SIX SIGMA DMAIC Financial Services

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REFLECTIVE PRACTICE

Using Six Sigma DMAIC to improve credit initiation process in a financial services operation Sameer Kumar, Anthony D. Wolfe and Katherine A. Wolfe Opus College of Business, University of St Thomas, Minneapolis, Minnesota, USA

Using Six Sigma DMAIC

659 Received May 2008 Accepted May 2008 Accepted

Abstract initiation tion proc process ess for mid-l mid-level evel corp corporat oratee cred credit it card cust customers omers involves Purpose – The credit initia dependencies on multiple people across divisions considered as a critical function for a US financial services company. Increasing efficiency and effectiveness of the process could save time and money for the company. The purpose of this study is to analyze the process using Six Sigma DMAIC tools in order to determine inefficiencies; specifically, to decrease the number of days it takes from the time a comp co mpan any y su subm bmit itss a re requ ques est, t, to th thee ti time me it is ap appr prov oved ed fr from om 20 da days ys to 15 da days ys,, re resu sult ltin ing g in a 25 pe perc rcen entt improvement in throughput. Design/methodology/approach – The process improvement tool used is the Six Sigma DMAIC methodology, in addition to cause-and-effect diagrams and the development of poka-yoke poka-yokes. Findings – This study found several areas for improvement in the process studied. Using statistical testing, bottlenecks in the process were identified. Process changes are suggested, as well as, new measures that can be implemented to prevent variance in the process. Business ness oper operation ationss can can bene benefit fit from from eval evaluatin uating g key key proc processes esses in this way Practical Pract ical impli implicatio cations ns – Busi to strengthen procedures and eliminate variation. The managers at the financial services operation studied will be able to implement the recommended process to improve efficiency and throughput. Research limitations/implications – Limitations exist that may prevent the recommendations from being carried out. These limitations lie in elements that are outside the control of the credit manager, such as the actions of the sales team and the approval of executive management. Success of this project hinges on cooperation from these parties. examined with Originality/value – The process under evaluation in the study has never before been examined such scrutiny. The outcome of the study and recommendations for improvement will be of great value to the financial services operation studied. Other service organizations, however, can learn from the Six Sigma process executed for this study as well. Six Sigma is a valuable methodology that can be applied to a wide variety of organizations and business processes. Keywords Six Sigma, Poka-yoke, Cause and effect analysis, Credit management, Financial services Paper type Case study

Introduction The purpose of this case study is to analyze the credit initiation process for mid-level corporate credit card customers at a major US financial services operation, using Six Sigma Sig ma too tools ls in ord order er to det determ ermine ine ine ineffic fficien iencie ciess and mak makee rec recomm ommend endati ations ons for improvement. Currently, the process is taking longer on average than the expected timeframe established by management. By examining five months of historical data using Six Sigma analysis, this study will identify inefficiencies in the process to

International Journal of Productivity and Performance Management Vol. 57 No. 8, 2008 pp. 659-676 q Emerald Group Publishing Limited 1741-0401 DOI 10.1108/17410400810916071

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ascertain which steps in the process are the best targets for improvements. Additionally, concrete recommendations will be made to improve the process based on the data studied. The study will evaluate a single process within a major financial services company in the USA. The company operates in over half the states in the USA and has over 1,000 locations. The company serves consumers, businesses, and institutions in the areas of banking, mortgage, insurance, investment, and brokerage. The process being analyzed is the underwriting of customers for the commercial card programs through the Payment Systems Division (PSD). The Payment Systems Underwriting Division (PSUD) is responsible for underwriting applications received by the PSD. The purpose of the underwriting process is to determine the level of credit risk. “Credit risk can be defined as the possible decline in value of an institution’s assets due to the potential failure of counterparties to honor their financial obligations” (Kuritzkes et al., 2002). The division is set up to underwrite both large and mid-market customers. In addition, customers apply for a variety of programs that will allow them to manage their accounts payable more efficiently. Programs also help companies do a number of tasks more efficiently. For example, cards can be distributed to employees to allow them to travel and spend money. More sophisticated PSD programs allow companies to purchase various types of goods and manage their freight systems. The PSUD is made up of Credit Initiation, Portfolio Management, and Administrative and Documentation Groups. The Credit Initiation Team is responsible for underwriting all new requests. Within the Credit Initiation Team are a credit manager and team of four analysts. Responsibilities of the analysts include underwriting all new requests and making recommendations as to whether or not the credit should be given to the applicant based on underwriting criteria established in the credit policy. In addition to managing the credit analysts, the credit manager is responsible for approving credit requests. if the size of the exposure or risk rating is outside the scope of the credit manager’s approval authority, the credit must be approved by the senior credit approver. The volume of requests received by the Credit Initiation Team varies from month to month, but averages roughly 100 to 120 requests per month. The majority of these credits, roughly 80 percent, are “Mid-Market Requests” meaning that the company size is roughly $5 million to $50 million in total annual revenues. This paper will evaluate the Credit Initiation Team’s process for analyzing and approving “Mid Market Requests.” Currently, the process takes an average of 19.744 days. The objective of this paper is to make recommendation that will reduce the process to 15 days. The rest of the paper is organized as follows. Next is the Literature review section where existing literature on credit analysis and Six Sigma DMAIC approaches related to this study are examined. The Methodology section describes the application of Six Sigma DMAIC process improvement tool. A value of the case study to researchers and practitioners is presented. research findings, recommendations and limitations of the case study derived from the analysis are outlined. The Practical implications section summarizes the impact of implementation of recommended changes to the credit initiation and approval process in the financial services operation. In the Conclusion section, the contribution and benefits derived from the study are documented.

Literature review Credit analysis is an important function in the financial services industry, enabling financial services operation to mitigate risk while maximizing profit. It is much more valuable to conduct credit analysis of a customer rather than hoping they do not default on a loan: Loan classification by examiners is . . . a better indicator of poor quality than actual default . . . Ex ante measures such as loan examiner ratings of loan quality are superior because they are made prior to loss or default and so give financial services management the opportunity to take corrective action (Haslem and Longbrake, 1972).

However, despite the widely accepted importance of credit analysis, variance in the credit analysis process is common. Petrie (2006) raises numerous concerns with credit analysis in a typical underwriting operation. These arise from lenders often getting the credit into the queue as rapidly as possible with less-than-adequate information. In the absence of critical relevant information, meaningful evaluation by the analyst is constrained thus slowing the underwriting process significantly. This can have an adverse effect on overall quality of the underwriting process. The analyst ends up dealing with several credits concurrently. It is normal for the analyst to work on multiple tasks; however, the greater the number of credits under active underwriting, the greater the likelihood for errors, inconsistencies, or oversights in the analysis. Petrie recommends that the credit department can minimize the opportunity for errors by defining exactly what constitutes the critical amount of information for each type of credit product before the underwriting process is initiated. Following Petrie’s (2006) recommendation, correct information is necessary to precede the credit analysis process. It is expected that the lack of information will prove to be a cause of variation within the process studied in this paper. Further research shows that financial services firms have had success underwriting credit differently depending on the size of the request. One financial services firm split transactions into two groups: under $250,000 and over $250,000. They, then, created a streamlined credit memorandum for the under-$250,000 category (Beans, 2005). This method of classification may prove to be beneficial when looking for ways to remove variance and create efficiency within the studied process. As stated, this paper will analyze a process according to Six Sigma methodology applied to a financial services operation, which was developed by Motorola in 1987 (Dasgupta, 2003). The notion of Six Sigma is derived from previous quality schemes in which a process is considered to produce quality results if 99.74 percent of the products were within specification. (Revere and Black, 2003) A Six Sigma level of performance is equivalent to producing 3.4 defects per million opportunities (Dasgupta, 2003). Many business organizations have developed and implemented Six Sigma approaches (Scalise, 2003, Yang et al., 2007). Among the noted ones include: General Electric (GE), DuPont, Honeywell, and Samsung. A brief perspective is provided in the following paragraphs on how these companies have applied Six Sigma to similar needs. We begin with General Electric. A key element of GE’s approach to Six Sigma is tailoring underlying methodologies to specific needs and characteristics of its business units. GE has taken the generic Six Sigma methodology for process innovation, and has tailored their specific needs of system design and implementation, as well as, product development activities.


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DuPont combines Six Sigma principles with the Supply Chain Operations Reference (SCOR) model, which scopes five core management processes – including plan, source, make, deliver, and return. DuPont’s Six Sigma approach utilizes a quality function deployment (QFD) tool – a method for converting customers’ requirements to products, processes or services. Honeywell is known for extensive application of lean methodologies, which has become a major tool in their Six Sigma implementation. Honeywell developed a proprietary Six Sigma approach called Six Sigma Plus which links lean manufacturing concepts and tools, such as value stream maps and thought process maps, into a general Six Sigma framework. At Samsung, Six Sigma projects usually focus on either redesigning processes and systems or improving performance levels of existing systems. In Six Sigma parlance, the former is addressed most commonly through define, measure, analyze, design, optimize, verify (DMADOV), while the latter is addressed using define, measure, analyze, improve, control (DMAIC). Samsung’s Business Team estimated that among the supply chain management (SCM) projects at Samsung, about 75 percent would involve redesigning processes, while the remaining 25 percent would focus on process improvement. The DMADOV methodology, while useful, could not provide the necessary support to execute the entire range of SCM projects at Samsung. In most cases, SCM Six Sigma projects result in system development. Accordingly, a new approach was needed. define, measure, analyze, enable, and verify (DMAEV) is the resulting approach. Additionally, Samsung incorporated the concept of five design parameters (process, operation rule and policy, organization role and responsibility, performance measure, and system), process modeling and value chain map techniques, and SCM related investment value analysis methods. Samsung’s endeavor for global optimum is continuing and SCM Six Sigma is expected to play an enabling role (Yang et al., 2007; Knowles et al., 2005). Six Sigma is also being used in the service world. The banking industry is also utilizing Six Sigma extensively. Citi Financial, Huntington, Commonwealth, UBS, Chase, US Bank and Bank of America are all employing Six Sigma. In all, Six Sigma and other quality tools have become part of the Bank of America’s culture and have created benefits of more than $ 2 billion according to Milton Jones (2004), a quality and productivity executive in the Bank of America. Methodology In order to determine the best ways to improve the process, we will utilize the Six Sigma DMAIC tool. This tool is meant to decrease variation in the process by identifying and improving specific areas. Overall, Six Sigma is a quality improvement tool, employed in a systematic project-oriented fashion through five steps: define, measure, analyze, improve, and control (Chase et al., 2006) In the define phase, a process in need of improvement is identified. Then, data relating to the process are gathered in the measure phase. In the analyze phase, the data are analyzed to determine the most likely causes of defect or problems. Statistical tools are utilized to ensure analysis is fair and impartial. Once the data are gathered and analyzed, the improvement phase will begin to establish ways to eliminate the identified causes of variation. Finally, in the control phase, a plan will be established to maintain

systematically the improvement set in place. The five phases of DMAIC process are described in the rest of this section. Define This project will focus on the process steps involved in the underwriting of mid-market customers by the Credit Initiation Team in the PSUD. The scope of the process analysis will begin at the end of the Regional Sales Manager’s sales process, when, he or she will have the customer submit an application for a PSD program. The process will be analyzed through the point where the credit decision is reported back the customer. Steps taken by the regional sales manager, the Credit Initiation Team and members of both teams administration will be analyzed. The process is outlined in Figure 1. Measure A process flow chart was provided by the company to illustrate the steps of the current credit approval process (see Figure 1). In addition, five months of historical data was collected on the process through a database that monitors progress of the Credit Initiation Team (CI Database). The data collected ranges from how many days it took to complete each step in the process to who was involved with each step. All Mid-Market requests were examined to determine the length of time to approve each credit request. Analyze In total, there were 442 mid-market credit requests that were approved in the five-month period. The requests came from entities of all different types of industries. Notably, 90 requests came from government entities, and nearly all were for under $200,000. Overall, the requests varied in size from $1 to $2 million. Figure 2 shows the number of credit request by limit size. Overall, the data analysis showed several areas in need of improvement. Since the goal of the project is to reduce the number of days for a credit to be approved to 15, the average amount of time it historically took to approve a request was examined. Of the total requests, 190 requests took longer than the 15-day goal. According to the statistical summary in Figure 3, the mean of the data was 19.774. The histogram below (Figure 4) shows that most credits were approved in around 15 to 20 days. Many instances, however, took much longer and therefore pulled the average up even more. Having established that a large percentage of the population of requests took longer than 15 days, further analysis was necessary to determine the causes of the delays. The following Cause and Effect Diagram (Figure 5) illustrates a number of the potential causes of credit request approvals taking over 15 days to be contingently approved. Further statistical analysis is needed to show which of the inputs (Sales, Underwriting, and Approval) is the biggest contributor to the delays. Analysis of sales team The sales team’s portion of the process was the first area examined. The sales team is responsible for providing complete information on the borrower, including full financial information, to the credit analysis team. Information on the borrower is entered into a credit request by the sales administrator and, if financial information is available electronically, the complete financial statements outlined in the credit policy


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Figure 1. Original PSUD credit approval process


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Figure 1.

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Figure 2. Number of credit requests by limit size

Figure 3. Summary for the number of days from receipt of request to approval

should be attached. In cases where financial statements are not available electronically, the sales administrator forwards hard copies of the financial statements to the credit administrator. If the request is received and complete financial information is not provided, the request is pended by the credit analyst. In reviewing the data provided, 147 or 33 percent of all requests were pended. See Breakdown of pended requests (Figure 6). Of the 33 percent, about two-thirds were pended due to incomplete financial statements.


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Figure 4. Histogram of the number of days from a credit request to approval

Figure 5. Cause and effect diagram

To determine the impact pending a request has on the number of days to approve a request, a one-way ANOVA test was conducted to illustrate correlation. To determine correlation, two measures will be examined: p-value and r -squared. The p-value is a measure of probability that a difference between groups during an experiment happened by chance. For example, a p -value of 0.01 ( p ¼ 0 :01) means there is a one in 100 chance the result occurred by chance. The lower the p -value, the more likely it is that the difference between groups was caused by treatment. R -squared refers to the

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fraction of variance explained by a model. Results can range between zero and one, with one signifying that the model perfectly predicts results. The results of the test, shown in Table I, indicate that there is a moderate correlation between pending requests and greater days to approve a request as illustrated by a p-value of 0.000 and R -sq. of 46.6 percent. A similar test was conducted of the “Date entered to approved” versus “Salesperson” and we found statistical significance shown by a p-value of 0.000 and R -sq. of 30.78 percent (see Table II). Overall, the previous tests have identified the salesperson’s actions as an area that if improved, could improve the number of days to a contingent approval. Analysis of credit analysis team A one-way ANOVA test (Table III) on the “Date Entered to Contingently Approved” versus the “Credit Analyst” showed no statistical relationship as shown by the p-value of 0.446 (see Table III). Therefore, we can rule out the credit analyst as an area in need of improvement.

Figure 6. Breakdown of pended requests

Table I. One way ANOVA test results for time from date entered to approved versus total days pended

Table II. One way ANOVA test results for time from date entered to approved versus sales person

Source

DF

SS

MS

F

p

Total days pended Error Total

38 403 441

115,797 135,075 250,871

3,047 335

9.09

0.000

Notes: S ¼ 18:31 R -sq: ¼ 46:16 percent R -sq: ð adjÞ ¼ 41:08 percent

Source

DF

SS

MS

F

p

Sales Person Error Total

27 355 382

7,1292 160,289 231,581

2,640 452

5.85

0.000


Analysis of approval Finally, analysis must be done to determine if the approver is playing a role in the long approval times. The one-way ANOVA test in Table IV shows that there is in fact some correlation between “Time entered to Approval” and the “Time from referred for approval to approval.” The average number of days it took between referred for an approval to approval of a credit was 3.13 days.


669 Improve After analyzing the data, it is clear that the two areas of the process in most need of improvement are the sales team and the approval stages. Each of the areas has failures that are leading to slow downs in the process. These failures can be identified in the cause and effect diagrams shown in Figures 7 and 8. In this section, the failures will be Source

DF

SS

MS

F

p

Credit Analyst Error Total

13 428 441

7,428 243,443 250,871

571 569

1.00

0.446

Notes: S ¼ 23:85 R -sq: ¼ 2 :96 percent R -sq: ð adjÞ ¼ 0:01 percent

Source

DF

SS

MS

F

Time from Referr Error Total

23 418 441

38,417 212,455 250,871

1,670 508

3.29


Table III. One way ANOVA test results for time from date entered to approved versus credit analyst

p

Table IV. One way ANOVA test 0.000 results for time from date entered to approval versus time from referred for approval to approval

Figure 7. Cause and effect diagram for sales team

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outlined and process changes and/or poka-yokes will be suggested to allow for improvements of suggested areas, namely, sales team and approval stages. A poka-yoke is a procedure that blocks the inevitable mistake from becoming a service defect (Chase et al., 2006).

670 Sales team Failure: untimely financial information. Financial information is consistently untimely. Often times, the implementation team receives a request and the financial statements are not available to the credit analyst for a number of days. This is often because financials are sent in the mail. Recommended poka-yoke: require all financial data, where applicable, be submitted electronically. Although this poka-yoke will require the sales team to have access to scanners, it will ensure that each request is accompanied by financial data. Failure: incomplete financial information. Depending upon the request, the credit policy dictates that certain financial information be provided for initial analysis. Frequently, the request is submitted without the required financial information. The requests are pended until the required information is received. Recommended poka-yoke: develop an Excel form that automatically tells sales administrator what financial data is required depending upon certain credit variables, such as the type of entity requesting credit and the entities fiscal year end. Recommended poka-yoke: train the sales administrator and sales team as to the proper amount of financial data needed for each request. Failure: sales teams unresponsive to credit teams request . Recommended poka-yoke: hold salespeople accountable. The sales representatives are responsible for retrieving information requested by credit analysts. If they do not submit the proper data, they should be held accountable for their actions. A quarterly report should be sent to the sales manager to allow him or her to identify repeat offenders. Failure: incorrect financial statements. Recommended poka-yoke: require sales administrator to verify submitted financial data against company name submitted on the credit request as well as on the credit application. Many requests are accompanied by financial data of an incorrect entity or variation of the company. The sales administrator needs to ensure there is a match.

Figure 8. Cause and effect diagram for approval area

Approval Failure: approvals taking too long . Poka-yoke: develop folder system that indicates the priority level of an approval request. Currently, analysts drop off the approval requests into the inbox of the credit manager. Any request that needs to be approved in less than two days should be submitted to the credit manager for approval in a red folder. Poka-yoke: hold the credit manager responsible for making decisions regarding requests within three days. A monthly report should be generated and forwarded to his/her manager that indicates the number of approval requests that took over three days. Process alteration In addition to implementing poka-yokes, the process can be revised to allow the average number of days from the receipt of a credit request to approval to 15 days. The suggested changes are reflected in a revised credit approval process flow chart shown in Figure 9. Process change no. 1. Develop an automated scoring process for less risky and/or smaller requests. Currently, a complete financial analysis is being completed for all requests. To improve this process, less risky credit requests, namely government entities, should be put through an automatic scoring model for requests up to $200,000. Additionally, small requests, which are $50,000 or under, should be scored through a similar automated system. This will allow for benefits throughout the process. Sales administrators will be responsible for submitting fewer requests and can therefore spend more time submitting other requests properly. Similarly, analysts will have fewer requests in their queue, allowing for faster review completions. Finally, approvers will have fewer requests to approve. Process change no. 2 . Allow analysts to have approval authority. Under the current policy, two approvers are necessary to approve a request over a certain dollar amount. However, analysts have no approval authority so two separate approvers must review the request in addition to the credit analyst. By allowing analysts to have credit approval authority, only one credit manager will need to review the majority of the requests. In the approval area, we can decrease approval time in two ways. One way is to enforce a two-day limit on approvals and to hold approvers to that standard. Another way is to change the approval authority limits on the approvers so that additional approvers are not needed for certain credits. If the credit manager was allowed to have final authority on approvals less than $2 million, then we could save days of time when the credit was previously waiting for an extra approver. This recommendation is also reflected in the improved process. Control Integral to the Six Sigma DMAIC process is the control phase. This portion of the process sets in place a plan to ensure changes and improvements are maintained. It is suggested that the management team meet monthly to review progress and compliance with the changes. Before the management meeting, the credit manager will survey the sales team to get their feedback on issues they are having with the implemented changes. In addition, the credit manager will run a report to compare updated data with the data from this project. Adjustments will be made to the process on an ongoing basis.


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Figure 9. Revised PSUD credit approval process


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Figure 9.

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Value of the study Available research on the process improvement of commercial underwriting was minimal. However, numerous studies have been completed regarding process improvements in the insurance industry and consumer credit. Therefore, this study is valuable as it fills a gap in available research. Results of this study can be used by the current credit manager of the initiation group within the financial services operation. In addition, other groups within the financial services operation can learn from this analysis and similar steps can be taken within their respective credit groups to promote efficiencies. Furthermore, other financial services institutions with similar processes may benefit from the study. On a higher level, the study provides an excellent example of how the Six Sigma DMAIC process can be applied to a process in the service industry. Other companies that provide services, regardless of the industry, can learn from the analysis techniques used in the study to improve the customer service they provide. Research findings, recommendations and limitations Using the Six Sigma DMAIC process, this study found several areas for improvement in the process studied. Bottlenecks and variation in the process were identified through in-depth analysis of the statistical information. The analysis showed that the sales individuals and the credit approvers largely contributed to the inefficiencies in the process. This paper suggests process changes, as well as new measures, that can be implemented to prevent variation in the process in the future. The recommendations in this paper may be difficult to implement given the lack of control the credit manager has over the sales team and its action. Individuals on the sales team are managed by the sales team manager. The success of many of the recommendations in this paper will rely heavily on the sales team’s willingness to support the changes. Though the sales team stands to benefit from the improved process, they have other goals and incentives that motivate their behavior. It is recommended that future similar studies be conducted as a joint effort between member of all involved parties in the process to ensure buy-in of the results and recommendations. Furthermore, a number of recommendations, most notably the process changes, require changes to financial services operation policy. The credit manager alone does not have the authority to approve such changes. Approval of the changes by management up to and including the chief credit officer is required. Lastly, the implementation of automated scoring system involves large initial costs whether it is a new scoring system and/or developed by the financial services operation. Also, employees from number of areas of the organization would need to develop and monitor the system to ensure the recommendations are in line with the credit risk. Practical implications Analysis of this process has led us to believe that it is possible to decrease the number of days from when a credit request is received to when it is approved by five days, a 25 percent reduction in total days. The implementation of suggestions will result in improved customer service and satisfaction. However, it should be noted that the process that was analyzed is not unique. Companies, not only in the financial services

industry, but also in the vast majority of service industries, can benefit from similar research. Complex processes, no matter how finely tuned, have areas of bottlenecks or consistent delays, which can be identified and resolved through the Six Sigma process. The resolution of indicated delays will invariably result in a decrease in variation, and ultimately, improved service to customers. The improved service will ultimately translate into improved profitability, which should be at the forefront of every manager’s mind. Conclusion As a critical process in the division and company studied, the credit initiation process for mid-level corporate credit card customers had room for improvement. Currently, the process takes an average of 20 days to complete. The purpose of this study was to decrease that process cycle time to 15 days, a 25 percent improvement. Such an improvement in efficiency would save time and money for the division and company, as well as increase customer satisfaction. The process was studied using the Six Sigma DMAIC tool. The tool involves several steps: Define, measure, analyze, improve, and control. Using the tool, data surrounding this process was statistically analyzed to pinpoint the areas of the process where the most valuable improvements could be made. High correlation was found between delays in the process and both the sales team and the approval portion of the process. From the high correlation, it was inferred that improvements to those areas of the process would have the most significant impact to decrease the process time. Next, several poka-yokes and process improvements were recommended to improve those specific areas. The poka-yokes block possible failures in the process from occurring. Similarly, the process improvements suggested target the sales team and the approval process and were designed to increase efficiency greatly. References

Beans, K.M. (2005), “Improving the efficiencies of delivering service to your lending customers”, The RMA Journal , Vol. 87 No. 5, pp. 78-82. Chase, R.B., Jacobs, F.R. and Aquilano, N. (2006), Operations Management for Competitive Advantage, McGraw-Hill Irwin, New York, NY. Dasgupta, T. (2003), “Using the six-sigma metric to measure and improve the performance of a supply chain”, Total Quality Management , Vol. 14 No. 3, pp. 355-66. Haslem, J.A. and Longbrake, W.A. (1972), “A credit scoring model for commercial loans, journal of money”, Credit and Banking , Vol. 4 No. 3, pp. 733-4. Jones, M.H. (2004), “Using quality & Six Sigma to grow America’s leading financial solutions company”, International Quality & Productivity Center Conference, December 31 , available at: http://newsroom.bankofamerica.com/index.php?s ¼ speeches&item ¼ 18 Knowles, G., Whicker, L., Femat, J.H. and Canales, F.D.C. (2005), “A conceptual model for the application of six sigma methodologies to supply chain improvement”, International Journal of Logistics: Research and Applications, Vol. 8 No. 1, pp. 51-65. Kuritzkes, A., Schuermann, T. and Weiner, S.M. (2002), Risk Measurement, Risk Management, and Capital Adequacy in Financial Conglomerates , Netherlands-United States Roundtable on Financial Conglomerates, Washington, DC. Petrie, J.C. (2006), “Credit department management: uncovering hidden value”, The RMA Journal , Vol. 88 No. 8, pp. 34-41.


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Revere, L. and Black, K. (2003), “Integrating Six Sigma with total quality management”, Journal of Healthcare Management , Vol. 48 No. 6, pp. 377-91. Scalise, D. (2003), “Six Sigma in action”, Hospitals & Health Networks , Vol. 77 No. 5, May, p. 57. Yang, H.M., Choi, B.S., Park, H.J., Suh, M.S. and Chae, B. (2007), “Supply chain management Six Sigma: a management innovation methodology at the Samsung Group”, Supply Chain Management: An International Journal , Vol. 12 No. 2, pp. 88-95.

676 About the authors Sameer Kumar is currently a Professor of Decision Sciences and Qwest Endowed Chair in Global Communications and Technology Management in the Opus College of Business, University of St Thomas. Major areas of research interests include optimization concepts applied to various aspects of global supply chain management, information systems, technology management, product and process innovation, quality engineering and capital investment justifications. Sameer Kumar is the corresponding author and can be contacted at: [email protected] Anthony D. Wolfe is currently pursuing an MBA in the OPUS College of Business at the University of St Thomas, Minneapolis. Anthony has worked for over five years in Financial Services Industry where he has managed credit services operation. He has a BS in Business Management and Marketing from the Minnesota State University, Marshall, Minnesota. Katherine A. Wolfe is currently pursuing an MBA in the OPUS College of Business at the University of St Thomas, Minneapolis. She has been involved in marketing communications functions over the past five years in the software industry. Katherine has completed training as a Six Sigma Green Belt and has a BS in Public Relations and Marketing from the Minnesota State University, Marshall, Minnesota.

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SIX SIGMA DMAIC Financial Services

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