventory management cost as a percentage of
revenue is subsumed into this metric, reflecting the aforesaid connection between inventory and operating margin. 3.1.2.2 Intra-Competence (I-C) In this case the theses include indicators belonging to various performance attributes. The performance attributes, however, lie on one side of the equation, i.e., they are without exception either of a customer-orientated or business internal nature. Examples of these are fill rate and response time. Geimer and Becker assume that the indicator within a competence (customer centric vs. business internal) represents the variety of performance perspectives, which guarantee the balance between the
various objectives. This balance is important for the company’s overall success. It would not be purposeful to shorten order fulfillment lead time, for example, without taking into consideration the effect upon production flexibility.451 A further example can be found within the business-related competence. The Supply-Chain Council deliberately differentiated between the terms lead time and cash-to-cash cycle time within the SCOR model. Lead time precipitates itself not only in the form of the total Supply Chain Management cost, but also in the form of inventory days of supply. This differentiation between the terms is
relevant for a company’s competitive position and can be made as follows: T h e lead time is associated with a product or service provided by the Supply Chain. Lead time is therefore “imposed upon” the Supply Chain and is dependent upon customer expectations, Supply Chain innovations, and competitive pressure. The cycle time is based directly upon the Supply Chain processes. The lowest possible cycle time for a product’s Supply Chain is represented by the sum of all single cycle times, i.e., the sub-processes’ cycle times. The main reason for carrying out an
inventory and incurring the associated cost lies in the inequality between lead time and cycle time. If the lead time demanded by the market is lower than the respective cycle time, inventory of stock is required. It is also valid to say that the larger the inequality, the greater the required stock level. For this reason, leading companies are constantly striving for synchronization of the operating procedures and efficient design of the procurement and production processes with the aim of reducing the difference.452 The company Dell453 has, for example, reduced the lead times for customer-specific computers so far that
it possesses a positive cash-to-cash cycle time.454 Dell therefore receives payments from customers even before the suppliers’ invoices have to be paid. However, this must not be taken as proof that inventory stock is no longer necessary: it means rather that Dell has purely reduced its own stock holdings, and the burden of this has in turn to be borne by its suppliers.455 3.1.2.3 Inter-Competence/Performance Attribute (I-CP) In this case the work theses refer to indicators, which belong to various performance attributes and additionally lie on differing sides of the equation (i.e., customer-orientated as well as
business-related). An example here is the case of fill rate and total SC cost. The SC competences – performance capability and efficiency – have already been addressed in more detail elsewhere.456 The connection between the two terms can be illustrated by an example of the fundamental conflicting objectives: It makes no sense for a supplier to invest intensively in the construction of production capacities and inventory in order to build up a potential loss of sales in products with low margins. On the other hand, it could be thoroughly purposeful to do this in the case of products with high profit margin.457 A high value of the one
variable is linked to a low value of the other variable, and vice versa. This connection was already illustrated in the so-called SC driver/SC competence approach according to Hugos. In this approach high production capacities determine a high Supply Chain performance capability (responsiveness), but on the other hand a simultaneously low level of efficiency. In opposition to this, low production capacities determine a high level of efficiency, but on the other hand a low level of responsiveness. The following applies to the respective products: High stock levels determine a high level of responsiveness, but lead on the other
hand to low level of efficiency. Contrary to this, low inventory stocks may determine a high level of efficiency but determine on the other hand a simultaneously low level of 458 responsiveness. Meyr et al describe the connection as a trade-off between customer service and inventory investments.459 A further example of this dynamic is the connection between customer service and investment in assets, i.e., capital expenditures. The cash-to-cash cycle time depends amongst other things upon the industry within which the business finds itself. For example, in the Food and Beverage industry relatively
short payment and cycle times are normal. However, a tendency can be recognized whereby leading companies have a cash-to-cash cycle time half as long as that of their competitors. Alternatively formulated, these companies receive their payments within half the time, which creates an obvious competitive advantage for them, because they have lower for working capital expenses.460 Typically, the associated performance indicators correspond to the delivery performance: Companies showing a high delivery performance often have fewer customer problems with regards to wrong or belated deliveries, incorrect delivery documents or incorrect invoices. This in turn results
in shorter cash-to-cash cycle times for the firms in question, because, for instance, customers have less reason to defer a payment.461 There are a number of further reasons why a higher level of delivery performance has financial advantages and connects customer service on the one hand and costs with investments in assets on the other. Companies with shorter lead times and a high degree of customer service can significantly increase their operating procedures. By these means a business may undertake a strategic adjustment of customer groups and financial and business objectives. In addition to this, the development of
better customer relations is made possible, and this has a direct influence upon the customer retention rate and the costs associated with it.462 Schary and Skjott-Larsen combine the facts of the illustrated case as follows: “The objectives of the supply chain become a difficult balance. (_) The dominant purpose is to provide service to final customers, to deliver products reliably, as rapidly and with as much variety as possible. Service, however, commits resources and incurs costs. Supply chain management must seek to
control assets and cost to obtain profit as a return on the assets employed.”463 3.1.2.4 Formulation of hypotheses and theses model In this case we are dealing primarily with bivariate suppositions of correlation which – in the face of continual variables (level of intervalscale) – can be investigated by means of correlative procedures with the aid of the usual Product-Moment Correlation according to Pearson.464 The respective theses or hypotheses contain relationships between variables, whereby the variables represent SCOR
performance measures. These types of relationship may be defined more exactly using several criteria, whereby a differentiation can be made between various fundamental instances, as is the case with deterministic or statisticallyvariable relationships.465 Several of these relationships are of particular relevance within the framework of theses formulation and they are applied in conjunction with this. The theses model is built up hierarchically as already explained, on the levels Performance Attributes – Level 1 Metrics and Performance Measures. The theses formulation follows this hierarchical structure,
whereby the starting point is the lowest level, i.e., the performance measures. The respective theses foundations or model assumptions, on the other hand, start at the base of the highest level, i.e., the performance attributes. The theses investigation therefore takes place upon the performance measures level and the conclusion of model assumption investigation is carried out by means of theses aggregation in the form of Meta theses based upon the performance attributes. The allocation of each SCOR monitoring process to each performance measure is possible, but has only a predetermined influence upon the theses
formulation. The reason for this is that a comparison of the performance measures allocated to the various monitoring processes can occasionally make sense. This may be seen in the performance measure Percentage of purchase orders received on time and complete allocated to the procurement process, and the performance measure Backorder value allocated to the delivery process. The investigation of a potential connection between these two quantities is of great interest. The following illustrations show the theses model in graphical form, and differentiate between the derived SCOR model groups. In this case, the cross-
hatched areas purely represent examples for the actual SCOR model group. Diag. 3-2: Supply Chain competence and key performance indicators as building blocks of a SCORbased theses model: Intra-Performance Attribute (I-P)
Diag. 3-3: Supply Chain competence and key performance indicators as building blocks of a SCORbased theses model: Intra-competence (I-C)
Diag. 3-4: Supply Chain competence and key performance indicators as building blocks of a SCORbased theses model: Inter-Competence/Performance Attribute (I-CP)
In this way, and with regards to the model group Intra-performance attribute (I-P), the other three performance attributes (customer service, costs, assets) are investigated in addition to the illustrated Flexibility. This also applies to the other SCOR model groups and their associated performance indicators.
Before the work theses due to be examined are extracted on the basis of the developed theses model, we must consider possible variants. As the theses model to be investigated is concretely designed around Supply Chain specific performance indicators, particular attention must be paid to alternative approaches for the evaluation of the Supply Chain’s performance potential.466 3.1.2.5 Variations in approach and models for the illustration and measurement of the Supply Chain performance Rummler and Brache identify three
fundamental performance dimensions in their approach to the measurement of Supply Chain performance. They differentiate between quality, productivity and cost-orientated performance indicators. Furthermore, they differentiate between three performance levels: the organizational, process, and workplace or employee level.467 The first level performance indicators are characterized by the individual market requirements and success-determined functions of the organization. Additionally, the type and scope of the performance indicators being observed are orientated upon the organizational strategy, the organizationwide objectives, and the organizational
structure. Neuhäuser-Metternich and Witt define four performance areas and divide them into time, cost, quality, and performance-related measures.468 Sellenheim differentiates five varying dimensions and uses flexibility and preparation-orientated indicators, in addition to cost, quality and time-related performance indicators.469 Beischel and Smith similarly identify five dimensions critical to success and use cost, quality and flexibility, as well as service and resource-related performance indicators.470 The aforementioned approaches are
united in their premise that non-monetary performance dimensions (i.e., quality, time, flexibility and productivity) are of evidently higher importance and that monetary performance dimensions should only receive marginal consideration.471 One reason for this can be seen in the fact that non-financial performance indicators can be accommodated in a process model like SCOR,472 because the causes of deviation in performance are made apparent and targeted corrective measures are made possible. If a fall in quality (i.e., increase in reject rate or increase in repair quota) is noted, respective measures can be taken immediately.473
However, the use of non-monetary performance indicators carries with it inevitable respective disadvantages or restrictions, e.g., that non-monetary indicators cannot be aggregated so easily.474 Furthermore, an association between those improvements determined by non-monetary indicators and the achieved profit is difficult to establish. Therefore, it cannot be basically assumed that the improvements measured upon the basis of nonmonetary quantities really have an effect upon financial results. In this way, seeking a monetary quantification of falling production cycle times can become a difficult venture. On the other
hand, the occurrence of a slump in sales due to falling degree of delivery ability and the associated effects upon profits can be easily estimated.475 It was for this reason that expanded models or respective approaches were developed, and these explicitly include monetary in addition to non-monetary indicators.476 Greene and Flentov set down three performance levels as a basis for this: The business or factory level, the functional level, and the work place level. The first level performance indicators measure the respective company’s or factory’s performance with regards to the achievement of critical market and competition-related
success factors. The performance capability of functionally overlapping processes is measured on this level. The functional level’s indicators are used in order to measure the contribution of each function towards the achievement of the company’s strategic objectives. They measure how effectively the resources are used to fulfill the given strategic and tactical objectives. Finally, the performance indicators of the third level measure the production performance at workplace level. Their main task is the early highlighting of deviations, so that corrective actions can be taken at an early stage.477 Utzig
differentiates
four
performance levels: Market level, corporate level, factory level, and workplace level. The market level indicators are to measure the overall business compared to the competition, in order to determine the company’s competitive position. The performance indicators applied here can refer to quality, service, costs, and market share. On the corporate level we are dealing with assessment of the actual revenue situation, as well as the assessment of revenue potential. The indicators are for instance the annual surplus, Return on Assets (ROA) or Return on Sales (ROS),478 and the market share. On the factory level, financial and non-financial indicators are applied; for example cost,
productivity and delivery times. On the lowest level, workplace performance is assessed and indicators such as stock levels and cycle times play a supportive role.479 Apart from scientific approaches there are also a number of models from practical business that contain monetary as well as non-monetary performance indicators, as for example the Tableau de Bord. This mainly integrates quantitative performance indicators; qualitative indicators are merely allocated an inferior importance. The individual indicators are extracted topdown from the business strategy in order to enable an association between the
operational activities and the strategic business objectives. The performance indicators contained therein are orientated towards both the long-term and the short-term.480 The J. I. Case approach comprises a multitude of non-financial and financial performance indicators, and assesses not only quantitative but also qualitative measures (i.e., for the purpose of estimation of customer satisfaction).481 With the Harman approach, monetary and non-monetary performance indicators are also integrated. Qualitative measures are, on the other hand, not included. The indicators are extracted top-down from
the critical success factors, whereby a consistent targeting of all business levels is sought.482 The Caterpillar approach integrates monetary and non-monetary, as well as hard and soft performance indicators (for example customer and employee satisfaction).483 On the other hand, the Skandia-Navigator contains a multitude of non-financial indicators and thereby integrates quantitative and qualitative performance indicators. Apart from internal stakeholders (i.e., employees), it also includes external stakeholders (i.e., customers) in the concept.484 T h e Data Envelopment Analysis provides an ultimate indicator, the so-
called efficiency value. However, it is still possible to consider several inputand output-related performance indicators. In this case the approach is sufficiently flexible enough to include the time, adjustment and monitoring objective and the dimensional, formatand output-related dimension. However, the performance data is so strongly aggregated that it is no longer transparent enough, especially in the case of operational areas.485 The performance measurement matrix contains both monetary and nonmonetary, in addition to internally- and externally-orientated performance indicators. The approach necessitates the development of strategy-conformant
figures as well as their level-specific adjustment.486 T h e Performance Pyramid combines monetary and non-monetary as well as internally- and externallyorientated performance indicators. These indicators are extracted top-down from the business strategy. There is differentiation between various performance levels, and highly aggregated financial information is prepared for the higher business levels, in addition to transparent performance data prepared for the operational business areas. During this, primarily quantitative performance indicators are applied. Qualitative indicators, on the
other hand, are only considered within a limited scope.487 Kaplan and Norton’s Balanced Scorecard (BSC), which has already been addressed488 uses the categories of finances, customers, and processes, in addition to those of innovation, and growth. The applied performance indicators refer in this instance to the dimensions of cost, quality, time and productivity.489 The BSC has increasingly developed itself into a respective de facto-standard or reference model for performance measurement.490 The Quantum Performance Measurement Approach also integrates costs (monetary) in
addition to quality- and time-related (non-monetary) performance indicators. The dimensions represented are therefore cost, quality and time. In addition to quantitative data, qualitative performance data is taken into consideration here. The approach differentiates between three performance levels: organizational (more long-term orientated), process, and employee levels (set up for more middle- or shortterm).491 Of the respective approaches or models represented, only the Performance Pyramid, Balanced Scorecard and Quantum Performance Measurement Approach show a
relatively strong process orientation. As a process reference model, then, they are decidedly compatible with the SCOR model. The other approaches and models are only weakly or moderately highlighted in this aspect. In turn, of the three approaches the Balanced Scorecard alone shows an individual perspective for process-related performance indicators. It is therefore, as far as the model structure is concerned, the most closely related to the concrete model due to be examined, i.e., a performance indicator-specific depiction of the SCOR model. It must, however, be borne in mind that the BSC in its original condition is not explicitly directed at Supply Chain processes. The
special requirements to be considered in order to focus upon Supply Chain processes have already been discussed in Chapter 1.492 As the associated effects fundamental to the BSC and the SCOR model are consequently founded upon a variety of performance indicators, no applicable examples can be found for comparison with the association effects contained within the theses introduced in the study. The study is exploring new territory, as it were. Furthermore, it must be borne in mind that the SCOR model falls under the above category of a model that explicitly focuses upon nonmonetary performance indicators. The
restriction inevitably resulting from this is that financial quantities are not expressly included.493 Within the framework of the questionnaire chosen for the implementation of the empirical examination, however, financiallyrelated data was partially collected, as for example in the case of revenue or Return on Assets (ROA). During the evaluation, this information (if it was considered worthwhile) was used for additional discriminatory evaluations in order to identify whether this would lead to relevant changes in the respective results extracted from them. This will be dealt with more closely later.494 Although efforts do exist to arrive
at a standardized, SCOR-based Supply Chain Scorecard (a quasi Reference SCORCard),495 these efforts have, up until now, remained of a project-specific nature.496 To date, no empirical examination of these approaches from a scientific viewpoint has, in the author’s knowledge, taken place.497 This point will be taken up again in connection with suggestions for further research at the end of the work.498
3.2 Derivation of the Central Work Theses Building upon the theses model and its foundations, as discussed above, the hypotheses to be examined must be
extracted and formulated.499 Here the available and applied data was of great importance and had an immediate effect upon the derivation of the hypotheses. The basic variables that were assigned during this can be taken from the Appendix: Section 3 of the Appendix is an overview of the applied performance indicators and section 4 contains the exact definitions and calculative formulas, along with information on each of the sixty or so performance measures adopted. The theses that follow refer directly to the performance measures or, more exactly, to a combination of two performance measures (the bivariate
assumption of correlation). The Meta theses that are outlined at the beginning of every paragraph are situated upon a higher level, namely that of the performance attributes. It must be borne in mind that not every single thesis allocated to a Meta thesis correlates as strongly with the respective Meta thesis. It is also necessary to take into consideration the indicator-specific differences in performance terms explained at the end of paragraph 3.1.1. For this reason, an operational exploration of the single theses is essentially more precisely and immediately possible than in the case of the Meta theses. Thus, the Meta theses
serve purely as verification upon an aggregated level, whereas the detailed evaluation takes place upon the single theses, and therefore upon the performance measures level.
3.2.1 Theses of the SCOR model Groups IntraPerformance Attribute (I-P) META THESIS I: The Performance Measures within one Performance Attribute conform to one another (Performance Measures Consistency Criteria). 3.2.1.1 Performance Attribute Customer Service (reliability and
responsiveness)500 a. Delivery performance and fill rate: Thesis 1: A high on-time delivery percentage – inbound and outbound leads to a high customer retention rate. b. Delivery performance and perfect order fulfillment: Thesis 2: A high perfect orders rate leads to a high customer retention rate. Thesis 3: A low on-time delivery percentage – inbound and outbound correlates with a low perfect orders rate.
c.Delivery performance and order fulfillment lead time: Thesis 4: A high average manufacturing cycle time correlates with a low on-time delivery percentage – inbound and outbound. d. Fill rate and perfect order fulfillment: Thesis 5: If a high percentage of purchased orders received on time and complete is present, there is simultaneously also a high perfect orders rate. e.Fill rate and order fulfillment lead time: Thesis 6: A high average MPS plant delivery performance – work
orders leads to a short average manufacturing cycle time. f. Perfect order fulfillment and order fulfillment lead time: Thesis 7: A short average purchase requisition to delivery cycle time determines a high lines on-time fill rate. 3.2.1.2 Performance Attribute Flexibility501 a. Supply chain response time and production flexibility: Thesis 8: A high inventory stockout percentage leads to a high backorders value.
3.2.1.3 Performance Attribute Cost502 a. Total supply chain cost and cost of goods sold: Thesis 9: High purchasing cost as a percentage of revenue correlates with high inventory management cost as a percentage of revenue. b. Total supply chain cost and value added productivity: Thesis 10: High inventory management costs as a percentage of revenue accompany a high inventory management cost per FTE. Thesis 11: High transportation cost as a percentage of revenue
correlates with high transportation cost per FTE. c. Total supply chain cost and warranty cost or returns processing cost: Thesis 12: A high transportation cost as a percentage of revenue correlates with a low amount of damaged shipments. d. Cost of goods sold and value added productivity: Thesis 13: High purchasing cost as a percentage of revenue accompanies a high purchasing cost per FTE. e.
Value added productivity and warranty cost or returns processing
cost: Thesis 14: High customer service cost per FTE correlates with a low amount of customer disputes. Thesis 15: High transportation cost per FTE correlates with a low amount of damaged shipments. 3.2.1.4 Performance Attribute Assets503 a. Cash-to-cash cycle time and inventory days of supply: Thesis 16: A high inactive inventory percentage accompanies a low average inventory turnover. b. Cash-to-cash cycle time and asset turns:
Thesis 17: A high inactive inventory percentage accompanies a h i g h average warehousing space utilization. c. Inventory days of supply and asset turns: Thesis 18: A high average inventory turnover correlates with a l o w average warehousing space utilization.
3.2.2 Theses of the SCOR model Groups IntraCompetence (I-C) 3.2.2.1 Customer-facing indicators504
Customer service (reliability responsiveness) vs. flexibility:
and
META THESIS II: A high (low) customer service correlates with a high (low) flexibility. a. Delivery performance and supply chain response time: Thesis 19: A high on-time delivery percentage – inbound and outbound accompanies a low backorders value. b. Delivery performance and production flexibility: Thesis 20: A low inventory stockout percentage correlates with
a high on-time delivery percentage – inbound and outbound. c. Fill rate and supply chain response time: Thesis 21: A high percentage of purchased orders received on time and complete contributes to a low backorders value. Thesis 22:A high percentage of purchased lines received on time and complete contributes to a low backorders value. d. Fill rate and production flexibility: Thesis 23: A high inventory stockout percentage correlates with a
l o w average MPS plant delivery performance – work orders. e. Perfect order fulfillment and supply chain response time: Thesis 24: A high lines on-time fill rate contributes to a low backorders value. f.
Perfect order fulfillment and production flexibility: Thesis 25: A low inventory stockout percentage correlates with a high perfect orders rate. Thesis 26: A low inventory stockout percentage contributes to a high lines on-time fill rate.
g. Order fulfillment lead time and supply chain response time: Thesis 27: A short average manufacturing cycle time contributes to a low backorders value. h. Order fulfillment lead time and production flexibility: Thesis 28: A high inventory stockout percentage correlates with a h i g h average manufacturing cycle time. 3.2.2.2 Internal-facing indicators505 Cost vs. assets: META THESIS III:
High (low) costs correlate with high (low) asset investment. a. Total SCM cost and cash-to-cash cycle time: Thesis 29: High inventory management cost as a percentage of revenue correlates with a high average received finished goods turnaround time. Thesis 30: High inventory obsolescence cost as a percentage of revenue accompanies a high inactive inventory percentage. b. Total SCM cost and inventory days of supply: Thesis 31: High inventory
obsolescence cost as a percentage of revenue accompanies a low average inventory turnover. c. Total SCM cost and asset turns: Thesis 32: High inventory management cost per customer order correlates with a low average warehousing space utilization. d. Value added productivity and cash-tocash cycle time: Thesis 33: High inventory management cost per FTE is found in conjunction with a high average received finished goods turnaround time.
e.
Value added productivity and inventory days of supply: Thesis 34: High inventory management cost per FTE is found in conjunction with a low average inventory turnover.
f. Value added productivity and asset turns: Thesis 35: A high average throughput per FTE correlates with a high average plant capacity utilization for finished products. Thesis 36: High inventory management costs per FTE accompany a low average warehousing space utilization.
g. Warranty cost or return processing cost and cash-to-cash cycle time: Not applicable.506 h. Warranty cost or return processing cost and inventory days of supply: Thesis 37: A low average orderto-shipment lead time correlates with a low amount of customer disputes. i. Warranty cost or return processing cost and asset turns: Not applicable.507
3.2.3 Theses of the SCOR model Group?Inter-
Competence/Performance Indicator (I-CP) 3.2.3.1 Customer Service (reliability and responsiveness) vs. cost508 META THESIS IV: A high (low) customer service correlates with high (low) costs. a. Delivery performance and total SCM cost: Thesis 38: High inventory management costs as a percentage of revenue correlate with a low backorders value. b. Delivery performance and cost of goods sold:
Thesis 39: High customer service cost as a percentage of revenue accompanies a high on-time delivery percentage – inbound and outbound. c. Delivery performance and value added productivity: Thesis 40: High customer service cost per FTE correlates with a high on-time delivery percentage – inbound and outbound. d. Delivery performance and warranty cost or returns processing cost: Thesis 41: A low number of customer disputes accompanies a high customer retention rate.
e. Fill rate and total SCM cost: Thesis 42: A high cycle count accuracy percentage correlates with a high inventory management cost as a percentage of inventory value. f. Fill rate and cost of goods sold: Thesis 43: A high percentage of purchased orders received on time and complete corresponds to a high purchasing cost as a percentage of revenue. g.
Fill rate and value added productivity: Thesis 44: A high percentage of purchased orders received on time and complete, accompanies a high
purchasing cost per FTE. Thesis 45: A high manufacturing cost per FTE correlates with a high average MPS plant delivery performance – work orders. h. Fill rate and warranty cost or returns processing cost: Thesis 46: A high percentage of purchased orders received on time and complete correlates with a low amount of damaged shipments. Thesis 47: A high average MPS plant delivery performance accompanies a low amount of customer disputes.
i. Perfect order fulfillment and total SCM cost: Thesis 48: High inventory management costs per customer order correlate with a high perfect orders rate. Thesis 49: A high inventory management cost per customer order accompanies a high lines on-time fill rate. j. Perfect order fulfillment and cost of goods sold: Thesis 50: A high customer service cost as a percentage of revenue correlates with a high perfect
orders rate. k. Perfect order fulfillment and value added productivity: Thesis 51: A high customer service cost per FTE contributes to a high lines on-time fill rate. l. Perfect order fulfillment and warranty cost or returns processing cost: Thesis 52: A low perfect orders rate correlates with a high amount of customer disputes. m. Order fulfillment lead time and total SCM cost: Thesis 53: A low average purchase requisition to delivery cycle time accompanies a high inventory
management cost as a percentage of revenue. n. Order fulfillment lead time and cost of goods sold: Thesis 54: A high average purchase requisition to delivery cycle time correlates with a low purchasing cost per purchase order. o. Order fulfillment lead time and value added productivity: Thesis 55: A high purchasing cost per FTE correlates with a high average purchase requisition to delivery cycle time. Thesis 56: A high manufacturing
cost per FTE stands opposite to a low average manufacturing cycle time. p. Order fulfillment lead time and warranty cost or returns processing cost: Thesis 57: A low average purchase requisition to delivery cycle time accompanies a low amount of customer disputes. 3.2.3.2 Flexibility vs. cost509 META THESIS V: A high (low) Supply Chainflexibility correlates with high (low) costs.
a. Supply chain response time and total SCM cost: Thesis 58: A high inventory management cost as a percentage of inventory value accompanies a low backorders value. b. Supply chain response time and cost of goods sold: Thesis 59: A high manufacturing cost as a percentage of revenue correlates with a low backorders value. c. Supply chain response time and value added productivity: Thesis 60: Low customer service costs per FTE take place in
conjunction with a high backorders value. d. Supply chain response time and warranty cost or returns processing cost: Thesis 61: A low backorders value correlates with a low amount of customer disputes. e. Production flexibility and total SCM cost: Thesis 62: A high inventory stockout percentage correlates with a high inventory obsolescence cost as a percentage of revenue. f. Production flexibility and cost of goods sold:
Thesis 63: A low inventory stockout percentage accompanies a high manufacturing cost as a percentage of revenue. g. Production flexibility and value added productivity: Thesis 64: A high manufacturing cost per FTE correlates with a low inventory stockout percentage. Thesis 65: A high customer service cost per FTE takes place in conjunction with a low inventory stockout percentage. 3.2.3.3 Customer Service (reliability and responsiveness) vs. assets510
META THESIS VI: A high (low) customer service correlates to high (low) assets. a. Delivery performance and cash-tocash cycle time: Thesis 66: A high on-time delivery percentage – inbound and outbound correlates with a low inactive inventory percentage. b. Delivery performance and inventory days of supply: Thesis 67: A high average inventory turnover is simultaneous to a low backorders value. Thesis 68: A low average order-
to-shipment lead time accompanies a high on-time delivery percentage – inbound and outbound. c. Delivery performance and asset turns: Not applicable.511 d. Fill rate and cash-to-cash cycle time: Thesis 69: A high cycle count accuracy percentage correlates with a low inactive inventory percentage. e. Fill rate and inventory days of supply: Thesis 70: A high percentage of purchased lines received on time and complete accompanies a low average order-to-shipment lead time. f. Fill rate and asset turns:
Not applicable.512 g. Perfect order fulfillment and cash-tocash cycle time: Thesis 71: A high inactive inventory percentage takes place mutually with a high lines on-time fill rate. h. Perfect order fulfillment and inventory days of supply: Thesis 72: A high average order-to-shipment lead time correlates with a high lines on-time fill rate. i. Perfect order fulfillment and asset turns:
Not applicable.513 j. Order fulfillment lead time and cashto-cash cycle time: Thesis 73: A high amount of transactions processed via web/EDI accompanies a low average received finished goods turnaround time. k. Order fulfillment lead time and inventory days of supply: Thesis 74: A high percentage of sales via web/EDI correlates with a low average order-to-shipment lead time. l. Order fulfillment lead time and asset turns.
Not applicable.514 3.2.3.4 Flexibility vs. assets515 META THESIS VII: A high (low) flexibility correlates with high (low) assets. a. Supply chain response time and cashto-cash cycle time: Thesis 75: A low average received finished goods turnaround time correlates with a low backorders value. b. Supply chain response time and inventory days of supply: Thesis 76: A high average
inventory turnover occurs simultaneously with a low backorders value. c. Supply chain response time and asset turns: Not applicable.516 d. Production flexibility and cash-tocash cycle time: Thesis 77: A low inventory stockout percentage correlates with a low average received finished goods turnaround time. e. Production flexibility and inventory days of supply: Thesis 78: A low inventory
stockout percentage accompanies a low average order-to-shipment lead time. Thesis 79: A high inventory stockout percentage accompanies a low average inventory turnover. f. Production flexibility and asset turns: Thesis 80: A low inventory stockout percentage correlates with a high average operating-equipment efficiency rate – OEE for finished products.
3.3 Planning and Design of the Empirical Examination
3.3.1 Sources of information (types of method for information retrieval) There are two types of information retrieval: primary and secondary research. Primary research, also referred to as field research, is the case if one conducts one’s own investigations in order to receive information. In this case one is therefore working with data unknown prior to commencing one’s own research.517 Secondary research occurs when, during the acquisition of data, material already available, which was collected by other institutions for other purposes,
is used. In this case, the object of the secondary research is the collection and evaluation of data that was identified and retrieved at an earlier point in time and for other purposes.518 Data is therefore submitted to a second or third evaluation, which is why we speak of secondary research as opposed to primary research, i.e., the first survey of data referring to a concretely described objective. Since the renewed processing of the data can take place mainly at a desk or in an office, the term desk research519 may also be found to describe this procedure. This study employed a two-stage approach with regards to the type of
information retrieved. In the first stage during the primary research, data was collected within industrial companies. In the second stage this data was used as secondary research for a purpose other than the original, within the framework of the examination carried out.
3.3.2 Data collection and sampling methods The relevance of any observational or informative source in a primary survey is governed above all else by nature of the information required. During the course of a survey the range of elements used to reach specific conclusions is described as the total mass or universe. A full survey (or total survey) is one in
which every individual element is examined for characteristics of interest. In most cases, however, such a total survey proves to be practically impossible within a large scope of population, often for financial, time or organizational reasons. For reasons of research economy, the examination is limited to a part of the population, the so-called partial mass or sample.520 The selection procedures or spot check techniques used today may be judged according to two criteria which partially overlap each other: the result validity or obligation, and the application of selection criteria:521
1. Result validity or obligation: In accordance with the validity of the results, a differentiation can be made between those procedures that lead to representative results and those that do not. Representative procedures can be further divided into simple and complex random sampling.522 2. Application of selection criteria: The selection criteria to be applied are then re-classified as random sampling and non-probability sampling. Those selection procedures considered as non-probability sampling include the accidental sampling or sampling of available subjects on the one side, and quota sampling, sampling of typical cases, snowball sampling and
quota sampling on the other side. In those cases, subjective decisions are required at some phase of the selection.523 A similar division can be found in the work of Hammann and Erichson, who differentiate between random sampling and non-probability 524 sampling. Therefore, whilst random sampling is based upon random selection mechanisms, the procedure of purposive sampling comprises a selected sub-set. The selection takes place in a targeted and considered way and according to factually relevant characteristics.525
Sampling of typical cases is especially suitable for examinations that test hypotheses. The premise of this method lies in its restriction of the analysis to a relatively low number of population elements, which are considered to be characteristic or especially typical. Each individually selected case should therefore represent a larger number.526 As this study is concerned with the examination of the SCOR model’s developed depiction, the selection criteria for typical data orientated
themselves upon the SCOR-specific characteristics of the companies found within the data pool. The SCOR model’s illustration reflected in the theses model also covers all SCOR process areas (chevrons). For this reason, all companies whose data were actually present in all SCOR processes and complete were selected for the purpose of secondary research. A sample size of 73 companies resulted from this. The reason for the failure of some companies to give information regarding individual SCOR processes can be traced primarily in their Supply Chain strategy. In this way, companies that have, for instance, displaced their production (Outsourcing) could inevitably not make any statements
on the SCOR process Make. The same principle applies to the other SCOR process areas.527 Particular attention was also paid to seeking the maximum data available, with the minimum of missing data. To this end, sets of data were omitted from the selection when a principal declaration to all the SCOR process areas had been given, but where partly incomplete information was present within one or more areas. By these means, a distortion of the results due to a fluctuating sample size – and associated sample consistency – could be avoided, and the set of data could be monitored as exactly as possible. This will be more
closely dealt with in paragraph 3.3.5.
3.3.3 Survey types When conducting primary research surveys, observations and experiments are considered possible methods of data collection. The most commonly used method in this respect is that of the survey. Surveys involve the subject contributing either factual information or an expressed opinion (judgment). Depending upon the type of approach it is possible to differentiate between an unsystematic (improper) and a 528 systematic survey. Hammann and Erichson use the following differentiation between the types of survey:529
1. Written survey 2. Verbal survey: Personal interview, telephone survey 3. Electronic survey, either as an online or offline survey. Often, the survey is carried out with the aid of a questionnaire. In this case, the questions are posed in a standardized form, and answered accordingly. The persons in question therefore receive survey stimuli via the questions they are given, and provide their own information as input to the 530 questionnaire. An electronic survey (or precisely, an online survey)
was assigned in the case of the primary research. This, in addition to the data applied within the framework of the work submitted, formed the basis for the secondary research.531 Data retrieval for primary examination purposes took place in the period between the middle of 2001 until the end of 2003. In total, 170 companies were included in the survey. The answers to the questions were divided in accordance with the SCOR main processes (chevrons). The responsibility for the provision of the answers lay with the managers of the respective process
areas. For instance, the procurement manager was responsible for the questionnaire block Source (purchasing).532 The answers to financerelated questions were the responsibility of the financial manager or Chief Financial Officer (CFO), who in most cases also acted as the sponsor of the company surveyed.533 This, of course, does not exclude the fact that further levels within the hierarchy were included into the examination during data collection at various points.
3.3.4 Design of the applied questionnaire The issue of the questionnaire’s construction plays an important role in
determining the success of a survey, particularly in the arrangement and sequence of the questions. Over time, a number of basic rules have been developed for the creation of questionnaires, which can now be considered as binding. Four phases, and consequently four groups of questions, are differentiated:534 1. Introductory phase (contact questions) 2. Information retrieval phase 3. Monitoring phase (control questions) 4. Personal or organizational information. In the case of each type of question, a differentiation can be made between
closed questions and alternative questions. In turn, closed questions can be subdivided by looking at the radius within which the categories are determined by the questionnaire, and the extent to which these categories are actually revealed to the person being interviewed.535 Apart from this, attention must also be paid to the external structure of the questionnaire.536 Alternative questioning, scale questioning, and catalogue questioning were assigned in the primary survey, which served to collect the data used within the study. The build-up followed the four phases
mentioned above. The structure of the survey took place in the form of an online questionnaire (KPI Benchmarking Questionnaire).537 The primary research questionnaire was developed within the framework of an internal project by the business consultancy BearingPoint (formerly KPMG Consulting),538 in conjunction with a business consultancy specializing in the design of questionnaire-based surveys, and completed by the beginning of 2001. At this time, SCOR Version 4 was available. The changes to this version through Version 5 up to Version
6 (which formed the basis for the study) did not, however, influence the extrication of the theses model. This is because the model structure of this time already represented the structure due to be operationalized and analyzed in the relevant parts of the study.539
3.3.5 Practical examples for analysis Companies included in the study were drawn from various regions or countries, and also from various industrial groups. This was done in order to avoid a weak degree of generalization in the results, and to make them as universally valid and industry-independent as possible. This follows a fundamental
benchmarking idea, namely to apply best practices for optimization of individual processes, as well as the industryspanning definition of the Supply Chain’s capability. The sample size orientates itself, as described, upon the data retrieved as part of the primary research. The selection of practical examples took place upon the basis of available empirical data material using typical cases.540 Consequently, the results of more than seventy actual companies were enlisted as examples and the respective items of data were processed. In this case, an ideal representation could not be reached.541 Nevertheless, it
is assumed that the results obtained via the random sampling characteristics – which are to be more closely explained in later sections – can be taken as an empirical and scientifically strong declaration. In this context, it is also noted that empirical works with large sample sizes and with a variety of criteria, which have the SCOR model as their topic, are still rare. A differentiation according to regional distribution and industryaffiliation leads to the following results in the study: The companies have their locations in the following respective regions or countries and are distributed as
follows:542 North America (USA and Canada): 75.3 percent Europe (France, Germany, Hungary, Italy, Turkey and the UK): 16.5 percent Asia (India, Indonesia and Singapore): 8.2 percent. The companies cover the industrial sector and comprise the following specific industries or industrial groups:543 Aerospace and Defense
Agriculture and Biotechnology Apparel Automotive Biotechnology Chemicals and Pharmaceuticals Computers and Consumer Electronics Consumer Packaged Goods, CPG Electronic equipment Household appliances Machinery and Equipment Metal Products Office and Printing Machines Rubber and Plastic Products Telecommunications Retail and Distribution. The above-mentioned distribution
refers to the 73 selected companies, but also applies to the primary survey’s data pool. The companies must remain anonymous on the grounds of client protection. A study of company distribution on the basis of revenue and total employees leads to the following results, attained using the German Commercial Code’s (Handelsgesetzbuch, HGB) guidelines for the measurement of companies’ size:544 Small companies: 2 percent Middle-sized companies: 37.5 percent Large companies: 60.5 percent.
With regards to their business success, the companies under observation distribute themselves as follows, whereby Return on Assets (ROA)545 is the measure for 546 judgment: ROA negative: 9.6 percent of the companies ROA between 0 and 10 percent: 63 percent of the companies ROA over 10 percent: 27.4 percent of the companies. Of the 73 companies observed, by
the end of 2006 seven no longer existed in the same form in which they found themselves during the survey period. Four of them had been taken over by other companies or merged into new companies. Three were no longer present on the market, or no information could be obtained as to their whereabouts. Of those three companies none had shown a negative ROA during the period of the survey.547 The resulting variety in the data with respect to industry-affiliation represents the fundamental principle upon which the SCOR model is based. It reminds us that it is intended to be an industry-independent business process
reference model.548 This variation inevitably conceals certain disadvantages. Under normal circumstances generalized statements regarding the illustrated model’s universal suitability (or more exactly in this case: the SCOR model’s developed depiction) are particularly targeted during such sampling. As a result, less emphasis is placed upon sub-group specific statements (whereby for example in the sense of a clustering, the examined sample size had to appear critical for the latter intention).549 The data appeared to permit an exploratory approach towards this objective despite the immanent sampling restrictions.550
A process of differentiation according to varying types of strategy or, in the present case, Supply Chain strategy types (e.g., mass 551 customization) was not possible here, since the data required in order to enable such a differentiation was not acquired within the field research. In turn, the primary research did not focus upon these data elements, because the SCOR model in the form observed here (i.e., on SCOR levels 1 to 3) does not provide for such a differentiation.552 Those differences in this form that address the issue of distribution according to industry affiliation may be found from the fourth level onwards (i.e., upon a project-specific
granularity).553 In turn, the accompanying heterogeneity of the set of data conceals, in the variables resulting from the industry-independent observation represented above, the risk of unusual effects which may influence results. As far as the extent of this risk is concerned, closer quantification is not possible due to the above-mentioned lack of relevant data. However, it must be pointed out in this context that the focal point of analysis should, in the first instance, be the illustrated model’s fundamental capacity, and not those unusual effects which are difficult to determine.554
Apart from these issues, as part of the data evaluation within the empirical study and in addition to the parameters for revenue and number of employees (see above), a company’s success in the form of Return on Assets (ROA) was differentiated for test purposes using the aforementioned classification. This was done in order to intercept heterogeneity within the set of data.555 When this led to an incremental accumulation of knowledge it is noted in the results. If no further declaration is made in the course of the study, no additional conclusions could be reached via this discriminating observation.
3.4 Execution of the
Empirical Examination 3.4.1 Applied method for the data survey?(primary research) The business consultancy BearingPoint (formerly KPMG Consulting)556 implemented the SCOR model as an immanent component of the methods applied for analysis and Supply Chain optimization. The methods used for Supply Chain transformation and the tools for that purpose are expressly adapted and tuned to the SCOR model. The company did, however, modify and enhance the SCOR model slightly. The most significant differences are as follows:
1. The delivery process (Deliver) was split into two sub-processes: a storage process (Store) and a transport process (Transport). According to BearingPoint, the logistical functions can be more clearly and effectively represented in this manner. 2. The sales process (Sell) was introduced within the delivery process. The reason given for this step was the need to accommodate the increasing importance of the connection between Supply and Demand Management. 3. New Product Development was added. BearingPoint states that this is
because, although the process was not a part of the present SCOR model, it will become all the more important for the business success in the future, for example in connection with core competences and the constantly increasing importance of Outsourcing. 4. The planning process was integrated into the other above-mentioned processes and therefore is not explicitly apparent. The consultancy explains that this is so the planning processes’ functions appear throughout the Supply Chain. One method developed by BearingPoint for Supply Chain analysis is represented by a questionnaire based
upon the SCOR model.557 Here, the focal point is the Key Performance Indicator (KPI) which has already been introduced, and which is supposed to be compared to the values of other companies (Benchmarking). In this case, the survey is a quantitatively-orientated one (KPI Benchmarking questionnaire). According to BearingPoint, the aim of this method is the comprehensive analysis, representation, and comparison of firm’s Supply Chains with each other within a relatively short period. As an additional advantage, the consultancy claims that this procedure also promotes the build-up and transfer of 558 knowledge.
3.4.1.1 Course of the examination The planning and preparation of the examination was done jointly by the customer and the business consultancy. The individual stages were as follows:559 1. Preparation: Customer: • Identification of the company’s employees taking part in the examination and filling out the questionnaire • Identification of an (executive) sponsor to ensure a high return quota.560
BearingPoint: • Granting access rights for the online questionnaire completion • Support during data collection 2. Execution: Customer: • Completion of the questionnaire561 BearingPoint: • Support during data collection. 3. Analysis: Customer:
• Answers to further questions and availability of further information material, if required. BearingPoint: • Evaluation of the collected data562 • Compilation of action points for Supply Chain’s improvement. 4. Results: Customer: • Validation of the results • Validation of the action points. BearingPoint: • Presentation of the results
• Presentation of the suggested action points. 3.4.1.2 Examination results The result report contained the aggregated results of collected data throughout all the survey’s participants. The submitted results comprised individual reports of performance comparison supported by graphics. Potential improvements were also contained within it. In addition to this, the results could be aggregated in a SCORcard format based upon the SCOR processes.563 The result report included a combination of graphical forms of
representation; for example, bar charts for the illustration of comparison group averages and quartile charts for standardization of results compared to the average value of a comparison group.564 From these it was possible to build up a detailed view for every SCOR process, originating from the overall view. To take this line of analysis further, the respective performance measures behind these results could be viewed in detail. As this also applied to the diagnosis level, suggestions for improvement there were also contained in the report.565
3.4.2 Evaluation of the results of the empirical
examination (secondary research) This study evaluated the results of the empirical survey. This process must be differentiated from that of the survey, which the BearingPoint consultancy used to handle their type of field research, as explored in the previous section. In this study, the results collected were used more in the form of secondary research (desk research). 3.4.2.1 Evaluation of data It is the general task of data evaluation to sort, investigate and analyze the surveyed data, and to condense the data into clearly visible proportions, in order that decisions can be reached. Data
evaluation is therefore, ultimately, about arriving at assertive and informative measurements so that immanent data correlations can be recognized.566 The findings (or the diagnosis part of the study) are orientated around the theses outlined previously and are clearly and visibly arranged in accordance with them. Such an approach is in line with the established guidelines for empirical research.567 In this sense, the data present was evaluated in order to investigate the connection between the individual variables on an empirical basis and in accordance with the theses. Interpretations are thereby restricted exclusively to the statistical durability of
the theses. Estimates or concrete recommendations for action do not play a role as far as the findings part of the study is concerned. On the contrary, the respective conclusions are extracted during the later parts of the work.568 Originating from the data collected in the survey phase, which has been checked for completeness, the evaluation of the data has four stages:569 1. Data preparation 2. Data processing 3. Interpretation 4. Report and presentation of the results. 3.4.2.2 Methodology of evaluation for
the single hypotheses For the purpose of data preparation, data processing, and result presentation, an application program by the corporation SPSS with the same name SPSS (abbreviation for Statistical Product and Service Solutions, formerly Statistical Package for Social Sciences)570 was applied in this study. 571 During this process, the data available to the author was transferred from an Excel file into the evaluation program. Based upon this, the following statistical values were investigated:572 Arithmetic Mean:573 A measure of location574
in
conjunction with metrical (i.e., at least interval-scaled) data. This is often also described as measure of central tendency, which is (strictly speaking) incorrect due to the existence of other central measures (such as a geometrical or harmonic center). It is calculated as the sum of the individual values of the data package, divided by the number of data elements Range: The span in variation represents a diffused measure. It is calculated as the difference between the highest value (Maximum) and the lowest (Minimum) of a data package. Standard Deviation:
The standard deviation is calculated as the root of the variance of a data package. As with the variance, a distinction must be reached between the standard deviation, which characterizes the given data (empirical standard deviation), and that which is calculated from sample data as an estimated value for the population. Type I Error: The erroneous declination of a null hypothesis575 is described as a socalled Type I Error. 576 It reflects the risk of declining a neutral hypothesis purely upon the grounds of the randomness of the respective sample, therefore also of assuming
a connection or difference that de facto does not exist. A value of P(a) = 0.05 stands for example for a respective Type I Error or error margin of 5 percent.577 Correlation Coefficient: Correlative measures emphasize the strength of the correlation between two variables. In this case, if correlations between these and further variables are taken into consideration, one speaks of partial correlation. Measures for the strength of the correlation are described, as a rule, as correlation coefficients. Correlation
coefficients can often take on values of a minimum of –1 and a maximum of +1. In this instance –1 indicates a perfect negative and +1 a perfect positive correlation. The choice of correlation coefficients is dependent upon the standard of measurement of the variables. Often in the case of two metric characteristics, the so-called Product-Moment Correlation (PM Correlation) and the corresponding Bravais-Pearson’s correlation coefficient (BPC) are used, which in statistics is usually abbreviated by using the letter R. The BPC is calculated as the covariance578 of both variables of
interest, divided by the product of the standard deviation of both the variables. It can adopt values between +1 (perfect positive correlation) and –1 (perfect negative correlation). A value of 0 indicates the absence of a (linear) correlation.579 Those methodical influences that may interfere with the BPC, on account of the absence of the linearity of variable relationships (such as polynomial progression), and the respective safety insurance measures within the work submitted, will be dealt with more closely in the following sections. In the case of the examined theses, we are dealing with bivariate
correlation assumptions based upon pairs of respective characteristics or continual variables (interval-scaled) in the shape of SCOR performance measures. Therefore, the BPC was chosen to be the correlation coefficient. The minimum requirement for a significant conclusion was fundamentally established as P(a) < 0.05 within the usual (business) scientific and statistical criteria.580 In conjunction with the statistical significance, it was presumed that the result of a hypotheses test could be considered significant if the assumption is correct that a theoretically assumed correlation or difference between the characteristics
found within the data is not explained purely by the _blurring_ associated with the sampling approach (typical cases).581 In addition to the respective inferential statistic or conclusive data analysis,582 it is possible to achieve an illustration of the acquired knowledge for exemplary findings – provided unambiguous systematic coupling of the variables is present – by means of additional and descriptive graphics.583 In concrete terms, and for the representation of the usual measurement of central tendency, the use of block diagrams was deemed the best option.584 During this process – and in accordance
with each diagnostic situation – special attention was paid to the issue of whether companies deviate from a given basic tendency, dependent upon factors such as turnover and employee numbers, or whether they _cover_ these extremely well. This was done in order to enable the derivation of later attempts at clarification.585 Each correlation-immanent null hypothesis refers to _parallel running,_ (i.e., no correlation) of the bivariate number of measures involved in the present case. Because missing data played no role within the present case, significance-modulating effects following a variety of sample sizes
require no further consideration.586 The illustrated statistical values are enlisted in order to confirm (verify) or reject (falsify) the established theses and arrive at an interpretation resulting from this. The ultimate purpose of this process is the accommodation of appropriate answers to the questions fundamental to the examination.587 The above indication of the level of significance applied to the inferential statistics must not be overlook the fact that this approach can by all means accommodate the acceptance of model images which – taken individually – contain no significant effects, but do collectively contain a homogenous
pattern. In view of this fact, this approach is also used during the following explanation of the first stage theses results (classic inferential statistic, in this case: correlation analysis) to separate totally unsystematic results (i.e., roughly in the area of the absolute correlation level 0 to 0.1, in order to demarcate partially tendencial or _strictly_ substantial and therefore statistically significant results). The conventional respective dichotomy or differentiation between significant and non-significant will be closely observed during the following. This binary examination represents the secured fundamental and statistically
inferential evaluation criteria for the examined theses. It does, however, represent a momentous difference as to whether: A thesis is correlatively confirmed in a significant way, A postulated bivariate correlation is to be highlighted as quite simply unsystematic (see above), or The example referred to presents itself contrary to expectation in a statistically significant way. The question of the classification to be used within this study in order to incorporate the examined bivariate
correlations can – irrespective of the relevance of basic binary division – contribute to a varied understanding of the outcome.588 In accordance with this, the following groups can be differentiated: Significantly model-conformant Significantly model-contrary (i.e., a correlation counteracting the expectancy in a significant way) Unsystematic within the R-range from -0.10 to +0.10 Tendentially model-conformant or tendentially model-contrary (i.e., indicative of the respective opposite but not yet significant
correlative direction). So far, we have dealt with a purely pragmatic illustration. During the collective theses findings, binary division will also be used.589 3.4.2.3 Special evaluation procedure for the Meta theses For examples chosen to concretely investigate the developed Meta theses in a second stage, it was decided to assign a so-called Structural Equation Model (SEM) or, more exactly, a Covariance Structure Model590 as an examination model. Namely, it is the so-called AMOS procedure, whereby AMOS
stands for Analysis of Moment Structures.591 AMOS was chosen because this procedure is able to analyze and exchange data matrix results with SPSS.592 Because SPSS had already been used to investigate the single theses, the desired assignment of AMOS had research-economical advantages. Calculation procedures or programs such as AMOS or the related LISREL593 are, as mentioned, to be assigned to structural equation models. This is a form of statistics which goes beyond inferential statistics and descriptive statistical procedures applied for investigation of the single theses. In the recent past, it has
sometimes been the case that people have spoken of _new generation_ in conjunction with AMOS and other related procedures. The fact is, however, that these procedures were already conceived around 30 years ago. In any case, the respective software has in recent times increasingly developed in the direction of better usability, and is therefore available to an extended field of users.594 Collectively observed, therefore, programs such as LISREL and AMOS allow more complex models to be tested _en bloc_ for compatibility. The models to be tested thereby usually contain respective statements or hypotheses for
the effect association of ?so-called constructs, which in turn normally comprise particular single variables (indicators). AMOS and LISREL enable the conclusive decision as to whether the model ideals can be retained (model confirmation), or whether they must be declined (model rejection). High sample sizes, i.e., N>150 are optimal for model investigation, whereby AMOS and LISREL can also calculate sample sizes from 40 onwards. It is something of a precondition for this process, however, that the model due for investigation does not show an excessively high degree of complexity.595 These circumstances will be explored more closely in Chapter 4.596
Various statistical measurements are available for model evaluation within the framework of structural equation procedures, whereby the socalled Goodness-of-Fit Index (GFI) has gained an increasingly substantial presence. The GFI determines a guideline, similar to a conventional level of significance, as to the point from which a postulated model is still seen as compatible (i.e., suitable _Fit_ between data and model assumptions), and from which point it is considered to be incompatible. The GFI measures the relative amounts of variance and covariance justified in total by the model and illustrates the stability index within
the framework of regression analysis.597 The GFI can accommodate values between 0 and 1, whereby a value of 1 means that all empirical variance and covariance are exactly reflected by the model (_perfect model fit,_ usually a theoretical case). Another noteworthy tool within the context of structural equation models is the Adjusted-Goodness-of-Fit Index (AGFI). The AGFI is similarly a measure for the variance expressed within the model which, however, additionally takes into consideration the model complexity in the form of degrees of freedom.598 The AGFI also accommodates for values between 0 and
1. A model’s _Fit_ may be seen as more suitable the closer it becomes to a value of 1.599 Without preempting the illustration of results that follows, it must be noted here that inevitable conclusions can already be drawn as to the model’s compatibility from within the framework of the conventional inferential statistic (therefore correlation analytical) observation of the theses. The structureanalytical approach strives to make a _holistic_ contribution to the model evaluation.
Chapter Four
Comparison of work hypotheses and acknowledged results of the empirical study Chapter four continues the context of justification within the framework of the research-logical course, started in the previous chapter and as defined by Friedrichs. Evaluation, investigation
statistical and result
interpretation transform data into findings and conclusions. The evaluation does not take place randomly, but is led by the hypotheses. Description, analysis and explanations are the most important parts of the interpretation process.600
4.1 Results of the Evaluations of the Theses In this section the evaluation results for the theses developed previously will be explained using statistical procedures. In this context, statistics can be defined as the art of analyzing, illustrating, and interpreting accumulated data so that the user arrives at new knowledge.601
The data gathered from the more than 70 actual companies available for the evaluation was of essential importance here. The calculations for the individual variables or respective performance indicators, and the further information thereto, can be found in section 4 of the appendix. The complete set of data is available from the author. The close subject matter proximity between the data basis and the central questions made it easier to meet the requirement of a reflective reference to the examination-leading hypotheses in the course of interpreting the examination results.602 Several factors can constitute the cause for a possible
“Mis-Match” between accumulated data and empirical reality in a study like this.603 On the sampling procedure level, for instance, surveying units or persons questioned for information on the decisive examination object could simply prove to be unsuitable, or lead one to a restricted or distorted selection. This could occur in the case of a survey whose results are intended to be applicable to large industrial companies, but where results were gained following a survey of companies that belonged exclusively to a particular industry.604 Amongst several other artifact sources,605 a “Mis-Match” can result from the fact that – despite the partial
basic suitability of a selection – examiners and persons questioned have differing frames of reference available. This risk is to be classified as especially “delicate”, because it can lead to substantially false content conclusions without this always immediately becoming obvious. This can happen for example in the form of a weak questionnaire return quota, or criticism articulated by the persons questioned, or similar peculiarities.606 In this study such a difference in frames of reference could have existed in the fact that examiners and persons questioned had a varying comprehension of the relatively highly specialized question categories. In the same way, the case could be
possible where, for socially desirable reasons, the persons questioned submitted information pertaining to question categories not applicable to company reality. An example of this would be the issue of estimative statements as to certain process attributes not followed by the company itself, and therefore not specifically included into the survey. As has already been pointed out elsewhere,607 after the focus had been placed upon those answers obtained within the consolidation of the primary research which contained gapless and differentiated statements, the previously mentioned risks become more
improbable (reversed conclusion of high statement authenticity). The conclusion of risk improbability therefore became more applicable to the study as the statements were made by the executives within each business specializing in Supply Chain topics.608 In the case of the presence of a “MisMatch” between the screening questions and company reality, those respondents would have been more likely to express criticism than if the questions had been put to less professionally experienced contact persons. The case of gapless and differentiated statements being given as to the individual Supply Chain attributes (if the question categories had not had close proximity to business reality) must
be considered relatively improbable. However, the aforementioned risks may not be completely ruled out, as total avoidance of artifacts is not practically achievable in any empirical examination in which the “Human Factor” plays a role (surveys or interview for information, etc.)609 All following evaluations took place discriminately and in accordance with the groupings represented in paragraph 3.3.5. This was done in order to capture heterogeneity within the set of data and respectively monitor input measures on the evaluation side with regards to possible result-influencing distortions. Those cases in which this
led to an incremental knowledge accumulation are noted with their respective results. If no further declarations are made, additional recognition could not be gained from the discriminate observation. The diagnostic situation presents itself as a rough comparison for the companies characterized by various discriminating characteristics, i.e., no inferentialstatistically relevant interactions were present.610 The representation and interpretation of the results was performed by means of the following rough screening process: Description
of
the
evaluation
results by using statistical figures as for example arithmetic mean, standard deviation, etc. Explanation of the bivariate correlation by means of statistical values, as for example the BravaisPearson’s correlation coefficient (R) and the Type I Error (P(α). The direction of conclusion being whether the thesis is confirmed (verification), rejected (falsification) or judged to be unsystematic.611 Exemplary graphic illustration, if meaningful. In this case, it was decided for inferential-statistical as well as content reasons to carry out visualization of the bivariate
correlations from a correlation level of R = 0.30 (absolute).612 For better comprehension of the following illustrations it did, however, seem important to first illustrate the pertinent descriptive-statistical measures of the central tendency and dispersion (especially arithmetic means and standard deviations) in addition to the respective units of measurement (i.e., percentages, hourly or daily recording, etc.) of all model parameters involved in the assumed correlations. The necessary information is made apparent by the following tables 4-2a to 4-2e. An overview of the examined sample’s
distribution takes place beforehand in the tables 4-1a to 4-1e. Tbl. 4-1a: Distribution of the examined companies (N = 73) by region and country613 Country
Region
Quantity Percentage
Canada
North America 2
2.74
USA
North America 53
72.6
France
Europe
1
1.37
Germany
Europe
1
1.37
Hungary
Europe
1
1.37
Italy
Europe
5
6.85
Turkey
Europe
1
1.37
UK
Europe
3
4.11
India
Asia
2
2.74
Indonesia Asia
3
4.11
Singapore Asia
1
1.37
Tbl. 4-1b: Distribution of the examined companies (N =
73) by industry614 Industry
Quantity Percentage
Aerospace and Defense
2
2.74
Agriculture and Biotechnology
3
4.11
Apparel
3
4.11
Automotive
2
2.74
Chemicals and Pharmaceuticals
7
9.59
Computers and Consumer Electronics
7
9.59
Consumer Packaged Goods
7
9.59
Electric Utilities
4
5.48
Household Appliances
3
4.11
Machinery and Equipment
6
8.22
Metal Products
5 6.84
Office and Printing Machines 4 5.48 Rubber and Plastic Products 5 6.85 Telecommunications
7 9.59
Retail and Distribution
6 8.22
Others
2 2.74
Tbl. 4-1c: Distribution of the examined companies (N = 73) by group size based on revenue according to German Commercial Code (HGB)615 Size range
Revenue
Quantity Percentage
Less than 6.77m Euros Small-size (approx. 8.5m UScompanies Dollars)
3
4.1
6.77 to 27.5m Euros Mid-size (approx. 8.5m to 35m companies US-Dollars)
14
19.2
Greater than 27.5m Large Euros (approx. 35m US- 56 companies Dollars)
76.7
Tbl. 4-1d: Distribution of the examined companies (N = 73) by group size based on FTE number according to German Commercial Code (HGB)616 Size range
Number of Employees
Quantity Percentage
Small-size
Less than 50
2
2.7
companies Mid-size companies
50 to 250
Large companies Greater than 250
16
21.9
55
75.4
Tbl. 4-1e: Distribution of the examined companies (N = 73) by Return on Assets (ROA)617 Return on Assets (ROA) Quantity Percentage Negative (< 0 percent)
7
9.6
0 to 10 percent
46
63.0
Greater than 10 percent
20
27.4
Tbl. 4-2a: Description of Source629 Parameter No. (Performance ME618 Measure)
1
Purchasing cost as a percentage of revenue Percentage
Percent
X619 s 620 Min 621 Max622
1.33
0.64
0.23
3.67
of purchased orders Percent received on time and complete
80.64 18.56 5624
99
3
Percentage of purchased lines Percent received on time and complete
81.79 20.32 0625
99
4a
Transactions processed Percent via web
4.45
13.18 0626
90
Transactions 4b processed Percent via EDI
3.27
13.19 0627
90
2
5
Number of active Quantity 81.92 121.95 1628 suppliers per FTE
550
6
Purchasing Dollar cost per FTE
199,200
72,692 45,614 66
Tbl. 4-2b: Description of Produce631 Parameter No. (Performance Measure)
ME
x
s
Min Max V
1
Manufacturing cost as Percent 60.11 20.17 8.89 65.38 56.49 percentage of Revenue
2
Average operatingPercent 84.80 14.25 45.00 99.80 54.80 equipment efficiency rate
3
Average manufacturing Percent 201.52 356.70 1630 2,160 2,159 cycle time
Average MPS 4 plant delivery Percent 87.16 Performance
13.93
15
95
Average plant 5 capacity Percent 70.35 utilization
19.85
20
94
6 Manufacturing Dollar
387,266 752,362 29.851 3.81818m
cost per FTE Average 7 throughput per FTE
Pieces 496,616 909,367 481
5.454555m
Tbl. 4-2c: Description of Deliver – Store634 Parameter No. (Performance ME Measure)
x
s
Min
Max V
1
Inventory management cost as Percent percentage of revenue
1.95 1.54 0.02632 7.88 7.86
2
Inventory mgmt. cost as percentage of Percent inventory value
37.16 33.04 0.79
3
Average inventory turnover Inactive
Quantity 15.08 19.73 2
80
79.2
109
107
4
inventory percentage
Percent
10.16 17.28 3
67
5
Inventory obsolescence cost as Percent percentage of revenue
1.07 2.29 0.50
11.64 11.1
6
Cycle count accuracy percent.
90.34 19.22 1.89
98
96.1
240
239
Percent
64
Average received finished 7 goods turnaround time
Number of 19.20 hours
38.34
1
Inventory 8 stockout percentage
Percent 14.00
20.13
5633 60
55
Average warehousing 9 Percent 84.45 space utilization
16.27
30
66
96
Inventory 10 management Dollar cost per FTE
253,331 1.07689m 480
7.75 7.74 Mio.
Inventory mgmt. cost 11 Dollar per customer order
219,83 320,62
1,500 1,49
10
Tbl. 4-2d: Description of Deliver – Transport636 Parameter No. (Performance Measure)
ME
x
s
Min Max V
1
Transportation cost as Percent percentage of revenue
3.93 5.20 1
35.23 34.23
2b
Percentage of Percent inbound cost
40.04 25.86 5
80
75
2b
Percentage of Percent outbound cost
62.34 26.01 10
80
70
3
Damaged shipments
Quantity 1.56 2.16 0635 10
10
On-time
4a
delivery percentage (inbound)
Percent
92.04 8.91 60
On-time delivery 4b Percent 94.01 percentage (outbound) Transporta5 tion cost Dollar per FTE
7.29
98
60
38
99
1.04822m 1.61365m 1.8298m 8.1032m
Tbl. 4-2e: Description Deliver – Sell639 Parameter No. (Performance ME Measure)
1
2
x
Customer service cost as Percent 3.21 percentage of revenue Customer retention
Percent 85.03
s
Min
Max
7.87
0.50
56.1
18.17
30
97
rate 3
Customer disputes
Percent 4.33
7.56
1
50
4
Perfect orders rate
Percent 85.12
16.28
25
98
5
Lines ontime fill rate
Percent 88.95
10.14
60
98
6
Backorders value
Dollar
11.17801m 28.7459m 10.000 1.38
7
Average order- toshipment lead time
Hours
281.88
378.21
1
8
Customer service cost Dollar per FTE
108,577
254,600
221,900 1.81
9
Percentage of sales via web
33.32
0637
Percent 22.07
2,00
100
4.1.1 Results of the theses of
the SCOR model group Intra-Performance Attribute (I-P) 4.1.1.1 Performance Attribute Customer Service (reliability and responsiveness) Thesis 1: The thesis that a high percentage of ontime delivery percentage – inbound or outbound would lead to a high customer retention rate, was correlationanalytically confirmed for the “inbound”-component (supplier side). An increased customer retention rate was also coupled with an increased percentage of on-time deliveries. An unsystematic result was consequently
present for the “outbound”-component. The respective Product-MomentCorrelation (PM-Correlation) can be collectively found in Tbl. 4-3.640 The additional consideration of the parameters company revenue and FTENumber in a multiple regression (percentage of on-time deliveries as a criterion, manufacturing cycle-time in addition to the am. parameters as the predicates) were of no incremental value. This means that the correlation to be substantially assessed mainly represented itself independently from the revenue and employee-related operationalized company measure.
This statement was valid on the whole for all other theses, which is why these aspects are only enlisted in the following in cases where incremental information value is actually present (additional explanatory value) for the p a r a me te r s company revenue and number of FTE. Tbl. 4-3: Correlation between on-time deliveries and customer retention rate Correlation
N641 R642 P(α)643 M 644
on-time delivery percentage – inbound & customer 73 retention rate
+0.23 < 0.05
I-P
on-time delivery percentage – outbound & customer 73 retention rate
–0.04
nonsignif.
I-P
Thesis 2:
For the thesis whereby a high perfect orders rate determines a high customer retention rate, only a tendencial confirmation could be provided. The correlation does not therefore represent itself as significant, but is at least rudimentarily present. Tbl. 4-4: Correlation between perfect order rate and customer retention rate Correlation
N R
P(α)
M
perfect orders rate & customer retention rate
73 +0.17
nonsignif.
IP
Thesis 3: The position whereby a low on-time delivery percentage – inbound or outbound correlates with a low perfect orders rate was confirmed in the case of
the “outbound”-component. In addition to this there was an extremely homogeneous relationship between a high percentage of on-time deliveries and a strong proportion in percentage of perfect customer orders. Therefore, a reversible relationship of variables was present.645 On the other hand, and with reference to the “inbound”-component, only a tendencial correlation could be assumed. The respective PM-Correlations and other evaluation results may be taken from Tbl. 4-5.646 The factual situation can be differentiated further by means of a collective observation.647
Tbl. 4-5: Correlation between on-time deliveries and perfect order rate Correlation
N R
P(α)
M
on-time delivery percentage – inbound & perfect orders rate
73 +0.16
non- Isignif. P
on-time delivery percentage – outbound & perfect orders rate
73 +0.48
< I0.001 P
Thesis 4: The thesis of a “counter-rotating” correlation between the manufacturing cycle-time on the one side and the percentage of on-time deliveries – inbound or outbound on the other side could be corroborated by correlation analysis.The negative PM-correlation coefficients mean in this context that with a high manufacturing cycle-time, the percentage of on-time deliveries
decreases. Seen from the other side, it increases with decreasing manufacturing cycle-time, and this points towards the presence of a reversible variable relationship. As a result of this, only the correlation for the “outbound”component was to be deemed to be substantial. Diag. 4-1: On-time deliveries (inbound or outbound) and perfect customer orders
On the basis of these results, a “counter-rotating” relationship can be assumed between the amount of on-time “outbound” deliveries and the manufacturing cycle-time. In the case of “inbound” deliveries, a purely marginal correlation could be noted. Tbl. 4-6: Correlation between on-time deliveries and manufacturing cycle-time Correlation
N R
P(α)
M
on-time delivery percentage – inbound – non- I73 & average manufacturing cycle time 0.09 signif. P on-time delivery percentage – outbound & average manufacturing cycle time
73
– I< 0.05 0.22 P
The reliable delivery of purchase orders was therefore unequivocally bound to defect-free (perfect) customer
orders on the basis of the empirical data, and as a result of this a positive relationship of both parameters could be corroborated. Thesis 5: A positive correlation between the percentage of purchased orders received on time and complete and the perfect orders rate was confirmed as statistically significant. Therefore, a deterministic variable relationship could be proven. Tbl. 4-7: Correlation between perfect purchase orders and perfect order rate Correlation
N R
P(α) M
percentage of purchased orders < Ireceived on time and complete & perfect 73 +0.27 0.05 P orders rate
As a result, the respective correlation level lay in an area which already allowed for the conclusion of a measurably narrow parameter648 coupling. Thesis 6: It was possible to find a significant degree of support for the model’s concept that a “counter-rotating” coupling exists between average MPS plant delivery performance – work orders and average manufacturing cycle time. High average MPS plant delivery performance – work orders
accompanied shorter average manufacturing cycle times. Contrary to this low average MPS plant delivery performance – work orders accompanied an increase in average manufacturing cycle time to a significant degree. An opposing relationship could therefore be proven between the two parameters, and this relationship can also be seen as deterministic in nature. Tbl. 4-8: Connection between MPS plant delivery performance (work orders) and manufacturing cycletime Correlation
N R
P(α) M
average MPS plant delivery performance – work orders average manufacturing cycle time
73 −0.26
< I0.05 P
Thesis 7: The position that a short average purchase requisition to delivery cycle time determines a high lines on-time fill rate could not be empirically corroborated to a sufficient degree. The correlation of the two parameters can, for the most part, be characterized as unsystematic. Tbl. 4-9: Correlation between delivery cycle-time for purchase requisitions and perfect customer order lines Correlation
N R
P(α)
M
average purchase requisition to non- Idelivery cycle time & lines on-time fill 73 +0.09 signif. P rate
4.1.1.2 Performance attribute Flexibility
Thesis 8: In a similar fashion, no empirical confirmation could be found for the assumption that a high inventory stockout percentage leads to a high backorders value. Both parameters stood in a positive relationship, but conclusively had to be characterized as purely unsystematic (Tbl. 4-10). Tbl. 4-10: Correlation between stockout and backorders Correlation
N R
P(α)
M
inventory stockout percentage & backorders value
73 +0.06
non- Isignif. P
4.1.1.3 Performance attribute Cost Thesis 9: It was expected that a positive
relationship would be found between purchasing cost as a percentage of revenue and inventory management cost as a percentage of revenue . On the basis of the data gathered, this assumption received sustained confirmation. Tbl. 4-11: Correlation between revenue-related purchase and inventory management cost Correlation
N R
P(α) M
purchasing cost as a percentage of revenue & inventory management cost as a percentage of revenue
73 +0.34
< I0.01 P
H i g h purchasing cost as a percentage of revenue therefore occurred significantly with high inventory management cost as a
percentage of revenue . On the other hand, low purchasing cost was respectively present with low inventory management cost. The unison of both parameters involved and the associated deterministic relationship can also be convincingly corroborated by collective observation, as can be taken from Diag. 4-2. Diag. 4-2: Revenue-related purchase and inventory management cost
Thesis 10: Only tendencial indications were given by PM-correlation to support the thesis that a high inventory management cost as a percentage of revenue would accompany a high inventory management cost per FTE. In accordance with this, the calculated
relationship cannot be described as significant. However, a slightly positive correlation could be proven. Tbl. 4-12: Correlation between revenue and FTErelated inventory management cost Correlation
N R
P(α)
M
inventory management cost as a percentage of revenue & inventory management cost per FTE
73 +0.16
non- Isignif. P
Thesis 11: The assumption of a positive correlation between high transportation cost as a percentage of revenue and high transportation cost per FTE was corroborated. Both parameters indicated an extremely homogeneous and statistically significant relationship.
Tbl. 4-13: Correlation between revenue and FTErelated transport cost Correlation
N R
P(α) M
transportation cost as a percentage of < I73 +0.67 revenue & transportation cost per FTE 0.001 P
The unambiguous deterministic relationship of both parameters is also apparent in the following graphical illustration (collective observation mode). Thesis 12: A “counter-rotating” relationship was expected between the expression of transportation cost as a percentage of revenue and the amount of damaged shipments. As can be seen from the
following table, a negative correlation was calculated on the basis of the empirical data for both parameters involved. The latter was, though, finally marked as unsystematic when measured upon the height of the PM-Correlation. Diag. 4-3: Revenue and FTE-related transport cost
Tbl. 4-14: Correlation between revenue-related transport cost and damaged shipments Correlation
N R
transportation cost as a percentage of revenue & damaged shipments
73
P(α)
M
– non- I0.06 signif. P
Thesis 13: The thesis that a high purchasing cost as a percentage of revenue accompanies a h i g h purchasing cost per FTE was empirically confirmed. Despite the unusually strong correlation, the criterion of statistical significance was still achieved. Tbl. 4-15: Correlation between revenue and FTErelated purchase cost Correlation
N R
P(α) M
purchasing cost as a percentage of revenue & purchasing cost per FTE
73 +0.20
< I0.05 P
A deterministic variable relationship was therefore proven upon the basis of the examined data, as can be taken from Tbl. 4-15. Thesis 14: An empirical confirmation could not be produced for the correlation of a high customer service cost per FTE with a low number of customer disputes. Absolutely no factual relationship between customer service cost per FTE and amount of customer disputes exists in the submitted data pool. Tbl. 4-16: Correlation between FTE-related customer service cost and customer disputes Correlation
N R
P(α)
M
customer service cost per FTE & customer disputes
nonI73 – 0.01 signif. P
Thesis 15: As was the case with thesis 14, a completely unsystematic result arose here and therefore offered no justification for acceptance of the model assumption. In the end, purely a “noncorrelation” was given between the parameters transportation cost per FTE and the amount of damaged shipments. Tbl. 4-17: Correlation between FTE-related customer service cost and damaged shipments Correlation
N R
transportation cost per FTE & damaged shipments
73
P(α)
– non0.03 signif.
M IP
4.1.1.4 Performance attribute “Assets”
Thesis 16: A “counter-rotating” relationship was expected between the parameters inactive inventory percentage and average inventory turnover. The tendencial contrary relationship of both parameters could, in fact, be empirically proven. The criteria of statistical significance were, however, missed (albeit narrowly) (Tbl. 4-18). Tbl. 4-18: Correlation between inactive inventory percentage and inventory turnover Correlation
N R
inactive inventory percentage & average inventory turnover
73
P(α)
M
– non- I0.18 signif. P
As a basic tendency, it can be assumed from the results that a high
inactive inventory percentage would accompany a low average inventory turnover. Thesis 17: No empirical confirmation could be found for the thesis according to which a high inactive inventory percentage was coupled with a high average warehousing space utilization. Both parameters extensively stood in an unsystematic relationship with one another. Tbl. 4-19: Correlation between inactive inventory percentage and warehousing space utilization Correlation
N R
P(α)
M
inactive inventory percentage & non- I73 +0.08 average warehousing space utilization signif. P
Thesis 18: The presumption that a high average inventory turnover accompanies a low average warehousing space utilization could not be empirically corroborated either. The respective PM-correlation did show a negative prognosis, but the correlation lay within a completely unsystematic area. Tbl. 4-20: Correlation between inventory turnover and warehousing space utilization Correlation
N R
P(α)
M
average inventory turnover & average non- I73 +0.04 warehousing space utilization signif. P
4.1.2 Results of the theses of the SCOR model group
intra-competence (I-C) 4.1.2.1 Customer-facing indicators Thesis 19: A “counter-rotating” relationship was expected between the on-time delivery percentage – inbound or outbound and the backorders value. In this respect, a high amount of on-time deliveries should accompany a low value of back orders (assumption of a negative correlation). Positive correlations as to this question formulation could still be seen tendentially as a threshold value, even if this was in a direction contrary to that of the model. Nevertheless, the coupling of both components of on-time delivery (“inbound” and “outbound”) with the
respective value of backorders was not sufficiently unequivocal. It must therefore be noted that the results obtained did not precipitate in a modelconformant manner, although this itself was not in a significant way. Tbl. 4-21: Correlation between on-time deliveries and backorder value Correlation
N R
P(α)
M
on-time delivery percentage – inbound & backorders value
73 +0.13
non- Isignif. C
on-time delivery percentage – outbound & backorders value
73 +0.12
non- Isignif. C
Thesis 20: It is significant with this thesis that in the case of the “inbound”-component, the postulated correlation between a low inventory stockout percentage and a
high on-time delivery percentage could not be substantiated. In the case of the “outbound”-component, the postulate was more than meaningfully illustrated. Tbl. 4-22: Correlation between stockout and on-time deliveries Correlation
N R
P(α)
M
inventory stockout percentage & onnon- I73 –0.05 time delivery percentage – inbound signif. C inventory stockout percentage & on< I73 +0.46 time delivery percentage – outbound 0.001 C
In this way, it must be noted that the said “counter-rotating” variable correlation exists overtly and primarily for the “outbound“-situation, i.e., outgoing inventory or customer side respectively. Diag. 4-4 illustrates the
factual situation in a graphical form. Diag. 4-4: On-time deliveries (inbound or outbound) and inventory stockout649
Thesis 21: The thesis according to which a high percentage of purchased orders
received on time and complete would contribute to a low backorders value was confirmed for the empirical data pool. The respective contrary correlation appeared to be moderately revealed, but not statistically unambiguous. It could therefore be proven that both parameters stood in a “counter-rotating” relationship with one another.650 Tbl. 4-23: Correlation between perfect purchase orders and backorders Correlation
N R
P(α) M
percentage of purchased orders received – < I73 on time and complete & backorders value 0.21 0.05 C
Thesis 22: The data situation with regards to this
thesis can be compared to the one for the next thesis, the content of which is similar: A strongly evident percentage of purchased lines received on time and complete significantly contributed to a low backorders value.651 Tbl. 4-24: Correlation between perfect purchase order lines and backorders Correlation
N R
P(α) M
percentage of purchased lines received – < I73 on time and complete & backorders value 0.37 0.01 C
It can be taken from the associated Diag. 4-5 that this relationship may be characterized as thoroughly unequivocal (grouped view). Thesis 23:
The correlation analysis for the postulate of a “counter-rotating” correlation between stockout and MPS plant delivery performance (work orders) produced a supportive corroboration. In actual fact, both parameters’ characteristics showed themselves to be extremely contrary to one another. This means that a high stockout percentage and a reduced average MPS plant delivery performance accompanied each other (and vice versa for low stockout percentage, i.e., reversible variable relationship). The constellation of the results was therefore unequivocal, as is clearly expressed in the associated Diag. 4-6.
Diag. 4-5: Perfect purchase order lines and backorders
Tbl. 4-25: Correlation between stockout and MPS plant delivery performance (work orders) Correlation
N R
P(α) M
inventory stockout percentage & – < Iaverage MPS plant delivery performance 73 0.63 0.001 C (work orders)
Thesis 24: Although not expressed so strongly as
was the case with the last thesis, this postulate of a “counter-rotating” relationship between perfect customer order lines and backorders was also confirmed as statistically significant. High lines on-time fill rates were in this way empirically proven to contribute to a reduced backorders value. In this case, both parameters appeared to react in a distinctly contrary manner to one another. Diag. 4-6: Stock out and MPS plant delivery performance (work orders)
Tbl. 4-26: Correlation between percentage of perfect customer order lines and value of backorders Correlation
N R
lines on-time fill rate average MPS plant delivery performance (work orders) backorders value
73
P(α) M
– < I0.19 0.05 C
Thesis 25: The model assumption whereby a low inventory stockout percentage would
accompany a perfect orders rate was convincingly confirmed. A “counterrotation” of both the parameters was clearly evident. The contrary constellation of both parameters is also highlighted by the graphical illustration in Diag. 4-7. Tbl. 4-27: Correlation between stockout and perfect customer orders Correlation
N R
P(α) M
inventory stockout percentage & perfect 73 – < Iorders rate 0.63 0.001 C
Diag. 4-7: Stock out and perfect customer orders
In accordance with this, low percentages of stockout were very clearly coupled with the increased probability of perfect customer orders. The reverse appeared to be the case for increased stockout, i.e., a reversible variable relationship was present.
Thesis 26: Thesis 26, whose content was closely related to that of thesis 25, whereby a l o w inventory stockout percentage contributed to a high lines on-time fill rate, was unequivocally corroborated. Both parameters stood in a markedly contrary relationship to one another.652 Tbl. 4-28: Correlation between stockout and perfect customer order line Correlation
N R
inventory stockout percentage & lines on-time fill rate
73
P(α) M
– < I0.49 0.001 C
The non-ambiguity of the contrary relationship (deterministic variable correlation) also becomes apparent in
the respective visualization, as can be taken from the following Diag. 4-8. Diag. 4-8: Stock out and perfect customer order lines
Thesis 27: The thesis by which a low average manufacturing cycle time contributes to a decreased backorders value was not confirmed by the calculated correlation. Moreover, a rather “counter-rotating”
relationship was shown by both these parameters. The relationship, though, proved itself to be relatively weak. A conclusion of statistical significance was therefore respectively eliminated. Thesis 28: No empirical confirmation could be reached for the model assumption of a positive correlation between the inventory stockout percentage and a high average manufacturing cycle time. The relationship of both parameters was, in fact, proven to be positive, but nevertheless – measured by the correlation level calculated – it also appeared totally unsystematic. Tbl. 4-29: Correlation between manufacturing cycle-
time and backorders Correlation
N R
average manufacturing cycle time & backorders value
73
P(α)
M
– non- I0.15 signif. C
Tbl. 4-30: Correlation between stockout and manufacturing cycle-time Correlation
N R
P(α)
M
inventory stockout percentage & average manufacturing cycle time
73 +0.03
non- Isignif. C
4.1.2.2 Internal-facing indicators Thesis 29: The postulated model relationship between inventory management cost as a percentage of revenue and average received finished goods turnaround time was given clear confirmation on the basis of the data pool. Both parameters,
in fact, stood in an expressly positive relationship. In this respect, high inventory management costs, expressed as a percentage of revenue, accompanied an increase in average received finished goods turnaround time to a marked degree. Tbl. 4-31: Correlation between inventory management cost and received finished goods turnaround time Correlation
N R
P(α) M
inventory management cost as a percentage of revenue & average received finished goods turnaround time
73 +0.41
< I0.001 C
On the strength of this a “synchronous” (and therefore also a deterministic)
variable relationship could be proven between the two parameters. The factual situation is graphically illustrated in Diag. 4-9. Diag. 4-9: Inventory management cost and received finished goods turnaround time
Thesis 30: The thesis according to which a parallel
would seem to exist between increased inventory obsolescence cost as a percentage of revenue and a similarly increased inactive inventory percentage proved itself to be empirically stable. Both parameters substantially correlated positively with one another. A coupling of the inventory obsolescence costs with the inactive inventory percentage was therefore considered as proven. Tbl. 4-32: Correlation between inventory obsolescence cost and inactive inventory percentage Correlation
N R
P(α) M
inventory obsolescence cost as a percentage of revenue & inactive inventory percentage
73 +0.29 <0.01
IC
Thesis 31: No enduring confirmation was found for the model assumption that a high inventory obsolescence cost as a percentage of revenue would return a low average inventory turnover. On the one hand, the expected negative correlation between the two parameters was apparent. On the other hand, this correlation expressed itself in an equality – i.e., a correlation level that did not allow the evaluation “substantially narrow” in the last analysis. Tbl. 4-33: Correlation between inventory obsolescence cost and inventory turnover Correlation
N R
P(α)
M
inventory obsolescence cost as a percentage of revenue & average inventory turnover
73 −0.13
non- Isignif. C
Thesis 32: No supporting evidence was produced based on the evaluated data material for the thesis of a “counter-rotating” relationship between inventory management cost per customer order and a low average warehousing space utilization. In this case, one was not dealing with a systematic correlation. Both parameters showed the absence of systematic coupling, absent in both a positive and negative direction. In accordance with this, the existence of a “non-correlation” was proven. Tbl. 4-34: Correlation between inventory management
cost/customer order and warehousing space utilization Correlation
N R
P(α)
M
inventory management cost per customer order & average warehousing space utilization
73 +0.03
non- Isignif. C
Thesis 33: In the same way, no convincing empirical proof could be found for the thesis wherein a high inventory management cost per FTE would be present with a high average received finished goods turnaround time. The calculated correlation did show a slight positive indication. In the last analysis the correlation level lay, however, in a range that could only be marked as unsystematic.
Tbl. 4-35: Correlation between FTE-related inventory management cost and received finished goods turnaround time Correlation
N R
P(α)
M
inventory management cost per FTE & average received finished goods turnaround time
73 +0.05
non- Isignif. C
Thesis 34: The postulate of a negative coupling of the inventory management cost per FTE with the average inventory turnover was confirmed in a statistically significant way. Although this contrary correlation was not abundantly evident, a significantly increased occurrenceprobability of high inventory management cost per FTE simultaneous with a low average inventory turnover was found.
Tbl. 4-36: Correlation between FTE-related inventory management cost and inventory turnover Correlation
N R
P(α) M
inventory management cost per FTE & average inventory turnover
73 −0.21
< I0.05 C
Thesis 35: The thesis that a high average throughput per FTE would correlate with a high average plant capacity utilization for finished products could not be corroborated sufficiently or with any certainty. Both parameters correlated, as expected, in a positive way. Nevertheless, in the face of the correlation level found, a meaningful correlation could not be assumed. All the same, at least, a rudimentary positive
relationship could be proven. Tbl. 4-37: Correlation between FTE-related throughput and plant capacity utilization Correlation
N R
P(α)
M
average throughtput per FTE & average plant capacity utilization
73 +0.11
non- Isignif. C
Thesis 36: No certain statistical significance could be produced for the assumption of a “counter-rotating” relationship between the inventory management cost per FTE and the average warehousing space utilization. In a similar manner to the case of thesis 35, the expected (in this case negative) direction in the relationship of both parameters was apparent. However, measured upon the
correlation level, the variable correlation could not be viewed as substantial. Tbl. 4-38: Correlation between FTE-related inventory management cost and warehousing space utilization Correlation
N R
P(α)
M
inventory management cost per FTE & average warehousing space utilization
73 −0.12
non- Isignif. C
Thesis 37: A far-reaching unsystematic diagnosis was apparent with regards to thesis 37, whereby a degree of unison between a l o w average order-to-shipment lead time and a low amount of customer disputes was assumed. The correlation level lay within the marginalized area,
as illustrated in Tbl. 4-39. Tbl. 4-39: Correlation between order to shipment lead time and customer disputes Correlation
N R
P(α)
M
average order-to-shipment lead time & non- I73 −0.05 customer disputes signif. C
4.1.3 Results of the theses of the SCOR model group Inter-Competence/ Performance Attribute (ICP) 4.1.3.1 Customer Service (reliability ? and responsiveness) vs. cost Thesis 38: The model assumption that a high
inventory management cost as a percentage of revenue would accompany a low value in backorders could not be empirically supported. The correlation identified between the two parameters could only be marked as unsystematic in the last analysis. The respective correlation values can be taken from Tbl. 4-40. ?In view of these facts, a systematic coupling of the parameters involved was assumed on the basis of the examined data. Tbl. 4-40: Correlation between inventory management cost and backorders Correlation
N R
P(α)
M
inventory management cost as a percentage of revenue & backorders value
73 +0.03
non- Isignif. CP
Thesis 39: It was assumed that a positive correlation exists between customer service cost as a percentage of revenue and the on-time delivery percentage – inbound and outbound. Analysis of the correlation could not, however, confirm this. Negative relationships were found between the cost for customer service on the one hand, and on-time deliveries on the other. However, in the face of the empirically identified correlation levels, the contrary constellations were not to be viewed as substantial. Thesis 40: With regards to the thesis whereby a hi gh customer service cost per FTE
would correlate with a high on-time delivery percentage – inbound and outbound, no “strict” pattern of correlation became apparent. Having said that, the correlations on the whole consistently fell positively in the expected direction. The criteria for statistical significance were met in the case of the “inbound”-component. Tbl. 4-41: Correlation between customer service cost as a percentage of revenue and on-time deliveries Correlation
N R
P(α)
M
customer service cost as a percentage of revenue & on-time delivery percentage – inbound
73 −0.03
non- Isignif. CP
customer service cost as a percentage of revenue & on-time delivery percentage – outbound
73 −0.08
non- Isignif. CP
Tbl. 4-42: Correlation between FTE-related customer service cost and on-time deliveries Correlation
N R
P(α)
M
customer service cost per FTE & onI73 +0.19 < 0.05 time delivery percentage – inbound CP customer service cost per FTE & onnon- I73 +0.03 time delivery percentage – outbound signif. CP
It could be collectively recorded in this case that high FTE-related costs for customer service do positively accompany a higher degree of on-time deliveries. Thesis 41: No convincing empirical proof was found for thesis 41, according to which a low amount of revealed customer disputes was assumed to accompany a high customer retention rate.
The respective correlation direction was negative and therefore “counter-rotating” as expected, but measured by the correlation level the correlation could not be described as anything other than unsystematic. Tbl. 4-43: Correlation between customer disputes and customer retention rate Correlation
N R
P(α)
M
customer disputes & customer retention rate
73 −0.04
nonIsignif. CP
Thesis 42: The model assumption of a positive relationship between cycle count accuracy percentage and inventory management cost as a percentage of
inventory value was not corroborated with empirical certainty in the last analysis. On the one hand, a “parallelrunning” of both parameters was present (positive correlation). On the other hand, the correlation level did not lie in an area that would have allowed the assumption of a substantial correlation. Tbl. 4-44: Correlation between cycle count accuracy percentage and inventory management cost Correlation
N R
P(α)
M
cycle count accuracy percentage & inventory management cost as a percentage of inventory value
73 +0.06
non- Isignif. CP
Thesis 43: A non-uniform result was noted as far as the model assumption is concerned, by
which a high percentage of purchased orders received on time and complete would correlate with a high purchasing cost as a percentage of revenue. The correlation prognosis of the two parameters proved itself to be negative, whereby however the respective “counter-rotation” was, after the last analysis, to be classified as unsystematic in the face of the correlation level. Thesis 44: For the thesis of a positive correlation b e t w e e n percentage of purchased orders received on time and complete a nd purchasing cost per FTE, it was possibly to derive at least rudimentary
confirmation from the empirical data used for analysis. Tbl. 4-45: Correlation between perfect purchase orders and purchasing cost as a percentage of revenue Correlation
N R
P(α)
M
percentage of purchased orders received on time and complete & purchasing cost as a percentage of revenue
73 −0.09
non- Isignif. CP
Both parameters correlated positively with one another, i.e., a high percentage of perfect purchase orders increasingly accompanied high FTErelated purchasing costs. However, in the case of the correlation present, statistical significance was not reached. Tbl. 4-46: Correlation between perfect purchase orders
and FTE-related purchasing cost Correlation
N R
P(α)
M
percentage of purchased orders received on time and complete & purchasing cost per FTE
73 +0.14
non- Isignif. CP
All the same, a fundamentally positive coupling of both parameters could be considered proven. Thesis 45: The relationship between the manufacturing cost per FTE and average MPS plant delivery performance took shape in the direction of the prognosis, i.e., resulted in the expected positive correlation coefficient. Despite the significance of the diagnosis, a narrow correlation
between the two parameters could not be assumed in the face of the empirically discovered correlation level. On the whole, though, a degree of “parallelrunning” could be assumed by means of the evaluation results between FTErelated production costs and average plant delivery performance. Tbl. 4-47: Correlation between FTE-related manufacturing cost and MPS plant delivery performance (work orders) Correlation
N R
P(α) M
manufacturing cost per FTE & average < IMPS plant delivery performance (work 73 +0.20 0.05 CP orders)
Thesis 46: The thesis whereby a strongly marked measure of percentage of purchased
orders received on time and complete would preferentially occur in conjunction with a reduced amount of damaged shipments was completely confirmed in the submitted data pool. Reliably carried-out purchase orders and damaged shipments therefore stood in a positive way to one another in the postulated negative relationship. Tbl. 4-48: Correlation between perfect purchase orders and damaged shipments Correlation
N R
P(α) M
percentage of purchased orders received on time and complete & damaged shipments
73 −0.27
< I0.01 CP
In accordance with this, the contrary relationship of both variables
was convincingly proven.653 Thesis 47: A contrary relationship between the average MPS plant delivery performance and the number of customer disputes was convincingly corroborated on the basis of the empirical data material. Although not drastic, a significant negative correlation did exist between both parameters, i.e., a high average MPS plant delivery performance occurred in significant conjunction with a reduced number of customer disputes. The correlation can be taken from Tbl. 4-49. Thesis 48:
The thesis that a high inventory management cost per customer order correlates with a high perfect orders rate was confirmed in the empirical data pool. A significant, but not strictly deterministic relationship existed in the expected positive direction between both parameters, as highlighted in Tbl. 4-50. Tbl. 4-49: Correlation between MPS plant delivery performance (work orders) and customer disputes Correlation
N R
P(α) M
average MPS plant delivery performance & customer disputes
73 −0.22
< I0.01 CP
Tbl. 4-50: Correlation between inventory management cost/customer order and perfect order rate Correlation inventory management cost per
N R
P(α) M <
I-
customer order & perfect orders rate
73 +0.19 0.05 CP
Thesis 49: The assumption, according to which a positive relationship was to be expected betw een inventory management cost per customer order and perfect purchase order lines , might be considered empirically secured. The postulated positive relationship between both parameters was apparent in a significant way. High inventory management costs per customer order therefore accompanied equally high perfect purchase order lines, and a deterministic variable relationship could be 654 proven.
Tbl. 4-51: Correlation between inventory management cost/customer order and perfect purchase order lines Correlation
N R
P(α) M
inventory management cost per customer order & lines on-time fill rate
73 +0.25
< I0.05 CP
Thesis 50: The expected “parallelism” between customer service cost as a percentage of revenue and a high perfect orders rate could not be confirmed. Contrary to such an assumption, a “counter-rotating” relationship was present between both parameters. In the face of the identified correlation level, the correlation could, on the whole, rather be classified as unsystematic. Tbl. 4-52: Correlation between customer service cost
as percentage of revenue and perfect order rate Correlation
N R
P(α)
M
customer service cost as a percentage of revenue & perfect orders rate
73 −0.07
non- Isignif. CP
Thesis 51: It was assumed that a positive correlation between the customer service cost per FTE and a lines ontime fill rate would be identified. In actual fact such a positive correlation was present between both parameters. Tbl. 4-53: Correlation between FTE-related customer service cost and perfect purchase order lines Correlation
N R
P(α)
M
customer service cost per FTE & lines on-time fill rate
73 +0.13
non- Isignif. CP
The correlation, however, lay within an area which did not show conclusively that high FTE-related customer service costs contribute to a high lines on-time fill-rate in the sense of a strict and unambiguous accordance to rule. Therefore, based upon the fundament of the data observed, only a tendencial “synchronous” relationship between the variables concerned could so far be presumed. Consequently, at least a rudimentary “synchronous” correlation might be assumed upon the basis of the examination results. Thesis 52: The thesis of a “counter-rotating” correlation between the perfect orders
rate and the number of customer disputes was not corroborated by means of the empirical data. The respective correlation level moved within a statistically almost completely unsystematic range. In actual fact therefore, no substantial or even tendencial correlation existed between the two parameters. Tbl. 4-54: Correlation between perfect customer orders and customer disputes Correlation
N R
P(α)
M
perfect orders rate & customer disputes
73 +0.04
nonsignif.
ICP
Thesis 53: A “synchronism” was expected between a low average purchase requisition to
delivery cycle time and a high inventory management cost as a percentage of revenue. However, the correlationanalytical investigation brought forward no adequate proof for the suitability of such an assumption. No factual and recognizable correlation existed between the two parameters. On the contrary, the calculated correlation showed itself to be unsystematic. Tbl. 4-55: Correlation between purchase requisition to delivery cycle time and inventory management cost as a percentage of revenue Correlation
N R
P(α)
M
average purchase requisition to delivery cycle time & inventory management cost as a percentage of revenue
non- I73 +0.03 signif. CP
Thesis 54: No unequivocal confirmation was found upon an empirical level as regards the thesis that a high average purchase requisition to delivery cycle time would correlate with low order-related purchase costs. On the one hand, the correlation direction was, as expected, negative and therefore indicated a “counter-rotating” relationship between both parameters. On the other hand, measured by the correlation level, it would not have been suitable to conclude that a substantial correlation existed. Tbl. 4-56: Correlation between purchase requisition to delivery time and order-related purchase cost
Correlation N R P(α) M average purchase requisition to non- Idelivery cycle time & purchasing cost 73 −0.08 signif. CP per purchase order
Thesis 55: The thesis gave unambiguous support to the presence of a positive relationship between purchasing cost per FTE and t h e average purchase requisition to delivery cycle time. Both parameters correlated significantly positively with one another and produced a respective pattern of correlation. As far as this is concerned, increased FTE-related purchase costs actually did appear in conjunction with an increasing purchase requisition to
delivery cycle time. Tbl. 4-57: Correlation between FTE-related purchase cost and purchase requisition to delivery cycle time Correlation
N R
P(α) M
purchasing cost per FTE & average purchase requisition to delivery cycle time
73 +0.25
< I0.05 CP
Thesis 56: With respect to the thesis that a “counter-rotating” correlation existed between manufacturing cost per FTE and the average manufacturing cycle time, a picture of a threshold value was found within the data material. As expected, both parameters correlated negatively with one another, i.e., increased manufacturing cost per FTE
accompanied a reduced average manufacturing cycle time (Tbl. 4-58). Nonetheless, the identified correlation coefficient missed – albeit narrowly – the criteria of statistical significance, so that a substantial correlation could not be presumed in the final analysis. The accumulated evaluation results did, however, permit the conclusion that, on the whole, a contrary relationship exists between the two parameters. Tbl. 4-58: Correlation between FTE-related manufacturing cost and manufacturing cycle time Correlation manufacturing cost per FTE &
N R 73 +0.16
P(α)
M
non-
I-
average manufacturing cycle time
signif. CP
Thesis 57: A threshold value also resulted in the case of thesis 57: A homogeneous relationship was expected between average purchase requisition to delivery cycle time and an equally reduced number of customer disputes. The empirical data indicated an exactly opposing coupling, as both parameters correlated negatively with each other, as can be taken from Tbl. 4-59. Tbl. 4-59: Correlation between purchase requisition to delivery cycle time and customer disputes Correlation
N R
P(α)
M
average purchase requisition to delivery cycle time & customer disputes
non- I73 −0.17 signif. CP
Despite this fact, a substantial effect could not be assumed in the face of the explicit correlation level. For all that, this thesis could not be supported (for an attempted explanation, see also the comments and explanations following the presentation of the theses).655 4.1.3.2 Flexibility vs. cost Thesis 58: With thesis 58, a “counter-rotating” relationship between inventory management cost as a percentage of inventory value and the backorders value was assumed. The empirical examination of the facts produced an at
least rudimentary indication of the suitability of this thesis. Tbl. 4-60: Correlation between inventory management cost as a percentage of inventory and backorder value Correlation
N R
P(α)
M
inventory management cost as a percentage of inventory value & backorders value
73 −0.13
non- Isignif. CP
Both parameters correlated negatively with one another as expected, i.e., strongly marked inventory management costs as a percentage of inventory actually accompanied a rather reduced amount of backorders. A secure proof of the tendencial modelconformant constellation was, however, not found.
Thesis 59: The thesis by which an increased manufacturing cost as a percentage of revenue would accompany a low backorders value was enduringly confirmed. The identified correlationanalytical diagnosis underlined the “counter-rotation” of both parameters in a significant way, as can be taken from Tbl. 4-61. Tbl. 4-61: Correlation between manufacturing cost as a percentage of revenue and backorder value Correlation
N R
P(α) M
manufacturing cost as a percentage of revenue & backorders value
73 −0.30
< I0.01 CP
The
following
Diag.
4-10
emphasizes the opposing course directions ?of manufacturing cost as a percentage of revenue and the backorder value. Thesis 60: The empirical proof necessary in order to support thesis 60, ?in which a low customer service cost per FTE increasingly appears in conjunction with a high backorders value, could not be produced with sufficient security. It was, however, apparent that both parameters correlated negatively with one another, as expected. But in the last examination, the relationship lay in a far-reaching and statistically unsystematic area. Diag. 4-10: Manufacturing cost as a percentage of
revenue and backorder value
Tbl. 4-62: Correlation between FTE-related customer service cost and backorder value Correlation
N R
P(α)
customer service cost per FTE & backorders value
73 −0.06
non- Isignif. CP
Thesis 61:
M
The thesis by which a low backorders value would accompany a low number o f customer disputes could not be adopted. A negative correlation consequently existed between both parameters. However, the level of correlation lay within an area of value that would only permit the conclusion that an extensively unsystematic relationship exists between both parameters. Tbl. 4-63: Correlation between backorder value and customer disputes Correlation
N R
P(α)
M
backorders value & customer disputes
73 −0.05
nonsignif.
ICP
Thesis 62:
The model assumption suggested a positive relationship between inventory stockout percentage and inventory obsolescence cost as a percentage of revenue. A positive correlation of both parameters was actually calculated. Tbl. 4-64: Correlation between stockout and percentage of inventory obsolescence cost as a percentage of revenue Correlation
N R
P(α)
M
inventory stockout percentage & inventory obsolescence cost as a percentage of revenue
73 +0.13
non- Isignif. CP
It could therefore be confirmed that a high stockout percentage accompanied high inventory obsolescence cost. Nevertheless, the correlation did not lie at a level that could be described as
substantial. Thesis 63: It was expected that a low inventory stockout percentage would appear in conjunction with a high manufacturing cost as a percentage of revenue . The negative correlation assumed thus far was actually to be found upon the basis of the empirical data material. Tbl. 4-65: Correlation between stockout and manufacturing cost as a percentage of revenue Correlation
N R
P(α)
M
inventory stockout percentage & manufacturing cost as a percentage of revenue
73 −0.14
non- Isignif. CP
In the case of this inferential-
statistical investigation, however, no level of correlation extent was present which would have justified the respective conclusion of clearness or “stringency” of the (“counter- rotating”) correlation between both parameters. Thesis 64: The model expectation for thesis 64 also comprised an opposing relationship between the two parameters. It was assumed that a high manufacturing cost per FTE would correlate with a low inventory stockout percentage. The model expectation could be corroborated using the data pool. The relationship between the two parameters did not manifest itself as expressly
narrow, but it was statistically significant, as can be taken from Tbl. 466. Tbl. 4-66: Correlation between FTE-related manufacturing cost and stockout Correlation
N R
P(α) M
manufacturing cost per FTE & inventory stockout percentage
73 −0.19
< I0.05 CP
Thesis 65 The thesis, whereby a high customer service cost per FTE should often appear in conjunction with a low inventory stockout percentage could not be confirmed with sufficient certainty. This result can, though, be seen tendentially as a threshold value. On the one hand, the expected negative
correlation was apparent, i.e., “counterrotation” of both parameters was present. On the other hand, however, the correlation level had not reached a statistically significant mark, as can be taken from Tbl. 4-67. As a result of this, the classification as “unequivocal” within the basic tendency of a model-conformant correlation had to be disregarded. Tbl. 4-67: Correlation between FTE-related customer service cost and stockout Correlation
N R
P(α)
M
customer service cost per FTE & inventory stockout percentage
73 −0.12
non- Isignif. CP
4.1.3.3 Customer Service (reliability ?
and responsiveness) vs. assets Thesis 66: No empirical support could be found for the thesis that a high on-time delivery percentage – inbound and outbound would correlate with a low inactive inventory percentage, as reflected in the following Tbl. 4-68. Tbl. 4-68: Correlation between on-time deliveries and inactive inventory percentages Correlation
N R
P(α)
M
on-time delivery percentage – inbound & inactive inventory percentage
73 +0.02
non- Isignif. CP
on-time delivery percentage – inbound & inactive inventory percentage
73 +0.07
non- Isignif. CP
Therefore, the analysis of the data
material produced the result that the percentage of on-time deliveries in the case of both components (inbound and outbound) did not stand in the assumed negative relationship with inactive inventory percentage. It was rather a largely unspecific correlation, and showed therefore an unsystematic pattern of correlation. Thesis 67: An increased appearance-probability of a high average inventory turnover in conjunction with a reduced backorders value was expected. The identified correlation was in accordance with this assumption and turned out to be negative.
However, in the face of the resulting correlation level only a tendencial, as opposed to a strongly marked, correlation could be concluded. Tbl. 4-69: Correlation between inventory turnover and backorder value Correlation
N R
P(α)
M
average inventory turnover & backorders value
73 −0.15
non- Isignif. CP
Thesis 68: The thesis of a “counter-rotating” correlation between average order-toshipment lead time and a high on-time delivery percentage – inbound and outbound was convincingly confirmed based on the empirical material.
In the case of both components – “inbound” and “outbound” – a high degree of on-time execution accompanied a reduced average orderto-shipment lead time, i.e., a deterministic variable relationship was proven. Tbl. 4-70: Correlation between order to shipment lead time and on-time deliveries (inbound or outbound) Correlation
N R
P(α) M
average order-to-shipment lead time & < I73 −0.22 on-time delivery percentage – inbound 0.05 CP average order-to-shipment lead time & < Ion-time delivery percentage – 73 −0.43 0.001 CP outbound
Diag. 4-11: On-time deliveries (inbound or outbound) and order to shipment lead time 656
In turn – for both named components – the contrary relationship also proved itself to be statistically significant. The “counter-rotation” for the “outbound” component, i.e., on the customer side, became especially apparent. The Diag. 4-11 serves the purpose of further visualization.
Thesis 69: The thesis whereby a “counter-rotating” relationship existed between cycle count accuracy percentage and inactive inventory percentage was, on the whole, confirmed. The correlation upon the basis of the empirical data was negative. With this, the data relationships presented themselves in a model-conformant manner. The correlation was not proven to be strongly expressed or “stringent”. However, the criteria of inferentialstatistical significance were met, as Tbl. 4-71 indicates. Tbl. 4-71: Correlation between cycle count accuracy percentage and inactive inventory percentage
Correlation cycle count accuracy percentage & inactive inventory percentage
N R
P(α) M < I73 −0.20 0.05 CP
Thesis 70: The model assumption according to which a high percentage of purchased lines received on time and complete would appear in conjunction with a low average order-to-shipment lead time could not be corroborated by means of the empirical data. No unambiguous “counter-rotation” of the two parameters could be identified. They stood moreover in a widely unsystematic relationship with one another (non-correlation).
Tbl. 4-72: Correlation between perfect purchase order lines and order to shipment lead time Correlation
N R
P(α)
M
percentage of purchased lines received on time and complete & average order-to-shipment lead time
73 −0.02
non- Isignif. CP
Thesis 71: The thesis by which an increased occurence probability of a high inactive inventory percentage would be present together with a high lines on-time fill rate could not be confirmed. According to the data situation the relationship between the two parameters was even shown as negative, as illustrated in the following Tbl. 4-73. Tbl. 4-73: Correlation between inactive inventory percentage and perfect customer order lines
Correlation N R P(α) M inactive inventory percentage & lines non- I73 −0.09 on-time fill rate signif. CP
However, in the face of the identified correlation level only the assumption of an unsystematic correlation could be adopted. Thesis 72: A stringently positive relationship was expected between the average order-toshipment lead time and perfect customer order lines. The calculated correlation of the two parameters did not confirm this assumption. A recognizable negative correlation was present, but this did not meet the criteria of statistical significance, as reflected in Tbl. 4-74.
Tbl. 4-74: Correlation between order to shipment lead time and perfect customer order lines Correlation
N R
P(α)
M
average order-to-shipment lead time & lines on-time fill rate
73 −0.14
non- Isignif. CP
Nevertheless, the conclusion may be drawn from this that within the empirical data pool, a high average order to shipment lead time does not accompany a similarly high amount of perfect customer order lines, but tendentially rather accompanies a reduced amount. In accordance with this, the constellation has a tendentially model-contrary character, as the evaluation results above showed.
Thesis 73: In the case of thesis 73, a stringently negative relationship between transactions processed via web/EDI and the average received finished goods turnaround time was assumed. The data observed, however, showed that a “reversed” – and therefore positive – correlation existed between the two parameters, via the web as well as EDI. Both correlations appeared as recognizably positive, although not expressly “stringent”, as can be taken from the following Tbl. 4-75. Tbl. 4-75: Correlation between purchasing transactions processed via web/EDI and received finished goods turnaround time Correlation
N R
P(α)
M
transactions processed via web & average received finished goods turnaround time transactions processed via EDI & average received finished goods turnaround time
73 −0.13
non- Isignif. CP
73 −0.20 < 0.05
ICP
During the correlation of the transactions processed via web/EDI and the received finished goods turnaround time, a statistically significant result was actually identified. Possible explanations of this so far basically model-contrary diagnosis will be commented upon in detail elsewhere.657 Thesis 74: With regards to thesis 74, according to which a high percentage of sales via
web would correlate with a low average order-to-shipment lead time, the tendencial negative relationship of the two parameters was present in the empirical data pool, as expected. However, the correlation level was situated in a value range that was regarded as totally unsystematic, as emphasized in Tbl. 4-76. The conclusion of a confirmation of the model assumption in a substantial way would therefore not have been justifiable. Tbl. 4-76: Correlation between sales transactions processed via web and order to shipment lead time Correlation
N R
P(α)
M
percentage of sales via web & average order-to-shipment lead time
non- I73 0.00658 signif. CP
4.1.3.4 Flexibility vs. assets Thesis 75: In the case of thesis 75, conformity of the bivariate parameter relationship was expected. This is to say that a low average received finished goods turnaround time was assumed to correlate with a low backorder value (vice versa for high values of both parameters). Tbl. 4-77: Correlation between received finished goods turnaround time and backorder value Correlation
N R
P(α)
average received finished goods turnaround time & backorders value
73 −0.02
non- Isignif. CP
The
thesis
could
not
M
be
corroborated by means of the observed data, as can be seen in the above Tbl. 477. The received finished goods turnaround time and the backorder value stood, moreover, in an almost totally unsystematic statistical relationship to one another (as such, a “noncorrelation” existed). Thesis 76: No convincing empirical confirmation could be produced for the thesis that a high average inventory turnover would establish itself parallel to a reduced backorder value. A negative correlation existed between the two parameters. The respective correlation level, however, reached only a poorly expressed (and
therefore unsystematic) value range, as can be taken from Tbl. 4-78. Tbl. 4-78: Correlation between inventory turnover and backorder value Correlation
N R
P(α)
M
average inventory turnover & backorders value
73 −0.08
non- Isignif. CP
Thesis 77: The model assumption that a low inventory stockout percentage would accompany a low average received finished goods turnaround time (and vice versa for a high value of the two parameters) could be unequivocally confirmed by the data. In accordance with this, a reversible deterministic
variable relationship could be proven. Tbl. 4-79: Correlation between stockout and received finished goods turnaround time Correlation
N R
P(α) M
inventory stockout percentage & average received finished goods turnaround time
73 +0.27
< I0.05 CP
The characteristics of the percentage of stockout and the received finished goods turnaround time indicated the model-conformant “synchronism”, as illustrated in Tbl. 4-79. In addition to this the respective correlation was statistically significant. Thesis 78: A parallel-running relationship was expected between the inventory
stockout percentage and the average order-to-shipment lead time. According to this, a low percentage of inventory stockout should appear in conjunction with a reduced average order-toshipment lead time (“synchronism” also in the reverse case, i.e., high values of both parameters). A model-conformant correlation was in fact present within the data pool. Beyond this, the applicability of the model assumption was corroborated as statistically significant, and a reversible deterministic variable correlation could be proven. Tbl. 4-80: Correlation between stockout and order to shipment lead time
Correlation inventory stockout percentage & average order-to-shipment lead time
N R
P(α) M < I73 +0.24 0.05 CP
Thesis 79: In accordance with the model idea, a “counter-rotating” relationship was expected between the inventory stockout percentage and the average inventory turnover. This relationship allowed itself to be corroborated as model-conformant based on the submitted data material. The respective correlation coefficient also proved to be statistically significant, as can be seen from Tbl. 4-81. Tbl. 4-81: Correlation between stockout and inventory turnover Correlation
N R
P(α) M
inventory stockout percentage & average inventory turnover
73 −0.21
< I0.05 CP
Thesis 80: A “counter-rotating” relationship between the inventory stockout percentage and the average operatingequipment efficiency rate (OEE) for finished products was assumed. The identified correlation did in fact show that such a contrary relationship existed between the two parameters: Weakly marked stockout percentages therefore increasingly appeared in conjunction with an increased degree in operating equipment efficiency rate. Tbl. 4-82: Correlation between stockout and operating equipment efficiency rate (OEE)
Correlation inventory stockout percentage & average operating-equipment efficiency rate (OEE)
N R
P(α)
M
73 −0.10
non- Isignif. CP
Nonetheless, the result could only be considered a threshold value, as the empirical parameter correlation was not present to an extent that could be judged as “stringent” or meaningful. The criteria of statistical significance were respectively not met, as illustrated in the above Tbl. 4-82.
4.2 Rating of the Examination Results of the Single Hypotheses In the present section the following questions are to be answered upon the
basis of the described examination results: Which theses could be confirmed, and how are these results to be classified?659 Which theses were not confirmed or had to be rejected? What are the possible reasons for this? Which theses should be changed and in what way, so that they can be either confirmed or validated in future empirical examinations?
4.2.1 Consequences from the respective differences
and conformities between the theses and actual results Of the 80 single theses evaluated, about 60 percent were confirmed as significant, or at least indicative of the prognosis of correlation direction. Roughly 35 percent proved themselves to be unsystematic, 5 percent were factually model-contrary and therefore had to be unconditionally rejected (falsified). The details of the confirmed theses may be taken from the aforesaid evaluation results. The theses conformant with the SCOR model corroborate the central assumption established at the beginning of Chapter 3 and the system of hypotheses found there.
In the following, the second and third categories will be dealt with in detail. During this, the four modelcontrary theses will be individually discussed and explanations for their status will be sought. The unsystematic theses will be discussed in expedient blocks of topics.
4.2.2 Approaches to clarification ?of the unsystematic theses The statistically totally unsubstantial and therefore unsystematically classified theses do not, in the last instance, disprove the respective adopted central
assumption or the fundamental system of hypotheses. It was moreover not possible to confirm or reject them in a statistically unambiguous way. In the following, possible explanations are to be found for the unproven correlations (“non-correlations”) of each involved parameter for those theses. Factors will be discussed here which could have contributed to create an unsystematic picture. In this context, the limitations (boundaries) of the SCOR model highlighted in Chapter 2 are also considered. In accordance with this, the model does not lay claim to explicitly include every company process or every
activity.660 Unsystematic results can therefore quite possibly manifest themselves for the performance measures respective to that type of company process. The cases in which this correlation is possible with unsystematic results will be specifically indicated. Based on the results, the unsystematic theses allow themselves to be allocated to the following four categories: Customer order management, inventory management, transport, and purchasing. 4.2.2.1 Customer order management Fifteen theses fell beneath this heading, namely:
Within the SCOR model group Intra-Performance Attribute (IP):Nos. 7, 8, 14661 Within the SCOR model group Intra-Competence (I-C): Nos. 27, 37662 Within SCOR model group InterCompetence/Performance Attribute (I-CP): Nos. 8, 39, 41, 50, 52, 60, 61, 70, 74, 75.663 In the category customer order management, the theses could be further allocated to two sub-categories: Time: On-time deliveries, lines ontime fill rate, order to shipment
lead time, backorders Quality: Perfect customer orders, customer disputes. Both the further competitive factors in Supply Chain Management’s soc a l l e d strategic square, cost and flexibility, are implicitly contained within those factors which are allocated to the formerly-named parameters time and quality. 664 The following correlations could not be individually proven: On-time deliveries, lines on-time fill rate, backorders and:
Order to shipment lead time: It could be possible that the allocation of both variables to differing SCOR processes (Deliver on the one side and Source on the other side) represents the reason for the fact that only an unsystematic correlation could be proven between both the variables. The correlation would otherwise have to extend over the whole Supply Chain, so to speak.665 It is possible that in order to prove the correlation and receive a sufficiently strong indication, a larger sample size would be necessary.
Stock out: A possible reason could be that the examined companies had built-up a sufficient inventory or applied powerful production and inventory stock planning systems in order to make themselves independent from delivery bottlenecks – at least partially. In conjunction with the companies to which this does not apply this could, in actual fact, lead to an unsystematic picture on the whole, and thus contribute to a correlation that cannot be proven.666 Customer service costs: It is possible that delivery reliability is
so strongly influenced by further parameters that higher assets alone cannot possess a significant influence. An unsystematic total picture could result from this. This train of thought is based upon the assumption that delivery reliability alone is not sufficient in order to secure the customer service necessary to uphold a competitive position.667Another potential explanation could lie in the present efforts to be in a position to ensure a better customer service by means of suitable Supply Chain concepts, with simultaneously low costs.668 An opposing, yet also interesting question would be whether
companies would be better off investing in other areas and should save on customer service costs.669 This factual situation will be dealt with more closely in Chapter 5. Inventory management costs: The failure of the study to prove this correlation could be attributed to the fact that some of the companies examined already work with the previously described concept of Vendor Managed Inventory (VMI) . Larger amounts of unfinished or delivered material would then only have a restricted influence upon the inventory costs. To be more exact, the costs would be shifted onto the supplier and “externalized”, as it
were.670 Similar conditions should be valid for the correlation with turnaround times: Longer turnaround times do not adequately equate to higher inventory management costs, because respective inventory strategies are applied to counteract this.671 Average received finished goods turnaround time: The companyinternal turnaround time has a substantial influence upon the final delivery of the finished product to the customer, and with this the ontime contract fulfillment. Under normal circumstances, the performance indicators for the Supply Chain contain this internal
component as a constituent of the delivery performance.672 It can therefore be presumed that the data material in conjunction with this was not consistent.
Customer disputes: This correlation falls under the aspect of customer service after delivery which the SCOR model takes, amongst other things, as a given entity. A possible way to operationalize this correlation could be a questionnaire to investigate the customer’s opinion of the delivery performance.673 It must be assumed
that further parameters have a substantial influence upon the frequency of customer disputes. On-time purchase orders: It can be presumed that the procurement strategy and the associated objective of on-time purchase orders have an influence upon the delivery performance.674 However, purchase orders alone probably do not have a sufficiently large influence upon the total order processing duration. One reason could lie in the fact that the companies have built up enough inventory stock in order to balance out delivery fluctuations. This in turn would indicate potential
savings. Another approach at explanation could lie in respective procurement strategies for cooperation with suppliers – Strategic Sourcing, for example.675 Sales via the internet: The internet has apparently not been able to contribute to the acceleration of order processing cycle time. According to this, internet usage in sales seems to have made a sales channel available which can contribute to increasing market shares and revenues, but does not necessarily have to lead to a substantial shortening of process lead time.676
Perfect customer orders and: Cost of customer service: Those issues mentioned above for the ontime deliveries also apply here. Perfect customer orders are possibly so strongly influenced by other parameters that high capital expenditure alone has no identifiable influence. The question also arises here as to whether companies would be better advised to invest in other areas and save on customer service costs. This will be more closely dealt with in Chapter 5. Customer disputes: If perfect
customer orders have no identifiable positive influence upon the number of customer disputes, it would mean in reverse that imperfect customer orders do not have to lead to ?an increased number of disputes. This would suggest the conclusion that some of the examined companies apply large sums in order to “mend” or “touch-up” their systems, as it were, in order to avoid disputes.677
Similarly, no identifiable correlation could be proven between the amount of customer disputes and customer service
costs. Also, no significant correlation between customer disputes and customer retention could be proven. This correlation however, falls under the category of customer service after delivery, which the SCOR model – as explained above – takes as a given entity. On the one hand, improvement possibilities with regards to the SCOR model result from this. On the other hand, possibilities for process optimization arise for the 678 respective companies. Both cases will be addressed in the course of Chapter 5.
4.2.2.2 Inventory management Eight theses fell within this category, namely: Within the SCOR model group Intra-Performance Attribute (IP):Nos. 17, 18679 Within the SCOR model group Intra-Competence (I-C): Nos. 28, 32, 33680 Within SCOR model group InterCompetence/Performance Attribute (I-CP): Nos. 42, 53, 66.681 Sub-categories could be constructed within the category of inventory
management: Effectiveness: Inventory turnover frequency, material availability Efficiency: Warehousing space utilization, inventory turnover periods, inventory management costs. The following correlations could not be proven: Warehousing space utilization and: Inventory turnover frequency: It can be assumed that the same applies
here as in the above mentioned case with regards to the relation between the ability to deliver and inventory management costs. Inventory turnover frequency has probably no identifiable influence upon warehousing space efficiency, because some of the examined companies work with the concept of Vendor Managed Inventory (VMI). In this case too, the costs would be shifted onto the suppliers and “externalized”, so to speak.682 This would also apply for the inventory management costs: Inventory management costs would, in this case, be displaced and “uncoupled”, as it were, from the
warehousing space utility. That would also explain why no identifiable correlation between inventory management costs and cycle count accuracy percentage could be proven: The responsibility for the cycle count accuracy would lay – at least partially – with the supplier.683
Inactive stored material: A possible reason for the fact that no significant correlation could be identified may be the fact that some of the examined companies specialize in products with a relatively short life cycle. The life
cycle of stored material has a direct influence upon inventory aging: The shorter the life cycle is, the more frequently old or inactive material must be replaced by new. 684 In addition to the life cycle, however, the service level strived for is also relevant: The higher this is, the higher the risk of building up inactive material.685 Inventory management costs: The non-proven correlation could represent a further indication of the fact that the examined companies already enlist to a greater extent the use of the concept of Vendor Managed Inventory (VMI) or related concepts.
Stock out and manufacturing cycle time: It can be presumed that amongst the examined companies a partial uncoupling of the stocking levels and the production process has already taken place.686 As a result of this, the dependency or respective correlation between the factors would be minimal, and the correlation therefore not identifiable. 4.2.2.3 Transport Two theses were allocated to this category and fall into the SCOR model group Intra-Performance Attribute (I-P): Nos. 12 and 15.687
Both theses could be allocated to costs. Concretely, no correlation was found between transport costs (as percentage of revenue and per FTE) and the extent of damaged shipments. This may be because a number of the examined companies are already cooperating with external logistics services (Third-Party Logistics Service Provider, 3PL) . This would be consistent with present tendencies, whereby the cooperation with a 3PL plays a great role for an increasing number of companies.688 4.2.2.4 Purchasing Two theses fell within this category in
the SCOR model group InterCompetence/Performance Attribute (ICP): Nos. 43 and 54.689 In this case, both theses also allow themselves to be allocated to costs. In accordance with this, no significant correlation could be proven between purchasing costs and: On-time purchase orders: Higher costs in the purchasing area did not seem to contribute to guaranteeing a better processing of purchase orders. This could be because a major problem within the procurement process normally lies in the expense involved in
information retrieval, complex process chains, and the multitude of manual tasks.690 Purchase requisition to delivery cycle time: As in the previous case, the same would be applicable here: The reason for the inability to prove the correlation may lie in high procurement process 691 complexity.
4.2.3 Clarification possibilities of the modelcontrary theses In the case of those hypotheses that proved to be model-contrary in a
significant or at least tendencial way, a factual more exact “reverse” correlation was found than was presumed during the initial thesis development. In this respect, the original theses had to be represented in the shape of new “counter-theses”, in order to draw the model closer to the empirical reality (model-conformant correlation). The derivation and suitability (or rather the “non-defensibleness” of the content) of such counter-theses will be dealt with more closely here in conjunction with the possible reasons for the model-contrary result constellation. Recommendations for improvement of the SCOR model result
from the extrication of potential reasons, which will be dealt with in detail in Chapter 5.692 The model-contrary cases undoubtedly represent the most problematic group, as they can invalidate the adopted central assumption. However, the percentage they represent of the total amount of theses investigated is less influential than it would need to be to cause serious problems. In this study they account for somewhere in the proximity of five percent, which is way short of the amount that would be necessary in order to consider the basic hypotheses system as fundamentally unsuitable.693 One
thesis fell within the SCOR model group Intra-Competence (I-C): No. 19. The other three theses had to be allocated to SCOR model group InterCompetence/Performance Attribute (ICP): Nos. 58, 72 and 73. 4.2.3.1 Model-contrary thesis of SCOR model Group ?Intra-Competence (I-C) The “counter-rotating” correlation within the customer-facing indicators, i.e., between customer service (reliability and responsiveness), concr etel y delivery performance – inbound or outbound, and flexibility, concretely supply chain response time, had to be rejected as model-contrary. The respective correlations were exactly
“reversed”. The resulting counter-thesis would be as follows: A high on-time delivery percentage – inbound and outbound accompanies a high backorders value. The counter-thesis content does not make sense from the aspect of plausibility, because it is the primary intention of on-time deliveries to avoid backorders.694As far as this is concerned, the counter-thesis cannot therefore be theoretically supported within the model. It is to be assumed that inconsistencies within the data material available account for this examination diagnosis. In addition to this, it must be
noted that in the face of the empirical constellation, the model-contrary “counter-rotation” was to be derived from the original theses. However, the correlation fell short of being significant. The conclusion that inconsistencies or “random fluctuations” exist within the data material was therefore corroborated. 4.2.3.2 Model-contrary theses of SCOR model group InterCompetence/Performance Attribute (ICP) The (positive) correlation between customer service (reliability and responsiveness), concretely order fulfillment lead time, and cost,
concretely warranty cost or returns processing cost adopted in thesis 57 had to be rejected. The constellation upon the basis of the empirical data proved itself to be “counter-rotating”. The counter-thesis can therefore be formulated as follows: A low average purchase requisition to delivery cycle time appears in conjunction with a high number customer disputes. From the point of view of the content, such a counter-thesis would seem highly plausible: The high complexity of the procurement process determines that, even with procurement
of the simplest or respectively lowvalue products, several departments must be involved within the procurement process.695 The SCOR model in its present form obviously does not (yet) make enough allowance for the influence of present-day procurement process complexity upon customer satisfaction. A possible reason for this could be that SCOR represents a respective functionor process-orientated model, as opposed to a data-orientated model. Dataorientated models are in a position to illustrate data and relationships, under which for example the relationship between delivery data and customer data
would fall. One possibility for furtherdevelopment of the SCOR model in that direction could be the extension of the process model to illustrate associated material and information flows between customers and suppliers.696 The (positive) correlation adopted in thesis 72 between customer service (reliability and responsiveness), concretely perfect order fulfillment, and assets, concretely inventory days of supply, had to be rejected, as a recognizably contrary relationship existed between both parameters upon the basis of the empirical material. The possible counter-thesis is therefore:
A low average order-toshipment lead time correlates with a high lines on-time fill rate. Such a counter-thesis may be justified on the following bases: It can be assumed that the reduction in order to shipment lead time ranks high in the importance of today’s Supply Chain strategies. A possible effect of this would be the achievement of a perfect orders rate.697 The optimization of the processes regarding order to shipment lead time already represents a substantial component of the respective Supply
Chain and competitive strategy respectively for a multitude of companies.698 And if one presumes that beyond this fact a high rate of perfect orders has a positive influence upon payments on the customer side, the process acceleration in the sense of a decrease in average order-to-shipment lead time is of substantial importance for the competitive factor time in the context of contemporary competitive strategies.699 It may be concluded that the SCOR model does not yet make sufficient provision for the circumstance whereby, due the contemporary prioritizing of decreased average order-to-shipment
lead times, a “counter-rotating” influence upon the order completion rate is no longer present.700 The correlation adopted in thesis 73 between customer service (reliability and responsiveness), concretely order fulfilment lead time, and assets, concretely cash-to-cash cycle time, was rejected on the grounds of the significantly “counter-rotating” diagnosis. The possible counter-thesis in accordance with this is as follows: A high number of transactions processed via web/EDI accompanies a high average received finished goods turnaround time.
A possible explanation for the justification of this counter-thesis could lay in the fact that – although the electronic processing of procurement procedures, according to industry affiliation, can make possible cost savings of 10 to 20 percent of process costs and 3 to 12.5 percent of total costs – the savings are probably not achieved via the shortening of process cycle time. Moreover, a lack of networking and synchronization between the departments involved leads to redundant stages of work which mainly have to be manually carried out, and are therefore relatively personnel- and time-intensive.701 The SCOR model does not account enough for this and attaches too little importance
to this factor. A reason for this fact could be that the concept of E-Business was only first introduced into the model in Version 6.702
4.2.4 Summary of the examination results accumulated for the single hypotheses Of the 80 evaluated single theses, roughly three fifths were proven to be significant or at least modelconformingly indicative of the correlation direction in the prognosis.703 About a third of the theses were proven to be unsystematic in the defined sense.704 A small amount of roughly a
twentieth had to be classified as significant or at least tendentially contrary to the model and presumed correlation direction.705 By the enlisted use of pure binary division according to p(α)-error probability (significant vs. nonsignificant), roughly 37 percent of the theses were to be classified as significantly model-conformant and about 1.5 percent as significantly modelcontrary.706 In connection with this it must be considered that a summarizing string of single theses is only restrictedly suitable for the purpose of confirming a theory –
understood as a hypotheses system – in a statistical-methodical sense. Several hypotheses or a system of hypotheses belong to a theory in this sense.707 As was made clear at the beginning of the study, the objective here was, amongst other things, to make an initial contribution towards a theory of this kind. This study does not, however, claim to have conclusively investigated a theory. 708 Rather, it adopted an exploratory approach, in which a successive accumulation of knowledge stood in the foreground. Such an approach must be continued beyond this study by means of further iterative examinations building upon it.709
Furthermore, a multitude of alternative illustration options for the model exist in addition to the one developed in the context of the work at hand. The inference to the SCOR model, in a general sense, must really be seen with this as a background. Beyond this, interfering influences occurring with probable certainty and the limitations present during the examination must not be disregarded. These will be dealt with at the end of the present chapter. The derivation of respective recommendations for improvement or innovative indications with regards to the SCOR model and its application, gained on the basis of the accumulated results, needs to continue. This aspect
will be addressed in Chapter 5.710
4.3 Attempt at Application of Structure-analytical Procedures ?to Verify the Meta Theses 4.3.1 Design of the examination Directly after the investigation of the single theses by means of inferentialstatistical procedures, an attempt was made to examine the seven established Meta theses711 using a structureanalytical procedure. During this, concretely the AMOS procedure came into use.712 Because the Meta theses
focus upon a superior level, the assignment of this procedure seemed appropriate. It is important to note in this instance, though, that the single theses subsumed beneath a Meta thesis for the purpose of structural analysis may in no way be allocated to the same extent of the respective Meta theses. This can already be seen by the fact that some single theses were emphatically modelconformant, whilst others were unsystematic and, in their turn, some were even partially model-contrary. The possible reasons for this state of affairs were equally described. The attempt to apply the structureanalytical procedure must be seen with
this as a background: It should serve to clarify whether the results accumulated within the framework of the detailed (inferential-statistical) examination and (interpretative) observation and regarding the single theses can be tendentially confirmed and, if not, what the possible reasons for this could be. During this it should not be about, as it were, hastily confirming (or rejecting) blocks of theses (on Meta theses level) using structural analyses. Firstly, the focus of the study lay clearly in the detailed investigation of bivariate assumptions of correlation based on the compiled single hypotheses. Secondly, the sample size of
N = 73 would in no way have been sufficient for the exclusive dependence upon the support of structural equation modeling procedures.713 Within the framework of the additional structureanalytical calculations – respectively split according to the seven Meta theses – it was decided to transpose the present parameters. Up to that point, these had been calculated on a bivariate level. Now, they were transformed into more complex models, or rather models on a more aggregated level. The investigation of these partial models for sufficient compatibility with the empirically identified data situation, was made possible by means of the previouslymentioned AMOS program.
With regards to the partial models to be tested, it must be noted that patterns of correlation should only be extracted when they fulfill the following two conditions: 1. The partial models arise logically from each single theses allocated to one Meta thesis. They therefore comprehend the bivariate correlations postulated by means of single hypotheses. 2. The partial models actually extend beyond the level of complexity of single hypotheses which exist exclusively upon a bivariate level, and at least illustrate the correlation presumptions associated with a Meta
thesis. The second condition therefore contained the illustration of a pattern of correlation comprising several parameters. Modeled upon pertinent literature,714 the theoretical measurement supportability was assumed if certain indicators715 could be identifiably allocated to a factor or respective fundamental dimension, and this statistical allocation was durable going by factor-analytical conventions, i.e., had a substantially high intercorrelation of the respective indicators amongst themselves.716 Because more than two variables were principally involved, “advanced” variable relationships were
present.717 A degree of orientation by structure-analytical conventions, according to which the following symbols are customarily used, was sought during the illustration of the formed partial models:718 An ellipse represents respective dimensions or factors consisting of indicators (single measures). In statistical literature, they are described as hypothetical constructs or latent, nonmeasurable dimensions, depending on the field of use.
Rectangular boxes represent soc a l l e d indicators as an operationalization of factors. Arrows visualize relationships which are postulated as substantial, whereby the arrow’s direction shows the direction of dependency (assumed causal direction) and gives an indication as to whether the observed measures converge or diverge. A variety of information can be found in literature with regards to the acceptance or rejection of structureanalytical models, whereby the orientation takes place by means of the previously mentioned Goodness-of-Fit
Index (GFI) or Adjusted Goodness-ofFit Index (AGFI).719 In some cases, a GFI or AGFI of ¾0.9 is required, and in others a value of ¾0.8 in conjunction with a positive degree of freedom720 of the model.721
4.3.2 Verification of the suitability ?of the Meta theses for creation ?of structure-analytical partial models As already introduced and outlined elsewhere,722 it is necessary to make use of so-called hypothetical constructs in order to investigate a structure-
analytical model and the causal dependencies postulated therein with the support of suitable measurement indicators. In scientific methodology, reference is made in this respect to a theoretical and an observational language. The theoretical language or language on a general model-descriptive level works primarily in conjunction with hypothetical constructs, whilst the observational language uses terms that refer to the directly observable empirical phenomena.723 In order to describe the correlation between the hypothetical constructs, one cannot avoid defining every latent variable by means of an indicator or,
better still, several indicators: The indicators – as explained above – represent the empirical illustration of the non-observable latent variables. The mapping takes place with the aid of corresponding hypotheses, which connect the theoretical terms with the terms of the observational language.724 The AMOS approach at causal analysis, useful for model evaluation in the submitted work, is also founded upon the previously-mentioned concepts: A structural model is formed, which illustrates theoretically or subjectlogically derived relationships between hypothetical constructs. The dependent latent variables are represented as
endogenous, and the independent variables as exogenous measures. Subsequent to this, a measurement model is determined for the latent exogenous as well as for the latent endogenous variables. The acquisition of the latent variables can only take place via the empirical indicators (constructional operationalization). Causal dependencies between the indicator variables are defined under AMOS by covariances and correlations. During this, differentiations can be established between latent variables and their indicators, as well as above all else, between latent endogenous and exogenous variables. On the whole, AMOS comprises an analysis on the
level of aggregated data material (covariance and correlation data) and is intended to evaluate a given hypotheses system en bloc, i.e., as a complete entity.725 In the work at hand and built upon the AMOS guidelines, an exemplary partial model allows itself to be drafted in the form of a graph, as depicted in the following illustration. In this case, we are actually dealing with the representation of the partial model belonging to Meta thesis VI.726 In this context, the postulated latent dimensions and overt indicators are pictured, whereby the Performance Attributes fundamental to the Meta theses – which
cannot be immediately measured – represent latent dimensions. A “bottomup” mapping of (immediately measurable) Performance Measures therefore takes place in the shape of overt indicators (illustrated by rectangular boxes) to Performance Attributes in the form of hypothetical constructs (illustrated by ellipses). A representation of residual variables or measurement errors has been excluded for reasons of lucidity.727 After determination of the model structure by AMOS, it is necessary to investigate the extent to which the mathematical estimation of the model parameters is actually possible, or
whether the degrees of freedom are appropriate for the parameters to be estimated. In accordance with this, the number of degrees of freedom required in order to solve a structural equation model must be larger than, or equal to, zero. The following formula expresses this factual situation:729 t = p*= 1/2 (p+q) (p+q+q) With: t = number of parameters to be estimated p = number of the y-variables q = number of the x-variables Tbl. 4-83: Legend to Diag. 4-12: Index of the applied
performance measures728 Index Performance Measure CS-1 Customer retention rate CS-2 Backorders value CS-3
On-time delivery percentage (inbound and outbound)
CS-4
Percentage of purchased orders received on time and complete
CS-5
Percentage of purchased lines received on time and complete
CS-6
Average MPS plant delivery performance (work orders)
CS-7 Cycle count accuracy percentage CS-8
On-time delivery percentage (inbound and outbound)
CS-9 Perfect orders rate CS10
Lines on-time fill rate
CS11
Customer retention rate
CS-
Average purchase requisition to delivery cycle time
12 CS13
Transactions processed via web/EDI
CS14
Average manufacturing cycle time
CS15
Percentage of sales via web
A-1
Average received finished goods turnaround time
A-2
Inactive inventory percentage
A-3
Average order-to-shipment lead time
A-4
Average inventory turnover
A-5
Average operating-equipment efficiency rate (OEE)
A-6
Average plant capacity utilization
A-7
Average warehousing space utilization
The testing for the model structure’s ability to be identified is guaranteed by AMOS itself (by automatic parameter estimation), because in the case of non-identifiable
models, the model does not complete the calculation, in so far as that no unequivocal solution for the equation system is given. Also, the model’s quality is questionable in the presence of negative variances, correlations ¾1 (excluded anyway according to the convention) or very high standard errors, as such effects would suggest the conclusion of problems during identification of the model. Diag. 4-12: Mapping of structure-analytical partial model to Meta thesis VI
The implementation of structureanalytical calculations using AMOS is principally made easier by satisfactorily high levels of sample sizes.The Unweighted-Least-Squares’ method has sometimes verified its ability for the purpose of parameter determination.730 Unlike alternative estimation procedures, such as the Maximum-
Likelihood-Variant, this method offers the advantage that consistent estimates are possible and do not depend upon a minimum sample size.731 With a sample size compatible with the presently implemented examination, reliable and valid results – with consideration to the previouslymentioned reservations and in view of albeit rather simpler model structures – could be attained in other structureanalytical studies.732 It was also clear from the beginning that the achievable sample size for the work submitted lay at best at or even under the minimum limit required for the complete testing of a model.733 For this reason, the
investigation of the model structure for the partial models was not successfully realizable, in as much as all relevant single theses for this purpose had been processed. Sample sizes of at least N = 100, or possibly even larger samples as recommended by Backhaus et al. and other researchers, would have been necessary here. Consequently, an investigation of the total model, i.e., the complete combination of all the individual partial models, was not possible. It may be summarized that, in the present case, it was not possible to develop a structure-analytical model which, with regards to the associated variables, would have provided sufficient coverage and simultaneously
fulfilled the necessary assumption conditions to meet scientific requirements. It would perhaps have been principally possible to compose partial models which contain purely a restricted number of variables. By doing this, however, the significance of the statements would have been further reduced, in addition to the already critical sample size (N = 73) necessary for structure-analytical evaluations under application of strict scientific measures. Furthermore, it must be considered that structural equation models also demand, in addition to statistical criteria, certain content requirements
from the data material to be examined – for example, the presence of a secured theory (at least to a rudimentary degree).734 Because this study is of an exploratory nature, it cannot sufficiently account for this requirement and must only make an initial contribution towards it. This train of thought is captured again elsewhere in Chapter 6 in conjunction with the possibilities of the assignment of structure-analytical procedures within future studies.735
4.4 Identification of Interfering Influences and Errors 4.4.1 Criticism of the
selection procedure The procedure of typical case selection applied in the study shows several known disadvantages. The first problem comprises determining those criteria in accordance with which the elements observed are to be classified. These criteria can only be defined by the examination objective, and the influence that this can have upon the examination results may prove difficult to estimate. Secondly, the procedure already predetermines respective prior knowledge of the total population. One must know in advance, for example, how the relevant characteristics (according to which the typical cases are defined) are
distributed within the total population. Thirdly, in the empirical context, the selection cannot be adjusted to the characteristics of actual interest, but substitute characteristics have to be enlisted in order to determine the typical cases. During this, it must be considered that the substitute characteristics must also be typical with regards to the actual characteristics of interest.736 The first disadvantage is appropriate in this case, but it must be noted that the examination objective was clearly known. Disadvantages two and three are only restrictedly valid, because with the type of information retrieval in question, one is dealing with a case of secondary
research. As a result of this, knowledge of the total population was already present and a typical selection was simplified. The person conducting the secondary analysis is similarly restricted during the hypotheses testing by the quality of the material used, which is determined by factors such as the method of the primary research, the sample, etc.737 In addition to this, the actual characteristics of interest were known at the time of the examination implementation, so that no substitute characteristics had to be defined. It must still be recorded, though, that the sampling method applied does not lay claim to representing the examination results.
4.4.2 “Blurring” of the hierarchical assignment ?of performance indicators “Blurring” is possible with regards to the allocation of performance indicators from latterly to formerly situated levels (i.e., performance measures to level 1 metrics). The Supply-Chain Council admits that within the SCOR model, the performance indicators may not be, or are not always clearly able to be, assigned to the SCOR main processes (chevrons).738 In addition to this, overlapping sometimes occurs, which is due to the comprehensive detail of the model.739 On the other hand, the
performance indicators used within the framework of the examination refer directly to a SCOR main process.740 An attempt was made to confront this disadvantage by means of investigating where the reasons lay in case of the rejection (falsification) of a thesis. Within the context of this additional and interpretative attempt at explanation, the “blurring” in assignment already mentioned was taken into consideration.741
4.4.3 Realization of the examination ?as a secondary analysis
The examination submitted was implemented as a secondary analysis, which conceals advantages as well as disadvantages. The biggest advantage can almost certainly be seen in the fact that the necessary data did not need to be accumulated, but was already present. The aspects of research economy and time were decisive criteria during the selection of the analytical procedure. A substantial disadvantage of secondary analysis exists, however, in the fact that the evaluation must inevitably be restricted to the data already present. The researcher planning the examination therefore needs, in all cases, an example of the questionnaire
assigned within the primary survey, the interview instructions, and the resulting data.742 In this study, the required information was unrestrictedly available to the author. For this reason, an assessment on the extent to which the data could be used was possible to a sufficient extent. The complete questionnaire and the instructions for completion may be taken from the appendix;743 the data material in its entirety is available from the author.744
4.4.4 Scope of the examined sample The sample selected for the purpose of
the secondary examination contained the data from 73 (typical) companies. The scope of this sample seemed thoroughly adequate for meaningful descriptivestatistical and correlation-analytical results. With the given scope, the risk of the correlation level being substantially blurred by extreme values is also minimized.745 However, as has already been mentioned, a sample size of over 100 is usually favored for structural equation models similar to the applied AMOS procedure.746 A two-stage approach was chosen to counter this possible point of criticism: First, a correlation analysis was carried out and, following this and
purely for selected special cases – concretely for investigation of the Meta theses – an attempt was made to apply a structural equation model. The restriction therefore only refers to those cases in which the second stage was applied. Beyond this, the second stage primarily served as the attempt at an additional corroboration of the results previously compiled by means of inferential-statistical methods and 747 descriptive-analytical descriptions.
4.4.5 Inadequacies in the terminology ?for the performance terms In Chapter 3, the insufficiencies present
concerning the definition of terms in conjunction with the SCOR model were dealt with in detail.748 An attempt was made to confront this problem by adopting Seibt’s incitement and creating a consistent definition of terms, which was adhered to throughout the study.749
Chapter Five
Summary of conclusions and innovative assessments This chapter addresses the context of realization, as seen within the framework of the course of research logic according to Friedrichs. This context, which has consistently been identified as a central theme from the point of view of critical social-scientific research theory, 750 may usefully be described as follows:
Under context of realization, the effects of an examination are to be understood, as is its contribution to solving the initially posed problem. The examination has a knowledgetheoretical function, in that it expands our knowledge of social connections.751
5.1 Overall Appraisal and Interpretation of the SCOR Model due to the Results of the Examination In this chapter the empirical results are collectively analyzed once again by the
use of single theses blocks within the SCOR model groups. This is done with the aim of undertaking a complete evaluation of both the illustration developed within the framework of the work submitted, and the SCOR model operationalized by means of the derived theses model. Consequently, a 752 hermeneutic observation of the single theses’ results can take place upon an aggregated level. Together with the detailed explanation of the examination results and the interpretational work in Chapter 4, it accounts for the postulate according to which the analysis is constituted by (statistical) evaluations and (content-theoretical) interpretations, together with the data collected by
means of empirical instruments (in other words: the empirical material present in the form of numbers).753 Chapter 4 also contains a discussion on the possible reasons for results that are barely or definitively not compatible with the model concepts.754 As emphasized in the introduction, this study cannot (and does not wish to) lay claim to having investigated the suitability and validity of the SCOR model per se. Rather, it represents an exploratory contribution in this field, which must be taken up and continued by further studies. The following explanations must be seen with this premise in mind.
5.1.1 Reflection of the SCOR model based upon the results of the SCOR model groups The first Meta thesis established referred to the SCOR model group IntraPerformance Attribute (I-P). 755 In this thesis, the performance measures within a performance attribute conform to one another, and thus a degree of consistency must exist between these performance measures. The Meta thesis was unambiguously confirmed by the empirical data. Of the eighteen single theses subsumed beneath this Meta thesis, eleven could be classified as
significantly or at least tendentially model-conformant. An unsystematic diagnosis was reached for seven of the investigated theses; i.e., a “noncorrelation” had to be correlationanalytically assumed.756 Consequently, a clear model-contrary relationship was not identified for any of the examined parameters of the (single) hypotheses associated with this Meta thesis. During the application of pure binary division according to the p(a)-error probability (significant vs. nonsignificant), 38 percent of the theses were therefore classified as significantly
model-conformant, and 0 percent classified as significantly modelcontrary.757 The SCOR model group IntraPerformance Attribute (I-P) could therefore be validated as tentatively confirmed. The second and third Meta theses referred to the SCOR model group IntraCompetence (I-C).758 The second Meta thesis contained the correlation between customer service and flexibility. In this area it was expected that the general statement that a high (low) customer service would accompany a high (low)
flexibility would be confirmed. This statement may be considered as supportively corroborated upon the basis of the observed factual connections. For the ten single theses relevant here, in two cases a tendencial – not statistically meaningful – modelcontrary constellation was present, and in one case a totally unsystematic constellation was given. The results of all other theses were covered on the whole by the model postulate represented by Meta thesis II. During the application of pure binary division according to the p(a)-error probability (significant vs. non-
significant), 58 percent of the theses were therefore classified as significantly model-conformant, and 0 percent classified as significantly modelcontrary.759 Meta thesis III assumed a parallel correlation between costs and assets. In the case of the nine single theses established, a model-contrary diagnostic situation (even if it was tendencial or rudimentary in nature) was not unexceptionally present. Only in the case of three single theses did a conclusion of largely unsystematic circumstances have to be drawn in the face of an explicit
correlation level within the area of 0.00 to 0.10 (absolute). On the whole, however, nothing contradicted the fundamental suitability of the respective Meta thesis. During the application of pure binary division according to the p (a)-error probability (significant vs. nonsignificant), roughly 33 percent of the theses were therefore classified as significantly modelconformant, and 0 percent classified as significantly model-contrary.760
With this, the SCOR model group Intra-Competence (I-C) could therefore be validated as tentatively confirmed. Finally, Meta theses IV to VII referred to the SCOR model group InterCompetence/Performance Attribute (ICP).761 A comparatively high number of single theses were assigned to these Meta theses, according to which a high (low) customer service correlates with high (low) costs. Of these twenty single theses, half proved themselves to be significantly model-conformant. The majority of the remaining single theses were more or less statistically classified as unsystematic. A model-contrary
parameter relationship could only be identified for one single thesis (expected “synchronization” between purchase requisition to delivery cycle time and number of customer disputes).762 On the whole therefore, a model-compatible diagnostic situation existed for Meta thesis IV, even if this compatibility was not as strongly expressed as in the case of theses I to III.763 During the application of pure binary division according to the p(a)-error probability (significant vs. nonsignificant), roughly 32 percent of the theses were classified as significantly
model-conformant, and 0 percent classified as significantly modelcontrary.764 Meta thesis V, which assumed that a positive correlation existed between flexibility and costs, included eight single theses. Only a quarter of these single hypotheses were classified as unsystematic, and a model-contrary correlation was not given. All other theses proved themselves to be modelconformant in the face of the identified parameter relationship directions, albeit with an emphatic and only tendentially model-compatible percentage. So far, this Meta thesis seemed an acceptable
measurement based upon the empirical facts. During the application of pure binary division according to the p(a)-error probability (significant vs. nonsignificant), roughly 25 percent of the theses were classified as significantly model-conformant, and 0 percent classified as significantly model765 contrary. A different conclusion had to be drawn with respect to the sixth Meta thesis. In this case, a positive correlation
or a “parallelism” between customer service and assets was assumed. Roughly half of the respective nine single theses were judged as modeladequate. Two single theses, on the other hand, proved themselves to be substantially, or at least rudimentarily, model-contrary, and the remaining single theses proved themselves to be unsystematic to the largest extent. Faced with this empirical diagnostic situation, it has become necessary to reflect more closely upon the possible reasons for such a model inadequacy (even if it was partial), and to work out respective options for innovation. This occurs in the following paragraph.
During the application of pure binary division according to the p(a)-error probability (significant vs. nonsignificant), roughly 25 percent of the theses were classified as significantly model-conformant, and 8 percent classified as significantly model766 contrary. In the case of Meta thesis VII, whereby a positive correlation was assumed between flexibility and assets, a model-conformant pattern became apparent at large. Of the six single theses assigned to this Meta thesis, half were
model-conformant in a statistically significant way. Two single theses could be tendentially confirmed, only one was statistically unsystematic.767 During the application of pure binary division according to the p(a)-error probability (significant vs. nonsignificant), 50 percent of the theses were classified as significantly modelconformant, and 0 percent classified as significantly model-contrary.768 With this, the SCOR model group Intra-
Competence/Performance Attribute (I-CP) could therefore also be validated as tentatively confirmed. Consequently, the following statement arises regarding the central assumption, which was established for the purpose of the empirical 769 examination: After consideration of the aforementioned restrictions, and for the developed depiction of the SCOR model in hypotheses form, it can be considered as tentatively
confirmed that the Performance Metrics assigned to the Performance Attributes within one of the two Supply Chain competences (performance capability and efficiency) are consistent with one another, i.e., point in the same direction. The performance metrics assigned to the Performance Attributes between the two competences mutually compensate each other, i.e., they guarantee a balance between the various objectives. This assessment rests upon the
substantial predominance of theses confirmed in a statistically significant way over significant modelcontrary theses. Empirically, the last named case hardly played a role. Based on the attained examination results, and in addition to the derivation of the above-mentioned conclusions, further conclusions may be drawn in the form of recommendations. These will be spelt out in the following paragraph. As already mentioned, it must be borne in mind that the recommendations refer, in the first instance, to the operationalization of the SCOR model.
As previously indicated, other forms of model operationalization are 770 possible. An inference to the (abstract) SCOR model must therefore be seen in the respective context. As a result, generalizations are only possible in a restricted manner and must inevitably be seen with the premise of an exploratory approach.771
5.1.2 Potentials for improvement and recommendations Above all, the results of Meta theses VI indicated that potential for improvement to the SCOR model seems to exist with regards to the connection between
customer service and assets. In detail, the Meta thesis assumed that a high (low) customer service correlates with high (low) assets. As already mentioned, the SCOR model does not attempt to describe every company process or activity within the Supply Chain.772 The components consciously “omitted” are marketing and sales (i.e., demand generation), research and technology development, product development, and some areas of post-delivery customer service.773 The results of the submitted examination suggest that in the context outlined here, the inclusion of marketing and sales would be necessary,
especially as these represent a substantial component of customer service in the present-day competitive environment.774 Modern Supply Chain strategies775 assume the existence, as a rule, of sales and distribution-controlled delivery ability. 776 Inevitably, consequences arise for the stock levels and range of stock.777 In conjunction with this, it is important to note that the simultaneous requirement for a high customer service, especially with respect to high delivery reliability and short delivery lead times, has a decisive influence upon the SC strategy and with that also upon the required assets, especially the inventory days of supply.778 Additionally, the step from
traditional Supply Chain Management towards a value-generating approach is introduced – previously described elsewhere as the Value Chain.779 The value generating approach focuses upon the gradual increase in value and, accordingly, it explicitly includes marketing- and sales-orientated elements in addition to the actual physical availability, disposal, usage and exploitation of goods.780 However, no efforts have been made by the Supply-Chain Council to change the SCOR model in Version 7.0 and 8.0 in this direction.781 Hence, the recommendation below can be derived.
First recommendation: Due to the results of the examination as well as present-day developments in the competitive company environment, tendencies are apparent whereby the inclusion of marketing and sales into a future SCOR model version could contribute to address weaknesses and therefore further optimize the model. As explained in Chapter 4 in the context of the interpretation of modelcontrary diagnoses,782 potential for improvement obviously exists upon the
basis of examined Version 6.0 of SCOR with regards to the procurement processes. This situation has not changed in Version 7.0 and 8.0 of SCOR.783 Consequently, the following recommendation is derived from this: Second recommendation: The examination results suggest that the SCOR model in its present form does not (yet) fully account for the influence of today’s procurement process complexity upon customer satisfaction. A possibility for further development of the SCOR model could, therefore,
be the expansion of the process model to include the illustration of associated material and information flows between customer and supplier. Another finding of the study, also reached through the model-contrary results, was that the electronic execution of the procurement processes does not seem to be sufficiently expressed within the SCOR model Version 6.0. 784 There were also no significant changes made in this regard in the subsequent versions of SCOR.785 Thus, a further recommendation may be made:
Third recommendation: The inclusion of the concept of EBusiness into the SCOR model, which began with Version 6.0, should be consequently continued with specific regard to the electronic execution of purchasing processes. By these means and amongst other things, the requirement for an increasing network and synchronization between the departments involved in procurement could be better accounted for. In addition to this, suggestions for best practices would offer support
for the realization of improvements in process execution. It must be borne in mind that, in the case of the aforesaid recommendations, we would not be dealing with an extension to the SCOR model structure, but rather a continuative “supplementing” of the model structure already present. This aspect will be invoked again at a later stage within the framework of the recommendations for further research.786
5.1.3 Recapitulatory appreciation of the
operationalization of the SCOR model The three SCOR model groups illustrated above mutually cover the developed operationalization of the SCOR model as a whole, with the SC c o mp e t e nc e s customer-facing and internal-facing on the one side, and the hierarchically arranged key performance indicators (performance attributes – level 1 metrics – performance measures) on the other side.787 A hermeneutic appraisal788 of the results of the seven aggregated Meta theses and the associated blocks of single hypotheses respectively allow the conclusion that those are to be deemed to be supportive.
Based on the examination results, the depiction of the SCOR model developed within the framework of the work can be seen respectively as suitable, or as having a close proximity to the truth.789 In this case, it must be borne in mind that we were dealing with an exploratory examination of the model. As a result of this, a universally valid statement as to the suitability or close proximity to the truth of the model as such can in no way be made based upon the accumulated knowledge. The findings rather represent an initial contribution in the form of a confirmed central assumption790 towards a universally valid statement.791
On the whole, noticeable discrepancies could not be found between the model concept and empirical reality. 792 However, certain deficits became apparent in the case of Meta thesis VI, which contained the relationship between customer service and assets, and in the case of which possible areas for improvement were indicated. A representation of modern concepts and tools will now be initialized for Supply Chain formation under application of the SCOR model and based on the accumulated examination results. For this purpose, the following continuative questions are formulated and raised:
Which innovative approaches for the Supply Chain’s formation and optimization are currently being discussed in academic science and company practice? Which possibilities and modern tools exist that could be applied for the improvement of the SCOR model’s usage and consequently the formation and optimization of the Supply Chain?793 As described earlier, this study was particularly concerned with answering the following research questions:794
How could the SCOR model be transposed onto a thesis model and operationalized based on the model-immanent performance indicators? How could the SCOR model’s conveyed depiction be submitted to an exploratory examination based on empirical data? This study had the explicit objective of developing a special form of SCOR model operationalization and investigating its adequacy and proximity to truth. It is therefore to be left to advanced studies to undertake an attempt
at more exhaustive answers to the aforementioned questions, as well as other questions resulting from them in a similar exploratory context, and to corroborate them by means of scientific investigation. In Chapter 6, an attempt is made to derive concrete suggestions as to in which areas and in which form this could take place.795
5.2 Innovative Approaches for the Formation and Optimization of the Supply Chain 5.2.1 Representation of the Adaptive Supply Chain
It has become apparent since around the year 2000 that the Supply Chain’s performance capability and efficiency represent necessary conditions for companies’ success in the contemporary competitive environment, especially in sectors of the production industry like wholesale and retail. To ensure the fulfillment of these conditions, so-called Adaptive Supply Chains or Adaptive Business Networks are currently being discussed.796 Adaptive Supply Chains (ASC) highlight known redundancies in order to help companies cope with unforeseen events. They are at present (beginning of 2007) in the process of replacing the
traditional SC approaches outlined earlier, including the advanced virtual SC networks. ASC possess the flexibility to continuously adjust themselves to changing market requirements and therefore react in an optimal way to environmental variables, i.e., with maximum efficiency and in real-time.797 In order to fulfill these requirements, ASC combine order management, planning, production and distribution management procedures into integrated business processes and provide the SC network with real time information. In this way, they enable quick decisions in addition to their efficient and effective execution.798 The advantages of ASC may be collectively
set down as follows: “Adaptive Supply Chains provide a cohesive process infrastructure that connects network participants, provides visibility, and monitors for changing conditions. When conditions change, the consequences are immediately determined and affected parties are notified with recommended courses of action for optimal results. Once approved, a new action is executed and the plan is adapted within context of this new process. The result is
improved performance across the global supply chain network.”799 The transformation of a traditional Supply Chain into an ASC necessitates the investigation of and necessary changes to the basic Supply Chain processes in order to remain competitive. The Supply Chain then no longer represents a static system, but moreover a dynamic, self-changing and adaptive, high-performance network. The changes to market conditions determined by the internet play a major role in this case.800 The definition of the ASC resulting from this must be seen in context with the presently available
internet-based possibilities.801 For this purpose, the definition of the Information Management View, as well as the E-Business concept according to Seibt already mentioned elsewhere, is called-upon:802 An Adaptive Supply Chain (ASC) is based upon a Supply Chain integrated by means of information technology, in which the flow of information between diverse parties represents the integration factor. In this sense, it possesses a communal information basis, in addition to mechanisms enabling an
exchange of this information amongst the participants. For this purpose, several to all of an organization’s processes with reference to the Supply Chain within a company between it and its business partners between it and a third party (e.g., authorities) are totally or partially realized by electronic communication networks, and supported by the assignment of Information and Communication
Technology (ICT) systems.803 Having set down what an ASC involves, the way in which this kind of Supply Chain can be put into action – or rather, can be realized – is to be addressed in the following paragraph.
5.2.2 Realization of Adaptive Supply Chains The realization of ASC stems from an existing Supply Chain and can be best explained in the form of a staged process. Heinrich and Betts, for example, describe the procedure to achieve an ASC by means of a fourstage process:804
First stage: Visibility: Exchange of information with SC partners, standard processes for most routine transactions with SC partners, information exchange by means of internet-based technology, and additional insight into a company’s processes and data problems.
Second stage: Supply Chain Community: Execution of regularly occurring transactions by means of so-called portals,805 introduction of minimum and maximum monitoring values (for example for inventory stocks), reduction of inventory stock, and increased efficiency of
process flows by means of automation. Third stage: Collaboration: Exchange of customer requirement information amongst SC 806 partners, determination of target inventory replenishment 807 measures, transferal of responsibility for stock replenishment to the suppliers,808 and the possibility of allocating stock in accordance with order receipt to fulfill the maximum amount of orders.809 Fourth stage: Adaptability: Significant reduction of process times, multiple elimination of work stages, significant reduction of
inventory levels and working capital, release of new market chances through strategic partnerships, and the introduction of new products. Stages one to three were previously developed and applied in the past within the framework of traditional Supply Chain and Value Chain strategies. The substantial difference in the ASC lies in the presence of the fourth stage, as conventional Supply Chains are of a static nature. Only the fourth stage enables the development up to and into an ASC. 810 Heinrich and Betts characterize this fourth stage and the
changes accompanying it as follows: “In step four, companies begin to automate many more business processes (…). In addition, the move from step three to step four involves increased technology complexity and a heightened degree of automation among an expanding number of network partners.”811 Lawrenz and Nenninger also call the transition from a linear Supply Chain to an ASC, i.e., from stage three to stage four, a transition to E-Business Networks.812 In agreement with this,
Schäfer, in his empirical study, arrives at the conclusion that nearly 97 percent of companies surveyed agreed with the statement that the internet – as an instrument of E-Business – offers completely new possibilities of arranging business processes in a company-spanning manner.813 The special challenges with regards to ASC result from the requirement to exchange information within a company, as well as with relevant SC partners. To arrange the creation of electronic partnerships and alliances effectively, firms must link-in a variety of information systems in order to avoid inefficiencies and
redundancies. The SC environments presently found are of a progressively complex nature and comprise a multitude of sub-processes and activities. In this case, industrial companies do not represent the only group that has the optimization of the company processes in general and the SC processes in particular as their objective. The same applies to the public sector, as described elsewhere by the example of the US Department of Defense, DoD.814 The requirements on the competitive side literally force revolutionary changes to the existing SC processes. The result is the mutation of traditional linear and static Supply
Chains into dynamic value chains, or in other words, ASC. 815 Radjou et al. postulate as follows in conjunction with this: “To cope with volatility firms need to migrate their static supply chains to adaptive supply networks.”816 The existing tools and applications for management of the Supply Chain, as already described elsewhere within the context of Supply Chain Management, are often no longer (or only restrictedly) capable of keeping up with the changes that are required in order to achieve higher efficiency.817 The modern tools to
remedy this will be outlined in the next section. Because these tools are based upon the SCOR model, it would also have been plausible to imagine them in correlation with the present condition of the SCOR model’s development. 818 However, as will be emphasized, since they are founded upon the Adaptive Supply Chains discussed in the previous section, it seemed more appropriate to introduce them subsequent to these. For this reason, an illustration of existing established Supply Chain concepts and SCOR applications may be found at the beginning of the work, whereas in the previous chapter an actual outlook upon new, although not yet further diffused and tested concepts and assignment
possibilities, is presented.
5.3 Modern Tools for Improvement of the Assignment and Application Possibilities of the SCOR model As already often mentioned, the SCOR model represents a descriptive but not a formative model of the Supply Chain. The model does highlight Supply Chain weaknesses and therefore potential areas for optimization, but these must then be removed or realized by using other measures. Such a measure for Supply Chain improvement is represented by Business Process Reengineering
(BPR).819 Within the framework of BPR literature, Information Technology (IT) is often seen as an essential enabler820 for the change process. No other factor is said to possess such large potential for the achievement of radical process improvements.821 Hofmann collectively and tersely unites the requirement for a continuous (re-) design in the face of a permanently changing Supply Chain or ASC as follows: “The faster the supply chain changes, the more important supply chain design 822 becomes.”
The SCOR model does not deal closely with levels beneath the third level. This can, for example, be seen in the fact that the SCC describes the fourth level as the Implementation Level, upon which companies implement specific Supply Chain flows.823 On these lower levels of the SCOR model, process, design and modeling tools for analysis and documentation can be assigned. Tools are available today for static, as well as dynamic simulations of such modeling. Most companies begin to analyze their existing Supply Chain for potential improvements using the SCOR model, as
well as definitions and performance indicators contained within it. In this way, gaps in processes and inefficiencies in process performance can be revealed. Improvements can then be executed by means of a Supply Chain-related BPR initiative. As a rule, this includes the definition, implementation, and continuous measurement of performance indicators. During this, there is a dominating aspiration to achieve a leading level of performance in all relevant SC processes that would set the company above the competition.824
5.3.1 The concept of Supply Chain Design Management
(SCDM) Supply Chain Design Management (SCDM) is based upon a given SC model and aims at the simulation and continuous optimization of the ASC. It is a new kind of tool that seeks to help firms to identify and improve SC processes, performance indicators, and information flows within companies and with other SC partners.825 In the following, the focus is exclusively placed upon such applications and tools as are based upon the SCOR model. SCDM primarily has the following objectives:826
Validation of the present SC model by means of existing or actual company processes (As-Is business processes); Simulation and prediction of the influence upon SC performance in the case of changes to the SC structure, up to the To-Be configuration; Application of performance indicators in accordance with industrial standards for the execution of the analysis of alternative SC scenarios (What-if impact analysis); Measurement, prediction, and monitoring of SC influence factors for the identification of
improvement potentials; Connection of those corporate and SC processes upon lower levels which are relevant for the monitoring of operational procedures and systems. A Supply Chain model is above all highly effective if it is recognized and accepted not only internally (i.e., within the company) by executives responsible for decisions, but also by external SC partners. In this way, changes in demand and alternative scenarios resulting from them can be quickly analyzed in order to determine what influence they have upon corporate-policy, financial, and Supply Chain specific performance
indicators.827 The result is a clear comprehension of the options, risks and effects upon the Supply Chain. In this way, SCDM creates greater flexibility and adaptability for companies. On a strategic level, SCDM serves not only to initially design, but also to continuously redesign the complete Supply Chain, and therefore to enable the support of an ASC.828 The benefit of a SCDM, dependent upon the planning level, can be described as follows:829
On a strategic level, it serves for example to analyze the Supply Chain’s performance capability by means of the application of alternative scenarios to analyze possible effects of changes in SC structure in the context of redesign, the identification of optimal SC strategies – beneath which for example the previously mentioned Postponement Strategies fall – and the investigation of the influence of new systems and applications to support SC processes, i.e., ERP systems or APS. On an operational level, it serves for example to forecast demand and the analysis and simulation of
possible changes in demand, the support of the Collaborative Planning, Forecasting and Replenishment (CPFR) concept, the identification of available capacities to react to changes in demand, and the determination of best practices in the case of delivery failure by suppliers. With consideration to these premises and with the inclusion of the listed ASC definitions, SCDM can be embedded into this context as follows: Supply Chain Design Management (SCDM) makes a universally valid language
convention available as a minimum requirement for Supply Chain processes (preferably upon the basis of the SCOR model), illustrates the extended (i.e., companyinternal and companyspanning) Supply Chain, enables an analysis of possible effects in the case of changes in supply and demand factors in addition to the simulation of changes in Supply Chain structure and processes. It therefore serves to realize and support Adaptive Supply Chains (ASC) and their design and
redesign. It must, as a component of E-Business systems and in the total system life cycle, be continuously adjusted to market developments and altered business requirements.830 The following sections relate to an evaluation undertaken into which ITbased applications are available today in order to fulfill the aim of supporting SCDM.
5.3.2 Applications for Supply Chain Design Management
The applications for Supply Chain formation are not to be confused with applications for planning of company procedures. This latter group includes, amongst others, Enterprise Resource Planning (ERP) solutions. Some manufacturers of such integrated, SC-orientated solutions – as for example International Business Systems (IBS)831 or Business Objects 832 – state that their products orientate themselves or are respectively based upon the SCOR model.833 These applications are also not to be confused with those that are assigned to the initial design and possible
sporadic redesign of the Supply Chain. These are for instance the previously named ERP systems or the Advanced Planning Systems (APS).834 Until a short while ago, no electronic tools for the continuous design or redesign of the Supply Chain existed. For the further course of the work, the term SCDM application was used for tools of this kind. The objective of the SCDM applications is to design complex Supply Chains for strategic, tactical and operational predictions. The research company Gartner835 assumes the market for SCDM applications is presently gaining in importance, and that this will carry on in the future.836
The author’s research revealed that at the beginning of 2007 only a few applications existed which immediately supported Supply Chain formation within the framework of SCDM. The following three manufacturers and applications, which may be taken seriously and are based in unison upon the SCOR model, fall within these applications:837 e-SCOR by Gensym ARIS EasySCOR by IDS ADOLog by BOC. The applications that are to be explored in more detail below share the
explicit purpose of supporting the respective formation or design and continuous redesign of the Supply Chain upon the SCOR model basis, as stated in the previous paragraph. This is not an appraisal of the applications, but rather an exemplary overview of the scope of their functions and their main assignment possibilities.838 5.3.2.1 e-SCOR by Gensym The software manufacturer Gensym839 has developed an application by the name of e-SCOR. This application designs, simulates and controls graphically-supported SC scenarios. eSCOR is based upon the SCOR model with its associated processes and
performance attributes. Its main strengths are perceived to be its ability to simulate SC structures and affect the Supply Chain’s behavior, in addition to being able to analyze the exactness of an existing SC model. This takes place by means of description of the SC structure, processes and information flows upon an aggregated level, and spans the extended Supply Chain. The aggregated analysis level is connected to the SCOR model’s lower levels (below the third level). After each respective role has been determined (in the sense of SC participants and functions) and the SC processes have been recorded, the
existing model (As-Is Model) can be represented and validated by using performance indicators and information about the work course. In this way, a basis for comparison (Baseline) is created for investigating the effects of changes.840 Furthermore, alternative To-Be scenarios can be simulated, which automatically project the SCOR-based performance indicators into the future. This includes the measurement and simulation of effects of changes to the SC structure and processes upon the relevant performance indicators (Whatif scenarios). Apart from that, model changes can be undertaken parallel to
this in order to demonstrate their effects in real-time. Additional, distinctive characteristics of e-SCOR are the analysis possibilities with regards to order fulfillment and distribution strategies, planning and procurement strategies, as well as financial performance.841 Due to the fact that the performance indicators correspond with the SCOR performance indicators, customer-facing indicators are included on the one side, such as delivery ability and order execution performance. On the other side, internal-facing performance indicators are taken into consideration, as for example cash-to-cash cycle time
and Return on Assets (ROA). In this way, the known conflict in objectives (trade-off) between the performance indicators can be analyzed and their effects can be highlighted.842 5.3.2.2 ARIS EasySCOR by IDS The consultancy IDS843 distributes an application by the name of ARIS EasySCOR. This product belongs to the ARIS family of products, which serve the purpose of Business Process Design in the general sense. According to IDS, Business Process Design means that companies adjust their business processes according to their own requirements and necessities
and to those of the market in three stages. The associated section in the Continuous Improvement cycle is comprised of the three aspects design, analysis, and optimization. The process design, i.e., the graphical representation of existing procedures, answers the questions as to who does what and in which order, which services are provided, and which software systems are assigned for this purpose.844 The next stage is concerned with the analysis and appraisal of the actual processes. During this, weaknesses in the procedures are revealed and improvement potentials are exploited. The adjoining derivation of To-Be
processes, i.e., the ways and means by which these are to support the company with value generation in the future, is based upon the results of the prior analysis and rounds off this approach.845 ARIS EasySCOR especially focuses upon the Supply Chain field and contains the following functionalities:846 Design of the company’s processes Design of dynamic business process models Implementation of simulations with regards to changes in the SC design Continuous monitoring and, if necessary, adjustment (redesign) of the business processes
Integration of automated decision processes into the existing (subjective) procedures for decision making. EasySCOR therefore represents an application for the analysis, design and continuous redesign of Supply Chains. It combines a company’s process design, which may be already present within ARIS, with the SCOR model. The combination resulting from this is supposed to enable efficient design, analysis, and optimization of SC processes in accordance with the SCOR model and the standards defined by the SCC. The application ARIS EasySCOR comprises process definitions, best
practices, and performance indicators for the SCOR main processes Plan, Source, Make, Deliver, and Return. 847 EasySCOR is supposed to simplify and accelerate the identification of bottlenecks, weaknesses and improvement potentials within the Supply Chain by use of predefined and standardized elements. Beyond this, it allows the comparison of a company’s specific SC processes and their performance capability with those of competitors based on the SCOR performance indicators and within the framework of benchmarking. Furthermore, existing processes can be defined and
documented in an easy way, and most various scenarios of SC models can be simulated and investigated before changes are undertaken within the framework of a required SC redesign.848 The following illustration gives an insight into the application. The strong integration with SCOR is visually reflected by the model’s known constitutional elements (process elements, performance indicators, and so on). Version 6.0 of ARIS EasySCOR is consistent with Version 6.0 of the SCOR model. It is already being assigned by several previously named organizations, as for example Intel and the US
Department of Defense (DoD).850 5.3.2.3 ADOLog by BOC The SW provider BOC851 has developed another SCDM application by the name o f ADOLog. This application is based upon the SCOR model and is consistent with the processes and performance indicators contained therein. It offers support during strategic planning and decision processes with the framework of Supply Chain Management. ADOLog is supposed to offer support for the Supply Chain manager and the employees in the SCM field, in order to achieve the desired profitability and performance targets more efficiently. The modeling of the SC processes upon
the basis of the SCOR model should, in connection with this, serve as a basis for Supply Chain optimization. The description and representation of the SC processes present in the application should enable the process integration and the undertaking of socalled transversal analyses852 from the point of view of each individual company. These analyses aim to abolish the separation of individual business areas. During this, a connection is created with the documented processes within the SCOR model that therefore function as reference processes.853 Because the modeling method implemented in ADOLog is based
completely upon the SCOR model, the application also contains all concepts developed by the SCC in order to be in accordance with SCOR and the corresponding descriptions and evaluation standards for Supply Chains. Diag. 5-1: Representative example from ARIS EasySCOR849
It is presumed that SCOR represents the best option of undertaking a thorough investigation (audit) of the Supply Chain, even if some companies possess their own developed applications for the evaluation of internal processes. In conjunction with this, all advantages of the SCOR model are used, for example, a standard terminology, company-spanning Supply Chain descriptions, predefined performance indicators as a basis for benchmarking, and best practices as an orientation guide. Building upon this, ADOLog should corroborate decisions pertinent to the Supply Chain by facts and
numbers, identify deviations from the performance metrics strived for, and enable adjustments to the SC design resulting from this.854 ADOLog is highlighted by the following four progress stages.855 1. Geographic Product Flow: Locations and infrastructure: The so-called Geographic Product Flow (GPF) models serve the purpose of regional-geographical illustration of locations and infrastructure. The user has a number of plans available for this purpose, with the aid of which the physical positioning and the distance of individual locations can be visualized
and illustrated. As far as locations in the GPF model are concerned, production plants and procurement and distribution activities are of interest. Material and information flows can also be visualized. In addition to this, the specification of the resources necessary for individual material flows (methods of transport) is supported. 2. SCOR Level II: Configuration: In this case, the physical illustration of the Supply Chain is transposed from the GPF model into the SCOR model’s process-orientated terminology, i.e., transferral into the SCOR standard processes by means
of the known process categories takes place. Products thereby go through a series of ideal and typical SCOR basic processes (Source, Make and Deliver), as well as various alternative editions of the Supply Chain – from make-to-stock right up to make-to-order. The depth of the modeling depends upon the importance of the formerly and latterly situated production stages. In this way, continual Supply Chains can be modeled (i.e., from supplier’s supplier to customer’s customer). 3. SCOR Level III: Decomposition: Here the results gained in Level II are broken down into single,
standardized process elements. Each process category contains a reference to a particular Level III model. The process elements represent SCORdefined standard processes, which concretely describe Supply Chain activities. Input and output information, performance indicators and best practices are defined for each process element. 4. Level IV: Processes and organization: Level IV models illustrate the individual, company-specific procedures and generate data for the optimization of SC configuration by means of simulation. In conjunction with this, Level IV process models
are supposed to enable the detailed illustration of the individual company processes. In this case, each stage of work is represented by an activity. The various procedural variants are illustrated and later simulated with the aid of variables and alternative decisions. Beyond this, Level IV organizational models represent the necessary personnel and business resources. Apart from this, the organizational model comprises business resources, such as IT infrastructure and production equipment. 5.3.2.4 Recapitulating observation of
the SCDM applications Conventional applications only support decision making by means of simple, static business rules, which are embedded into the companies’ procedures. The majority of decisions, as well as the fundamental business processes, still require expertise and specialized knowledge, which includes the continuously changing environmental variables. For this reason, many of the events associated with this cannot be automated at present, which results in the fact that organizations’ decisions still primarily lie with respective experts and, at least in a partial degree, are consequently of a subjective nature.
In order to ensure an automated execution within the context of SCDM, the associated applications must enable transparency with regards to the rules for business operations processing, dynamic modification, scenario planning and continuous improvement. Existing applications, like ERP systems and APS applications for example, are at present not in a position to provide the required functionality.856 The SCDM applications described go beyond this and have the main objective of continuously analysing and improving business processes, or more exactly: Supply Chain processes. As a result of this, they should support the SCOR model-based Supply Chain competences – customer-facing
performance capability and internalf a c i n g efficiency – and therefore contribute towards an improvement of performance.857 Schäfer and Seibt collectively describe the corresponding situation as follows: A decisive factor for future company success will be the competence to be able to manufacture top quality, innovative products at marketable prices and faster than the competition. In order to realize this, company processes must be continuously improved and formed more effectively and
efficiently by the integration of new, innovative ideas.858 The SCDM applications, as well as the underlying ASC, will be examined at the end of Chapter 6 within the framework of suggestions and possibilities for further research. Now, however, again focus needs to be placed upon the SCOR model, and its present limitations discussed.
Chapter Six
Limitations of the presently available SCOR model “Organizations today face multiple environmental forces affecting their survival, growth, and success. In such a complex reality it is desirable to see an organization as a social, technological, economic, and human system. In this context, it is important to see the interrelationship of individual, group, and organization development processes
as needed for organization renewal. Renewal, in this context, implies purposeful and planned change.”859
6.1 Observation of the Formation Dimensions of Organization and Personnel in the Submitted Context The general weaknesses and limitations of the SCOR model have been addressed in Chapter 2.860 Chapter 5 discussed potential areas for improvement as identified in the results of the empirical examination.861 A comparison of the general limitations and the potential improvements shows that the functional areas given as preconditioned by the
Supply-Chain Council – the respective employee or personnel areas of Quality Assurance and Training862 – did not produce any indicators worth mentioning with regards to the examination results. This is self-explanatory to the point that the quantitative questionnaire developed for the primary data survey did not – in keeping with the SCOR model – deal closely with these functional areas. The issue of quality assurance was, however, implicitly questioned by means of performance measures, as for example damaged shipments and customer disputes.863 Having said this, the area of employee
and personnel matters (Human 864 Resources, HR) was not dealt with at all. The same applies to the issue of training which, on account of its association to personnel development, can be considered as a component of the personnel area.865 An attempt therefore needs to be made to define the context of this case more clearly. For this purpose, the organization is determined by means of the following framework.866 According to Leavitt, an organization is constituted by four independent system variables:867 1 . Strategy/task: This means the preparation of goods and services, inclusive of all associated operational
“sub-tasks.” 2 . People/actors: In the main, the observation of persons or actors and their respective behaviour falls within this category. 3 . Structure: Communication systems, role models and work procedures. 4 . Technology: This is taken to mean inventions for the immediate solution of problems – for example, technologies for the measurement of work performance or computers. These variables are often used to categorize differing approaches to organizational change. Whilst technology-, organizationaland structural-based approaches mainly
focus upon mechanisms to solve problems, the personnel-based approach concentrates above all upon organizational change. Ultimately, however, all the approaches mentioned seek the improvement of the performance capability of partial areas.868 The personnel-based approaches assume that a change in the organization predetermines a change in the behaviour of the members of the organization, i.e., the actors, and therefore attribute a special importance to the people variable.869 In conjunction with this, two main trains of thought may be identified:870
Influencing and changing the behaviour of the actors themselves: Guest871 and O’Shaughnessy872 can be named as advocates of this school of thought, as they view the style of leadership as the focal point. In contrast, Lawrence and Lorsch focus on the interaction between the actors, the organization and the company’s environment.873
Issuing “suitable” employees with permission to give instructions in order to implement changes (Power-Equalization Theory):874
Group behaviour also falls within this category (changing groups), as portrayed for example by Lippitt.875 On the other hand, there are those (like Likert876 or Litterer877) who observe the changes to the organization by looking at the employees issued with permission to give instructions. There is not sufficient space here to discuss in depth the differing approaches to organizational development.878 It should be noted, though, that the system variable people represents a substantial factor during the implementation of organizational changes.879 As opposed
to the others, however, this variable has not been closely analyzed for the aforementioned reasons. If one transfers the system variables onto the Supply Chain area, Supply Chain Management is made up of five elements of organizational design:880 1 . Strategy development: This is to enable the conception of a Supply Chain based upon company objectives and market requirements.881 2. Process formation: Here the tasks are described that are necessary for the procedures and management of the Supply Chain. Included in this are the relationships between the processes and the respective best practices.
3 . Performance measurement: In order to measure, assess and monitor the Supply Chain’s performance capability and efficiency, a balanced selection of process-related performance indicators is required.882 4 . Organizational model: This comprehends the description of the organization’s structure, the responsibility of the departments, as well as the tasks and jurisdiction of the individual employees. The management of Human Resources also falls within this category.883 5 . Technology formation: Integrated Information Systems (IS) represent a necessary aid for the planning and implementation of Supply Chain
processes.884 It is apparent that the aforesaid concept by Leavitt, in addition to the system variables contained within it, also applies to the management of the Supply Chain. This is consistent with the presumption that Supply Chain Management represents a partial area of company management and, as a result, must be adjusted to suit superior corporate objectives.885 The special importance of the “people” factor in correlation with the concept of E-Business – a field that also represents, as indicated, the framework of reference for Supply Chain
Management, and therefore represents the application of SCOR as well886 – was proven by Schäfer in an empirical examination. More than three quarters of the companies questioned agreed upon the fact that the “human” factor represents the largest challenge for successful E-Business. Apart from this, it became apparent that potential benefits cannot be fully exploited without a systematic accumulation of knowledge in the form of qualifying measures.887 Kuglin and Rosenbaum accentuate the people-orientated variable by explicitly postulating its alignment to the other system variables: “People are the key to success
in any organization. Therefore, it is essential that organizational structures and performance metrics be established so that everyone is working together to achieve the overall strategic goals of the company.”888 This view inevitably highlights limitations to the application of SCOR. As the “human factor” is not included explicitly in the SCOR model’s structure, no performance indicators are ear-marked to enable the measurement of this particular performance capability. As a result, only limited improvement potentials can be identified in this
direction. Finally, no best practices are contained within the model description that could contribute to process improvements. Given the focal point of this study, the following respective areas of observation or dimensions of Supply Chain formation arise: . Strategy/task: Supply Chain expression 2 . Processes and Organizational structure: Supply Chain reference model 3. Technology: Supporting Supply Chain applications 1
4 . People: The human resources for the execution of all activities pertinent to the Supply Chain, as well as their
influence within the framework of the change process (renewal).889 The illustration that follows represents an attempt to place the observed approaches and procedures into a frame of reference under the aforementioned focal point. No claim to entirety is made in this case; it is moreover the provision of an overview. The illustrated time stream purely serves to provide a rough orientation; flowing transitions de facto often exist. The arrow between the system variables people and structure is deliberately shown in form of a dashed line in order to graphically represent the described problem.
The structural dimensions shown on the left hand side of the illustration represent, in a transposed sense, the organizational transition. The right hand side describes the concrete structuring of the changes associated with it, i.e., the ways and means of the concrete manifestation of the change process. From this, it also becomes apparent why the time stream mainly applies to the right hand side: Change Management determines a permanent transition process.891 Diag. 6-1: Dimensions of the formation in the context of the Supply Chain890
6.2 Consequences of the Peripheral Conditions and Errors in Own Work What follows is an attempt to find possible answers to the following questions:
Which respective events or conditions had a restrictive or disadvantageous effect during the execution of the study? How can the influence of the aforementioned points be characterized with regards to the study, and what steps were taken to minimize the occurrence of negative influences? What could future researchers look to do better or differently? Unfortunately, despite intensive literature research, no previous studies could be found that both contained an empirical examination of the SCOR model and would satisfy scientific
requirements. In the literature reviewed it was either the case that generally held optimization potentials founded upon the value of experience were proven,892 or there were respective studies or projects that applied the SCOR model or built upon it.893 One certain reason for this is that the SCOR model originated from company practice, rather than from the scientific or academic environment.894 As a consequence of that, this study entered new territory, so to speak, and was therefore obliged to assume an exploratory nature.895 Amongst other things, a large amount of contemporary literature was enlisted and evaluated (both from academic research and from
company practice) in order to take into account the balance and plurality required by scientific aspects. Furthermore, consideration must be given to the fact that in the case of the SCOR model, we are dealing with a relatively new concept that was only initiated within the last decade.896 This may be the reason why, despite constantly increasing membership numbers, the model is still not universally present in science and practice.897 The 2002 study by Göpfert and Neher is significant here, as 85 percent of the firms questioned by the authors stated that they did not use the SCOR model or did not plan to assign it
in the future.898 Although it must be noted that this study was directed particularly at the German market, the percentage is still high. The examples described in Chapter 2 from the American and Asian regions show that development there has progressed substantially further. Apart from this, it can definitely be assumed that the SCOR model’s importance and diffusion will increase considerably in Europe, and in particular in Germany, within the next few years.899 With regards to those influences that affect the context of the empirical examination, reference is made to the detailed explanations in Chapter 4.900 During the
resulting measurement of the empirical examination’s contribution with the restrictions and insufficiencies, consideration must be given to the fact that it is only the first stage towards a scientific establishment within the context of an exploratory approach. The submitted work cannot be generalized to apply to the industrial or logistic situation in Europe or even in North America or Asia. Similarly, it in no way claims to deliver a conclusive empirical appraisal of the SCOR model and its suitability for Supply Chain analysis. On the contrary, this study stands upon its claim as the basis for further advanced studies, as should be emphasized by the suggested examples in the following
section. The method of data acquisition in the form of secondary research, as chosen within the framework of the work, inevitably conceals the disadvantage of a time delay. An alternative would have been to proceed with a primary survey. In this case, however, the research-economical aspect must be considered, especially as data was included from companies in the European, North American and Asian regions. Apart from this, the influence of a time delay should only have a restricted effect upon the quality of the examination results: Although the SCOR model finds itself in a continuous
development process, the basic assumptions on the part of the model are mainly fundamental in nature, and are only submitted to changes within a very limited scope. The aspect of change applies primarily to further developments and improvements, as for example the fully implemented inclusion of E-Business in Version 6.0 or a potential innovation in the model’s company-spanning aspects that is sought, yet still outstanding.901 The recommendation for optimization with regards to an explicit inclusion of marketing and sales into future model versions would fall within this sphere.902
6.3 Suggestions for Further Research in the Fields of Supply Chain Management and SCOR This study seeks to provide a helpful insight into the subject matter of Supply Chain Management in general and the SCOR model in particular. Given the complexity, scope, and ever-changing nature of the field, however, a number of points inevitably remain unanswered. These can be more closely addressed in future studies. One of the postulates in the context of empirical research is that a theory should, amongst other things, provide an indication of gaps in present knowledge.903 This study stands, then, as
an initial and exploratory contribution to the development of such a theory. Upon the basis of the knowledge gained within the framework of the empirical examination – the present developments introduced in Chapter 5, and the restrictions discussed in section 6.1 – an attempt will here be made to extricate continuative research suggestions. During this, reference will be made to existing research, and an attempt will be made to derive a concrete example of a theoretically founded empirical and adjoining research project. By enrolling the possible incentives stated, and also incentives going beyond
them for future research in this field, further steps can be taken to build upon and extend the SCOR model’s scientific nature above and beyond this study, and to continue to reduce the uncertainties associated with it.
6.3.1 Extensive research suggestions 1. Examination design and thesis model: • The investigation of the SCOR model should be repeated in a similar way in order to validate the results found. A time related component is seen as rather marginal, as the fundamental
validity of the model may only be restrictedly submitted to temporal changes. Further developments of the model represent a restriction to this, as they take place with every new version of the model. • Continuative examinations could also differentiate between particular criteria, such as company size or region.904 A substantial condition for the consideration of possible discriminating factors is represented by the presence of a respective data basis or a sufficiently high sample size.905 • In the present case, it was not
possible to achieve a more unequivocal model adaptation by means of the additional variables. Certain indicators, in the sense of incremental information, were present for the fact that the SCOR model’s illustration particularly corresponded to the situation of larger (and partially more successful) companies.906 In this case, however, the results gathered did not seem sufficient to generalize this statement. Continuous empirical work would seem to be necessary. • A number of theses showed themselves to be unsystematic upon the basis of the results. For several
of them, it was only possible to formulate a rudimentarily explanation, as explained in detail in Chapter 4.907 It would, in any case, be interesting to investigate the respective constellations more intensively in future studies, and extract explanations for what are at present inconclusive diagnostic situations. 2. Modern concepts and tools for Supply Chain formation: • It remains to be seen whether the concept of the Adaptive Supply Chain (ASC) establishes itself, or whether it is prematurely superseded by other concepts. At
present, a multitude of articles and dissertations exist to address this theme from a practical as well as a scientific point of view. These result from the possibilities provided by new Information and Communication Technologies (ICT), for example the internet, by means of which the structural possibilities of company-internal as well as company-spanning processes were placed upon a new platform.908 One effect in the field of the Supply Chain is the further development into an ASC. This must not obscure the fact that, at present, scientific studies into the ASC concept and its influence upon
a company’s performance abilities do not exist. There is, then, a clear need for further studies in this field.909 • The above point also applies to Supply Chain Design Management (SCDM) and the associated applications. Although the concept is classified as very relevant, there is at present little scientific proof as to the extent of its influence upon the performance indicators. The truly substantial applications in this context are, without exception, based upon the SCOR model. •
Further
leading studies
could
therefore build upon the results found within the framework of the work submitted, and contribute to the provision of objective and comparable measures of the influence of SCDM applications upon corporate success.910 • Finally, it would be of interest to investigate the influence of SCDM upon other E-Business concepts, as for example Customer Relationship Management (CRM) or Electronic Procurement.911 3. Expansion of the SCOR model structure:912 • The importance of the “Human Factor” within the framework of a
total observation of organizational system variables has been discussed in detail. This has highlighted the case for the inclusion of this factor into a comprehensive concept of Supply Chain Management in general, and the SCOR model structure in particular.913 • The additional performance indicators associated with the new system variables would also have to be included into an extended Supply Chain Scorecard (in the sense of an approach including the “Human Factor” for the measurement of Supply Chain performance), in order to make a
continuous monitoring of this new performance area possible.914 From a performance indicator-specific point of view, it would be practical to enlist the Balanced Scorecard (BSC), as this contains a special learning and growth perspective.915 Due to its similarity in content to the SCOR model (with reference to the performance indicator-specific SCOR model observation),916 the BSC seems predestined to serve as a basis for the inclusion of personnel into organizational performance measurement.917 It even explicitly preconditions the inclusion of the employees and therefore the “Human Factor” for
successful assignment.918 An extended Supply Chain SCORCard resulting from this could be used for the progress monitoring of Change Management, because this can be considered to be a special characteristic of the “Human Factor.”919 • The SCOR model comprises, as has been indicated, non-monetary measures with regards to the associated performance 920 indicators. The disadvantages connected with this, as for instance the problem of aggregating nonmonetary indicators, could be confronted by an expansion of the SCOR model structure or the
respective inclusion of financial performance terms. For the aforesaid reasons, the closest approach would seem to lie in the orientation upon the BSC. • The result would be a Supply Chain SCORCard expanded by one financial entity that would for example enable the creation of a reference between the improvements sought upon a SCOR model-based Supply Chain analysis, and the gains achieved.921 No referential results exist, at present, with regards to the suggested
expansion of the SCOR model structure. If the validity and effects of this extended model structure are to be assessed, further research in the field is needed as a matter of some urgency.
6.3.2 Example of a theoretically founded empirical research project as a possibility for adjoining research An attempt is made here to give a concrete example of a future empirical research project. As a basis for this, the examination results accumulated within the framework of the empirical study will be called-upon, as will
developments in company practice and business-economical research. This will enable the study to justify its claim to having exploratively contributed to a step-by-step accumulation of knowledge, which can then be continued by further focused investigations.922 Primarily, the application of structure-analytical procedures serves to investigate a thesis model, understood as a system of hypotheses. In the present case, however, the necessary procedural conditions were not sufficiently available.923 It would therefore appear appropriate to allow these procedures to be assigned within a study building upon a model. A decisive factor in
conjunction with this is the availability of a data basis as large as possible (with a sample size of at least 150 or more). To what extent this is achievable from a research-economical perspective must be concluded individually for each case. Furthermore, it must be taken into consideration that structural equation models, in addition to statistical criteria, require certain content from the data material being analyzed.924 The structural equation model to be investigated is directly derived from the thesis model. A substantial difference is, however, present from the development of a structural equation model sought in Chapter 4: The latter-named model set
out to validate the results of the inferential – statistical examination. It therefore relied upon Meta theses, and the hypothetical constructs were derived accordingly. The consequence was that one was dealing with a “bottom-up” approach, so to speak.925 Contrary to this, and within the framework of the submitted suggestion, this should be a superior point of view and the thesis model observed in its entirety. For this reason, a “top-down” three-stage correlation of hypothetical constructs is given: Firstly, the (not directly measurable) Performance Attributes are mapped to the (also not immediately measurable) Supply Chain
competences. Then, the (not directly measurable) Metrics Level 1 are mapped to the Performance Attributes. Finally, in the third stage, a mapping of the (measurable) Performance Measures to the Level 1 metrics takes place as overt indicators. The following diagram represents the level of the hypothetical constructs for reasons of graphical representation and legibility. In the illustration following this, the mapping of the Level 1 metrics (as the level of hypothetical constructs) to the performance measures (as the overt indicator level) is represented.926 Diag. 6-2a: Research suggestion for a performance indicator-based SCOR model represented in a structure-analytical form (Part 1)927
Diag. 6-2b: Research suggestion for a performance indicator-based SCOR model represented in a structure-analytical form (Part 2)928
Table 6-1: Legend to Diag. 6-2b: Index of the applied performance metrics929 Index Performance Measure DP-1 Customer retention rate DP-2 Backorders value DP-3
On-time delivery percentage (inbound and outbound)
FR-1
Percentage of purchased orders received on time and complete
FR-2 Percentage of purchased lines received on time and complete Average MPS plant delivery performance (work FR-3 orders) FR-4 Cycle count accuracy percentage FR-5
On-time delivery percentage (inbound and outbound)
POFPerfect orders rate 1 POFLines on-time fill rate 2 POFCustomer retention rate 3 OFLAverage purchase requisition to delivery cycle time 1 OFLTransactions processed via web/EDI 2 OFLAverage manufacturing cycle time 3 OFLPercentage of sales via web 4
RT-1 Backorders value On-time delivery percentage (inbound and RT-2 outbound) RT-3 Lines on-time fill rate PF-1
Inventory stockout percentage
PF-2
Average MPS plant delivery performance (work orders)
TSC- Inventory management cost as a percentage of 1 revenue TSC- Inventory management cost as a percentage of 2 inventory value TSC- Inventory obsolescence cost as a percentage of 3 revenue TSCPercentage of inbound or outbound cost 4 TSCInventory management cost per customer order 5 TSCPercentage of inventory in transit 6 TSC- Transportation cost per mile (inbound and 7 outbound) TSC-
Transportation cost as a percentage of revenue
8 TSCInbound transportation cost per supplier order 9 TSCOutbound transportation cost per customer order 10 TSCPremium freight charges 11 COGPurchasing cost per purchase order 1 COGPurchasing cost as a percentage of revenue 2 VAPPercentage of purchases from certified suppliers 3 VAPManufacturing cost per FTE 4 VAPAverage first-pass yield rate 5 VAPScrap/rework cost as a percentage of revenues 6 VAPAverage throughput per FTE 7 VAP-
Average machine availability rate
8 VAPAverage number of customers orders per FTE 9 VAPCustomer service cost per FTE 10 VAPInventory management cost per FTE 11 VAP- Transportation cost per FTE 12 WC-1 Damaged shipments WC-2 Customer disputes CTCAverage received finished goods turnaround time 1 CTCInactive inventory percentage 2 DOSAverage order-to-shipment lead time 1 DOSAverage inventory turnover 2 AT-1 Average operating-equipment efficiency rate (OEE) AT-2 Average plant capacity utilization AT-3 Average warehousing space utilization
A modification introduced in SCOR model Version 7.0 and continued in Version 8.0 would enable, beyond this, a (simplified) variant of the examination design. The new versions represent an initial attempt to make calculation operations available, which should make the calculation of the Level 1 metrics possible.930 These metrics will naturally still not be immediately measurable, because they are ratio indexes.931 They could, however, be identified as indicators in the structureanalytical analysis, as numerical values would already be present for them. In this case, one level of hypothetical structures would become obsolete. As a
result, it would be possible to perform an additional comparison of the aboverepresented model with the alternative model, which would allow additional and enhanced conclusions as to the examined illustration of the SCOR model’s structure.
6.4 Balance Between Standardization and Individualization In the meantime, many companies have introduced the SCOR model into operational planning and business practice. However, the expectations from the SCOR model must not be set too high, as it is neither a “panacea” nor
an end in itself. It is not intended to be the sole means of realizing effective monitoring of the Supply Chain. The application of SCOR by no means replaces management expertise.932 The SCOR model’s application for the standardization of processes and structures often also has an ambivalent character. On the one hand, a number of companies see a great potential for optimization in standardization, as expressed in the following interpreted quote.933 The largest potential with respect to business improvements and results lies
in the harmonization, improvement and standardization of processes.934 On the other hand, however, the standardization of structures and processes can mean that some strengths, which resulted directly from the previous differences, become lost.935 In this case, the model’s application would not contribute to the achievement of competitive advantages. In an extreme case, it could actually lead to competitive disadvantages.936 Thus, a company must find a suitable balance between standardization and individualization in order to be able to
supportively improve and expand its competitive position.937 In a survey conducted in the year 2001 amongst so-called DAX938 Companies to establish the value of E-Business concepts and corresponding activities within corporations, Schäfer came to the conclusion that E-Business was seen as being particularly relevant. In that case, it must be taken into consideration that Supply Chain Management, and as a result also SCOR, fall within the E-Business frame of reference.939 The activities going on in this field were, however, comparatively low-key at the time of this study, and could certainly be strengthened and
given greater priority. Furthermore, although an important value was assigned to the concept of company-spanning Supply Chain Management, an evident backlog existed. In this case, it was the concrete necessity for detailed information and assistance in addition to supporting standards and interfaces required by companies in order to introduce company-spanning concepts. The increasing difficulty regarding the unequivocal “demarcation” of company boundaries was pointed out in this case. The requirement for adequate measures on an information technology and organizational level was consequently
highlighted, which enables companies to flexibly arrange and integrate interorganizational SC processes.940 Based upon the knowledge accumulated, the SCOR model as introduced within the course of the work represents a good aid for the analysis and optimization of Supply Chains by present day standards. Apart from this, it offers standards for company-spanning process description and performance comparison. It has, as indicated, clear advantages, but does not represent a form of “universal recommendation.”941 Advanced approaches to the realization and support of Adaptive Supply Chains (ASC), similar to the introduced Supply
Chain Design Management (SCDM) and the applications available for it, indicate possible ways in which future Supply Chain models will be able to continuously adapt to market requirements. At present, due to the novelty of the subject matter, there is a lack of sufficient practical experience and scientifically founded examinations to produce a conclusive study of those. Seibt collectively expresses the requirements that arise for companies in the predominantly dynamic competitive environment as follows: Each organization must learn to adjust itself to a multitude of flexible and quickly
changing cooperation with an ever increasing number of changing partners, to comprehend them as a chance. Only in this way will it become a valuably participating cooperation partner, i.e., in the generation of value which brings dynamic changes with it.942 This statement is particularly noteworthy with regards to the present subject matter, and unequivocally affirms the need for further work in the field of Adaptive Supply Chains and the processes and tools necessary for their
management. Such a field is not only guided by the need to generate value, but is concerned with continuously redefining this value throughout the whole Supply Chain.943 The final objective is to manage an evolutionary process, which enables a “balancing” between the (external) customer requirements on the one side, and the (company-internal) competences or, more exactly in the present case, the SC competences on the other side.944 The SCOR model can make a valuable contribution to the continuous analysis and necessary optimization of the Supply Chain built upon it. This is reflected, amongst other things, by its
constantly rising usage. However, in its present formation it has clear limitations as to its assignment possibilities with respect to the Supply Chain’s design and redesign. It has been shown that the SCOR model initially originated from the requirement to create a universal language for the Supply Chain’s description in the sense of a “standardization of the SC nomenclature.” It therefore represents a descriptive model, and in its original condition does not claim to possess a structuring character.945 This, of course, does not mean that
incentives and suggested improvements can and should arise from it. Their implementation must just be seen as external to the SCOR model’s application.946 This correlation is not always made sufficiently clear by companies and consultancies.947 This study has highlighted the difference as much as possible, and deliberately refrained from an appraisal of the implementation successes in conjunction with formation measures. Modern approaches with respect to the further improvement of the model are very promising and have the primary intention of developing the model further, namely into a combined
descriptive and structural model.948 However, firm proof of their application ability and, furthermore, of their quantifiable success is still absent. Hence, they require an expansion of their application in corporate practice, as well as further scientific foundation. A continuous improvement of the SCOR model and its application possibilities could usefully be produced by a close cooperation between corporate practice and academic research. It would also be a very valuable contribution to the continuous improvement and advanced application of the SCOR model.
References 1. (Latham, 1999, p. 91). The author quotes Gerry Perez, Senior Vice President – Technical Operations of Boehringer-Ingelheim, Inc., the American subsidiary of the company Boehringer-Ingelheim (headquartered in Darmstadt, Germany). 2. The period between 1995 and 2007 is referred to here. 3. For the usage of the term Supply Chain, cp., for example, Werner (2002, p. 28). The term logistical chain or logistics chain is also sometimes found. The difference lies in the fact that the focal point of the logistics chain extends to cover the (physical) activities in a narrower sense. In addition to this the Supply Chain also covers the accompanying cash and information flows and has a substantially wider conception, cp., for example, Thaler (2003, p. 43) and von Steinäcker and Kühner (2001, p. 45). The terms introduced and their meanings will be dealt with in more detail in the course of the submitted
work. 4. For discussion on Supply Chain Management as a management discipline in the context of business economics, see the respective explanations in sect. 1.2. 5. Cp. Supply & Demand-Chain (2005a, 2005b). A personal website and a respective magazine exist for executives from the field of Supply Chain Management with the title Chief Supply Chain Officer (CSCO) – Insights for the Supply Chain Executive, cp. CSCO (2005). 6. A method or a procedure is used in order to get from a defined starting condition to a defined end condition. In opposition to this, with a model we have an illustration of a defined starting structure seen from certain points of view. Models are each designed for particular question or problem conditions; they are moulded by the basis forming question (i.e., by the uses required of the model) (cp. Kromrey, 2002, p. 204). 7. With reference to the terms Non-Profit Organisation and Not-for-Profit Organisation, cp., roughly, Kotler and Bliemel (1992, pp. 30, 42).
8. The respective terms or organizations (SCOR model, SCC and so on) will be explained in more detail at a later point in time. 9. Cp., for example, Bolstorff and Rosenbaum (2003, p. 1). 10. Cp., for example, Huan, Sheoran, and Wang (2004, p. 23), Lambert and Pohlen (2001, p. 1), Lockamy and McCormack (2004, p. 1192), and Gardner and Cooper (2003, p. 37). 11. For the practical applications of the SCOR model, see chap. 2, sect. 2.4; for assignment within the framework of scientific examinations see chap. 6, sect. 6.2. 12. For the discussion on an explorative advance to approaching a research area, cp., for example, Wollnik (1977). For a respective example of application, cp., roughly, Schäfer (2002). 13. This will be more closely dealt with in chap. 4, sect. 4.1. 14. This problematic will be dealt with more explicitly in chap. 5, sect. 5.1. 15. For the term exploration see the explanations at the beginning of chap. 3.
16. The subject matter will be dealt with in detail in chap. 3, para. 3.1.1. 17. We are dealing here purely with basic guidelines as to the methodical layout of the work. Detailed explanations of the processes, testing procedures and statistically acknowledged values are found in association with the disclosure of findings (see the explanations in and chap C, sect. 3.4). 18. Cp. Friedrichs (1990, p. 50). 19. Modelled upon Friedrichs (1990, p. 51). 20. The value generation resulting from the performance process of businesses is calculatively identified as revenue minus intermediate inputs; cp., for example, Böcker and Dichtl (1991, p. 169). 21. Cp. BearingPoint (2003a, p. 1). 22. For the terms bottleneck and bottleneck monitoring, cp., roughly, Goldrath and Cox (1992, p. 138) and Heinrich and Betts (2003, p. 14). 23. According to the Balancing Law of Planning (Ausgleichsgesetz der Planung) formulated by Gutenberg, the coordinated course of
happenings within a business require the continuous mutual synchronization of marketing possibilities, manufacturing capacities, procurement factors, etc. Resulting from this are areas which change in the course of time, and impede as bottlenecks the full quantative and/or qualitative development of the other operational partial areas due to their entanglement. For planning purposes the result is that partial plans have to be aimed at this “minimum sector” via particular synchronization measures. Compare to this end, for example, Gutenberg (1979, p. 164) and Schierenbeck (2003, p. 129). 24. Cp., for example, Thaler (2003, p. 19). 25. The term Service Level can be defined as follows: “The probability of being able to satisfy any order during the normal order cycle, from stock in hand” (Stephenson, 2004). 26. Cp. Kuhn and Hellingrath (2002, p. 45). 27. In the past, a number of compositions considered the basic question as to whether Supply Chain Management represents a self-contained management discipline in the context of business
economics, or moreover a type of fashion appearance of limited duration; cp. to this end roughly Eßig (1999, p. 106), Kieser (1996, p. 21), Müller, Seuring, and Goldbach (2003, p. 429), and Otto and Kotzrab (2001, p. 157). 28. In academic literature, there is an increasing recognition that Supply Chain Management actually constitutes a self-contained discipline within business economics, which needs to be taken appropriately. Representatively, Ayers comes to the following conclusion in this context: “SCM is a discipline worthy of a distinct identity. This identity puts it on a level with other disciplines such as finance, operations, and marketing” (Ayers, 2002a, p. 8). For further compositions which advocate a similar viewpoint, cp., for example, Bechtel and Mulumudi (1996, p. 1), Cooper, Lambert, and Pagh (1997, p.1), Göpfert (1999, p. 19), and von Steinäcker and Kühner (2001, p. 39). 29. In the following, the term Supply Chain and the abbreviation SC will be used synonymously. 30. Background information: “The Council of Logistics
Management (CLM) was originally founded as the National Council of Physical Distribution Management (NCPDM) in America’s St. Louis, in January, 1963. The NCPDM was formed by a visionary group of educators, consultants, and managers who envisioned the integration of transportation, warehousing, and inventory as the future of the discipline. At that time, physical distribution was just beginning to edge its way into the corporate lexicon and make its considerable presence felt in the business community. These early founders believed that high-level executives within their own companies needed to be made aware of the critical role that physical distribution could and should play in improving marketing efficiency and profits. They determined that there was an urgent need for an organization that would facilitate continuing education and the interchange of ideas in this rapidly growing profession that came to be known as logistics management” (CLM, 2004a). 31. The original text reads: “The supply chain is as all
that happens to a product from dirt to dust” (CLM, 2004c). 32. The product life cycle comprises the main stages from product evolution through production, right up to reutilization. Throughout the life cycle constant changes and improvements must be implemented, which only then make a continual success on the market possible; cp., for example, Wöhe (1984, p. 626). 33. Cp. Ayers (2002a, p. 5) and Lambert and Pohlen (2001, p. 1). 34. Cp. Christopher (1998, p. 4) and Hugos (2003, p. 40). 35. Cp. Banfield (1999, p. 3) and Daganzo (2003, p. 1). 36. Cp. Chopra and Meindl (2001, p. 1). 37. Cp. Lambert, Stock, and Ellram (1998, p. 14). Aitken defines the Supply Chain in this sense as follows: “A network of connected and interdependent organizations mutually and cooperationally working together to control, manage and improve the flow of material and information from supplier to end user” (Aitken, 1998, p. 19).
38. Cp. Govil and Proth (2002, p. 7) and Poirier (1999, p. 197). 39. Cp., roughly, Kuhn, Hellingrath, and Kloth (1998, p. 7). 40. Cp. Jansen and Reising (2001, p. 197) and Marbacher (2001). Christopher advocates a similar approach on the grounds that today’s Supply Chains are mainly driven by the market (Christopher, 1998, p. 18). 41. In the past the so-called push concept was the most widely spread. With this approach in the various stages of the Supply Chain, semicompleted and completed products are manufactured and stored until such time as they can be sold and delivered to the next stage in the Supply Chain on the basis of customer orders. Often, long delivery periods and high inventory stock result from this. In opposition to this, the demand vacuum approach (pull concept) is characterized by the fact that the customer decides to buy a certain product, whereby it quotes the exact requirements with reference to the product and delivery time. Building upon this
the necessary resource amount is acquired. The production and distribution process has then to lead to a delivery according exactly to the wishes of the customer (quality, time, etc.). In order to realize this approach, supply and demand must be synchronized. This synchronization represents one of the tasks and one of the objectives of Supply Chain Management (SCM), as will still be illustrated in detail in the course of the work; cp. Landvoigt and Nieland (2003, p. 4) and Poirier (2000, p. 26). 42. Cp. Hoover, Eloranta, Holmstrom, and Huttunen (2001, p. 70). 43. Cp. Bovet and Martha (2000, p. 17). For the term process in the context of the Supply Chain, q.v. (Schönsleben, 2000, p. 22; Thaler, 2003, p. 17). 44. (Stephens, Gustin, & Ayers, 2002, p. 360). 45. Cp. Premkumar (2002, p. 368). 46. The concept of Collaborative Planning, Forecasting and Replenishment (CPFR) enables a companyspanning cooperation for the buyers and sellers to make demand and marketing prognoses, as
well as a regular actualization of plans, which is based upon a dynamic exchange of information and has reduced supplier stock as its objective; cp., for example, Handfield and Nichols (2002, p. 298) and Schneider and Grünewald (2001, p. 198). 47. Cp. Chakravarty (2001, p. 402). 48. Cp. Govil and Proth (2002, p. 17). 49. The term internet stands as a synonym for a global association of local, country-specific and regional networks. The impression of a complete network is created via connection to network centers (backbones) for private, scientific and commercial users. Due to this, the internet is a global-spanning, public net that connects worldwide institutions, businesses and private users to the so-called Transmission Control Protocol/Internet Protocol (TCP/ IP) via Gateway Servers; cp. Rebstock (2000, p. 6) and Schoder (2004, p. 60). 50. Cp. Kuglin and Rosenbaum (2001, p. 59). For the Supply Chain’s penetration of the internet and the implications thereof, cp., for example, Coppe
and Duffy (1999, p. 521). 51. In this context see the concept of Collaborative Planning, Forecasting and Replenishment (CPFR) described earlier in this paragraph. 52. The concept of Vendor Managed Inventory (VMI) falls within this category and can be described as follows: “Traditional responsibilities have changed. Large retailers obtain more and more sending orders to their suppliers, i.e., the customer goods manufacturers. Instead they install consignation stores whose contents are owned by their suppliers until the goods are withdrawn by the retailer. A supplier is responsible for filling up his inventory to an extent which is convenient for both the supplier and the retailer. Such an agreement is called Vendor Managed Inventory” (Meyr, Rodhe, and Stadler, 2002a, p. 65). 53. Cp. Gensym (2001, p. 2). 54. In the framework of a Supply Chain Strategy, the company’s desired value generation must be clarified. This involves answering questions, as for example: With which products and services
is the business going to compete? Is a standard product of a certain series going to be offered to all customers, or are customer-defined series products going to be on offer? Which magnitude of numbers is strived for (few, many)? Is purely a product on offer, or is additional service performance, such as inventory replenishment, also offered? To what extent does the depth of production suffice? Cp. Geimer and Becker (2001a, p. 26). 55. Cp. Christopher (1998, p. 15) and Geimer and Becker (2001a, p. 26). The so-called Value Chain follows a similar approach in thought, already defined by Porter; cp. Kuglin (1998, p. 106), Porter (1995, p. 126), and Porter (1999, p. 59). 56. (Normann & Ramirez, 2000) Value Chain, p. 186 57. This term allows itself to be operationalized in the following way: “Customer satisfaction is the degree to which expectations of attributes, customer service, and price have been or are expected to be met” (Hilton, Maher, & Selto, 2004).
58. Profitability in this context is understood to be the capability of a business to attain a suitable rate of interest for invested capital (Return on Investment, ROI); cp. Horváth (2001, p. 571), Schierenbeck (2003, pp. 6, 635), and Wild (2004). 59. The so-called Strategic Triangle describes three decisive factors of competition: cost, time and quality; cp. Thaler (2003, p. 12). The Strategic Square adds the further factor of flexibility; cp. Werner (2002, p. 10). Due to this, flexibility can be defined as follows: “Flexibility is defined as the ability of the process to handle changes in its environment and in the requirements on the process” (Seibt, 1997a, p. 22). 60. The term Information Technology (IT) is to be defined in the submitted work as “(…) denoting the technologies used for processing, storing, and transporting information in digital form” (Carr, 2003, p. 12). For the term Digitalization, cp., for example, Negroponte (1995) and Rai, Patnayakuni, & Patnayakuni (2005, p. 2). 61. Cp. Bovet and Martha (2000, p. 4). For the term
Value Net, q.v. Andrews and Hahn (1998, p. 7) and Cartwright and Oliver (2000, p. 22). 62. A substantial development within the framework of the Supply Chain is the construction of so-called virtual networks. A virtual network describes the temporary melting together of core competences (i.e., to be in command of special capabilities or success potentials in particular fields of the participating businesses – q.v. Prahalad and Hamel (1990, p. 79). The resulting construction represents itself to the customer as one unit. Inwardly, a virtual enterprise possesses no juristic and construction organisational interlocking; cp. Aldrich and Sonnenschein (2000, p. 35), Kaluza and Blecker (1999, p. 4), Schäfer (2002, p. 1), and Werner (2002, p. 12). 63. Nickles et al. describe the connection as follows: “Business strategies have traditionally driven IT development, but IT can now be used to enable new business strategies.” (Nickles, Müller & Tabacs, 1999, p. 495). 64. Cp. BearingPoint (2004a, p. 2). 65. Cp. Forbath and Chin (2000, p. 3) and Rayport and
Sviokla (1995, p. 75). 66. For background information on the Dell Company see chap. 3, para. 3.1.2.2. 67. Cp. Dell and Fredmann (1999, p. 101). 68. Cp. Seibt (2000, p. 11). 69. The starting point of the description and systemization of E-Commerce is the dimension of business transactions, as for example market types, market services and people acting within them; cp. Klein and Szyperski (2004). For the connection between SCM and E-Commerce, q.v. Drummond (2002, p. 29). For the integration of E-Commerce within E-Business and for the boundaries of the two terms, q.v., for example, Schäfer (2002, p. 11). 70. Cp., for example, Kämpf and Martino (2004). For the term Electronic Supply Chain Management (E-SCM), q.v. Hillek (2001, p. 1) and KPMG (2001, p. 2). 71. For the integration of SCM solutions into the operational architecture of information systems, cp., roughly, Gronau, Haak, and Noll (2002, p. 385) and Kämpf and Roldan (2004).
72. Cp. Ross (2003, p. 18). 73. Cp. Ayers (2002c, p. 245). 74. Cp. Hughes, Ralf, and Michles (1998, p. 4). For the direction of Strategy definition and implementation, q.v. Fuchs, Young, and Zweider-McKay (1999, p. 8). 75. The term Competitive Advantage can be determined as follows: “The challenge of competitive strategy – whether it is overall lowcost, broad differentiation, best-cost, focused low-cost or focused differentiation – is to create a competitive advantage for the firm. Competitive advantage comes from positioning a firm in the marketplace so it has an edge in coping with competitive forces and in attracting buyers” (Johnson & Strickland, 2004). 76. For the term fixed costs, cp., for example, Schierenbeck (2003, pp. 233, 654) and QM (2004a). 77. The fundamental competitive strategy is the management of costs. The common acceptance of Porter’s strategy alternatives Cost Management (i.e., a strategy that focuses upon
the competitive advantage of lowest cost compared to competition), Differentiation (i.e., the achievement of at least one of the unique performance attributes) and Concentration (i.e., concentration upon a market segment or demand group respectively) means that a corporation can only be successful if it concentrates upon one of the three fundamental types of strategy and the competitive advantages resulting from the strategy. Otherwise the business is in danger of being “stuck in the middle”; cp. to this end, Porter (1995, p. 62) and Porter (1999, p. 19). 78. Cp. Stephens et al., 2002, p. 361). For achievement of competitive advantages via information technology, cp., roughly, Porter and Millar (1988, p. 62). 79. (Christopher, 1998, p. 16). Cp. in this context also (Unknown Author, 2006, p. 14). 80. For the description of Supply Chain Management as a unique discipline within Business Economics, see the explanations at the end of sect. 1.2. The fields of application demonstrated
in the further course of the work should moreover highlight the present importance of SCM for corporate practice. 81. In the further course, the term Supply Chain Management and the respective abbreviation SCM will be used synonymously. 82. Cp. Evans and Danks (1999, p. 19). For implementation of SC strategies, q.v. Easton, Brown, and Armitage (1999, p. 446). 83. Cp. Beech (1999, p. 92). 84. Cp. Tyndall, Gopal, Partsch, and Kamauff (1998, p. 65). 85. Cp. Raman (1999, p. 171). 86. (Hugos, 2003, p. 2). 87. For background information on the Council of Logistics Management (CLM) see sect. 1.3. 88. Cp. Novack, Langley, and Rinehard (1995, p. 27). Cp. also to this end CLM (2004c). 89. Cp. Bowersox and Closs (1996, p. 1). 90. Cp. to this Novack, Rinehard, and Wells (1992, p. 234) and Simchi-Levi, Kaminsky, and SimchiLevi (2000, p. 1). A differentiation of the planning levels can be undertaken as follows:
Operational: short-term (less than one year for the running accounting or reporting period respectively) and mainly applicable to one part of the company or activities respectively. Tactical: mid-term (time horizon 1 to 3 years) and mainly for a larger part of the company or activities respectively. Strategic: long-term (time horizon longer than 3 years) and mainly impartial, pertaining to the substantial product areas, company activities or the company as a whole, and the aspects critical for success; cp., roughly, Albach (2001, pp. 294, 329). 91. Logistics Service Providers can be described as follows: “Third-party logistics involves the use of external companies to perform logistics functions that have traditionally been performed within an organization. The functions performed by the third party can encompass the entire logistics process or selected activities within that process” (Skjoett-Larsen, 2000, p. 112). According to this definition, every relationship between a carrier and a logistic service provider is described as Third-Party Logistics.
92. Cp. CLM (2004b). 93. Cp. Kajüter (2002, p. 36). 94. Cp. Hellingrath (1999, p. 77). For the terms effectivity and efficiency in the submitted context, see para. 1.7.1. 95. Cp. CLM (2004b). 96. Cp. CLM (2004c). 97. Cp. Stephens et al. (2002, p. 360). Werner differentiates between the business-internal and the business-integrated Supply Chain, whereby the latter is directed at the intersections of a company with its external partners; cp. Werner (2002, p. 6). 98. For an overview of various term definitions and a proposal of classifications, cp., for example, Müller et al. (2003, p. 419) and Schäfer (2002, p. 47). 99. (Schönsleben, 2000, p. 152). The term Advanced Planning System (APS) can be described as follows: “An APS typically consists of several software modules (eventually again comprising several software components), each of them covering a certain range of planning tasks”
(Rohde, Meyr, & Wagner, 2000, p. 10). Cp. also to this end Meyr, Rodhe, and Stadler (2002b, p. 99) and Poluha (2001, p. 314). 100. As to the various planning levels, especially in the information management areas, cp., for example, Schoder (2004, p. 42). 101. For the core competence term, q.v., for instance, Prahalad and Hamel (1990, p. 79). 102. Cp. Schönsleben (2000, p. 54). “Lead time expresses the amount of time consumed for each functional transaction. (…) The main properties of the lead time of a process are the length and the predictability or precision of its executions” (Seibt, 1997a, p. 21). 103. Cp. Bowersox and Closs (1996, p. 4). 104. Cp. Ayers (2002a, p. 8). 105. Cp. Premkumar (2002, p. 368). 106. Cp. Kaczmarek and Stüllenberg (2002, p. 275). 107. Cp. Handfield and Nichols (2000, p. 2). 108. Cp. Ross (1997, p. 9). 109. The terms Lean Production or Lean Management respectively, describe the concept of the increase of efficiency, often in the sense of
decentralization, production displacement (outsourcing), flat hierarchies, compression of performances and therefore less personnel. This concept, as with other variants of Leanconcepts, stems back to an analysis by the Japanese car manufacturers in a study of the Massachusetts Institute of Technology (MIT) at the end of the 1980’s (for background information about the MIT, cp. the explanations in para. 1.7.1). According to this, the Japanese car manufacturers produced twice as efficiently and flexibly as the European and American competition, with simultaneously meaningful better quality. The comparison with “lean” in the sense of decentralization, outsourcing, shallower hierarchies, performance compression and therefore less personnel is a simplification of the Japanese concept, which represents embracive quality management (Total Quality Management, TQM) and only achieves the efficiency and flexibility advantages on the basis of this embracing concept, which are described as externally visible and are attributed to
organizational changes in the “lean” sense. Differentiated interpretations comprise therefore the substantial elements of quality management, however there are no unified meanings for the various concepts connected with “lean”; cp. Pollalis (2002, p. 333) and Thaler (2003, p. 113). 110. Distribution represents a partial area of the order management process, which focuses upon the final delivery to the customer. “For delivery, or distribution, the products are issued from stock (commissioning) and prepared for shipment, required transportation means and accompanying documents are provided, and delivery is executed” (Schönsleben, 2000, p. 139). 111. Safety stock in the framework of SCM can be described as follows: “Safety stock has to protect against uncertainty which may arise from internal processes like production lead time, from unknown customer demand, and from uncertain supplier lead time. This implies that the main drivers for the safety stock levels are production and transportation disruptions,
forecasting errors, and lead time variations. The benefit of safety stock is that it allows quick customer service and avoids lost sales, emergency shipments, and the loss of goodwill. Furthermore, safety stock for raw materials enables smoother flow of goods in the production process and avoids disruptions due to stockout at the raw material level. Besides the uncertainty mentioned above, the main driver for safety stock is the length of the lead time (production or procurement), which is necessary to replenish the stock” (Sürie & Wagner, 2002, p. 41). 112. Cp. Hoover et al. (2001, p. 9). 113. Cp. Mentzer et al., no year, p. 18). 114. Cp. Hugos (2003, p. 3). 115. The term indicator applies also to “pointer”; cp. Wahrig-Burfeind (2004, p. 164). 116. The business-external or business-integrated Supply Chains are meant by this, which have been previously more closely explained, cp. Werner (2002, p. 6). 117. The connection between actually competing
objectives gives reference to the competitive strategy of so-called Outpacing. Whilst – as previously explained in sect. 1.3.2 – Porter considers the strategic alternatives as unable to be unified, Gilbert & Strebel assume that these alternatives can be combined, if not simultaneously, then in chronological order. The so-called outpacing strategy is highlighted by the fact that a business changes between the two strategy-alternatives during its strategic adjustment in order to gain an enduring lead ahead of the competition. The reason is that if a corporation has gained a competitive advantage, it can be assumed that other corporations will attempt to position themselves on the market and, under certain conditions, will imitate the strategy. New bidders will push themselves onto the market until no more advantages can be realized and the leaders in cost or differentiating products can secure no further competitive advantage. Gilbert & Strebel however, critically indicate that only the best businesses are in a position to overcome the conflict in objectives
between customer benefits and low costs and achieve a low cost level if a customer benefit is given; cp. Gilbert and Strebel (1987, p. 28), Hamel and Prahalad (2000, p. 125), and Ekholm and Wallin (2004, p. 4). Beyond the outpacing strategy which assumes not a simultaneous, but more a successive application of varying strategy alternatives, so-called hybrid competitive strategies exist within which falls e.g. the hypothesis of simultaneousness, which presumes that a combined strategy usage is at least temporarily possible; cp. to this end Corsten and Will (1995, p. 1). 118. Cp. Kuglin (1998, p. 3). Cp. also Kuglin and Rosenbaum (2001). 119. Cp. Hoover et al. (2001, p. 48f). 120. Cp. Hugos (2003, p. 40). 121. Cp. Werner (2002, p. 10). 122. Cp. Schäfer and Seibt (1998, p. 365). 123. The competence with special reference to the Supply Chain has great importance advancing towards it within the framework of the SCOR model and the pertinent performance indicators,
which will be dealt with dedicatedly at the beginning of sect. 1.7. 124. For the term Competitive Advantage see para. 1.3.2. 125. The cash flow is a indicative index of the flow of capital from the profit process, from which insights into the solvency and the financial development of the company can be gained; cp. Schierenbeck (2003, p. 316) and Wöhe (1984, p. 740). In the context of the Supply Chain, the cash flow includes all financial transactions occurring as a result of the trading of goods, commodities and services; cp. Thaler (2003, p. 45). 126. The Supply Chain strategy describes what a business wishes to achieve with the Supply Chain and which services are achievable with it. With its SC strategy a business defines how it can contribute to its competitive capability using its SC processes and SC infrastructure. The objective of the strategy definition is the identification of relevant competitive factors and their implementation within the Supply Chain.
The SC strategy is subordinate to the respective business or competitive strategy, or respectively derived from them and must support them. Following this, a substantial characteristic of a successful SC strategy is the adjustment to the business strategy and therefore to the organization’s strategic core vision; cp. Geimer and Becker (2001a, p. 21). 127. The following definition of terms is to be used for OEM: “OEM is an acronym for Original Equipment Manufacturer. An OEM is a company that builds components that are used in systems sold by another company called a Value-Added Reseller or VAR. The practice of a VAR selling products with components from OEMs is common in the electronics and computer industry. Typically an OEM will build to order based on designs of the VAR. In common usage, a VAR is sometimes called an OEM, despite this being a complete reversal of the literal meaning of both terms. This misunderstanding arises from use of the term OEM as a verb. For example, a VAR might say
that they are going to OEM a new product, meaning they are going to offer a new product based on components from an OEM. In recent years, some OEM’s have also taken on a larger role in the design of the product they are manufacturing. The term ODM, Original Design Manufacturer, is used to describe companies that design and manufacture a product that is then sold under other brand names” (WordIQ, 2004b). Cp. also to this end Chakravarty (2001, p. 334). 128. Cp. Industry Directions (1998b, p. 2). 129. Cp. Goldrath (1999, p. 4) and Heinrich and Betts (2003, p. 14). 130. For the description of structural and process organizations, cp., for example, Wöhe (1984, pp. 156, 171) and Grochla and Wittmann (1974, pp. 1, 190). 131. Cp. Hines, Silvi, Bartolini, and Rachi (2002, p. 55). 132. The term integrated stands for “integral, combined or interlinked”; cp. Wahrig-Burfeind (2004, p. 189). Consequently de-integrated can be defined as “non-integral, non-combined or non-
interlinked.” 133. Self-representation of the business: “Smart GmbH is a 100% daughter company of DaimlerChrysler AG. Founded: April 1994, 1,140 employees (as at: Jan. 2003), administrative central office: Böblingen (Germany), production location: Hambach (France), with a presence in 24 countries (as at: January 2003)” (Smart, 2004). 134. The term mass customization represents a further variety of the hybrid competitive strategies mentioned in para. 1.4.2. In the core of this – expressis verbis – the customer-individual mass production of products for a large trading market is understood. In accordance with this, the products must meet the various needs of the demanding. During this the costs should roughly represent those of a mass production of standardized products. The approach seeks a balanced association between continually running mass production and non-continuous individual production; cp. Piller (1997, p. 16), Piller and Schoder (1999, p. 3), and Chakravarty
(2001, p. 132). Cp. to this end also Pine (1993). 135. Cp. van Hoek and Weken (2000, p. 3). 136. Cp. Campbell and Wilson (1995, p. 14). Cp to this end also Jarillo (1993). 137. Cp., roughly, Schönsleben (2000, p. 22). 138. Cp., roughly, Thaler (2003, p. 17). See to this end also Nippa and Picot (1995), Kuhn (1995), and Turner and Thaler (1995). 139. For an embracing description of the structure, the elements and the monitoring principles of a Supply Chain, q.v. Stewens (2005). 140. Cp., for example, Hagemann (2004, p. 5), Heck (2004, pp. 1, 48), and Werner (2002, p. 16). 141. The process chain as a basis for the model can be described as follows: “The process chain is defined as a set of chronological and logical related activities performed to achieve a defined business outcome” (Beckman, 1999, p. 27). 142. Cp. Banfield (1999, p. 205) and Handfield and Nichols (2002, p. 40). 143. Cp. Kaczmarek and Stüllenberg (2002, p. 275). 144. In the general sense, the term productivity can be defined as follows: “The rate of output per unit
of input.” Transposed upon the area of Industrial Management, this leads to the following definition of the term: “In factories and corporations, productivity is a measure of the ability to create goods and services from a given amount of labor, capital, materials, land, resources, knowledge, time, or any combination of those. Since capital goods tend to decline in value and wear out, most economists distinguish between gross capital productivity (total yield) and net capital productivity, which discounts depreciation” (Mintzer, 1992, p. 20). Cp. also, for example, Schierenbeck (2003, pp. 107, 638). 145. The terms effectivity and efficiency, in addition to the differences and connections between the terms, will be dealt with more closely in para. 1.7.1. 146. Cp. Preißner (2003, p. 123). 147. Whereby cost need to be distinguished from expenses (for the varying terms q.v. Scherrer (2001, p. 629). 148. Cp. Wöhe (1984, p. 871). 149. Cp. Kuglin (1998, p. 192).
150. Measurement of cost in the post-costing, estimation of the cost in the pre-costing (note of the author). 151. Cp. Stemmler (2002, p. 176). 152. Incremental or marginal costs are defined as the manufacturing costs of each of the previously brought out (produced) unit. As long as a the total cost curve of a product or cost area runs linearly, the incremental costs for every manufactured unit are equal and represent the proportional costs or product costs respectively. The terms product costs, proportional costs and marginal costs have the same meaning. For marginal cost calculation, the terms proportional cost calculation or Direct Costing are synonymously used; cp. Schierenbeck (2003, pp. 287, 676) and QM (2004b). 153. Expenses or payments respectively represent a negative payment flow in the sense of money outlet. They are compared to respective incomes or incoming payments; cp. Scherrer (2001, p. 628). 154. Cp. Govil and Proth (2002, p. 87). For the term
cash flow see the footnote under para. 1.4.3. 155. The term Key Performance Indicator, KPI, can be defined as follows: “A statistical measure of how well an organization is doing. A KPI may measure a company’s financial performance or how it is holding up against customer requirements” (ASQ, 2004). Cp. also Sürie and Wagner (2002, p. 32). The term is to serve as a collective term for other related terms within the frame of the work submitted, for example performance attributes, metrics and performance measures, which will be dealt with in detail in the further course of the work. 156. A company’s success can be identified as the difference between results and costs or as the difference between revenues and costs. In profit and loss accounting it is evidenced by the difference between earnings and expenses over a certain period, in the form of profit and loss; cp. Wöhe (1984, pp. 873, 1021). 157. Cp. Kerzner (2003, p. 63). 158. Cp., roughly, SAP (2003, 2004, p. 2), Reiner and Hofmann (2004, p. 1) and PMG (2002, p. 1).
159. Cp. BMA (2004b). 160. Cp. Novack et al. (1995, p. 235). 161. Ibid. 162. As to the term customer satisfaction and the relevant influencing factors, cp. also Seibt (1997a, p. 19). 163. For an overview of present developments in the field of Supply Chain Cost management, q.v. Cooper and Slagmulder (1999). 164. Activity-based costing is highlighted by the fact that the allocation of general product costs in the indirect performance areas does not occur on the basis of value-measured cost drivers, but according to the tasks necessary for production (processes, activities) with given consideration to the “cost drivers” influencing the processes; cp. Scherrer (2001, p. 655). Cp. roughly also Horváth and Mayer (1989, p. 214) and Cooper and Kaplan (1991, p. 130). 165. Cp. Dekker and Van Goor (2000, p. 41). 166. Cp. Thaler (2003, p. 89). Cp. to this end also Weber (1992). 167. For an overview of operational performance
measurements as building blocks of a Management Information System (MIS), q.v. Müller-Hagedorn (1999, p. 729). 168. See under para. 1.5.3. Cp. also Novack et al. (1995, p. 235). 169. The application possibilities and particulars of a questionnaire within the framework of an examination under inclusion of performance indicators will be dealt with in detail in chap. 3. 170. Cp. Novack et al. (1995, p. 235). 171. Self-representation of the organization: “Nolan Norton Institute carries out research in the areas of business strategy, organization development and IT strategy and management. The insights resulting from this are published and used in the consulting practice of Nolan, Norton & Co. The Nolan Norton Institute was founded after the American example in 1988 and since then became a leader in investigating, creating and disseminating knowledge on the management of information age organizations. Nolan Norton Institute designs and executes research on corporate and business strategy, corporate and
business governance and management” (NNI, 2004). 172. Self-description of the organization: “KPMG was formed in 1987 with the merger of Peat Marwick International (PMI) and Klynveld Main Goerdeler (KMG) and their individual member firms. Spanning three centuries, the organization’s history can be traced through the names of its principal founding members – whose initials form the name ‘KPMG’. KPMG International is a Swiss cooperational of which all KPMG firms are members. KPMG International provides no services to clients. Each member firm is a separate and independent legal entity, and each describes itself as such” (KPMG, 2004). 173. Cp. Kaplan and Norton (1997, p. VII). For an embracing overview of the Balanced Scorecard, q.v. Fitzgerald and Moon (1996). 174. Cp. Werner (2002, p. 269). 175. Cp. to this end also Kaplan and Norton (1996a, p. 12). For the subject matter of the Balanced Scorecard, q.v. Weber and Schäffer (1999) and
Horváth and Kaufmann (1998). 176. Above all, the large Strategy Advisory Firms, such as for example McKinsey or the Boston Consulting Group (BCG), have intensively assigned this procedure within the framework of their strategy projects, cp., for instance, Koller and Peacock (2002, p. 1). Apart from this, there are however smaller consultancies which have specialized themselves in the concept’s application. One of the leading firms of this kind is the advisory company Balanced Scorecard Collaborative (BSCol), specializing in the application of the Balanced Scorecard and founded by Kaplan and Norton, the creators of the Balanced Scorecard. The business describes itself as follows: “Balanced Scorecard Collaborative is a new kind of professional services firm that facilitates the worldwide awareness, use, enhancement, and integrity of the Balanced Scorecard (BSC) as a valueadded management process” (BSCol, 2004). 177. Cp. Industry Directions (1998a, p. 5). For the term Return on Investment (ROI) see the
explanations under para. 1.3.1. For the calculation of the ROI, q.v. Preißner (2003, p. 181). 178. Cp. Kaplan and Norton (1996b, p. 75) and Werner (2002, p. 169). 179. The strategic core vision describes a company’s task, core competences, competitive orientation for achieving competitive advantages, future competitive positioning, future product range, and financial objectives (growth, profits, etc.). From this it can be deduced in which areas a business must reach top performances. The successful implementation can be interpreted from the evaluation of the respective performance on the market; cp. Geimer and Becker (2001a, p. 22). 180. Cp. Kaplan and Norton (1996b, p. 75). 181. Cp. BMA (2004a). In this context it is important to understand that the listed perspectives are not generally valid, but of company-specific nature. 182. Data Warehouses can be defined as follows: “A logically consolidated store of data drawn from one or more sources within the enterprise and/or
outside the enterprise” (Simon & Shaffer, 2001, p. 9). 183. Cp. Industry Directions (1998a, p. 5), Zeller (2003, p. 8), and Kummer (2001, p. 81). For a scientifically founded overview as to operational and strategic management and controlling approaches for the measurement of the performance capability of Supply Chains, reference is made to Erdmann’s work; cp. Erdmann (2003). 184. Cp. Werner (2000a, p. 8; 2000b, p. 14). 185. Cp. Sürie and Wagner (2002, p. 33). 186. Cp. Zimmermann (2002, p. 413). Thaler speaks in this context of the area of conflict between manufacturer/customer and supplier, cp. Thaler (2003, p. 16). 187. Cp. for example, Geimer and Becker (2001b, p. 128), Heck (2004, p. 13), and Kanngießer (2002, p. 9). 188. Self-representation of the organization: “The Supply-Chain Council was organized in 1996 by Pittiglio, Rabin, Todd & McGrath (PRTM) and Advanced Manufacturing Research (AMR),
and initially included 69 voluntary member companies. The Supply-Chain Council now has closer to 1,000 corporate members worldwide and has established international chapters in Europe, Japan, Australia/New Zealand, South East Asia, and Southern Africa with additional requests for regional chapters pending. The Supply-Chain Council’s membership is primarily practitioners representing a broad cross section of industries, including manufacturers, services, distributors, and retailers. The site is divided into a ‘public’ and a ‘member’s only’ section. Nonmembers are welcome to browse the public section information including the Supply chain Operations Reference-model (SCOR) Overview materials, IT vendors, consultants, and researchers that support SCOR, calendar of upcoming events, links to other related organizations, and general information on the association. For a nominal annual fee members have access via password to the current version of SCOR, complete contact information on all members, email access to committee dialog on
SCOR, given at Supply-Chain Council conferences, and research study results conducted by members and others under the auspices of the Supply-Chain Council” (SupplyChain Council, 2006c). 189. Cp. roughly Schönsleben (2000, p. 152) and Meyr et al. (2002a, p. 45). 190. The SCOR monitoring processes will be dealt with closely in chap. 2. 191. Cp. Schönsleben (2000, p. 152). 192. Trommer combines all the facts as follows: “Various departments are now talking the same language (…) that’s a notable achievement. The framework (the SCOR model is referred to here; note of the author) helped to break down functional silos and allowed people to look at real issues and practices holding back supply chain management improvements. It gave people the chance to look at the supply chain with customer-wide needs in mind” (Trommer, 1996, p. 59). The author quotes Vinay Asgekar, head of the Business Process Reengineering (BPR) group of Rockwell Semiconductors.
193. The term Benchmarking can be defined as follows: “The process of measuring products, services, and practices against the toughest competitors or those known as leaders in their field. The subjects that can be benchmarked include strategies, operations, processes, and procedures. The objective of benchmarking is to identify and learn ‘best practices’ and then to use those procedures to improve performance” (SQN, 2004). Cp. also Schäfer and Seibt (1998, p. 365), Schönsleben (2000, p. 95), and Schumann (2001, p. 102). See for this purpose also the explanations in chap. 3, para. 3.1.1.2. 194. Cp. Seuring (2002, p. 20) and Werner (2002, p. 24). 195. Cp. Hellingrath (1999, p. 78). 196. Cp. Welke (2003, p. 13). The subject matter was also closely discussed in a personal conversation by the author with Welke; see Poluha (2004b). 197. The term best practices can be defined as follows: 1. Exemplary solutions or methods of procedure that lead to top performances, are “best practices.” 2. The action taken in order to
identify such procedures and to use them for improvements to one’s own processes, often as an extension to benchmarking. Best practice is a pragmatic procedure. It systemizes existing experiences of successful organizations (often also competitors), users, and so on. It compares various solutions which are assigned in practice, evaluates them with the aid of operational objectives, and determines on this basis which formations and methods of procedure best contribute to the achievement of targets; cp. O’Dell and Grayson (1998, p. 167). 198. Cp. to this end Burnstine and Soknacki (1979, p. 115). BIAIT originated within the framework of an IBM research project and was further developed within IBM. The range of application comprised the process areas of planning and execution of application development, marketing planning and organization analysis. A planning approach for application development which is founded upon the BIAIT principles is the socalled Business Information Control Study (BICS). The BICS approach leads to an
immediate identification of problem areas that have a high visibility for business executives and a large potential for the realization of advantages in the usage of computers; cp. Carlton (1980, p. 2). 199. Cp. Cremer (2005, p. 35). As to the Kölner Integrationsmodell (Cologne Integration Model), see also Grochla et al. (1974). 200. See chap. 2, sect. 2.2. 201. Cp. Fettke and Loos (2003b, p. 33). 202. B2B is focused on the interactions of institutional partner among themselves, B2C on the business with end customer, and G2C on the interactions among public sector and end customers; cp., for instance, Stadler (2002, p. 15f), Kodweiss and Nadjmabadi (2001, p. 75f), and Schäfer (2002, p. 16). 203. Cp. Werner (2002, p. 113). 204. Cp. Premkumar (2002, p. 367). 205. Cp. Fettke and Loos (2003b, p. 34). 206. See to this end chap. 2, para. 2.4.1.3. 207. (Razvi, 2002, p. 3). 208. Schäfer and Seibt define competence as the
ability to be able to manufacture innovative products in the highest quality at market capable prices faster than the competition. In order to realize this, the organization’s processes must be continuously improved and be formed more effectively and efficiently by the integration of new, innovative ideas; cp. Schäfer and Seibt (1998, p. 365). In the existing context this capability, as well as the associated processes refer specifically to the Supply Chain, which is emphasized by the term SC competence. Competence in turn determines the performance. 209. Cp. Hugos (2003, p. 34). 210. Cp. Hugos (2003, p. 37). 211. Cp. Werner (2002, p. 10). For effectivity Seibt synonymously uses the term effectiveness with the following definition: “Effectiveness is the result-factor that is frequently associated with output quality” (Seibt, 1997a, p. 20). 212. Profitability is defined as earnings in relation to expenses, or results in relation to costs. In opposition to this, productivity is the amount
produced in relation to the amount of factor assignment. Profitability is therefore also described as economic efficiency, and productivity is described as technical efficiency; cp. Schierenbeck (2003, p. 593) and Bea, Dichtl, and Schweitzer (1991, p. 2). 213. Cp. FHTE (2004). For the term business success see chap. 1, para. 1.5.3. Seibt defines efficiency as follows: “Efficiency is a measure of process economy and indicates the degree to which the process is able to produce a higher value of output with lower levels of cost” (Seibt, 1997a, p. 21). 214. In the general sense, Target Costing respectively represents a customer- or market-orientated cost determination of which all relevant cost flow measures are viewed as variables; cp. Scherrer (2001, p. 660). For the term Supply Chain-based marginal costing, cp., for example, Kajüter (2002, p. 35). 215. Cp. Goldbach (2002, p. 94). 216. For the term Activity-based costing (ABC) see the explanations under para. 1.5.3.
217. Cp. Slagmulder (2002, p. 86) 218. Self-representation of the organization: “The Massachusetts Institute of Technology – a coeducational, privately endowed research university – is dedicated to advancing knowledge and educating students in science, technology, and other areas of scholarship that will best serve the nation and the world in the 21st century. The Institute has more than 900 faculty and 10,000 undergraduate and graduate students. (…) A great deal of research and teaching takes place in interdisciplinary programs (…) whose work extends beyond traditional departmental boundaries” (MIT, 2004a). 219. The study named is described by MIT as follows: “The Integrated Supply Chain Management Program (ISCM) is a consortium of noncompeting companies that was started in January 1995 by a group of faculty and staff from the Sloan School of Management and the Center for Transportation & Logistics, where the Program is currently managed. The purpose
of the program is to accelerate the implementation of supply chain management principles within the sponsor companies, and to advance the state of the art of supply chain management. The ISCM Program enables sponsors to learn about the state-of-the-art and future supply chain practices in two main ways: 1. Facilitating best-practice-sharing and exchange among sponsors. 2. Creating new supply chain knowledge through ISCM and MIT research projects” (MIT, 2004b). 220. Cp. Hoover et al. (2001, p. 7). 221. Self-evaluation of the company: “PRTM works closely with leading companies worldwide to achieve breakthrough business results. Since 1976, we’ve delivered measurable value to our clients, earning one of the highest levels of repeat business in the management consulting industry. PRTM is also a recognized thought leader and innovator. There are 14 PRTM offices and more than 400 consultants in the U.S., Europe, and Asia. Our proven methodologies have become the industry
standard. The widely used Supply Chain Operations Reference (SCOR) model was originally developed by PRTM in collaboration with Advanced Manufacturing Research (AMR) and the Supply-Chain Council (SCC). PRTM has practices in product development, supply chain and operations, customer service and support, sales effectiveness, and strategic IT management, as well as a benchmarking subsidiary” (PRTM, 2004b). 222. Cp. PMG (2002, p. 1). Cp. to this end also PRTM (2001). 223. The following three types of inventory can be differentiated in association with inventory management costs: 1. Cycle inventory: is necessary in order to fulfil product demand amongst normally planned orders. 2. Seasonal inventory: represents produced and stored products in order to satisfy the expected demand that arises due to seasonal needs. 3. Safety inventory: is necessary in order to compensate for uncertain and unpredicted demand and order cycle times. With this, four fundamental
possibilities exist for the reduction of safety inventory: Firstly, the lowering of demand uncertainty by increased prognosis exactness. Secondly, shorter cycle times, which lead to a reduced safety inventory for coverage. Thirdly, a lowering in cycle time fluctuation. And fourthly, the reduction of uncertain product availability if demand exists; cp. roughly, Hugos (2003, p. 62). 224. Cp. Hoover et al. (2001, p. 50). 225. Ibid. 226. Cp. Stemmler (2002, p. 172). 227. (Bovet & Martha, 2000, p. 43). 228. (Hughes et al., 1998, p. 183). 229. For the term shareholder, cp., for example, Schierenbeck (2003, p. 86). 230. Shareholder Value can be described as follows: “At the end of the business cycle of a company, after all debts have been paid, money remains. This money, the free cash flow, is for the shareholder or shareholders. The free cash flow is the amount of money that is left after all creditors are paid within the appropriate period.
The definition of Shareholder Value is the value of the company (firm) minus the future claims (debts). The value of the company can be calculated as the Net Present Value (NPV) of all future cash flows plus the value of the nonoperating assets of the company” (Value Based Management, 2004). Cp. to this end also Albach (2001, p. 301) and Accenture (2004, p. 2). 231. Cp. Evans and Danks (1999, p. 20). 232. For the term profitability see para. 1.3.1. 233. Cp., roughly, Schierenbeck (2003, p. 635). The invested capital plays a large role for the profitability of an investment, which will be dealt with more closely in chap. 2, para. 2.3.1. 234. See chap. 2, para. 2.1.4. 235. Cp. Friedrichs (1990, p. 50). In the present context we are dealing in analogue with problems regarding business economics and Supply Chain Management respectively. 236. For background information on the Supply-Chain Council (SCC) see chap. 1, sect. 1.6. 237. Cp. Hellingrath (1999, p. 77). 238. The process term in the present context allows
itself to be defined as follows: “A coordinated (parallel and/or serial) set of process activity(s) that are connected in order to achieve a common goal. Such activities may consist of manual activity(s) and/or workflow activity(s)” (Hollingsworth, 1995, p. 52). The term Workflow will be dealt with in sect. 2.2. 239. Cp. Supply-Chain Council (2006b, p. 2). 240. For the term Best Practice see chap. 1, sect. 1.6. 241. Cp. Bolstorff and Rosenbaum (2003, p. 11) and Geimer and Becker (2001b, p. 116). 242. For the term Key Performance Indicator see chap. 1, para. 1.5.3 (note of the author). 243. (Industry Directions, 2001a, p. 9). 244. For background information on the copmany PRTM see chap. 1, para. 1.7.1. 245. Self-description of the organization: “As an independent research analyst firm, Advanced Manufacturing Research (AMR) is committed to providing unbiased, frank analysis on the enterprise software sector, working with both the users and providers of technology to ensure that we have a clear, objective picture of a
market or industry before publishing our research. Our research and advisory services are focused on the enterprise software applications and infrastructure – including Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM)” (AMR, 2004). 246. Cp. Supply-Chain Council (2006). Cp. also Werner (2002, p. 15), Handfield and Nichols (2002, p. 67) and Meyr et al. (2002a, p. 45). 247. Cp. Kaluza and Blecker (1999, p. 21) and Kanngießer (2002, p. 4). 248. Cp. McGrath (1996, p. 1). 249. The SCC’s internet page may be found at www.supply chain.org 250. The current SCOR model, valid at beginning of 2007, represented SCOR version 8.0. 251. Cp. Supply-Chain Council (2003a, p. 2), Schäfer (2002, p. 48), and Hellingrath (1999, p. 77). 252. Cp., roughly, Bolstorff and Rosenbaum (2003, p. 154) and Hugos (2003, p. 44). 253. (Supply-Chain Council, 2006b, p. 2). 254. Cp. Supply-Chain Council (2006b, p. 2).
255. Cp. Hagemann (2004, p. 6) and Geimer and Becker (2001b, p. 118). 256. Cp. Werner (2002, p. 21) and Hagemann (2004, p. 6). 257. Ibid. 258. Cp. Hagemann (2004, p. 6) and Werner (2002, p. 21). 259. Ibid. 260. Cp. Meyr et al. (2002a, p. 48). 261. (Supply-Chain Council, 2006b, p. 10). Detraction in quality of the illustration result from extraction from the original document, which can, if necessary, be accessed from the original for better legibility. 262. Cp. Corsten and Gössinger (2001, p. 141) and Becker (2002, p. 68). 263. Cp., roughly, Klaus (2005, p. 79). 264. Cp., roughly, Geimer and Becker (2001b, p. 123), Werner (2002, p. 26), and Bolstorff and Rosenbaum (2003, p. 225). 265. (Supply-Chain Council, 2005b, p. 13). 266. Cp. Meyr et al. (2002a, p. 47) and Kanngießer (2002, p. 6).
267. Cp. Kanngießer (2002, p. 4) and Supply-Chain Council (2006b, p. 4). 268. The performance terms will be dealt with more closely in chap. 3.1.1. 269. For an automated SC planning system the term Advanced Planning System (APS) is often used; for definition of the term see chap. 1, para. 1.4.2. 270. Cp. Heck (2004, p. 13). 271. Cp. Meyr et al. (2002a, p. 50), Bolstorff and Rosenbaum (2003, p. 60), and Kanngießer (2002, p. 9). 272. Ibid. 273. Cp. to this end also Bolstorff (2004, p. 22) and Geimer and Becker (2001b, p. 128). 274. (Supply-Chain Council, 2003b, p. 9). 275. A detailed representation of the various performance terms and their connections can be found in chap. 3. para. 3.1.1.1. 276. Cp. Hugos (2003, p. 154) and Heck (2004, p. 14). 277. Cp. Supply-Chain Council (2003b, p. 4). 278. Ibid. 279. For the term Electronic Business (E-Business),
q.v. Seibt (2001, p. 11) and KPMG (2000, p. 2). 280. Cp. Supply-Chain Council (2003b, p. 11). 281. Cp. Supply-Chain Council (2005b, pp. 1, 8). 282. Cp. Supply-Chain Council (2005b, pp. 1, 9). 283. Cp. Supply-Chain Council (2005b, p. 296). 284. Cp. Supply-Chain Council (2005b, p. 319). 285. As at beginning of 2007. 286. Cp. Supply-Chain Council (2006a, p. 1). 287. Cp. Supply-Chain Council (2006b, pp. 8, 368, 434). 288. Cp. Supply-Chain Council (2006b, pp. 386, 511). 289. The meaning and application of the ISA-95 standard can be collectively described as follows: “Reconciling plant floor system information with business information is the biggest benefit manufacturers find in the ISA-95 enterprise and control integration standard, especially those in the food and beverage and pharmaceutical industries, where Parts 1 and 2 have been streamlining paper processes into real-time automated processes and skyrocketing productivity. Now, with Part 3 in the mix, these companies can reap the rewards of new
manufacturing execution systems (MES) to help meet FDA (Food and Drug Association; note of the author) requirements with tracking and traceability” (ISA, 2006b). 290. Self-representation of the Organisation: “Founded in 1945, ISA is a leading, global, non-profit organization that is setting the standard for automation by helping over 30,000 worldwide members and other professionals solve difficult problems. Based in Research Triangle Park, North Carolina, ISA develops standards; certifies industry professionals; provides education and training; publishes books and technical articles; and hosts the largest conference and exhibition for automation professionals in the Western Hemisphere” (ISA, 2006a). 291. Cp. Supply-Chain Council (2006b, pp. 9, 419). 292. “BPM is a systematic approach to improving an organization’s business processes. BPM activities seek to make business processes more effective, more efficient, and more capable of adapting to an ever-changing environment. BPM
is a subset of infrastructure management, the administrative area of concern dealing with maintenance and optimization of an organization’s equipment and core operations” (Bitpipe, 2006). 293. The internet page is: www.supply-chain.org. 294. Cp. Supply-Chain Council (2006a, p. 1; 2006b, p. 9). 295. Cp. Supply-Chain Council (2006b, p. 9). 296. By the formulations “representation of Supply Chains” and “…to describe Supply Chains…”, it is indicated that the SCOR model is a descriptive model. It does not therefore represent a forming model, with the exception that it can positively contribute to the structuring of the Supply Chain by identification and implementation of necessary measures. 297. Cp. Supply-Chain Council (2005a, p. 2). 298. Reference models can be counted as normative models which are more closely described in chap. 1, sect. 1.6. The term reference information model can also be found for these models: An information model is a model which
represents an organization’s information system. A reference information model (reference model in short) is a concrete information model for a business, which is abstracted from a single case and is used to represent a standardized section of reality. Reference models support the development of an organization’s individual information model, cp., for example, Fettke and Loos (2003a, p. 35). For Supply Chain Management implementation, company-spanning information systems are necessary in addition to the businesses’ willingness to cooperate and reveal all processes that enable a prompt exchange of information; cp. Scholz-Reiter and Jakobza (1999, p. 7). 299. Cp. Migge (2002, p. 6) and Kämpf and Trapero (2004). For an exhausting overview of business process reference models, cp. also Scheer (1997). 300. (Supply-Chain Council, 2006b, p. 3). 301. Cp. Seuring (2002, p. 20). 302. (Supply-Chain Council, 2005a, p. 1). 303. Workflows and the associated system of
monitoring these workflows (Workflow Management System) can be defined in the context of business process reference models as follows: Workflow: The computerised facilitation or automation of a business process, in whole or part. Workflow is concerned with the automation of procedures where documents, information or tasks are passed between participants according to a defined set of rules to achieve, or contribute to, an overall business goal. While workflow may be manually organised, in practice most workflow is normally organised within the context of an IT system to provide computerised support for the procedural automation. (…) Workflow Management System: A system that completely defines, manages and executes workflows through the execution of software whose order of execution is driven by a computer representation of the workflow logic”; cp. Hollingsworth (1995, p. 5). 304. Cp. Hollingsworth (1995, p. 20). 305. For the subject matter of structure, implementation and explanation of business
regulations for the determination of business transactions in and between organizations within the framework of SCM, as well as various implementation aspects of these business regulations in information systems in support of SCM, cp. Klaus (2005). 306. Cp. Holten and Melchert (2002, p. 207). 307. Cp. Heinzel (2001, p. 51) and Thaler (2003, p. 48). 308. The “Human Factor” is dealt with in chap. 6, sect. 6.1. 309. (Supply-Chain Council, 2006b, p. 4). 310. Cp. Werner (2002, p. 6) and Supply-Chain Council (2006b, p. 4). 311. The submitted work follows the processorientated definition of E-Business according to Seibt, which was introduced at the end of chap. 1, para. 1.3.1., cp. Seibt (2001, p. 11), because the submitted reference models in general and especially the SCOR model are also considered process models. 312. Cp. Fettke and Loos (2003b, p. 31). Cp. also Fettke and Loos (2003a, p. 35).
313. Cp. Seuring (2001, p. 26), Kaluza and Blecker (1999, p. 22), and Hofmann (2004, p. 1). 314. Enterprise Resource Planning (ERP) Systems follow the primary objective integrating and making centrally available in one system the often functionally adjusted solutions for diverse business areas already present within an organization, such as procurement, production, sales and so on, as well as their relevant data. ERP systems, in that sense, represent transaction systems which mainly illustrate the actual condition of a business and administrate historical data, cp. Kansky (2001, p. 205). 315. (Handfield & Nichols, 2000, p. 53). 316. (Bolstorff & Rosenbaum, 2003, p. 9). Davenport provides the following further description as to the qualitative advantages: “Hundreds of organizations (…) have begun to use the SCOR model to evaluate their own processes; software vendors (…) have begun to incorporate SCOR flows and metrics into their supply chain software packages. Some companies have already benefited greatly from a SCOR-based
analysis of their supply chain processes” (Davenport, 2005, p. 102). 317. For the term profitability see chap. 1, para. 1.3.1. 318. The term project portfolio can be described as follows: “Project portfolio management refers to the selection and support of projects or program investments. These investments in projects and programs are guided by the organization’s strategic plan and available resources” (PMI, 2000, p. 10). 319. Cp. Hughes et al. (1998, p. 97). 320. Note of the author: The statement that quality defects were able to be lowered by 100% must be considered doubtful, even if this represents the objective of concepts such as Total Quality Management (TQM). For the concept of TQM, cp., for example, Pfohl (1992) and Zink (1989). 321. Cp. Stephens (2000, p. 39). The calculation of the specified performance indicators have still to be dealt with in detail in chap. 3, para. 3.1.1. 322. Cp. Stewart (1997, p. 62). 323. Schäfer and Seibt speak, in conjunction with this, of generic benchmarking and provide the
following description: The great advantage of benchmarking exists in the discovery and integration of innovative practices, which are not found in one’s own industry. Within the framework of generic benchmarking a comparison of business processes takes place that involve diverse functions and evolves from a variety of industries; cp. Schäfer and Seibt (1998, p. 376). 324. Business parameters: “Year founded: 1968; Number of employees: 78,000; Revenues: $30.1 bn. (2003); Products and services: over 450; Fortune 500 ranking: 65; Worldwide offices and facilities: 294” (Intel, 2004). 325. Cp. Intel Information Technology (2002, p. 6). 326. According to Seibt, the management of information and knowledge processes represents one of four core tasks of organizations’ information management (4-pillar-model); cp. Seibt (1993, p. 3). 327. Cp. Intel Information Technology (2002, p. 7). 328. Background information of the company: “SAP (Systems, Applications and Products in data
conversion) was founded in 1972 by five IBM colleagues and meanwhile has about 30,000 employees. In software-development alone, worldwide altogether 8,200 colleagues are employed. Aside from their main development center at their administrative seat in Walldorf SAP holds, amongst other things, development laboratories in Palo Alto (USA), Tokyo (Japan), Bangalore (India) and Sophia Antipolis (France) as well as in Berlin, Karlsruhe and Saarbrücken. With branches in over 50 countries SAP made a profit of 7.0 bn. Euros in the financial year 2003” (SAP, 2004). 329. For information on PRTM, see chap. 1, para. 1.7.1. 330. Cp. SAP (2003). 331. Notes pertaining to the company: “In their five business segments BASF attained revenues of 33,4 bn. Euros in the year 2003. It is the strategic objective to continue to grow profitably. On five continents, roughly 87,000 employees ensure BASF’s success” (BASF, 2004).
332. Cp. SAP (2003). 333. Self-description of the organization: “Meta Group helps companies (…) as a leading provider of IT research, advisory services, and strategic consulting since 1989. Publicly traded (NASDAQ: METG) since December 1995, Meta Group offers proven models to ensure that organizations are fully prepared to optimize their use of technology, respond to demand, manage risk, seize market opportunities, and avoid expensive mistakes. Serving as each client’s personal radar screen, Meta Group monitors the IT and business world to deliver an accurate, independent view” (Meta Group, 2004, p. 1). 334. Cp. SAP (2003, p. 3). The different performance terms and their variations within the SCOR model’s context will be dealt with in detail in chap. 3, para. 3.1.1 (only metrics are spoken of in the above text for the purpose of simplicity). 335. As for example E-Business, as illustrated in chap. 1, para. 1.3.1 and sect. 1.6. 336. Cp. Harmon (2004, p. 5). Cp. also Supply-Chain Council (2006b, p. 6).
337. Cp. Supply-Chain Council (2003a, p. 2). 338. This argument can also be found e.g., with Handfield and Nichols (2002, p. 68). 339. (Harmon, 2004, p. 6). 340. Cp. Carr (2003, p. 5) and Rai et al. (2005, p. 2). 341. Cp., for example, Klaus (2005, p. 79). 342. Cp. Werner (2002, p. 26). 343. This opinion is for example also shared by Seibt; see Poluha (2003a). 344. Self-description of Hewlett-Packard (HP): “HP delivers vital technology for business and life. The company’s solutions span IT infrastructure, personal computing and access devices, global services and imaging and printing for consumers, enterprises and small and medium business. HP has a dynamic, powerful team of 142,000 employees with capabilities in 170 countries doing business in more than 40 currencies and more than 10 languages. Revenues were $73.1 billion for the fiscal year that ended October 31, 2003” (HP, 2004). 345. The Customer-Chain Operations Reference Model (CCOR) and Design-Chain Process
Reference Model (DCOR) were initiated by the SCC. In conjunction with the SCOR model they can be assigned to improve organizations’ transaction processes. The further development takes place within their own specially purposeinitiated Special Interest Groups (SIGs) – Design-Chain Council (DCC) and CustomerChain Council (CCC); cp. Supply-Chain Council (2004). 346. Cp. Harmon (2004, p. 3). 347. A merger occurs when two corporations join together into one, with one corporation surviving and the other corporation disappearing. The assets and liabilities of the disappearing entity are absorbed into the surviving entity; cp. BFI (2004). Cp. also to this end, for example, Schierenbeck (2003, p. 427). 348. Additional background information: “HewlettPackard completed the largest technology merger in history by acquiring Compaq Computer. Closure of the deal was valued at an estimated $19 billion. (…) The combined company (with a combined work force of
150,000) will be the world’s biggest maker of computers and printers, as well as the thirdlargest provider of technology services, ostensibly able to tackle IBM and Sun Microsystems, while becoming more competitive against Dell Computer” (CNET, 2002). 349. Cp. Harmon (2003a, p. 7). 350. The term thread is defined in this context as follows: “Configuring a supply chain ’thread’ illustrates how SCOR configurations are done. Each thread can be used to describe, measure, and evaluate supply chain configurations. 1. Select the business entity to be modeled (geography, product set, organization). 2. Illustrate the physical locations of: Production facilities (Make), Distribution activities (Deliver), Sourcing activities (Source). 3. Illustrate primary point-to-point material flows using ’solid line’ arrows. 4. Place the most appropriate Level 2 execution process categories to describe activities at each location” (Supply-Chain Council, 2005a, p. 17). 351. The HP-approach to the First Generation
Business Process Change presupposed that the teams must fundamentally analyse and renovate each Supply Chain process. 352. Cp. Harmon (2003a, p. 7). 353. For the SCOR model’s limitations see explanations under para. 2.3.2. 354. Cp. Harmon (2003a, p. 7). 355. The term normative model has been dealt with in more detail under chap. 1, sect. 1.6. 356. (Welke, 2003, p. 30). 357. Information on the company Intel may be found in para. 2.3.1. 358. Cp. Intel Information Technology (2002, p. 6). 359. Ibid. 360. The ensuring of support and regulation of responsibilities refers to the role of the so-called project sponsor. The meaning and tasks of this role can be described as follows: “Sponsor: The individual or group within or external to the performing organization that provides the financial resources, in cash or in kind, for the project” (PMI, 2000, p. 16). 361. Cp. Intel Information Technology (2002, p. 6).
362. Ibid. 363. The mentioned approach has strong similarity with the concept of Collaborative Planning, Forecasting and Replenishment (CPFR), which can be described as follows: CPFR enables the company-spanning cooperation of buyers and sellers for demand and supply prognoses as well as a regular update of plans that is based upon a dynamic exchange of information via the Internet and should lead to optimal customer inventory stock and reduced supplier’s inventory stock. Supplier, manufacturer and customer develop a communal business plan, which comprises upcoming events (i.e., promotions) for the synchronization of supply plans and forecasts. Businesses achieve effective advantages by lower inventory levels as well as the resulting profits, improved financial flow and lower capital investment, which should enable an organization to simultaneously improve profitability and market share; cp. Schneider and Grünewald (2001, p. 198). Cp. to this end also Hellingrath (1999, p. 83).
364. The term Postponement can be described as follows: “Postponement refers to product and supply chain design that allows for last-minute customizing of a product for delivery” (Ayers, 2002b, p. 658). Cp. also Colehower et al. (2003, p. 1). 365. Cp. Harmon (2003b, p. 1) and Intel Information Technology (2002, p. 9). 366. “The Value-Chain Operations Reference (VCOR) model describes the business activities associated with all phases of delivering maximum value to the end user of value chain networks. VCOR enables collaboration between all partners and supports the activities of project engineers, product developers, marketers, suppliers, logistics, point of sale and aftermarket service providers across the extended enterprise of a product or service” (Value Chain Group, 2005b). 367. Self-description of the organization: “The Value Chain Group (VCG) is a non-profit organization that develops and maintains the Value Chain Operations Reference (VCOR) model. The
Value Chain Group exists to promote VCOR to be the Value Chain Reference framework of preference globally, enabling next generation Business Process Management” (Value Chain Group, 2005a). 368. Cp. Value Chain Group (2005b). 369. (Intel, 2005). 370. For normative models see chap. 1., sect. 1.6. For the term value chain see chap. 1., paras. 1.3.1 & 1.4.3. 371. Self-description of the organization: “The Supply Chain Integration Office has primary responsibility within the Logistics and Material Readiness secretariat (of the Office of the Secretary of Defense) for the following: 1. To facilitate DoD Component implementation of supply chain management practice. 2. To identify business process changes which could be enabled or strengthened through the implementation of E-Business capabilities. 3. To lead the development of modern supply chain policies in DoD, including the integration of acquisition logistics and e-commerce
capabilities. 4. To develop and maintain DoD component implementation of supply chain management and end-to-end distribution capabilities required to meet 21st century deployment and sustainment requirements. 5. To develop and maintain DoD policy regarding Materiel Management and Supply Distribution, including supply depot operations, storage and issue processing. 6. To develop and maintain DoD policy for Inventory Control, including item accountability, physical inventories, reconciliations and security. 7. To develop and maintain DoD policy regarding Petroleum Resource Management. 8. To act as the DoD focal point for DLA (Defense Logistics Agency)” (DoD, 2004c). 372. 80 bn. US-Dollars represented about 65 bn. Euros, calculated as at July 2006; cp. Tiago Stock Consulting (2006). 373. The DoD offers the following explanations for the term Integrated Supply Chain Management: “Vision: The transformation of DoD’s logistics system into a fully integrated supply chain based
on assured accountability and timely, accurate satisfaction of customer needs. Mission: To lead the implementation of a modern, integrated material supply chain process that fully supports military operational requirements. To promote customer confidence in the logistics process by building a responsive, cost-effective capacity to provide required products and services” (DoD, 2004b). 374. Cp. Gintic (2002). 375. Cp. DoD (1998, p. 7–1). 376. For the various E-Business areas see chap. 1, sect. 1.6. 377. Cp. DoD (2004a). 378. (DoD, 2004a). 379. Cp. DoD (2000, p. 77). 380. Cp. DoD (2000, p. 14). 381. Cp. (DoD, no year, p. 7–1). 382. (SCE, 2004). 383. Definition of the so-called Big Five: “Traditionally, the five largest Certified Public Accountants (CPA) firms in the world. They are: Andersen; PricewaterhouseCoopers; Deloitte & Touche
LLP; Ernst & Young LLP; and KPMG” (AICPA, 2004). Due to the bankruptcy of the firm Andersen in the year 2002, one speaks today of the four remaining organizations as the Big Four. 384. Self-representation of the company: “Business Objects has incorporated the Supply Chain Operations Reference model into Business Objects Supply Chain Intelligence” (Business Objects, 2002). 385. Background information: “mi services group is an IT and management consultancy offering a wide range of technical and professional expertise, combined with broad business knowledge. (…) mi Services is an international organisation with headquarters in Reading, UK and a network of offices across the UK, USA and New Zealand. We currently employ more than 200 people worldwide, of whom the vast majority are Consultants involved in the analysis, design, development, implementation and support of software systems” (mi Services Group Ltd., 2004).
386. The applications named will be dealt with more closely in chap. 5, sect. 5.3. 387. In this context, a tool can be defined as a computer-supported system or procedure, of which at least parts are programmed and made to run on computers; cp. Seibt (2004, p. 19). 388. The illustrated electronic Balanced SCOR e-Card represents a respective further development or automation of the Supply Chain Scorecard, as shown in chap. 1, para. 1.5.4. 389. The configuration of the Supply Chain is a substantial component of the SCOR model: “SCOR must accurately reflect how a supply chain’s configuration impacts management processes and practices” (Supply-Chain Council, 2005a, p. 13). The tools introduced serve the automation of the procedure. 390. Cp. mi Services Group Ltd. (2004, p. 2). 391. For background information on PRTM, see chap. 1, para. 1.7.1. 392. Self-representation of the organization: “The Performance Measurement Group, LLC was formed in 1998 to offer a pioneering
benchmarking service in core business process areas. Our service gives participants confidential, customized benchmarking analysis online. Hundreds of companies use PMG’s benchmarking services to measure their performance relative to best-in-class companies and identify opportunities to improve their business practices and use of IT. PMG’s services are based on PRTM’s leading thinking from 25 years of work in core business process management” (PMG, 2004). 393. Cp. PRTM (2004a). 394. Self-description: “BearingPoint is one of the world’s largest business consulting and systems integration firms. (…) BearingPoint provides business and technology strategy, systems design, architecture, applications implementation, network, systems integration and managed services. (…) BearingPoint has four industry groups in which we possess significant industryspecific knowledge. These groups are Public Services, Communications & Content, Financial Services, and Consumer and Industrial
Technology. We have existing operations in North America, Latin America, the Asian Pacific region, and Europe, the Middle East and Africa (EMEA). Our principal executive offices are located at 1676 International Drive, McLean, Virginia” (BearingPoint, 2004c). 395. Cp. BearingPoint (2004a, p. 8). 396. Becker and Geimer are also of the opinion that the formation of the Supply Chain spans five areas of action: Strategy development, process formation, performance measurement, organizational development, and technology formation; cp. Becker and Geimer (1999, p. 25). 397. Cp. BearingPoint (2004b). 398. Cp. to this end BearingPoint (2003b, p. 17). 399. For business information on SAP see para. 2.3.1. 400. Cp. SAP (2003, p. 1). 401. Cash-to-cash cycle time is used to measure how long a business requires from payment of the supplier up to receipt of the invoice sum from the customer. Due to many studies, this aggregate measurement is a good evaluation measure for the efficiency of order completion;
cp. Geimer and Becker (2001b, p. 130). 402. Cp. SAP (2004, p. 2). 403. Under application of the so-called Little’s Law, the inventory equals the produced amount in daily product units, multiplied by the cycle time; cp. WordIQ (2004a). 404. The reorganization of the planning processes plays an important role within the framework of SCM. The assignment of modern software systems offers great potentials here, which cannot be made accessible by the (successive) planning concepts of conventional and respective PPS or ERP systems. Due to the introduction of so-called Advanced Planning Systems (APS), the Supply Chain’s participants are integrated by communal data and planning, and the deficits of the PPS systems have been removed as extensively as possible, especially in the capacity planning area; cp. (Rohde et al., 2000), pp. 10. For an overview of the interplay of various applications, as for example ERP, APS, etc., cp. also Ayers (2002d, p. 35). 405. Cp. SAP (2003, p. 11).
406. Additional information: “The Singapore Institute of Manufacturing Technology (SIMTech) (…) contributes to the competitiveness of Singapore industry through the development of high value manufacturing technology and human capital. It is one of the research institutes of the Agency for Science, Technology and Research” (SIMTech, 2004). 407. Cp. to this end Gintic (2000, 2002) and SIMTech (2002, 2003). 408. Cp. SIMTech (2003, p. 2). 409. Cp. Friedrichs (1990, p. 52). 410. The terms thesis and hypothesis are being used synonymously. Under the term hypothesis one understands in conjunction with empirical research a presumption which is to be verified with the aid of empirical data. Within the framework of respective quantitative or standardized social research one means above all a presumption which can be submitted to a statistical test. This presumption is mainly directed towards the fact that a connection exists between two attributes or that a
difference exists between two groups. But there are numerous further hypotheses possible, such as about a particular form of connection (linear, exponential, etc.); cp. Ludwig-Mayerhofer (2004). 411. Cp. Kromrey (2002, p. 48). 412. Whilst under the term exploration the first empirical and theoretical orientation within one research area is often understood, the present case is to be based upon Wollnik’s definition, in which exploration can generally be considered to be informational exhaustion of systematically gained knowledge for the purpose of theory formation; cp. Wollnik (1977, p. 42). 413. For a similar approach within the framework of an empirical examination, cp., roughly, Schäfer (2002, p. 91). 414. Cp. to this end also Geimer and Becker (2001b, p. 129), Meyr et al. (2002a, p. 50), Schary and Skjott-Larsen (2001, p. 18), and Grünauer, Fleisch, and Österle (2000, p. 177). 415. For the term competence or in the present case the Supply Chain (SC) competence respectively,
see the explanations at the beginning of chap. 1, sect. 1.7. 416. This interpretation is also supported by Schoder; see Poluha (2005). 417. Hypothesis-investigative examinations are carried out with the primary objective of developing new hypotheses in a relatively unresearched scientific examination area, or to create conditional terms; cp., roughly, Bortz (1984, p. 26). 418. Cp. Heck (2004, p. 15). 419. This deficit and the requirement for a scientifically-founded examination of the model’s structure resulting from it are also seen as relevant by Seibt. He assumes, consistent with the requirements of exploratory and hypotheses-testing examinations, that the submitted empirical examination is a first step in this direction which may be grasped, deepened and expanded by further studies; see Poluha (2003b). 420. Thereby the aspect of a variety of different scientific and business practice objectives is
addressed. 421. According to the so-called Freedom of Value Judgement Posit (Werturteilsfreiheits-Postulat) of empirical science, evaluations pose a problem for the scientific concept of critical rationalism from several viewpoints; cp. to this end Kromrey (2002, p. 77). The Value Neutrality Posit (Wertneutralitätspostulat) refers exclusively to the association in reasoning which describes the methodical steps with whose help a problem is to be examined. To this end and to the differentiation between the associations to discovery, reasoning, as well as evaluation and effects in research, cp. Friedrichs (1990, p. 50). 422. Cp. Bolstorff and Rosenbaum (2003, p. 49). SCE also uses the term SCORcard and defines it as follows: “The resulting SCORcard provides a direct connection to the balance sheet. Performance requirements are established with respect to your competition and are prioritized by both definitions of a supply chain – product and channel. These priorities will help in the design phase of a SCOR project. The
SCORcard also summarizes actual performance against benchmark performance with a gap analysis that defines the value of improvements” (SCE, 2004, p. 8). 423. See to this end chap. 1, para. 1.5.4. 424. Cp. Supply-Chain Council (2003a, p. 7). 425. Cp. Supply-Chain Council (2003b, p. 242), Geimer and Becker (2001b, p. 129), and SIMTech (2003, p. 3). 426. As to the problematic and meaning of the conditions of adherence to delivery, cp., for example, Geimer and Becker (2001b, p. 131). 427. For an overview of the term Management Information System as well as related terms such as Business Intelligence, etc., cp. e.g., Kemper (1999, p. 25). 428. For the terms indirect and direct costs, the previously explained terms of fixed and variable costs are also used; cp. for instance Albach (2001, p. 252) and Schierenbeck (2003, p. 233). 429. Cp. Supply-Chain Council (2003b, p. 6). See to this end also Diag. 1-2 in chap. 1 and diag. 2-4 in chap. 2.
430. Cp. Geimer and Becker (2001b, p. 128). As opposed to these authors, the author of the work at hand uses the aforesaid term Supply Chain costs instead of the originally quoted term Logistic costs. This change was necessary due to reasons of consistency resulting from the differentiation between the terms Logistics and Supply Chain made in chap. 1. 431. Cp. Sürie and Wagner (2002, p. 33). 432. Cp. Bovet and Martha (2000, p. 43). See to this end also chap. 1, sect. 1.7. 433. Cp. Heinzel (2001, p. 54), Supply-Chain Council (2003b, p. 9), and Kanngießer (2002, p. 9). 434. In this way, for example, the order completion performance measures the total number of products delivered complete and on-timely at the desired time. Beneath this performance indicator we find, amongst other things, a performance indicator which measures the backorders value. For the allocation of Performance Measures to the Level 1 Metrics and furthermore to the Performance Attributes, cp. for example, Bolstorff and Rosenbaum (2003, p. 51) and
Christopher (1998, p. 107). 435. The exact definitions and calculative formulas of the performance measures may be taken from the appendix (see sect. 2 and 3 for an overview, and sect. 4 for the respective detailed information). 436. A quantitative examination was given preference, because in the SCOR model’s case we are dealing with a relatively known subject matter in principal, and a basis for universally valid knowledge and vocabulary was to be expected; cp. Lamnek (1995, p. 17) and Schnell, Hill, and Esser (1992, p. 389). 437. Reiner and Hofmann were faced with a similar task within the framework of an empirical study and also decided to build upon the SCOR model for the following reasons: “Benchmarking methods for process improvements are mostly developed and introduced by practitioners. Many practitioners use simple techniques rather than analytical solution methods. That is why a strong demand for effective methods of analyzing benchmarking results that can be used for
design, analysis and improvement of processes exists. (…) It is important to use an adequate Performance Measurement System for empirical research. We use a widely accepted industry standard, the Supply Chain Operations Reference (SCOR) model developed by the Supply-Chain Council (SCC)” (Reiner and Hofmann, 2004, p. 2). 438. Cp. BearingPoint (2003c, p. 5). The so-called strategic triangle describes three decisive factors in competition: Costs, time and quality; cp. Thaler (2003, p. 12). The productivity is determined by the cooperation and prioritization between these three factors, i.e., it is a result of those. 439. Under the term Diagnosis Zetterberg understands the description of an object or problem within the framework of a limited number of definitions and dimensions; cp. Zetterberg (1967, p. 67). Cp. also Friedrichs (1990, p. 108). 440. In this context, a measure could be considered to be the improvement in correlation between request date, commit date and delivery date; cp.
Supply-Chain Council (2003a, pp. 15, 20). 441. The shortcomings mentioned are to be clarified by several examples, whereby the list would enable itself to be further continued: Paul uses the terms Metrics and KPIs synonymously; cp. Paul (2002, p. 19). Christopher applies the term integrated supply chain metrics for the measures described by the Supply-Chain Council as Performance Attributes; cp. Christopher (1998, p. 106) and Supply-Chain Council (2003a, p. 8). Werner states that the respective procedures in the standardized Supply Chains are measured by metrics within the SCOR model; cp. Werner (2002, p. 16), whilst Thaler uses the term measures in the same connection; cp. Thaler (2003, p. 48). And Zeller applies the terms reference numbers, measurement numbers and metrics synonymously, and uses the term reference number class to mean performance attributes; cp. Zeller (2003, pp. 12, 19). 442. See Poluha (2004a). 443. Cp. Ossola-Haring (2003, p. 167). 444. Cp. Backhaus, Erichson, Plinke, and Weiber
(2003, p. 355). Possibilities of immediate collection and analysis of statistical data for such hypothetical constructions in conjunction with the Meta theses will be dealt with later in chap. 3 (see para. 3.4.2.3) as well as chap. 4 (see para. 4.3.2). 445. Cp. Preißner (2003, pp. 178, 195). See to this end also the explanations under chap. 2, para. 2.1.4. 446. Ibid. 447. Cp. Schumann (2001, p. 105). See also chap. 1. para. 1.5.3. 448. Schäfer and Seibt describe the issue as follows: It is not the metrics (stating how large the identified distance from other companies) that stand in the foreground of process benchmarking, but rather the practices which lead to exemplary results. Only by knowledge of the superior practices can changes be initiated which lead to the improvement of ones own business processes; cp. Schäfer and Seibt (1998, p. 27). 449. See to this end also the statements in chap. 1, sect. 1.7 for the Supply Chain drivers and
Supply Chain competences. 450. Cp. Ayers (2002b, p. 657). 451. Cp. Geimer and Becker (2001b, p. 129). 452. Cp. Ayers (2002b, p. 657). 453. Background information: “Headquartered in Round Rock, Texas, Dell is a premier provider of products and services required for customers worldwide to build their information technology and Internet infrastructures. Dell’s climb to market leadership is the result of a persistent focus on delivering the best possible customer experience by directly selling standard-based computing products and services. Revenue for the last four quarters totalled $45.4 billion and the company employs approximately 50,000 team members around the globe” (Dell, 2004). 454. The cash-to-cash cycle time shows the time required by a business until a certain amount has flowed back into it that was spent upon material procurement; cp. roughly, Supply-Chain Council (2003b, p. 262). 455. Cp. Ayers (2002b, p. 659). 456. See to this end also chap. 1, sect. 1.7.
457. Cp. Hoover et al. (2001, p. 9). 458. Cp. Hugos (2003, p. 37). 459. Cp. Meyr et al. (2002a, p. 52). 460. Cp. SIMTech (2003, p. 10). 461. Ibid. 462. Cp. PMG (2002, p. 2). 463. (Schary & Skjott-Larsen, 2001, p. 18). Cp. to this end also Grünauer et al. (2001, p. 177). 464. For the method of procedure, cp., for example, Rochel (1983, p. 143). 465. Basically a differentiation can be made between the following types of variable relationships: Deterministic (if X, then always Y) or statistical (if X, then probably Y); reversible (if X, then Y; if Y, then X) or irreversible (if X, then Y; if Y, then not X); sufficient (if X, then always Y) or conditional (if X, then Y, but only if Z is present); necessary (if X, then and only then Y) or substitutable (if X, then Y; but if Z, then also Y); cp. Zetterberg (1967, p. 82) and Friedrichs (1990, p. 105). 466. For an overview of the approaches represented in the following, as well as further approaches in
this respect, cp., for example, Barker (1993, p. 4), Kiesel (1996, p. 55), Gleich (1997, p. 358), Link (1998, p. 185), and Erdmann (2003, p. 59). 467. Cp. Rummler and Brache (1990, p. 143). 468. Cp. Neuhäuser-Metternich and Witt (1996, p. 266). 469. Cp. Sellenheim (1991, p. 51). 470. Cp. Beischel and Smith (1991). A very similar approach is followed by Taylor and Convey (1993, p. 23). 471. If one attempts to systematize the possible objective ideas which can influence the objective functions of a business, a fundamental differentiation between monetary and nonmonetary objective ideas is available. Under the term monetary (or financial) objective ideas, one understands objectives that enable themselves to be measured in monetary units, i.e., the striving towards profit and turnover. Further monetary objective ideas are for example ensuring liquidity and maintenance of capital. In opposition to this, the non-monetary objectives contain figures reflecting the achievement of certain growth or
productivity targets, the striving for increase in market share, or the insurance of particular requirements in quality; cp. Wöhe (1984, p. 110). 472. See to this end chap. 2, sect. 2.2. 473. Cp. Fisher (1995, p. 195). 474. Cp. Fickert (1993, p. 208). 475. Cp. Fisher (1995, p. 196). 476. Cp. Welz (2005, p. 6). 477. Cp. Greene and Flentov (1990, p. 53). 478. The Return on Assets (ROA) is identified by setting the net profit against the total assets. Because the asset profitability represents the return of interest on the capital invested in assets into the business, it is more capable of testimony than the Return on Equity (ROE); cp., for example, Wöhe (1984, p. 48). An exact description of the distribution can be taken from chap. 4, sect. 4.1. 479. Cp. Berliner and Brimson (1988, p. 161). 480. Cp. Epstein and Manzoni (1997, p. 29; 1998, p. 181). 481. Cp. Sellenheim (1991, p. 52).
482. Cp. Beischel and Smith (1991, p. 25). A very similar method of procedure is described by Taylor and Convey (1993, p. 22). 483. Cp. Hendricks et al. (1996, p. 21). 484. Cp. Edvinsson and Malone (1997, pp. 68, 82). For information about the Skandia-Navigator, q.v. Skandia (2005). 485. Cp. Brokemper (1995, p. 242) and Werner and Brokemper (1996, p. 165). 486. Cp. Keegan, Eiler, and Jones (1989, p. 45). 487. Cp. McNair, Lynch, and Cross (1990, p. 28) and Lynch and Cross (1995, pp. 66, 72). 488. See chap. 1, para. 1.5.4. 489. Cp. Kaplan and Norton (1992, p. 71). With reference to the Balanced Scorecard see also the statements in chap. 1, para. 1.5.4. For a similar categorization, cp. also Horváth and Lamla (1995, p. 82) and Wuest and Schnait (1996, p. 101). 490. Jacob roughly states that at the end of 1999, about 60 percent of all US-American businesses had already introduced the BSC; cp. Jacob (1999, p. 44).
491. Cp. Hronec (1996, pp. 17, 32). 492. See to this end chap. 1, para. 1.5.4. 493. Cp. Heinzel (2001, p. 53), Hagemann (2004, p. 10) and Heck (2004, p. 13). 494. See chap. 3, para. 3.3.5. 495. Cp., roughly, Bolstorff and Rosenbaum (2003, p. 49). 496. The 3Com Company has for example developed and assigned a so-called Supply chain Scorecard for 3Com, which is based explicitly upon the SCOR model, cp. Cohen and Russel (2005, p. 213). 497. As at beginning of 2007. 498. See chap. 6, sect. 6.2. 499. For the general method of procedure for hypotheses formulation, cp., for example, Friedrichs (1990, p. 103). 500. As to the derivation of the fundamental coherence of effects see chap. 3, para. 3.1.2.1. 501. Ibid. 502. Ibid. 503. As to the derivation of the fundamental coherence of effects see chap. 3, para. 3.1.2.1.
504. As to the derivation of the fundamental coherence of effects see chap. 3, para. 3.1.2.2. 505. Ibid. 506. Only an indirect coherence exists between the performance measures in so far as a low inventory level ties less capital, which conceals in itself a lower risk of inventory obsolescence. It can, however, lead on the other hand to insufficient delivery readiness. This in turn can result in dissatisfied customers and have as a consequence that the cash-to-cash cycle time increases; cp. Schumann (2001, p. 99). The assumption of such derived customer-orientated behavioral patterns within the Supply Chain’s context are not accommodated by the SCOR model and are therefore not to be researched any further in the submitted work. For a coherence between these two fields of research, Supply Chain Management and customer behavior, see e.g., Best’s fundamental work on deterministic demand fluctuations in Supply Chains; cp. Best (2003). 507. See the statements under the previous footnotes,
which may be used in analogue in the present case. 508. As to the derivation of the fundamental coherence of effects see chap. 3, para. 3.1.2.3. 509. Ibid. 510. Ibid. 511. The cash-to-cash cycle time represents by experience a good measure for the evaluation of the efficiency in the order transaction process. However, no (direct) dependency allows itself to be derived between the order transaction performance and the asset turns; cp. Geimer and Becker (2001b, p. 130). 512. See the statements under the previous footnote, which may be applied in analogue in the present case. 513. Ibid. 514. Ibid. 515. As to the derivation of the fundamental coherence of effects see chap. 3, para. 3.1.2.3. 516. The cash-to-cash cycle time represents by experience a good measure for the evaluation of the efficiency in the order transation procedure.
However no (direct) dependency allows itself to be derived between the order transaction performance and the asset turns; cp. Geimer and Becker (2001b, p. 130). 517. Cp. Schäfer and Knoblich (1978, p. 248). 518. Cp. Klingemann and Mochmann (1975, p. 178). 519. Cp. Rogge (1981, p. 49). 520. Because in the case of a representative survey the examination is in any case supposed to provide characteristics about the population, the selection of the sample must take place in such a way as to enable an exact and safe transposure of the results onto the entire mass. That is the case if the sample is representative, whereby representability is considered satisfactory with regards to the characteristics relevant to the examination. A partial mass is representative if it conforms to the total mass as far as the distribution of all characteristics of interest is concerned, i.e., represents albeit a small, but true-to-reality illustration of the population; cp. Berekoven, Eckert, and Ellenrieder (1987, p. 42) and Kromrey (2002, p.
257). 521. Cp., roughly, Selg and Bauer (1976, p. 87). 522. In both cases the partial mass forming the survey’s basis should represent a restricted, however proportionally true illustration of the population (so-called pars pro toto); cp. Monka and Voß (2002, p. 299). 523. As far as methods of non-probability sampling are concerned, those are often inapplicable for statistically controlled scientific statements. In the case of random sampling methods, the extraction of samples is completely arbitrary. Simple sampling and multi-stage sampling can be named in this connection; cp. Schäfer and Knoblich (1978, p. 255) and Friedrichs (1990, p. 130). 524. Hammann and Erichson thereby subdivide further as follows: 1. Types within random sampling are simple random sampling, stratified sampling, cluster sampling and multi-staged sampling. 2. Non-probability sampling (or also procedures of purposive sampling) are e.g., quota sampling, snowball sampling and sampling of typical cases;
cp. Hammann and Erichson (1990, p. 108) and Ludwig-Mayerhofer (2004). 525. Cp. Berekoven et al. (1987, p. 51). 526. For the principle of the representation of similar type cases, cp., roughly, Kromrey (2002, pp. 257, 273). 527. See also to this end the questionnaire in the appendix, sect. 1. 528. The unsystematic surveys, also called opportunistic surveys, mainly allow themselves to be completed in conjunction with other business procedures. The systematic surveys have an exactly defined examination objective and must be designed and prepared according to a plan. A division between a unique survey and a repeated survey is given by the characteristics of frequency and consistency of the survey. In the case of single surveys, also known as adhoc surveys, a very particular topic is examined. They therefore have a kind of diagnostic character, but also come under scrutiny to clarify relationships before important decisions are made; cp. Schäfer and Knoblich (1978, p.
247) and Friedrichs (1990, p. 192). 529. Cp. Hammann and Erichson (1990, p. 78). 530. Cp. Bortz (1984, p. 180) and Berekoven et al. (1987, p. 112). 531. For an example of an online questionnaire see section 5 of the appendix. 532. See to this end also the questionnaire in section 1of the appendix. 533. This topic will be reiterated in connection with the course of the examination in para. 3.4.1.1. 534. Cp. Schnell et al. (1992, p. 367). 535. The so-called scale question is an exemplary subform of the closed question. Here the persons interviewed are supposed to express their opinion about the intensity of a factual situation, which are provided in (verbally formulated) staged categories or as a continuum. In this way, an ordinal measurement of amounts or frequencies is enabled. Alternative or scaled questions are also described as selection questions, since only one answer is possible. The simplest form of alternative question is the YesNo Question. In the case of the so-called
catalogue questions, the selected person receives a number of qualitatively varying answer possibilities, which allow themselves to be allocated to a continuum (multiple-choice). As soon as they exclude each other, selective questioning is the case. Occasionally, more than one answer is permitted, one speaks in this case of multiple selection questions. In all the above mentioned cases, the suggested answers can take the form of catch-words or whole sentences; cp. Müller-Böling and Klandt (1996, p. 42) and Friedrichs (1990, p. 236). 536. This refers to the handy format, the clear and yet optimal use of space for the questions, and the reservation of enough free space for the protocolling of the answers (as long as the principle of multiple-choice is not followed); cp. Schäfer and Knoblich (1978, p. 294). 537. The complete questionnaire can be taken from the appendix (see sect.1), in addition to an example of the online data entry screen (see sect. 5). 538. For information regarding the consultancy BearingPoint, see chap. 2, para. 2.4.2.3.
539. As to the changes since SCOR version 4 see chap. 2, para. 2.1.5. 540. See to this end the statements under para. 3.3.2. 541. Guß and Walther for example quote a study conducted on Supply Chain Management in Germany and Switzerland by the business consultancy Deloitte Consulting in cooperation with the Fachhochschule (University of Applied Sciences) of Braunschweig/Wolfenbüttel in Germany in the period between 1999 and 2000. Within the framework of the study, around 70 questionnaires were finally evaluated and interpreted, whereby no further differentiation according to region, industry, business size and so on, were undertaken; cp. Guß and Walther (2001, p. 159). The study was identical to the work submitted with respect to the radius of the random sampling, as well as the selection method. For a similar study carried out in the North American region, cp. Deloitte (2000, p. 2). 542. A description of the exact distribution is repeated in chap. 4, sect. 4.1.
543. Ibid. 544. For the division of companies according to size classes according to HGB (German Commercial Code), cp. Schierenbeck (2003, p. 37). A description of the exact distribution is repeated in chap. 4, sect. 4.1. The differences in numbers result from the fact that in the case of the above listing, both criteria for the allocation to a size class (by revenue and number of employees) had to be simultaneously fulfilled. 545. For the term Return on Assets (ROA), see the explanations under the corresponding footnote in chap. 3, para. 3.1.2.5. 546. A popular suggestion for the objective ROA for industrial businesses lies on average around 10 percent; cp. Controlling Portal (2005). In accordance with this for example a rough definition can take place: Companies with a negative ROA are “losers”, such with a positive ROA up to 10 percent are “average”, and companies with a ROA larger than 10 percent are “winners”; cp. Aktien-Portal (2005). The range should accommodate for industrial or
regional fluctuations; cp. Deutsche Bundesbank (1997, p. 35). 547. The information results from research conducted by the author in July 2006. 548. Cp. Hinkelmann (2003, p. 14) and Supply-Chain Council (2005a, p. 21). 549. This is in accordance with the test concepts represented by methodicians such as Rochel, by which – in the sense of a hierarchical structure – in the first instance statements as to the general suitability of model images are to be extricated (main effects); cp. Rochel (1983, p. 118). Adjustment was also strived for in the work at hand. All the same, in order to be just to the exploratory character of the work, the possible explanatory value of the additional variables – i.e., sales volume and number of employees – was examined. Variables with a very heterogeneous structure, e.g., industryaffiliation, were not suitable for this observation, because the respective case number would have been too low. Variables with certain strongly outweighing characteristics which – due to this
fact - did not allow a sufficient differentiation as in the case of Return on Assets (ROA), were also only restrictedly suitable. 550. This does not make exempt from the necessity to deepen the examinations of restriction and particularities of the model image by means of further discriminating parameters in new and, if necessary, specific areas (see to this end also the statements within the following in addition to the notes in chap. 6, sect. 6.3). 551. For the various Supply Chain strategies, cp., for example, Werner (2002, p. 64). For the term mass customization see the explanation in chap. 1, para. 1.4. 552. Cp. Supply-Chain Council (2005b, p. 2). 553. See to this end the explanations in chap. 2, para. 2.1.3. 554. See to this end also the aforementioned notes on the subject of test concepts in context with the differentiation by strategy types and industryaffiliation supported by Rochel. 555. The company size based upon the aforementioned three-stage classification by employee numbers
and revenues according to the HGB (German Commercial Code) lead in most cases to identical allocation (see the detailed descriptive table in chap. 4, sect. 4.1). In the case of the Return on Assets it is to be restrictively noted that this lay in the “average” area between 0 and 10 percent for the greater majority of companies and was therefore only conditionally applicable for the discrimination; see to this end in the same way the descriptive table illustrated in chap. 4, sect. 4.1. A differentiation according to the respective business area or region was not suitable as a discriminating variable, because businesses in North America dominated with roughly three quarters of the sample, and the rest of the companies stemmed from a variety of regions. The SCOR model positions itself above this in a similar way to industry-affiliation, i.e. it follows the intention of staying on one level, upon which region specific factors are not taken into consideration; cp. Supply-Chain Council (2006b, p. 2). 556. For background information on BearingPoint, see
chap. 2, para. 2.4.2.3. 557. A method can be defined as a systematic procedure or rules applied to complete a number of activities in order to achieve certain structural objectives. A questionnaire or survey can be seen as a method in this sense. In opposition to this, a process represents a more refined or mature method which regulates the approach to the achievement of particular structural objectives down to the level of the individual stages of work; cp. Seibt (2004, p. 19). 558. Cp. BearingPoint (2003b, p. 4). 559. Cp. BearingPoint (2003b, p. 21). 560. As to the problematic of the return quota, cp., for example, Bortz (1984, p. 184) and Friedrichs (1990, p. 241). 561. The questionnaire and the exact questions may be taken from the appendix (sect. 1). 562. The associated Key Performance Indicators (KPI) and their calculation may be taken from the appendix (sect. 4). 563. For an example of representation of the results see sect. 5 of the appendix.
564. For the varying graphical possibilities of illustration, cp., for example, Wöhe (1984, p. 1245). 565. Cp. BearingPoint (2003b, p. 26). 566. Cp. Kromrey (2002, p. 405). 567. Cp., roughly, Friedrichs (1990, p. 54). 568. For this purpose refer to chap. 5 and chap. 6. 569. Cp., for example, Berekoven et al. (1987, p. 162). 570. For further information as to the product range in general and in particular SPSS for Windows, cp., roughly, SPSS (2003, p. 2; 2004b). 571. With respect to the application of programs for data analysis, such as SPSS, cp., for example, Allerbeck (1972). For the application of SPSS in particular, cp., for example,Bühl and Zöfel (2002) and Backhaus et al. (2003, p. 15). 572. Cp. to this end Bortz (1984, p. 534), Friedrichs (1990, p. 136), Kromrey (2002, p. 424), Voelker, Orton, & Adams (2001, p. 23) and LudwigMayerhofer (2004). As to the procedures of statistical data evaluation, cp. also Ehrenberg (1986). 573. No further consideration is given here to the
median as, above all, a suitable measure of central tendency for ordinal scales. 574. Measures of location are supposed to give information as to where the focal point of a onedimensional data package lies. They are therefore also known as the Measures of central tendency; cp. Monka and Voß (2002, p. 98). 575. During a statistical test, a so-called null hypothesis is set-up which generally states that the postulated correlation or difference does not exist. A test statistic is calculated which confirms whether a connection or difference found within the data is compatible with the null hypothesis. If the test statistic exceeds a particular, previously determined value, the null hypothesis is rejected and the actual research hypothesis, i.e., the alternative hypothesis, is valid as tentatively not proven to the contrary; cp. Voelker et al. (2001, p. 62). 576. In addition to the Type I Error, there is a Type II Error which describes the erroneous retention of a null hypothesis. It therefore reflects the risk of retaining a null hypothesis, even though in
actuality the alternative hypothesis is applicable; cp. Ludwig-Mayerhofer (2004). The type II error is not given any consideration within the further course of the work. 577. The letter P describes the confidence level (Probability), with which the hypothesis is supposed to be accepted as correct or applicable. The probability of error is then calculated as P(a) = 100% - P; cp. Gottwald (2000, pp. 51, 61). 578. The covariance describes the correlation between two metrical characteristics. It is identified by the fact that for each value of both variables, the deviation from the respective arithmetic mean is calculated (by subtraction of the same). For each case, the product of these two deviations is formed, the products added together and then divided by N-1 (where N represents the number of cases). In conjunction with this, it is to be noted that the calculation of the covariance is only meaningful with metrical variables; cp. Backhaus et al. (2003, p. 340). 579. As to the approach, cp., for example, Rochel
(1983, p. 143). 580. Cp., for example, Bortz (1985, p. 156) and Friedrichs (1990, p. 389). 581. Cp., for example, Ludwig-Mayerhofer (2004). 582. Inferential Statistics, also known as investigative, inductive or conclusive statistics, deal with the question as to how the results of a sample, i.e. a choice of examination units can be transposed upon a population from which the sample stems; cp. Monka and Voß (2002). 583. Descriptive Statistics deal with measurements to characterize data. They have the task of answering such questions as “how can the central tendency of a data package be characterized in the same way as the diffusion?” or “ how can correlations characterized between two or more variables be packaged like data?”; cp. Gottwald (2000, p. 4). 584. For the various possibilities of graphical illustration of statistical results, cp., for example, Voelker et al. (2001, p. 8) and Wöhe (1984, p. 1245). 585. Cp., roughly, Ehrenberg (1986, p. 48). For further going explanations as to the procedures used for
the evaluation and statistical principles, cp. Cramer (1999). 586. For reference to such a problematic, cp., for example, Bortz (1985, p. 156). 587. Cp. Berekoven et al. (1987, p. 162). 588. For example, it would play a role during a group of thesis tests whether all the theses lie within the totally unsystematic R-range at an absolute value of 0.00 or all within the model-conformant range with a “near” miss of the significance criterion. If the significance criterion was exclusively applied the conclusion would, though, be the same in both cases, namely that of unsystematic diagnoses. Even in the latest business administration essays whose hypotheses investigations are mainly correlatively directed a similar differentiation is quite positively undertaken by unequivocal hypotheses-conformant and hypothesestendencial diagnoses; cp. to this end Hannappel (2005, p. 129). 589. See to this end chap. 4, para. 4.2.3.3 and chap. 5, para. 5.1.1.
590. For an example of the assignment of the stated method see roughly Madeja and Schoder (2003, p. 4). For further going explanations of Structural Equation Models (SEM), cp. also Byrne (1998) and Kline (1998). 591. With reference to the AMOS procedure, cp., for example, Byrne (2000) and SPSS (2004a, p. 142). 592. Cp. Backhaus et al. (2003, p.11) and SPSS (2004c). 593. LISREL stands for Linear Structural Relationships. With reference to the LISREL model, cp., for example, SSI (2000) and Hayduk (1987). 594. Cp. SSI (2004). 595. Cp. Backhaus et al. (2003, p. 334). For the subject matter of applicability of structureanalytical procedures, cp. also Hoyle (1995) and Schumacker and Lomax (1996). 596. See chap. 4, sect. 4.3. 597. The stability index, also known as the Determination Coefficient, measures so to speak the compatibility quality of the regression
function to the empirical data (goodness of fit). The residual measures build the basis for this, i.e., the deviations between the values observed and the values estimated; cp. Gottwald (2000, p. 114). 598. The Adjusted-Goodness-of-Fit Index (AGFI) additionally takes into consideration the number of degrees of freedom in comparison to a socalled neutral model. The degree of freedom (df) is thereby the submitted number of data (N) within a row of data, lowered by 1. The number of degrees of freedom represents the number of independent measurement values in a row of data. It must be noted that in the case of a structure-analytical method of approach, the degrees of freedom are calculated according to a more complex scheme (case number- and variable-related) than the conventional statistics referred to, for example, within the framework of variance analysis; cp. Gottwald (2000, p. 25). 599. Cp. Backhaus et al. (2003, p. 374). 600. Cp. Friedrichs (1990, p. 53). 601. Cp. Gottwald (2000, p. 3).
602. Cp. to this end Kromrey (2002, p. 405). 603. The aspect of compatibility of theoretical statements and empirical reality (in the present case more exactly: business management and corporate practice respectively) will be returned to in chap. 5, para. 5.1.3 within the context of proximity to truth and the correspondence theory. 604. The mitigation of this risk in the examination being implemented here has already been previously discussed (see chap. 3, para. 3.3.5). 605. For a detailed list of various types of artifact sources during the construction of testing procedures and their application, reference is for example made to Wottawa (1980, p. 208). 606. Cp. Kromrey (2002, p. 359). Cp. also Alemann (1977, p. 209). 607. In this context see also the explanations in chap. 3, sect. 3.3.2. 608. See to this end chap.C, para. 3.3.3. 609. This conclusion also has implications for the discussion of theses which are not able to be confirmed. These types of “non-confirmability”
can arise due to (a) artifacts or respective lack in statement strength of the data or (b) an insufficient coverage of company reality by the not so extensively examined SCOR model illustrations (i.e., content changes or necessity for expansion of the model illustrations). In the work at hand, both possibilities were considered, whereby the position of origin was that – under critical observation of the indicated restrictions (see to this end also the explanations in chap. 3, paras. 3.3.2 & 3.3.5) – the validity of the data is present and must therefore outweigh the discussion about content. 610. For the term interaction within statistical methodology and for the various investigative procedures, cp., for example, Rochel (1983, p. 60). 611. In the sense of critical rationalism of research logic, respective statements or hypotheses must principally be able to fail by experience, i.e., be “falsifiable” (falsification criterion); cp. Popper (1989, p. 15). In accordance with this the term confirmed can be equalled to tentatively not
falsified. 612. The interpretation of “meaningfulness” of a correlation coefficient depends substantially upon the content aspects of an empirical survey. Voelker et al. describe the connection as follows: “Whether a correlation of a given magnitude is substantively or practically significant depends greatly on the phenomenon being studied” (Voelker et al., 2001, p. 101). Therefore in the submitted case and in the face of a level of R = 0.30, substantial correlations could be assumed for graphical illustration purposes which were significant on the “advanced” P(a)-level. A critical reflection of the correlation-analytical findings of the investigation will in any case be undertaken at the end of the chapter within the context of the discussion on possible errors and interfering influences. 613. Calculated based on BearingPoint (2003f). 614. Ibid. 615. Calculated based on BearingPoint (2003f). For the classification according to HGB (German
Commercial Code), cp. Schierenbeck (2003, p. 37). The values quoted were calculated by means of the exchange rate as at July 2006; cp. Tiago Stock Consulting (2006). 616. Calculated based on BearingPoint (2003f). For the classification according to HGB (German Commercial Code), cp. Schierenbeck (2003, p. 37). 617. Calculated based on BearingPoint (2003f). For the calculation of the Return on Assets (ROA), see chap. 3, para. 3.3.5. 618. UoM abbreviation for the Unit of Measure. 619. x abbreviation for Arithmetic mean. 620. s abbreviation for Standard deviation. 621. Min abbreviation for Minimum. 622. Max abbreviation for Maximum. 623. V abbreviation for Variation range. 624. By rounding up or down. 625. Ibid. 626. Ibid. 627. Ibid. 628. Ibid. 629. Calculated based on BearingPoint (2003f).
630. By rounding up or down. 631. Calculated based on BearingPoint (2003f). 632. Due to concepts of displacing inventory management and the associated costs, as for example the Vendor Managed Inventory (VMI) previously mentioned. 633. By rounding up or down. 634. Calculated based on BearingPoint (2003f). 635. By rounding up or down. 636. Calculated based on BearingPoint (2003f). 637. Correct extreme values due to various company adjustments. 638. Ibid. 639. Calculated based on BearingPoint (2003f). 640. As to the Product-Moment Correlation (PMCorrelation) and the associated Bravais Pearson Correlation Coefficient (R), see the explanations in chap. 3, para. 3.4.2.2. 641. N represents the number of total cases that have been examined, i.e., the sample size. 642. R represents the Bravais Pearson Correlation Coefficient which has been explained in detail in chap. 3, para. 3.4.2.2.
643. P(a) stands for the probability of error and is calculated as P(a) = 100% - P, with P for the confidence level (Probability); for detailed explanations see chap. 3, para. 3.4.2.2. 644. M abbreviates the SCOR model group that is associated to the respective coherence. In this conjunction, the following classifications are differenciated – analogues to the structure of the developed theses: I-P = Intra-Performance Attribute, I-C = Intra-Competence, I-CP = Inter-Competency/Performance Attribute; for detailed explanations, see chap. 3, para. 3.1.2). 645. For the differing cases of variable relationships see the respective footnote under chap. 3, para. 3.1.2.4. 646. The abbreviation n in this as well as the following graphics stands for number as a percentage of the respective group (as opposed to the total amount of examined cases represented by the capital letter N). 647. With this grouping – as also in the case of the following illustrations – a similar occupation was strived for to the “detriment” of similar interval
widths. For detailed indications as to the grouping and determination of the interval width or the requirement for balance, cp. also Bortz (1985, p. 35). 648. The variance clarification of the criterion variable percentage of purchase orders received on time and complete was visibly increased into a multiple regression by the introduction of the respective predicates company revenue or number of FTE. More simply expressed, this indicates a more meaningful correlation for the larger companies whereby, however, criteria of statistical significance were not achieved (see to this end also the respective notes as to the examination of the incremental function under thesis 1). 649. The parameter on-time deliveries was lowered to the ordinate here for reasons of clarity. 650. The variance clarification of the variable percentage of purchase orders received on time and complete was visibly increased into a multiple regression by the introduction of the respective predicates company revenue or
number of FTE. This indicates a more meaningful bivariate (here negative) correlation for the larger companies, whereby however criteria of statistical significance were not achieved (see to this end also further going notes as to the examination of the incremental function under thesis 1). 651. Here the variance clarification of the criterion variable percentage of purchase orders received on time and complete was also visibly increased into a multiple regression by the introduction of the respective predicates company revenue or number of FTE. In this respect, a more meaningful (negative) bivariate correlation is again reflected for the larger companies (see to this end also further going notes as to the examination of the incremental function under thesis 1). 652. The variance clarification of the criterion percentage of available inventory material presented itself – in a model-conformant direction – visibly more important for companies with smaller revenue than for larger companies.
A “non-significant” effect was therefore present in the case of multiple regression (see to this end also the respective notes as to the examination of the incremental function under thesis 1). 653. The variance clarification of the criterion percentage of purchase orders received on time and complete was visibly increased into a multiple regression by the introduction of the respective predicates company revenue or number of FTE. This indicates a more unambiguous bivariate (in this case negative) correlation for the larger companies (see to this end also notes as to the examination of the incremental function under thesis 1). 654. By the inclusion of the predicate Return on Assets (ROA) into a multiple regression it was possible to better highlight the above-mentioned coherence. More simply formulated, this indicates a more unequivocal correlation for the companies with an increased ROA, whereby however criterion of statistical significance were not reached (see to this end also further going notes as to the examination of the incremental
function under thesis 1, which may be applied in analogue). 655. See sect. 4.2 of chap. 4. 656. In this case the parameter on-time deliveries was lowered to the ordinate for reasons of clarity. 657. See to this end sect. 4-2 of chap. 4. 658. By rounding up or down. 659. Or respectively in the sense of the critical rationalism of research logic: Which theses (tentatively) do not have to be rejected? In the following therefore, confirmed means the equivalent to tentatively not rejected (see to this end also the comments at the beginning of sect. 4.1). 660. As more closely explained in chap. 2, para. 2.3.2, sales and marketing (demand generation), research and technology development, product development and partially after-sales customer service fall beneath this; cp. Supply-Chain Council (2005a, p. 3). 661. As to the detailed examination results see para. 4.1.1 of chap. 4. 662. As to the detailed examination results see para.
4.1.2. 663. As to the detailed examination results see para. 4.1.3. 664. As to the strategic square in the context of Supply Chain Management, cp. Ellram and Cooper (1990) and Weber, Dehler, and Wertz (2000, p. 264). See to this end also the respective explanations in chap. 1, para. 1.3.1. 665. Cp. Fleischmann, Meyr, and Wagner (2002, p. 76). See to this end also the Supply Chain Matrix with the two dimensions of Planning horizon and Supply Chain process by Rohde et al., whereby in the present context the Supply Chain process is above all important; cp. Rohde et al. (2000, p. 10). 666. Cp. Norek (1999, p. 381), Raman (1999, p. 174), and Chakravarty (2001, p. 372). 667. Cp. Hugos (2003, p. 96). Copacino defines a socalled Customer Service Pyramid, within which the reliability of the Supply Chain purely represents the basis upon which the Resilience and Creativity or respective Innovation build with the associated service elements; cp.
Copacino (1997, p. 74). 668. Cp. Christopher (1998, p. 12). The same direction is indicated by a collective research project implemented by the Institute for Supply Chain Management of the University of Münster in Germany and the consultancy McKinsey in the year 2003, in which Supply Chain Management was examined by 40 of the 74 largest German consumer goods manufacturers and 18 of the 40 largest German retail companies. During this, it became apparent that low costs and good service must no longer exclude each other as such. The authors use the term Supply Chain Champions for companies that perform better than the competition; cp. Behrenbeck and Thonemann (2003, p. 1). For detailed explanations see also Thonemann et al. (2003). 669. Cp. Miller (2002, p. 665). 670. Cp. Christopher (1998, p. 195) and Raman (1999, p. 182). Alt et al. presume that the concept of the previously described concept of Vendor Managed Inventory (VMI) influences all SCOR main processes as a part of the Supply Chain
strategy; cp. Alt, Puschmann, and Reichmayr (2001, p. 106). 671. Cp. Copacino (1997, p. 15), Poirier (2000, p. 57), and Handfield and Nichols (2000, p. 7). Thaler describes the varying inventory stock strategies as necessity-justified inventory logistics; cp. Thaler (2003, p. 212). 672. Cp. Kuglin (1998, p. 196). 673. Cp. Miller (2002, p. 665). Copacino postulates a customer service survey of a more general character, as he assumes that a continuous customer service strategy has a success-critical relevance; cp. Copacino (1997, p. 73). 674. Cp. Kuglin (1998, p. 256). 675. Cp. Banfield (1999, p. 19). For an overview of the strategic procurement concepts, cp., for example, Arnold and Eßig (2000, p. 122). 676. Cp. Schäfer (2002, p. 29). 677. Cp. Hoover et al. (2001, p. 27). 678. Cp. Bovet and Martha (2000, p. 217). 679. As to the detailed examination results see para. 4.1.1 of chap. 4. 680. As to the detailed examination results see para.
4.1.2. 681. As to the detailed examination results see para. 4.1.3. 682. See the connection between on-time deliveries, lines on-time fill-rate, backorders and inventory management costs under customer order management. 683. Cp. Werner (2002, p. 65) and Wildemann (1992, p. 391). 684. Cp. Meyr et al. (2002a, p. 57). 685. Cp. Hugos (2003, p. 168). 686. Cp. Schönsleben (2000, p. 116). Ayers uses the term production flexibility in conjunction with this; cp. Ayers (2002e, p. 110). 687. As to the detailed examination results, see para. 4.1.1. 688. Cp. Berger and Gottorna (2001, p. 73) and Handfield and Nichols (2002, p. 63). 689. As to the detailed examination results, see para. 4.1.3 of chap. 4. 690. Cp. Dörflein and Thome (2000, p. 47). 691. Cp. Schäfer (2002, p. 39). 692. See to this end chap. 5, para. 5.1.2.
693. Without wanting to preempt further explanations, the relationship – measured upon the correlation prognosis – of applicable theses to the unsystematic and contrary theses moreover indicates that the model is compatible with the empirical facts. Statistic special influences, as for example the so-called Type I Error Inflation, may not be called upon in the case at hand in order to question this fundamental compatibility; cp. to this end roughly Rochel (1983, p. 129). 694. Cp. Schönsleben (2000, p. 444) and Hugos (2003, p. 91). 695. Cp. Schäfer (2002, p. 39) and KPMG (1997, p. 2). 696. Cp. Thaler (2003, p. 48). 697. Cp. Ayers (2002c, p. 248). 698. Cp. Geimer and Becker (2001a, p. 28). 699. Cp. Werner (2002, pp. 54, 175). 700. Cp. Hugos (2003, p. 144). Cp. also in this context Hausman (2000). 701. Cp. Schäfer (2002, p. 39). 702. Cp. Supply-Chain Council (2003b, p. 8). 703. The percentage lay (rounded off) at 61 percent.
704. The percentage lay (rounded off) at 33 percent. 705. The percentage lay (rounded off) at 6 percent. 706. The remaining percentage proved itself to be nonsignificant (see to this end also the explanations under chap. 3, para. 3.4.2.2). 707. Cp. Kromrey (2002, p. 48). 708. In this sense the term of a central assumption to be examined (as opposed to a theory) was deliberately chosen. 709. See to this end the comments leading to this at the beginning of chap. 3. 710. See chap. 5, para. 5.1.2. 711. See to this end chap. 3, sect. 3.2. 712. Details as to structural equation procedures in general and AMOS, in addition to their application specifically, may be taken from chap. 3, para. 3.4.2.3. 713. Usually, for parameter estimation in conjunction with structure-analytical models, a sample size of N = 100 and partially even N = 200 are deemed to be sufficient; cp., roughly, Loehlin (1987, p. 60). An insufficient sample size can lead to the fact that based on the structure-
analytical estimation procedure, even a model which presents itself by respective dimensional or dimensional indicator relationships is not accepted. Ringle, in conjunction with this, points out a required minimum sample size of 200, which is also highlighted in methodical literature; cp. Ringle (2004, p. 16). 714. The so-called operationalization stands in the foreground here, i.e., the meaningful coverage of a total term by suitable indicators which – if one assumes a homogeneous term or homogeneous “construct” – must amongst themselves logically also show close relationships; cp. Wottawa (1980, p. 18). 715. Indicators are immediately measurable factual situations which highlight the presence of the meant, but not directly recorded phenomena; cp. Kroeber-Riel and Weinberg (2003, p. 31). 716. It is to be conclusively noted that within the framework of the AMOS program, the approval of the allocation of indicators (single measures) to respective postulated factors or structures or latent dimensions is investigated in all cases.
717. 718. 719.
720.
This means that a model with inadequate structural operationalization as a result of weak or unsystematic intercorrelation of the indicators in question is inevitably rejected; cp., roughly, Hair, Tatham, and Anderson (1995, p. 680). For the diverse types of variable relationships see the comments under chap. 3, para. 3.1.2.4. Cp. Backhaus et al. (2003, p. 335), Wottawa (1980, p. 198), and Hodapp (1984, p. 47). For closer comments on the Goodness-of-Fit Index (GFI) and Adjusted Goodness-of-Fit Index (AGFI) see chap. 3, para. 3.4.2.3. It is recommended during an empirical survey to ensure that as many indicator variables are accumulated as are necessary in order to attain a positive number of degrees of freedom. For the solubility of a structural equation model it is therefore unconditionally necessary (mandatory precondition) that the number of degrees of freedom is larger, or equal to, zero; cp., for example, Backhaus et al. (2003, p. 360). For the term degrees of freedom in conjunction with the AGFI see also chap. 3, para. 3.4.2.3.
721. In the case of the GFI or AGFI respectively, the extent of the index lies similar to a correlation coefficient, namely between 0 and 1. Values &60; 0.7 suggest a barely present coverage between model assumption and empirical data. As to the formation, relevance and controversy over these measures, cp., for instance, Hair et al. (1995, p. 686). 722. See to this end also chap. 3, para. 3.4.2.3. 723. Cp., for example, Backhaus et al. (2003, p. 336). 724. Cp. Hodapp (1984, p. 47). 725. Cp. SPSS (2004a, p. 142). 726. Single hypotheses 66 to 74 are assigned to this Meta thesis (see chap. 3, para. 3.2.3.3). The partial models can be illustrated, in analogue with further Meta theses, upon the basis of the theses model. 727. In this case, the graphical overview orientates itself by visualization recommendations aimed at immediate ability to be proven by Wottawa (1980, p. 199). For a visual example of the illustration of various structure-analytical variables within a graphical representation, cp.,
for example, Madeja and Schoder (2003, p. 5). 728. See to this end sect. 3 in the appendix. 729. Cp., for example, Backhaus et al. (2003, p. 360). 730. The method of Unweighted-Least-Squares represents one of the most assigned estimation procedures in market research in these parts; cp. to this end roughly Backhaus and Büschken (1997, p. 166). 731. Cp., for example, Backhaus et al. (2003, p. 363). 732. Cp. Schewe (1996, p. 55). Cp. also to this end Fritz (1984). 733. Cp. to this end roughly Backhaus and Büschken (1997, p. 13). 734. Cp., for example, Backhaus et al. (2003, p. 408). 735. See to this end chap. 6, para. 6.3.2. 736. Cp., roughly, Kromrey (2002, p. 274) and Kops (1977, p. 179). 737. Cp. Friedrichs (1990, p. 335). 738. The Supply-Chain Council describes the factual situation as follows: “Level 1 Metrics are primary, high level measures that may cross multiple SCOR processes. Level 1 Metrics do not necessarily relate to a SCOR Level 1
process (Plan, Source, Make, Deliver, Return)” (Supply-Chain Council, 2006b, p. 8). 739. Cp. Heck (2004, p. 14). 740. Cp. BearingPoint (2003b, p. 22). 741. See to this end para. 4.4.2. 742. Cp. Klingemann and Mochmann (1975, p. 178) and Kromrey (2002, p. 526). 743. See to this end sect. 1 in the appendix. Since it was an online survey in this case, the interview instructions are restricted to exact instructions on the completion of the questionnaire. 744. See to this end sect. 3 (Personal Sources) in the bibliography. 745. Cp. Bortz (1985, p. 269). A further risk of the correlation-analytical procedure chosen here can measurement-theoretically exist in the fact that strongly represented bivariate-nonlinear correlations may, as it were, be “overlooked”. Implemented test calculations on the basis of polynomials, i.e., procedures unambiguously resting upon such non-linear processes, produced no sort of indication of relevant additional gain in knowledge (with reference to
polynomial investigation, cp., for example, the concrete description by Rochel (1983, p. 162). 746. Cp., roughly, Backhaus et al. (2003, p. 410). 747. See to this end sect. 4.2 of chap. 4. 748. See to this end chap. 3, para. 3.1.1.2. 749. See Poluha (2004a). 750. Cp., roughly, Moser (1975, p. 123). 751. Cp. Friedrichs (1990, p. 54). In the present case, the knowledge expansion refers to associations respective to business or operations management or, more precisely, Supply Chain Management. 752. For the term Hermeneutics, cp. e.g., LudwigMayerhofer (2004). 753. Cp. Kromrey (2002, p. 405). 754. See to this end chap. 4, sect. 4.2. 755. See to this end chap. 3, para. 3.1.2.1. 756. In this context, it must be noted that the expected parameter relationships for the performance attribute assets were rather weakly represented. 757. The remaining percentage proved itself to be nonsignificant (see to this end also the explanations in chap. 3, para. 3.4.2.2).
758. See to this end chap. 3, para. 3.1.2.2. 759. The remaining percentage proved itself to be nonsignificant. 760. Ibid. 761. See to this end chap. 3, Para. 3.1.2.3. 762. For the reasons for the model-contrary constellation see chap. 4, para. 4.2.3. 763. For a possible approach at explanation see the statements in the following para. 5.1.2. The effect is presumably based upon the same reason as for assets, more closely described in the following, although it does not become so strongly apparent with regards to the costs. 764. The remaining percentage proved itself as nonsignificant. 765. Ibid. 766. The remaining percentage proved itself as nonsignificant. Although 9 single theses were given, the presence of subdivided single theses, due to the differentiation between an inbound and outbound component, meant that there were actually 12 result cases (see chap. 4, para. 4.1.3.3). Of these in turn, one case proved itself
to be significantly model-contrary. 767. The statistically unsystematic thesis can even be seen as a threshold value, i.e., it lay within the area of prognosis, but could not be considered to be a tangible corroboration of the model in the face of the identified correlation level (see chap. 4, para. 4.1.3.4). 768. The remaining percentage proved itself as nonsignificant. 769. See to this end the introduction to chap. 3. 770. See to this end the explanations in chap. 3, para. 3.1.2.5 as to alternative approaches and models for the illustration and measurement of Supply Chain performance. 771. See to this end also the explanations in chap. 1, para. 1.1.1. 772. See for this chap. 2, para. 2.3.2. 773. Cp. Supply-Chain Council (2006b, p. 2). 774. For support of this statement, cp. also Christopher (1998, p. 35) and Hieber, Nienhaus, Laakmann, and Stracke (2002, p. 6). To the inclusion of marketing and sales into the strategy concept, cp., roughly, Kotler and Bliemel (1992, p. 453).
775. The Supply Chain strategy as a component of a superior respective company or competitive strategy can be defined as follows: “Supply chain strategies, which are part of a level of strategy development called functional strategies, specify how purchasing/operations/logistics will (1) support the desired competitive business level strategy and (2) complement other functional strategies” (Handfield & Nichols, 2002, p. 247). 776. In the case of the order- or customer-controlled SC process (pull process), customer orders present do in fact initialize the planning and execution of the partial processes within the Supply Chain, beginning with procurement right up to delivery. The description market economy model or demand chain management may be found in some places for this. Contrary to this, in the case of the preview or necessity-controlled SC process (push process), the predicted demand development is the trigger for the SC partial processes. The term network economy model may sometimes be found for this; cp.
Schönsleben (2000, p. 30), Reddy and Reddy (2001, p. 192), and Hoover et al. (2001, p. 13). 777. Cp. Geimer and Becker (2001a, p. 34). See to this end also the principle of the so called risk spiral according to Christopher and Lee, whereby a lack of trust of the network participants in their partners, and the inherent insecurity accompanying it, can lead to a build-up in stocks. Due to this, the inventory levels inevitably increase, which in turn leads to a lack of transparency and the spiral is run through again; cp. Christopher and Lee (2001, p. 3). In this context, cp. also Markillie (2006c, p. 18). 778. Cp. Thaler (2003, p. 15) and Aldrich (2002, p. 155). 779. Cp. Bovet and Martha (2000, p. 2) and Kuglin (1998, p. 106). For the term Value Chain, cp. also Porter (1999, p. 59). See to this end also the explanations in chap. 1, sect. 1.3. 780. Cp. Werner (2002, p. 13). 781. Cp. Supply-Chain Council (2006b, p. 2). 782. See to this end chap. 4, para. 4.2.3. 783. Cp. Supply-Chain Council (2006b, p. 2).
784. See to this end chap. 4, para. 4.2.3. 785. Cp. Supply-Chain Council (2006b, p. 2). 786. See chap. 6, para. 6.3.1. 787. See to this end chap. 3, para. 3.1.2. 788. For the term Hermeneutic, see the footnote at the beginning of sect. 5.1. 789. With regards to the proximity to truth, reference is made to the truth criterion in the sense of the concurrence of theoretical statements, and in this case: business economics or corporate reality (theory of correspondence). With this, a continuous comparison of theoretical statement and observed reality is assumed; cp. Esser, Klenovits, and Zehnpfenning (1977, p. 167). The SCOR model accounts for this, as it represents an evolutionary model, so to speak, which is adjusted to the (changed) reality at regular intervals; cp. Geimer and Becker (2001b, p. 117) and Kanngießer (2002, p. 4). See to this end also the respective explanations under chap. 2, paras. 2.1.2 & 2.1.5. The indicated intrusional influences and errors highlighted with regards to the scientific proof of the SCOR model’s
suitability or respective proximity to truth, which was strived for within the framework of the work submitted, must by no means be ignored (see to this end also chap. 4, sect. 4.4). 790. See to this end the introduction to chap. 3. 791. This point of view is also supported e.g. by Schoder; see Poluha (2005). It must be noted that a special illustration of the model was developed and investigated. Generalizing inferences to the model must therefore be seen under this premise. 792. The model concept stands for the theoretical statement, the empirical reality for the respective economical or company reality. Therefore and in turn, reference is made to the theory of correspondence described in one of the previous footnotes in para. 5.1.3; cp. to this end also Friedrichs (1990, p. 27). 793. It has been indicated several times that the SCOR model represents, in the original sense, a descriptive model; cp. Supply-Chain Council (2005a, p. 3; 2006b, p. 2). See to this end also chap. 2, sect. 2.2, as well as para. 2.3.3.
Continuative thoughts lead in the direction of its possible further development into a forming model. 794. See to this end chap. 1, para. 1.1.1. 795. See chap. 6, sect. 6.3. 796. Adaptability can be described in the present concrete case as: “… capability to adapt or be flexible amid changing conditions” (Heinrich & Betts, 2003, p. 205). Christopher uses the term Agile Supply Chain, whereby the main difference lies in that in his case (and therefore in the case of Agile Supply Chains), the aspect of information technology or penetration of the Supply Chain by E-Business is only rudimentarily expressed; cp. Christopher (2001, p. 1). For the term Adaptability, cp. also Seibt (1997a, pp. 23, 32). 797. Cp. HP (2003, p. 2). 798. Cp. SAP (2004, p. 5). 799. (Industry Directions, 2003, p. 1). 800. Cp. CapGemini (2004). 801. Cp. Stephens et al. (2002, p. 361). As to the gain in competitive advantages by means of
information technology, cp. also Porter and Millar (1988, p. 62). For the importance of information technology in the formation of corporate networks, cp. also Klein (1996). 802. See to this end the close of chap. 1, para. 1.3.1. The named criteria represent necessary conditions, i.e., the listed criteria are urgently required in order to constitute an Adaptive Supply Chain (ASC). As to the necessity for conditions, cp. also Zetterberg (1967, p. 82). 803. Modelled upon Stephens et al. (2002, p. 361) and Seibt (2000, p. 11). 804. Cp. Heinrich and Betts (2003, p. 79). 805. A market place is described as a platform or portal if it enables users to execute electronic business transactions. Portals are used within the framework of SCM in various objective directions or application scenarios, as for example order execution and for the procurement process; cp. Lawrenz and Nenninger (2001, p. 329). Relevant company scenarios supported by portals represent the EBusiness areas explained earlier elsewhere, as
for example B2B, B2C and G2B (see to this end chap. 1, sect. 1.6). 806. The concept of Collaborative Order Planning that describes the exchange of order and planning data between various SC participants, for instance, fall beneath this; cp. Thaler (2003, p. 133). 807. See to this end for example the previously described concept of Collaborative Planning, Forecasting and Replenishment (CPFR). 808. See to this end roughly the previously described concept of Vendor Managed Inventory (VMI). 809. With regards to the optimal fulfillment of orders, a differentiation must be made between quantitative and qualitative components. Whilst the quantative perspective focuses upon the pure amount or percentage of on-time order deliveries, the qualitative perspective includes the classification of the customers. In this way, strategic customer management could mean that preference is given to the delivery to a key account customer over the delivery to several “B-rated” customers; cp. Industry Directions
(2001b, p. 7). 810. Cp. to this end Radjou (2001, p. 1). 811. (Heinrich & Betts, 2003, p. 152). 812. Cp. Lawrenz and Nenninger (2001, p. 335). 813. Cp. Schäfer (2002, p. 452). 814. See to this end also chap. 2, para. 2.4.1.3. 815. Cp. Gensym (2001, p. 2). 816. (Radjou, Orlov, & Porth, 2003, p. 3). 817. Cp. Segal (2003). 818. See chap. 2, sections. 2.2 and 2.3. 819. The term Business Process Reengineering (BPR) can be defined as follows with a view to SCM: “The terms transformation and business process reengineering are used rather loosely to describe three main types of business change efforts: 1. Business design, 2. ’Big’ business process reengineering, 3. ‘Little’ business process engineering. When translated into supply chain terms, these three change efforts have various meaning: Business design = Restructuring businesses, ‘Big’ business process reengineering = Transforming the supply chain order-to-cash cycle, ‘Little’ business process reengineering =
Transforming logistics functions (for example, transportation or warehousing)” (Kuglin, 1998, p. 99). 820. As stated in chap. 2, para. 2.1.3, enable represents one of the three types of a SCOR model process. It is noticeable that this type of process was formerly named infrastructure; cp. for example, Bolstorff and Rosenbaum (2003, p. 154). 821. Cp., roughly, Hammer and Champy (1993, pp. 44, 83), Davenport (1993, p. 16), and Venkatraman (1991, p. 127). Information technology alone, however, is not sufficient in order to change or improve company processes. Next to this, organizational and “human” factors also play a role; cp. Schäfer (2002, p. 2). These will be dealt with in chap. 6, sect. 6.1. 822. (Hofmann, 2004, p. 86). 823. Cp. Supply-Chain Council (2005a, p. 12; 2006b, p. 2). See to this end also chap. 2, sect. 2.1.3. 824. Cp. Heinzel (2001, p. 53). 825. For the term tools in the previous sense and as to the definition of other terms (procedures, etc.),
cp. roughly, Seibt (2004, p. 19). 826. Cp., roughly, Holcomb, Manrodt, and Ross (2003, p. 2) and Boyer, Frohlich, and Hult (2004, p. 171). 827. Cp. Goetschalckx (2002, p. 105). 828. Cp. Metcalfe (2003, p. 32) and Radjou et al. (2003, p. 14). 829. Cp. Industry Directions (2001b, p. 7). For the diffentiation between strategic and operational planning, cp. e.g. Schierenbeck (2003, p. 128). 830. Seibt assumes that companies must continuously adjust their E-Business systems to market developments and changed requirements throughout the whole system life cycle due to dynamic market development and the speed of development in the field of technology; cp. Seibt (1997b, p. 393). 831. Company representation: IBS, International Business Systems, is a worldwide leading software-supplier for (…) integrated ITsolutions for distribution and Supply Chainorientated companies. (…) Our internationally active customers comprise for example, ABB,
Cartier, Ciba Vision, Galenica, General Electric, Honda, Maxell, Miele, Pioneer and Volvo. In total, more than 5000 customers in over 40 countries have already chosen IBS’s software solutions. Since over 25 years ago our global network of branches and business partners has provided future-orientated companies with software, hardware and services. 2000 IBS employees work in 90 branches in 26 countries. In a further 10 countries IBS works successfully with business partners. IBS is noted at the Stockholm Stock-Exchange; cp. IBS (2004a). 832. Self-representation of the company: “Business Objects helps the world’s leading organizations track, understand, and manage their business in order to improve enterprise performance. With more than 26,000 customers in 80 countries worldwide, Business Objects is the clear market leader in the business intelligence industry. Founded in: 1990. 2003 Revenue: $560.8 million. Employees: 3,900. Headquarters: San Jose, California and Paris, France” (Business Objects, 2004).
833. Cp. IBS (2004b) and Business Objects (2002). The available work does not have the intention of verifying (or falsifying) the correctness of this statement. Moreover, purely the respective category of applications is to be highlighted. 834. An overview of the varying applications for Supply Chain Management (SCM) IT support can for example be found with Richmond, Burns, Mabe, Nuthall, and Toole (1999, p. 509). For an example as to how the various applications can be supported on the hardware side, cp., roughly, Sun and CGE&Y (2003, p. 2). 835. Self-portrayal: “Gartner, Inc. is the leading provider of research and analysis on the global IT industry. Our goal is to support enterprises as they drive innovation and growth through the use of technology. We help clients make informed technology and business decisions by providing in-depth analysis and actionable advice on virtually all aspects of technology. This year (2004; note of the author) marks the 25th anniversary of Gartner and the founding of our industry. We take pride in our pioneering work
to assist our clients and our industry in benefiting from the use of technology. Gartner clients trust in our rigorous standards that safeguard the independence and objectivity of our research and advice. With $858 million in revenue in 2003, and more than 10,000 clients and 75 locations worldwide, we are the clear market leader” (Gartner, 2004). 836. Cp. Gartner (2002, p. 1). In Gartner’s terminology, the term SCDM defined and used within the framework of the available work is a far-reaching combination of the automated execution of business events (Business Rules Engine, BRE) and the management of company processes (Business Process Management, BPM); cp. Sinur (2003, p. 2). 837. Cp. Hinkelmann (2003, p. 27). Under “applications to be taken seriously”, the author understands those that have already been widely assigned within larger and big companies – as to the classification of company sizes, cp. Schierenbeck (2003, pp. 34, 540). Next to this, other comparable applications exist, as for
example ProSCOR by Proforma; cp. Proforma (2004) or PowerChain by Optiant; cp. Optiant (2004), which up until now have only been engaged in the application of SCOR within a relatively narrow scope and by smaller SW vendors. 838. For an overview and an estimate as to the presently available SCDM manufacturers and applications, cp., roughly, Gartner (2002, p. 1) and Hall and Harmon (2005, p. 13). 839. Self-description of the Organisation: “Founded in 1986, Gensym Corporation (Burlington, Massachusetts) is a provider of software products and services that enable organizations to automate aspects of their operations that have historically required the direct attention of human experts. Gensym’s product and service offerings are all based on or relate to Gensym’s flagship product G2, which can emulate the reasoning of human experts as they assess, diagnose, and respond to unusual operating situations or as they seek to optimize operations” (Gensym, 2004a).
840. Cp. Gensym (2001, p. 1). 841. Cp. Gensym (2004b). 842. Cp. Industry Directions (2001b, p. 9). See to this end also chap. 3, para. 3.1.2. 843. Company description: IDS Scheer was founded in 1984 by Prof. Dr. Dr. h.c. mult. AugustWilhelm Scheer as a small consultancy company with employees of the University of Saarland in Germany. Today, the international company is represented by partners in 50 countries and a Global Services Partner of SAP. At IDS Scheer (…) over 2.000 employees are engaged as competent contact persons in all important questions about process organization – in the fields of Customer Relationship Management, Supply Chain Management and Enterprise Management, as well as the fields of Application Management, Outsourcing and Technology Consulting. IDS Scheer is represented in Germany in Saarbrücken Berlin, Düsseldorf, Frankfurt, Hamburg, Munich, Freiburg and Nuremberg and has branches in 20 countries, including Great Britain and France, the States of
Middle and Eastern Europe, Brasil, Canada and the USA, in addition to Japan, China and Singapore; cp. IDS (2004a). 844. Cp. IDS (2004b). 845. Ibid. 846. Cp. IDS (2003, p. 1). 847. Cp. Gunther (2003, p. 4). 848. Cp. IDS (2004c). 849. (Gunther, 2003, p. 8). Detraction in quality of the illustration result from extraction from the original document, which can, if necessary, be accessed from the original for better legibility. The consistency of the application with the SCOR model is primarily to be visualized by means of the illustration. 850. Cp. Sydow (2003, p. 18). 851. Company information: BOC Information Technologies Consulting GmbH, was founded in 1995 in Vienna, as a spin-off of the BPMS (Business Process Management Systems) group as part of the Knowledge Engineering Department of the University of Vienna. Thanks to the speedy expansion of the German market,
the first independent company was founded in Berlin in 1996. Originating from the headquarter in Vienna, further companies emerged in Madrid (1997), Dublin (1998), Athens (1999) and Warsaw (2002). BOC Information Technologies Consulting GmbH is an internationally operating advisory and software house, which has specialized itself in strategy, business process and IT management. At present, BOC employs over 100 people; cp. BOC (2004). 852. In the sense of the analysis of operational crosssectional functions (for the various operational organization forms, cp., for example, Schierenbeck (2003, p. 115). 853. Cp. BOC (2002a, p. 1). 854. Cp. BOC (2002b, p. 1). 855. Cp. BOC (2002c, p. 1) 856. Cp. Sinur (2003, p. 2) and Heinrich and Betts (2003, p. 14). 857. For the term Supply Chain Competence in the present context see chap. 1, sect. 1.7. 858. Cp. Schäfer and Seibt (1998, p. 365). In the submitted case, the focus lies upon the Supply
Chain and its management. In analogue to this, the factual situation refers to the competence with respect to the Supply Chain, which was referred to as Supply Chain competence in the work at hand. 859. (Lippitt, 1982, p. 1). 860. See chap. 2, para. 2.3.2. 861. See chap. 5, para. 5.1.2. 862. The Supply-Chain Council offers the following explanation of the model description in conjunction with this: “The Model is silent in the areas of human resources, training, and quality assurance among others. Currently, it is the position of the Council that these horizontal activities are implicit in the Model …” (SupplyChain Council, 2006b, p. 3). 863. See sect. 1 of the appendix. 864. The term can be defined as follows: “Human resources cover all process related human factors that are relevant to the improvement of the capabilities and motivation of the employees involved” (Seibt, 1997a, p. 13). Cp. to this end also Lawrence and Lee (1984, p. 54).
865. Development planning also covers on the one hand assignment planning that is supported by Training on-the-job, and on the other hand further educational planning that is assured by means of Training off-the-job; cp. Schierenbeck (2003, p. 352). 866. An organization can be fundamentally described as follows: “Organizations are composed of individuals and groups, created in order to achieve certain goals and objectives, operated by means of differentiated functions that are intended to be rationally coordinated and directed, in existence through time on a continuous basis” (Lawler & Rhode, 1976, p. 32). 867. Leavitt uses the term four-variable conception of organizations; cp. Leavitt (1965, p. 1144). Seibt also describes the system variables as Dimensions for Information System Structuring; cp. Seibt (1991, p. 252). For a thorough discussion of the subject matter, q.v. Seibt (1997a). 868. Cp. Schäfer (2002, p. 99).
869. Leavitt describes the special meaning of the People approach as follows: “By changing human behavior, it is argued, one can cause the creative invention of new tools, or one can cause modifications in structure (…) By either or both of these means, changing human behavior will cause changes in task solutions and task performance and also cause changes toward human growth and fulfillment …” (Leavitt, 1965, p. 1151). 870. Cp. Galbraith (1977, p. 26). 871. Cp. Guest (1962). 872. Cp. O’Shaughnessy (1976). 873. The approach already becomes apparent in the definition of an organization which the authors submit as follows: “An organization is the coordination of different activities of individual contributors to carry out planned transactions with the environment” (Lawrence & Lorsch, 1969, p. 3). 874. Cp. Leavitt (1965, p. 1153). 875. Cp. Lippitt (1982). 876. Cp. Likert (1961).
877. Litterer assumes that two substantial factors of influence exist for organizations: members and technology, cp. Litterer (1973, p. 104). 878. For the subject matter of organizational design, cp. also Grochla (1982, 1983). 879. For the meaning of the system variable people, especially within the context of organizational changes with regards to the Supply Chain, cp. also Cohen and Russel (2005, p. 229). 880. Cp., roughly, Becker and Geimer (1999, p. 25). 881. This is mainly in accordance with the Supply Chain competences fundamental to the SCOR model: Customer-facing performance capability on the one side, and company-related efficiency on the other side. 882. Alternatively expressed, the performance indicators serve to measure, assess and monitor the two SC competences that constitute the SCOR model. 883. Human Resources Management (HRM) is seen, as already represented in Porter’s Value Chain approach in chap. 1, para. 1.3.1, as an essential component of Supply Chain Management.
Porter assumes that HRM-related activities take place in various parts of the company, and that management of the human resources supports not only the individual primary and supportive activities, but also the total Supply Chain; cp. Porter (1999, p. 68). 884. In this context it is relevant to see the role of technology as an Enabler, as stated in chap. 5, sect. 5.3. Collins describes this as follows: “Technology can accelerate a transformation, but technology cannot cause a transformation” (Collins, 2001, p. 11). 885. Cp. Geimer and Becker (2001a, p. 22). 886. See to this end the statements in chap. 1, para. 1.3.1. 887. Cp. Schäfer (2002, pp. 358, 370). On the basis of empirical studies within the context of company process optimization, Kajüter quotes the following result: “Reengineering concepts confirmed the findings of earlier research identifying human factors (e.g., resistance against organizational change) as the major reason for the failure of corporate restructuring”
(Kajüter, 2002, p. 15). Handfield and Nichols come to a similar conclusion: “Organizations are continually faced with the challenge of managing the ‘people’ part of the equation. (…) A number of supply chain initiatives fail however due to poor communication of expectations and resulting behaviors that occur. (…) The management of interpersonal relationships between the different people in the organization is often the most difficult part” (Handfield & Nichols, 2000, p. 67). 888. (Kuglin & Rosenbaum, 2001, p. 170). 889. Hamel describes the renewal as follows: “Renewal is the capacity to reinvent not only processes and systems, but purpose and mission as well” (Hamel, 2002, p. ix). 890. Based upon Leavitt (1965, p. 1145). 891. Schumann for example describes Change Management as a “process of permanent change”; cp. Schumann (2001, p. 102). For the importance of Change Management in conjunction with Supply Chain Management, cp. also Poluha (2001, p. 317).
892. Cp., roughly, Hieber et al. (2002, p. 6). 893. For example, a project with the name ProdChain was carried out by the Forschungsinstitut für Rationalisierung (Research Institute for Rationalization) at the RWTH Aachen (fir) in Germany during the period from March 2002 until August 2004 under Promotion No. IST2000-61205. Its objective was the development of a procedure for analysis and improvement of logistic performances within production networks, and it was based upon the SupplyChain Council’s SCOR model. The project did not, however, investigate the SCOR model as such. It rather assumed its “suitability” and applied it to develop logistic metrics as indicators for the continuous improvement in logistic performance in production networks, by means of which targeted structural suggestions could be made according to the analyzed logistic performance; cp. Forschungsinstitut für Rationalisierung an der RWTH Aachen (fir) (2004). 894. Cp. Bolstorff and Rosenbaum (2003, p. 1).
895. For the term exploration, cp., roughly, Kromrey (2002, p. 67). See to this end also the statements under chap. 1, para. 1.1.2. 896. The first version of the SCOR model was published in November 1996; cp. for example, Hagemann (2004, p. 4). 897. Cp. Heck (2004, p. 15). 898. Cp. Göpfert and Neher (2002, p. 37). 899. Cp., for example, Heinzel (2001, p. 50). This opinion is also shared by Welke, who assumes that the development in the European region is being increasingly hastened by multinational companies which have possibly accumulated experience of the SCOR model in their American branches, as for example DaimlerChrysler and Siemens, or have their headquarters in America and are also active in Europe, as for example the companies HP and Intel already mentioned in conjunction with SCOR’s application; see Poluha (2004b). 900. See to this end chap. 4, sect. 4.4. 901. Cp. Supply-Chain Council (2003b, p. 1) and Hieber et al. (2002, p. 6).
902. In this context cp. also chap. 5, para. 5.1.2. 903. Cp. Kromrey (2002, p. 3). 904. As to the relevance of comparable data upon regional level, cp., for example, SIMTech (2002, 2003) and Gintic (2000, 2001). 905. See to this end the statements made in chap. 3, para. 3.3.5. 906. A possible approach at explanation for this would be that these companies invest increasing resources into the SCOR model or have respectively adjusted themselves more strongly to corporate-policy or logical decisions. 907. See chap. 4, para. 4.2.2. 908. Cp. Schäfer (2002, p.2). The concept of a new, expanded company, highlighted by strong relationships to external partners, and a shift from physical to virtual exchange in which information is primarily transfered along the value chain, was already introduced by Tapscott in 1996; cp. Tapscott (1996, p. 97). 909. For example, Radjou et al. (2003, p. 31) name practical application cases for Adaptive Supply Chains. However, the statements are of a purely
qualitative nature and are not based upon scientific examinations. 910. So that modern concepts like SCDM can be established within company practice, they must prove their strategic importance for the company success. In this case, the same requirements and present-day challenges apply that apply to information technology in general: “As information technology’s power and ubiquity have grown, its strategic importance has diminished. The way you approach IT investment and management will need to change dramatically” (Carr, 2003, p. 5). 911. For an empirical study of the influence of Customer Relationship Management (CRM) in the context of E-Business, cp. Madeja and Schoder (2003, p. 9). For an overview of selected E-Business concepts, cp., roughly, Schäfer (2002, p. 11). 912. The suggestions listed in the following for expansion of the SCOR model’s structure in the “human factor” area do not immediately stem from the results gained within the framework of
the work submitted, because inevitably no data was collected for this purpose. They are rather based mainly upon present developments in the field of Supply Chain Management, as they are shown to do in chap. 5 (see to this end chap. 5, sect. 5.2 & 5.3) Furthermore, they are to be differentiated from such suggestions which aim at a further “supplementing” of the existing model structure (see to this end chap. 5, para. 5.1.2). 913. See sect. 6.1; cp. also to this end Davenport (2005, p. 102). 914. Cp., roughly, Dhavale (1996, p. 50). Sharman (1995, p. 34) introduces the term employee or team performance in conjunction with this. As to the Supply Chain scorecard, see also the statements under chap. 1, para. 1.5.4. 915. Cp., roughly, Kaplan and Norton (1997, p. 121). 916. See to this end the explanations under chap. 3, para. 3.1.2.5. 917. Cp. Welz (2005, p. 17). 918. Cp. Ziegler (2005, p. 20). 919. As highlighted in sect. 6.1, the system variable
people represents a substantial factor during the implementation of organizational changes. 920. See to this end chap. 3, para. 3.1.2.5. 921. A distinguishing characteristic of the BSC is represented by the monetary (finance-related), as well as non-monetary performance indicators; cp., for example, Kaplan and Norton (1992, p. 71) and Horváth and Kaufmann (1998, p. 41). 922. Cp. to this end roughly Wollnik (1977). 923. See to this end chap. 4, sect. 4.3. 924. Cp. to this end roughly Backhaus et al. (2003, p. 408). 925. See to this end chap. 4, para. 4.3.2. 926. The graphical overviews also orientate themselves upon the recommendations for visualization by Wottowa, which target the immediate ability to be proven; cp. Wottawa (1980, p. 199). 927. Diags. 6.2a & 6.2b represent an entirety which, for graphic representation reasons, could not be reproduced in an illustration. 928. Ibid.
929. See to this end sect. 3 of the appendix. 930. Cp. Supply-Chain Council (2005c, p. 1); SupplyChain Council, 2006a, p. 1). For a detailed overview of the concerned performance indicators and their calculation cp. also SupplyChain Council (2005b, p. 296; 2006b, pp. 368, 434). 931. This means that two (or more) absolute figures are placed into relation with each other (see to this chap. 3, para. 3.1.1.2). 932. Cp. Geimer (2000, p. 56). 933. The advantages of such a standardization are seen not only within a company (between the functional departments), along the SCORspecific Value Chain (VC) and in cooperation between the partners within VC, but also between companies in varying industries (manufacturers, Logistics Service Provider, and so on); cp. Heinzel (2001, p. 49). See to this end also the explanations in chap. 1, sect. 1.6 and chap. 2, sect. 2.2. 934. Cp. Schmelzer and Sesselmann (2004, p. 162). The authors quote Johannes Feldmayer, member
of the central Board of Directors of Siemens AG. 935. Davenport, in conjunction with this, introduces the term Commoditization of processes; cp. Davenport (2005, p. 100). In this way, or example, the previously-mentioned Dell Company has selected velocity in association with a high multitude of products as a differential characteristic. The company has optimized and specially suited the relevant processes to such an extent that they run noticeably faster than the competition and have therefore created, and continuously built upon, a competitive advantage; cp. Geimer and Becker (2001a, p. 28) and Markillie (2006b, p. 4). As a final consequence, such a standardization – compatible to a competition benchmarking – would lead to stagnation, as at the end all competitors would have reached the same performance level, and a differentiation would no longer take place; cp. Schäfer and Seibt (1998, p. 376). For the term competitive benchmarking, cp. also Watson (1992, p. 5).
936. Cp. Heck (2004, p. 16). 937. Cp. Göpfert (2002, p. 38). 938. The DAX100 includes 100 large German stock corporations or public companies respectively comprising 30 DAX and 70 MDAX companies; cp., for example, Deutsche Börse (2004). 939. See to this end the statements in chap. 1, para. 1.3.1 and sect. 1.6. 940. Cp. Schäfer (2002, pp. 346, 381). 941. Cp. for example, Latham (1999, p. 91). 942. Cp. Seibt (1999, p. 56). 943. Cp. Normann and Ramirez (2000, p. 187). Abell combines the factual situation in a more general form under the term Dual Nature of Management and provides the following description: “Running the business and changing it are not sequential but parallel pursuits” (Abell, 1993, p. 3). 944. The fundamental dynamic process of continuously searching for new value creation and business opportunities, as well as realizing those within the framework of organizational change, was described by Schumpeter as the process of
creative destruction; cp. Schumpeter (1976, p. 81). Analogue to this, Lee states three fundamental conditions for an optimal Supply Chain according to his terminology: flexibility, adaptability and a balancing-out of interests; cp. Lee (2005, p. 72). 945. See to this end roughly chap. 2, sect. 2.2 & para. 2.3.3 in addition to the introduction to chap. 5, sect. 5.3. 946. Davenport describes the connection as follows: “The SCOR model is only a catalyst for change and a framework for analysis. As with any approach to process improvement, firms must still make difficult changes in how they do their work and to associated systems and behaviors” (Davenport, 2005, p. 102). 947. See to this end the practical examples under chap. 2, sect. 2.4. 948. By these means and amongst other things, the increasing demands upon the Supply Chain are to be taken into account, which in turn result from the changed competitive conditions; cp. Markillie (2006a, p. 3). Based upon Cohen and
Russel, the present connection allows itself to be described as follows: “Like its business, (the optimal) supply chain is flexible, agile and evolving constantly. What doesn’t change, though, is the company’s view of its supply chain as a key source of competitive advantage – one well worth the ongoing investment” (Cohen & Russel, 2005, p. 257). 949. Taken from BearingPoint (2003d). 950. Ibid. 951. Based on BearingPoint (2003d). 952. Taken from BearingPoint (2003e). For explanations concerning the graphical representation in the following diagrams, refer to para. 5.5 of the appendix. There is no direct correlation between the data used in the examples and the data used in the empirical study outlined in chapters 3 & 4. The data used in the following examples are only intended for exemplary and illustrative purposes. 953. Taken from BearingPoint (2003b, p. 23). 954. For detailed results refer to chapter 4, sect. 4.1; for a summary of the results refer to chapter 5,
para. 5.1.1. The existence of sub-cases for a single theses (e.g., 1a & 1b) is a result of the differentiation in an inbound and an outbound component. 955. For the abbreviations used, see the abbreviations list at the beginning of the book. 956. For each of the digitalized sources, the day of last access is given under the abbreviation DolA, i.e., the date when the internet site mentioned was last accessed. 957. Unpublished, available from the author.
Appendix One Quantitative Survey (KPI Questionnaire)949
Appendix Two Overview of the Performance Measures of the questionnaire950
Appendix Three Connection between Supply Chain Competences and Performance Indicators 951951 3.1 Customer-facing competence (customer service and flexibility)
3.2 Internal-facing competence (cost and assets)
Appendix four Details and Evaluation Examples of the Performance Measures952 4.1 Source
4.2 Make (Produce)
4.3 Deliver: Store
4.4 Deliver: Transport
4.5 Deliver: Sell
Appendix five Representation of the Resultsof the Questionnaire for the Acquisition of Performance Measures within the KPI Benchmarking Tool953 5.1 Example for online survey (KPI Questionnaire)
5.2 Results of SCOR on the first level
5.3 Results within a SCOR
process
5.4 Details of an exemplary performance measure and suggested measures for improvement
5.5 Clarification of details
of an exemplary performance measure representation
Appendix six Detailed Analysis Results of the Single Hypotheses954 6.1 Results of SCOR model group Intra-Performance Attribute (I-P)
6.2 Results of SCOR model group Intra-Competency (IC)
6.3 Results of SCOR model group InterCompetence/Performance
Attribute (I-CP)
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Table of Contents Application of the SCOR Model in Supply Chain Management Copyright Dedication Foreword Preface Acknowledgments Abbreviations 1. Objectives, methodology, approach and definition of terms 2. The Supply Chain Operations Reference Model (SCOR model) of the Supply-Chain Council 3. Empirical study based upon a quantitative questionnaire 4. Comparison of work hypotheses and
acknowledged results of the empirical study 5. Summary of conclusions and innovative assessments 6. Limitations of the presently available SCOR model References Appendices Bibliography
