An evaluation of just in time (JIT) implementation on manufacturing performance in Indian industry

An evaluation of just in time (JIT) implementation on manufacturing performance in Indian industry Gurinder Singh and Inderpreet Singh Ahuja

Gurinder Singh is based at Government Polytechnic College, Mohali, Khunimajara, Punjab, India. Inderpreet Singh Ahuja is a Professor based at University College of Engineering, Punjabi University, Patiala, Punjab, India.

Abstract Purpose – The purpose of this paper is to create awareness of contributions made by just-in-time manufacturing (JIT) practice towards building performance measures in Indian manufacturing industry. Implications of JIT implementation issues in Indian manufacturing industry have been critically evaluated in this paper. Design/methodology/approach – While conducting this study, survey of reasonable number of manufacturing organisations have been made so as to ascertain contributions made by JIT initiatives in the Indian manufacturing industries for achieving major performance measures. The correlations between various JIT implementation dimensions and performance parameters have been worked out by employing various statistical tools and bringing out significant factors contributing effectively towards achieving manufacturing performance measures. Findings – The study divulges that attitude of management, participation of workers, relationship of organisation with suppliers and customers, adoption of latest manufacturing methods and successful adaptation of effective JIT initiatives can significantly contribute towards enhancing performance measures in the organisation. The study also reveals that the holistic JIT manufacturing methods outscore the traditional manufacturing practices towards improving the manufacturing performance. The study highlights that detailed JIT implementation over a reasonable period can greatly contribute towards achievement in performance of organisation. Originality/value – The present study analyses the impact of JIT manufacturing method on performance of organisation and highlights the need for aligning organisational efforts in establishing manufacturing methods for attaining improvements in performance of manufacturing organisations. The paper highlighted the status of JIT manufacturing initiatives in the Indian context and the preparedness of Indian manufacturing industry to meet the challenges imposed by the Western world by employing aggressive JIT manufacturing strategies. Keywords Competitive advantage, India, Performance measures, Manufacturing industries, Just-in-time manufacturing (JIT) Paper type Research paper

1. Introduction

Received 22 September 2013 Revised 19 February 2014 Accepted 2 June 2014

In the present highly dynamic and continuously changing environment, the global competition among organisations has lead to higher demands on most manufacturing organisations (Miyake and Enkawa, 1999). Global competition among manufacturing organisations has heralded a tremendous change in approach of management, techniques of product and process, expectations of customer, attitude of supplier as well as competitive behaviour (Ahuja et al., 2006). The challenges thrown up by this competition has forced the manufacturing organisations worldwide to nurture high reliability, quality, availability and maintainability in the manufacturing systems by implementation of various strategic and proactive market-driven strategies to remain competitive in a highly dynamic environment (Ahuja and Khamba, 2008). Thus, to remain at the top, an organisation needs to change strategies, improve product quality and reduce cost of production at a faster rate than its competitors (Singh and Ahuja, 2012).

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DOI 10.1108/JABS-09-2013-0051

Manufacturing methods have usually been considered as an operating expense to be minimised, and not treated an investment in increasing reliability of process in many organisations (Patterson et al., 1996). The inadequacies of the manufacturing practices in the past have adversely affected the organisational competitiveness by reducing throughput and reliability of production facilities (Ahuja and Khamba, 2008). This has resulted in fast decline in quality of products and overall production, lowering of equipment availability due to excessive system downtime, increasing cost of product and increasing inventory, thereby leading to unreliable delivery performance. As organisations in today’s highly challenging scenario have working to reduce costs and enhance quality and responsiveness, the reduction in inventory and excess capacity have come out with serious weaknesses in the traditional manufacturing programmes (Lawrence, 1999). To meet all the challenges, organisations try to introduce different manufacturing techniques (Singh and Ahuja, 2012). Efforts have been made by management of organisations to reduce the manufacturing costs and to enhance the product quality. Various manufacturing techniques have been employed by organisations to remain competitive. The last quarter of the 20th century witnessed the emergence of three major programmatic operations improvement concepts that have drastically changed the way manufacturing firms manage their operations. These were just-in-time (JIT) production systems, International Standards Organization (ISO) 9000 certifications and total quality management (TQM) (Dreyfus et al., 2004). Adoption of world-class, lean and integrated manufacturing strategies such as JIT manufacturing leads to improvement of production performance (Fullerton and McWatters, 2002). Chauhan and Singh (2012) have emphasised that “elimination of waste” is the most important parameter of lean manufacturing, followed by “just in time deliveries”. JIT manufacturing is a systems approach to developing and operating a manufacturing system (Alawode and Ojo, 2008; González-R. et al., 2013). It is not a technique but a management philosophy, and it refers to meet the demand of customers well in time with enhanced quality. JIT philosophy is based on the concept of delivering raw materials when needed and producing products when there is a need (Singh and Ahuja, 2012). The main focus is on minimising all kind of inventories like raw material, work-in-process (WIP) and finished goods inventory with a view to cutting costs of inventory and also helping to expose other more serious inefficiencies (poor maintenance, poor quality, inspection, backlogs, etc.) in the manufacturing cycle (Vuppalapati et al., 1995). The fundamental objective of JIT is to eliminate all waste from the entire manufacturing cycle through continuous improvement (Frazier et al., 1988). It reduces the complexity of detailed planning of material, the need for shop floor tracking, raw material as well as WIP inventories, qualities and transactions associated with shop floor and purchasing systems (Manoj, 2011). However, benefits of JIT do not just happen but before a manufacturing organisation reaps the benefits of JIT, it must accept JIT as an organisational philosophy.

2. Literature review Holding of high inventory has been commonly considered as poor management (Boute et al., 2004). JIT has been depicted as an inventory control technique and the Japanese auto industry has recognised JIT as the developer of inventory management philosophy (Aghazadeh, 2003). It is a systematic approach which reduces inventory by supplying material at production and distribution points only when needed (Lee and Wellan, 1993). As the name implies, JIT is to produce goods JIT for use or sale (Adeyemi, 2010). Indeed, JIT is a method of production that has been developed to evolve a defect-free process (Cheng and Podolsky, 1996). Horngren and Foster (1987) listed four cardinal objectives of JIT, as shown in Figure 1. JIT is an approach to manufacturing based on waste reduction and rapid response to customer demand (Mullarkey et al., 1995). It is not like traditional forms of production, where fabrication, sub-assembly or assembly takes place as and when material is

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Figure 1 Objectives of JIT

availability. JIT is considered to be a “pull” system of manufacturing where production takes place only when there are requirements from downstream operations and specific demands from external customers (Mullarkey et al., 1995). The core of the JIT philosophy is continuous improvement in process through the elimination of all kinds of waste (Chakravorty and Atwater, 1995). According to Bicheno (1987), JIT is “to produce instantaneously with perfect quality and minimum waste”. In its broader sense, JIT is an approach of achieving perfection in a manufacturing industry based on the continuous elimination of waste. Thus, core elements of JIT focus on streamlining production process and eliminating waste in materials as well as labour through considerable reductions in WIP inventory, standardisation of work processes and elimination of all forms of non-value-added activities, like rework, end-of-line and in-line quality inspection and unnecessary material handling (Mullarkey et al., 1995). JIT production practices positively affect both efficiency and delivery (Danese et al., 2012). JIT is an innovative, non-traditional approach to manufacturing that is complementary to TQM, total productive maintenance (TPM), involvement of employee, management commitment, continuous improvement in performance and other world-class manufacturing strategies. JIT is a system or one can say culture that takes advantage of the skills and abilities of all individuals in an organisation (Patterson et al., 1996). JIT has garnered a great response from the manufacturing organisations worldwide since its evolution, and a very large number of manufacturing organisations are adopting JIT manufacturing, especially in developed and developing nations. JIT is a critical initiative to meet demand of customer on price, quality and lead times. JIT addresses entire production system over the entire life cycle and builds a concrete, shop floor-based mechanism to prevent various manufacturing losses and wastes (Ahuja and Khamba, 2008). Major aims of JIT are to eliminate all accidents, wastes, defects and breakdowns at the workplace. JIT is the foundation for world class manufacturing, as it facilitates the effective implementation of world-class lean manufacturing practices. Very few research papers on JIT implementation in Indian context are available for reference, at present. Lohar (2011) has conducted the survey of JIT implementation in Indian industries and found that the JIT has the potential to increase the operational efficiency, quality and organisational effectiveness of Indian industries, while some basic elements of JIT have been slightly difficult to implement in existing production system of industries. Study depicts that to gain the benefits of JIT, Indian industries should be willing to modify their procedure for dealing with supplier, analysis of operations to identify the areas of standardisation, simplification and automation and reengineering of operational processes and procedures are some important issues, which should be examined prior to


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implementation of JIT. It has been concluded that if these issues are not properly taken care of, the JIT efforts are sure to come across buyers-and supplier-related problems. Singh and Garg (2011) have explained about JIT movement, its concept, elements of JIT, motivational factors for JIT and benefits of JIT. Manoj (2011) has conducted his research in agro manufacturing units in Kerala (KAMCO) and found that philosophies like JIT have become imperative for survival and growth of any manufacturing company, rather than just an option. Road map for adoption of JIT in KAMCO has been explained in study. Pisuchpen (2012) has investigated the effect of varying number of kanban cards, mean inter-arrival time of demand and locations of the bottlenecks on the performance integration of JIT flexible manufacturing, assembly and disassembly systems using MANOVA. The study emphasised the interactions between the variables and their effects on system performance for improving performance processes. The research highlighted that minimised WIP can be obtained by higher percentage average fill rate, lower WIP, small average part cycles times and increase in kanban cards, while simultaneously retaining full customer satisfaction. Danese et al. (2012) in their research work developed six hypotheses on the relationships between JIT production, JIT supply, efficiency and delivery performance. Authors concluded that JIT production practices have positively affected both efficiency and delivery. JIT supply practices positively moderate the relationship between JIT production and delivery, while there is no significant moderating effect when considering the impact on efficiency. Chen and Tan (2013) have shown that organisation ownership not only impacts the implementation of JIT and operations performance but also impacts the relationship between JIT implementation and operations performance:

Moreover, the results revealed that, for firms operating in China, the implementation frequency of JIT practices varies with organisation ownerships. The foreign and joint venture firms (JVFs) were found to have a higher level of JIT implementation and can also achieve better performance from JIT implementation than state-owned and private-owned firms. Also, JIT implementation was found to have a significantly positive relationship with operations performance in all types of ownership firms.

Malik et al. (2011) have conducted their research on JIT-based quality management in Indian manufacturing industries, and after employing various statistical techniques in their survey, it has been depicted that the degree of difficulty in implementation of JIT-based quality management was found to be 3.18 on a scale of zero-five, implying that implementation of JIT-based quality management in totality is reasonably difficult in Indian industries. Kumar and Grewal (2007) have discussed the critical elements of the JIT in the context of Indian service industries. The study has been analysed by sending a questionnaire to about 60 service industries in Chandigarh, Delhi, Punjab, Haryana and Himachal Pardesh. On the basis of 30 responses received, authors identified critical elements of JIT. Attempt was made to examine the degree of importance and degree of difficulties, of these critical elements in Indian service industries. The results revealed that JIT played an important role in service industries. Authors suggested that elements that were less difficult but more important should be implemented in the initial stage. Mahadevan (1997) discussed the readiness of Indian industries in implementation of JIT. The survey was conducted by sending a questionnaire that identified 14 critical factors and participating organisations were asked about importance of these factors. It has been found that automobile industry in India made significant changes in many areas like JIT purchasing, implementation of TPM and multi-skill labour. These factors contribute towards

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successful implementation of JIT in manufacturing industry. Management’s commitment and participation of workers are the critical success factors.

3. Methodology and hypothesis The study has been conducted in the large-scale medium and on manufacturing industries of India that have successfully implemented JIT or are in the various stages of implementing JIT to study the JIT implementation issues and achievements made as a result of strategic JIT implementation. The manufacturing organisations having ⬎ 400 employees and having turnover ⬎Rs60 crores have been included in the present study. In this study, a reasonable number of manufacturing organisations (60 organisations) have been surveyed extensively to assure contributions made by JIT implementation initiatives in the Indian industries towards realisation of crux competencies. The approach of study has been directed towards justification of JIT implementation for its support to competitive manufacturing in Indian industries. The methodology employed in the study is shown in Figure 2. To ensure the contributions made by JIT manufacturing initiatives towards realisation of manufacturing performances, a detailed “JIT questionnaire” has been designed for accessing the JIT implementation capabilities of the Indian manufacturing industry and recognition of manufacturing performances. The JIT questionnaire has been designed through extensive literature review of studies by Benson (1986), Lee and Seah (2007), Golhar and Stamm (1991), Clark and Mia (1993), Ramarapu et al. (1995), Spencer and Guide (1995), Yasin and Wafa (1996), McLachlin (1997), Wafa and Yasin (1998), Claycomb et al. (1999), Canel et al. (2000) and Kumar (2010) and validated through peer review from academicians, consultants, JIT councillors and practitioners (JIT co-coordinators) from the industry. In the present study, the questionnaire survey technique has been used for gathering information on the status of JIT implementation issues and the recognition of various manufacturing performances in the Indian manufacturing industry. For carrying out the survey effectively, the JIT questionnaire has been designed by conducting extensive literature review, and it has been validated through peer review from academicians, consultants and JIT practitioners from the industry. To ensure the relevance and effectiveness of the questions, the questionnaire has been pre-tested on an exemplary sample of industry. The suggestions received from the peers, consultants, managers and senior executives from the industries and academicians have been added to make the questionnaire more accordant to the purpose and bring out major outcomes as a result of strategic JIT implementation. The JIT questionnaire delivers the purpose of divulging the exploits of Indian organisation with JIT practices and highlights the major contributions of Figure 2 Methodology utilised for study


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JIT in recognising the overall organisation’s goals and objectives. Figure 3 shows the steps undertaken in finalisation of the JIT questionnaire. A 4-point Likert scale has been used in this study to evaluate the extent of deployment of various JIT implementation success factors and enhancements in manufacturing performance parameters as a result of JIT implementation initiatives. The 4-point Likert scale has been used in the present study because it forces someone to agree or disagree. In this scale, there is no neutral or middle ground. It’s a forced choice that demands much more from respondents. The questionnaire was sent to 300 industries and 64 responses were received, out of which 60 responses are useable, and on the basis of these 60 responses further study has been carried out. Figure 4 shows the model for assessing the inter-relationships between critical JIT implementation dimensions and manufacturing performance measures. In the present study, the association between success factors of JIT implementation and manufacturing performance measures have also been assessed to evolve the recognition of the contributions of the various JIT implementation success factors towards achievement of specific manufacturing performance improvements, Further, the outcome of enhancements in manufacturing performance with respect to “time frame of JIT implementation” has been accessed to confirm the fact that JIT implementation is not an overnight process, and it requires quite a reasonable period that varies between three and five years to realise the true potential of JIT. The study has revealed improvements in manufacturing performance over time that extends up to five years and beyond. In this research, various statistical methods and tools has been employed for extracting out important factors and elements contributing towards realisation of improvement in

Figure 3 Questionnaire finalisation procedure

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Figure 4 Inter-relationships between JIT implementation dimensions and manufacturing performance measures

manufacturing performance. For the analysis, the various statistical tools like Cronbach’s alpha, Pearson correlation coefficient, multiple regression analysis, canonical correlation and two-tailed t-test have been employed to find out the contributions of JIT implementation initiatives towards achievement of manufacturing performance improvements in the Indian manufacturing organisations. For the present study, formulation of hypotheses is shown below: Manufacturing performance improvements-related hypothesis H1.

A significant overall association exists between JIT success factors and JIT manufacturing performance parameters.

H2.

There exists significant association between strategic JIT manufacturing performance parameters with major individual JIT implementation success factors.

Hypotheses related to manufacturing performance improvements H3.


Strategic enhancement in manufacturing performance depends on the gain in experience by manufacturing organisations with respect to time.

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4. Contributions of JIT in attaining manufacturing performance enhancement Indian manufacturing industries survey has acknowledged the fact that many organisations in the country have implemented or are going to implement JIT. The target respondents for the JIT questionnaire have been the industries that have made serious activities in the field of JIT and accomplished significant achievements by adoption of strategic JIT initiatives. First screening of manufacturing organisations across the country was done, and a database of industrials was created so that JIT questionnaire should be mailed to these industries. The JIT questionnaires were than mailed to the selected industries and were afterwards contacted through postal mail, telephonically and e-mail and the context of the present research work were explained, its significance and to clarify any queries/doubts to facilitate absolute and clear-cut responses to the JIT questionnaires. In total, 60 responses regarding the JIT questionnaire have been received from the major Indian manufacturing organisations at different stages of JIT implementation. Most of the replies of the “JIT Questionnaire” belonged to the top management and executives that included several Vice Presidents, General Managers (GM), Head – Quality Assurance, Head of Operations, Head – Process Engineering, Quality Coordinators, Head – Improvement Management, Chief Managers, Manages Manufacturing, GM – TPM, TPM Head, GM – Technical, Quality Managers, etc. The responses received from various organisations have been compiled and critically analysed to access the performance of various JIT-related issues of Indian manufacturing industry. Cronbach’s alpha has been applied to various JIT implementation success factors and JIT implementation performance measures to evaluate the reliability of the input and output data collected through the JIT questionnaire. The values of Cronbach’s alpha for various input and output parameters has been depicted in Table I. The values of Cronbach’s alpha for all the input and output parameters are in excess of 0.65. This indicates the significantly high reliability of data for various input and output parameters. The validation of all the input variables (A1, A2, A3, A4, A5 and A6) and output variables (B1, B2, B3, B4, B5, B6, B7, B8, B9 and B10) has been done by discriminant validity analysis. It is clear from the Table II that all the respective within covariance values of variances are more than the between variables covariance values. Therefore, input and output variables are further validated. On the basis of the responses received from the organisations, evaluation of association of various JIT success factors with key manufacturing performance parameters has been made. Table III shows the Pearson correlations between various JIT implementation success factors and manufacturing performance parameters. The Pearson correlations have been calculated to assure the significant factors contributing to success of the JIT implementation programme in the organisations. Only those pairs are considered as having a significant association which have Pearson correlation ⱖ 40 per cent and statistically significant at one per cent level of significance. To know about critical success factors for attaining results through holistic JIT implementation, the significant correlations have been obtained as a result of Pearson’s correlation and covariance, and these are validated through “Multiple Regression Analysis”, as shown in Table IV. The notations shown in the table include ␤ ⫽ regression coefficient (beta coefficient), R ⫽ multiple correlation coefficient. The significant factors (␤)

Table I Cronbach’s alpha values for input and output data A1 0.851

A2

A3

A4

A5

A6

A7

0.918

0.845

0.856

0.926

0.831

0.809

Cronbach’s alpha B1 B2 B3 0.870

0.816

0.828

B4

B5

B6

B7

B8

B9

B10

0.840

0.839

0.812

0.782

0.817

0.860

0.912

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Table II Covariance values for input and output parameters Covariance A1 A2 A3 A4 A5 A6 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10

A1

A2

A3

A4

A5

A6

B1

B2

B3

B4

B5

B6

B7

B8

B9

B10

0.016 0.011 0.012 0.011 0.009 0.009 0.012 0.013 0.015 0.015 0.013 0.016 0.015 0.012 0.013 0.016

0.011 0.014 0.012 0.011 0.012 0.012 0.013 0.013 0.012 0.013 0.015 0.016 0.014 0.014 0.015 0.017

0.012 0.012 0.015 0.012 0.010 0.012 0.013 0.013 0.016 0.015 0.014 0.018 0.016 0.017 0.018 0.019

0.011 0.011 0.012 0.013 0.011 0.010 0.012 0.012 0.012 0.013 0.011 0.016 0.015 0.015 0.015 0.017

0.009 0.012 0.010 0.011 0.017 0.012 0.011 0.011 0.010 0.011 0.012 0.016 0.014 0.013 0.012 0.014

0.009 0.012 0.012 0.010 0.012 0.021 0.013 0.011 0.013 0.014 0.013 0.018 0.015 0.016 0.017 0.017

0.012 0.013 0.013 0.012 0.011 0.013 0.020 0.015 0.013 0.014 0.018 0.016 0.016 0.015 0.016 0.020

0.013 0.013 0.013 0.012 0.011 0.011 0.015 0.019 0.014 0.016 0.016 0.019 0.015 0.017 0.016 0.020

0.015 0.012 0.016 0.012 0.010 0.013 0.013 0.014 0.030 0.022 0.017 0.021 0.019 0.017 0.022 0.020

0.015 0.013 0.015 0.013 0.011 0.014 0.014 0.016 0.022 0.025 0.018 0.023 0.020 0.017 0.020 0.020

0.013 0.015 0.014 0.011 0.012 0.013 0.018 0.016 0.017 0.018 0.026 0.016 0.017 0.015 0.017 0.022

0.016 0.016 0.018 0.016 0.016 0.018 0.016 0.019 0.021 0.023 0.016 0.031 0.020 0.021 0.023 0.022

0.015 0.014 0.016 0.015 0.014 0.015 0.016 0.015 0.019 0.020 0.017 0.020 0.027 0.019 0.021 0.023

0.012 0.014 0.017 0.015 0.013 0.016 0.015 0.017 0.017 0.017 0.015 0.021 0.019 0.025 0.020 0.023

0.013 0.015 0.018 0.015 0.012 0.017 0.016 0.013 0.015 0.015 0.013 0.016 0.015 0.012 0.030 0.025

0.016 0.017 0.019 0.017 0.014 0.017 0.020 0.013 0.012 0.013 0.015 0.016 0.014 0.014 0.025 0.033

with significance level, multiple correlation coefficient (R) and F-values for each performance parameter are indicated in Table IV. The results show that various JIT success factors depicted in the table have significant contribution with the respective manufacturing performance parameters reported. Finally, to calculate the inter-relationship between various JIT success factors and manufacturing performance, canonical correlation analysis has been used. The main aim Table III Values of Pearson correlation for all input and output categories Pearson correlation A1 A2 A3 A4 A5 A6 Note:

ⴱⴱ

B1

B2

B3

B4

B5

B6

B7

B8

B9

B10

0.640 0.800** 0.764** 0.703 0.615 0.634

0.724 0.796** 0.785** 0.763 0.612 0.544

0.694** 0.591 0.743** 0.586 0.445 0.506

0.762** 0.682 0.789** 0.707 0.537 0.600

0.656 0.788** 0.693 0.600 0.565 0.571

0.735 0.797 0.820** 0.768 0.692** 0.696

0.704 0.731 0.804** 0.797** 0.630 0.641

0.612** 0.759 0.852** 0.823** 0.632 0.681

0.598 0.730 0.813** 0.731 0.530 0.659

0.681** 0.786** 0.858 0.791 0.610 0.631

Correlation is significant at the 0.01 level (two-tailed)

Table IV Multiple regression between JIT implementation success factors with manufacturing performance parameters Performance parameter

Significance factor

B1

A2 A3 A2 A3 A3 A1 A3 A1 A2 A3 A5 A3 A4 A3 A4 A1 A3 A2 A1

B2 B3 B4 B5 B6 B7 B8 B9 B10


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Beta value ␤

t-value

Significance (p-value)

R-value

F-value

0.525 0.345 0.467 0.411 0.515 0.295 0.499 0.375 0.788 0.631 0.317 0.455 0.417 0.660 0.446 ⫺0.233 0.813 0.637 0.277

4.235 2.786 3.837 3.379 3.823 2.192 4.208 3.164 9.737 7.452 3.739 3.443 3.153 5.369 3.795 ⫺2.285 10.639 5.962 2.591

0.000 0.007 0.000 0.001 0.001 0.015 0.000 0.002 0.000 0.000 0.000 0.001 0.003 0.000 0.000 0.026 0.000 0.000 0.012

0.827

61.561

0.833

64.770

0.766

40.502

0.824

60.443

0.778 0.858

94.807 79.611

0.836

66.029

0.887

68.778

0.813 0.874

113.19 92.524

in utilising canonical correlation analysis for data analysis is to know the relationships between a set of multiple dependent (manufacturing performance parameters) and multiple predictor variables (JIT success factors). The bivariate correlation between linear composites of the predictor (JIT success factors) and criterion variables (manufacturing performance parameters) is measured with canonical correlation. The results obtained by applying canonical correlation analysis are shown in Table V. The results shown in column 1 of Table V show very strong and significant canonical correlation function (␤ ⫽ 0.975 at F-statistic probability of 0.00) between the predictor set of JIT implementation dimensions and the criterion set of manufacturing performance parameters. The observations of the multivariate test statistics have also been statistically significant (p ⬍ 0.001). The redundancy indices are 0.666 and 0.695 for the dependent and independent canonical variates, respectively. The redundancy index indicates the amount of variance in a canonical variate explained by the other canonical variate in the canonical function. The canonical loadings for predictor set of various JIT success factors (A1, A2, A3, A4, A5 and A6) on the independent variate has a range from 0.765 to 0.948. The criterion set of manufacturing performance parameter variates (B1, B2, B3, B4, B5, B6, B7, B8, B9 and B10) have also been found to be strongly loaded (range from 0.724 to 0.892) on the dependent variate. Because of the modest sample size, stability runs were made by dropping one variable at a time and re-executing the canonical correlation analysis to assess the validity of the canonical loadings. Of interest is the stability of the canonical loadings and the statistical significance of the univariate and step-down F tests for the canonical correlation function. The correlation between the individual predictor and criterion variables and their respective canonical variates is measured by canonical correlation, and are similar in interpretation to factor loadings. Columns 3, 4, 5, 6, 7 and 8 in Table V show the results of these stability runs Table V Canonical correlation analysis between JIT implementation dimensions and manufacturing performance results with stability analysis Results with all variables

Results after deletion of A3 A4

A1

A2

0.975 0.011 0.00

0.975 0.018 0.00

0.968 0.022 0.00

0.961 0.021 0.00

Dependent variate Canonical loadings B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 Shared variance Redundancy index

⫺0.842 ⫺0.846 ⫺0.724 ⫺0.809 ⫺0.787 ⫺0.892 ⫺0.848 ⫺0.881 ⫺0.835 ⫺0.887 0.700 0.695

⫺0.842 ⫺0.843 ⫺0.718 ⫺0.802 ⫺0.781 ⫺0.888 ⫺0.847 ⫺0.887 ⫺0.839 ⫺0.889 0.698 0.716

⫺0.820 ⫺0.834 ⫺0.743 ⫺0.828 ⫺0.744 ⫺0.894 ⫺0.869 ⫺0.899 ⫺0.840 ⫺0.892 0.702 0.671

Independent variate Canonical loadings A1 A2 A3 A4 A5 A6 Shared variance Redundancy

⫺0.790 ⫺0.933 ⫺0.948 ⫺0.887 ⫺0.765 ⫺0.788 0.731 0.666

– ⫺0.932 ⫺0.949 ⫺0.890 ⫺0.763 ⫺0.790 0.754 0.663

⫺0.797 – ⫺0.965 ⫺0.905 ⫺0.757 ⫺0.789 0.716 0.658

Canonical correlation Canonical root F-statistic probability

A5

A6

0.975 0.017 0.00

0.974 0.014 0.00

0.972 0.014 0.00

⫺0.844 ⫺0.846 ⫺0.689 ⫺0.798 ⫺0.803 ⫺0.893 ⫺0.843 ⫺0.854 ⫺0.810 ⫺0.861 0.682 0.669

⫺0.843 ⫺0.846 ⫺0.727 ⫺0.809 ⫺0.792 ⫺0.892 ⫺0.845 ⫺0.876 ⫺0.832 ⫺0.885 0.686 0.699

⫺0.843 ⫺0.846 ⫺0.722 ⫺0.809 ⫺0.785 ⫺0.887 ⫺0.849 ⫺0.887 ⫺0.844 ⫺0.890 0.723 0.699

⫺0.844 ⫺0.857 ⫺0.770 ⫺0.808 ⫺0.792 ⫺0.889 ⫺0.846 ⫺0.880 ⫺0.831 ⫺0.893 0.718 0.703

⫺0.802 ⫺0.960 – ⫺0.889 ⫺0.790 ⫺0.800 0.724 0.630

⫺0.793 ⫺0.935 ⫺0.946 ⫺ ⫺0.766 ⫺0.788 0.721 0.665

⫺0.786 ⫺0.908 ⫺0.926 ⫺0.868 – ⫺0.770 0.762 0.666

⫺0.798 ⫺0.936 ⫺0.951 ⫺0.889 ⫺0.766

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0.759 0.665

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corresponding to the deletion of criterion variables A1, A2, A3, A4, A5 and A6, respectively. It is clear from the results in columns 3-8 in Table V that there is a stability of the canonical loadings.

5. Result and discussion of relationship between various JIT success factors and manufacturing performance parameters The critical examination of the Pearson’s correlations shows that by adaptation of JIT initiatives in the organisations, all the input factors (A1-A6) have significant impact on realisation of overall manufacturing performance enhancements in the organisation. A3 is found to be more significant of all these factors. The results reveal that organisation culture, management’s commitment, employee’s involvement and workplace organisation (A1) JIT purchasing (A2), product and manufacturing flexibility, facility layout (A3), production system and process control, kanban and pull production, set-up time, quality (A4), daily schedule adherence and maintenance management, distribution and transportation system (A5) can strategically contribute towards realisation of manufacturing performance enhancements by affecting the organisational cultural transformations, thereby institutionalising a favourable environment towards managing change in the organisation. Validation of H1 The canonical correlation of r ⫽ 0.975 at F statistic probability of 0.00 from Table V depicts a significant overall association between criterion set of various JIT success factors and the pre-decided set of manufacturing performance parameters. Thus, H1 got validated in the present context. Further, the Pearson’s correlations also show that there exists a significant association between various JIT success factors and strategic manufacturing performance parameters. It has been observed from Table IV that culture of management, employee’s involvement and workplace organisation (A1) have significant effect of supplier’s coordination and relationship (B3), inventory levels (B4), maintainability (B8) and quality enhancement (B10). As in JIT environment, purchasing of materials plays a very important role and multiple suppliers are used to purchase the material. To give a fair deal to each supplier, an organisation must have ethical business values and should be fair to each supplier and it must imbibe ethical values to deal with its suppliers. JIT culture of management and involvement of workers helps an organisation to reduce its inventories, proper maintenance of equipment and machinery. JIT leads to enhancement in quality of products because of team work and multi-skilling of workforce. Job rotation under JIT systems create conditions for job enrichment and job enlargement and at the same time catering for employees’ social needs. In JIT environment, problem solving abilities, ability to face challenges and involvement of workers, reduces to great extent, WIP inventory, thereby leading to enhanced equipment maintainability. Holistic input factor JIT purchasing (A2) has a significant effect on output factors such as over all organisational achievements (B1), firm’s culture and values (B2), product variety and flexibility (B5) and quality enhancement (B10). The JIT purchasing leads to reduction in waste, increases cyclic times, quality of product gets enhanced and very less and few inspections are required and good quality material will be received. Effective JIT implementation initiatives can contribute towards improvement of the competitive position of the organisation leading to enhancement of productivity and more returns on net assets and returns on capital employed. The factor (A3) product and manufacturing flexibility, facility layout has exhibit significance on most of the manufacturing performance measures like overall organisational achievements (B1), firm’s culture and values (B2), supplier’s coordination and relations (B3), inventory levels (B4), set-up time (B6), production (B7), maintainability of equipment (B8), delivery compliance (B9) by commitment of management, workers participation, multi-skill labour, quality


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circles, better and ethical relationship with suppliers, improving synergy between production and maintenance functions, affecting improvements in equipment reliability, providing safe work environment, ensuring better upkeep of the production facilities, improvements in operational efficiency, increased emphasis on planning and control, elimination of procedural hassles, lowering in inventory and elimination of all kinds of wastes associated with the manufacturing system. Further, JIT initiative A4 can contribute effectively towards realisation of production (B7) and maintainability (B8) shows lessening in downtime of production equipment, production system stabilisation, enhancement in productivity and capabilities of human resource and improvements in the manufacturing system reliability. The results highlight that JIT implementation initiatives (A3) can contribute effectively towards improvement of set-up time (B6) in the organisation by affecting improvements in operational efficiency. In the end, from the above analysis, new JIT model for Indian manufacturing industry has been shown in Figure 5. Figure clearly shows the effect of various input factors on the performance measure. It is seen from the Figure 5 that most of the performance measures are affected by input factor A3, and input factor A5 has least effect on the performance measure.

Figure 5 JIT model depicting effect of implementation dimensions on performance measure

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Validation of H2 Many pairs of JIT success factors and JIT performance parameters (marked as ** in Tables III) having Pearson correlation ⱖ 40 per cent and statistically significant at 1 per cent level of significance are considered as having a significant association. Thus, this leads to validation of H2 in the present context.

6. Effect of JIT implementation on manufacturing performance improvements over time To check out the effect of JIT implementation with time on the manufacturing performance parameters, the responses collected from various Indian manufacturing industries have been grouped into three categories depending on the gain in experience with time, by various manufacturing organisations towards implementing JIT initiatives, as shown in Table VI. The gains in various parameters of manufacturing performance over time of the JIT implementation are shown in Table VII. The table summarises the average and SDs of gains gathered by various parameters of manufacturing performance due to implementation of effective JIT initiatives in the manufacturing organisations. It has been seen that the mean values of the improvements in manufacturing performance accrued in Phase III are significantly higher than those obtained in Phase II and mean values of Phase II are also significantly higher than those of Phase I. The significant change in manufacturing performance parameters in the all three phases can be attributed to the fact that JIT implementation in the Indian industry is gaining momentum, from the date of implementation to the maturity Phase III, with only a few manufacturing organisations having experience ⬎ five-six years regarding JIT practices. Table VIII shows the comparative analysis of gain in manufacturing performance improvements by effective JIT implementation programme with respect to time. The values are calculated by using a statistical tool, two-tailed t-test, at the significance level of p at 0.05. All the tests at various phases, i.e. t (II/I), t (III/I) and t (III/II) have been carried out to assure the statistical difference in effectiveness of various manufacturing performance improvements gained as a result of successful JIT implementation initiatives. The t (II/I) (stabilising phase), t (III/I) (maturity phase) and t (III/II) (intermediate phase between stabilising and maturity phases) values are depicted in Table VIII. The values of various manufacturing performance improvement parameters for t(II/I) phase indicates the significant realisation of improvement in manufacturing performance in stabilising phase as compared to introductory phase. Similarly, t (III/I) values show that maturity phases also have significant realisation of manufacturing performance improvements as compare to Table VI Classification of responses based on JIT implementation period Organization’s experience in JIT implementation

Serial Number

Category

1

Introductory phase (Phase I)

2

Stabilization phase (Phase II)

3

Maturity phase (Phase III)


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Less than three years Organization experiences cultural changes, devices mechanisms for overcoming internal resistances towards changes More than three years but less than five years Stabilizes the JIT improvement initiatives More than five years JIT improvement initiatives enter maturity phase with emphasis on manufacturing performance improvement Holistic deployment of all the JIT elements Improved performance in terms of cost reduction and enhanced manufacturing flexibilities

Number of responses (N) 22

18 20

Table VII Results of enhancement in parameters of manufacturing performance based on JIT implementation period Phase I N ⫽ 22

Performance Parameters B1 B2 B3 B4 B5 B6 B7 B8 B9 B10

Phase II N ⫽ 18

Phase III N ⫽ 20

Mean

SD

Mean

SD

Mean

SD

2.45455 2.77273 2.43636 2.37879 2.46591 2.14773 2.30682 2.46591 2.05682 2.47727

0.460566 0.435815 0.439204 0.507045 0.513482 0.375270 0.344444 0.347572 0.307948 0.481250

3.16667 3.33333 3.10000 3.05556 3.16667 2.75000 2.91667 3.16667 2.72222 3.20833

0.230887 0.383482 0.566724 0.235702 0.284398 0.514496 0.411239 0.428746 0.444649 0.523352

3.52500 3.76250 3.75000 3.55000 3.62500 3.48750 3.58750 3.75000 3.41250 3.88750

0.258411 0.383482 0.170139 0.394034 0.425348 0.384528 0.423605 0.229416 0.467883 0.151201

Table VIII Two-tailed t-test results for manufacturing performance improvement parameters with respect to variable periods Performance parameters

Phase I

Phase II

Phase III

t(II/I) (p-value)

t(III/I) (p-value)

t(III/II) (p-value)

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10

2.45455 2.77273 2.43636 2.37879 2.46591 2.14773 2.30682 2.46591 2.05682 2.47727

3.16667 3.33333 3.10000 3.05556 3.16667 2.75000 2.91667 3.16667 2.72222 3.20833

3.52500 3.76250 3.75000 3.55000 3.62500 3.48750 3.58750 3.75000 3.41250 3.88750

6.34 (0.000) 4.325 (0.000) 4.068 (0.000) 5.568 (0.000) 5.459 (0.000) 4.146 (0.000) 5.015 (0.000) 5.592 (0.000) 5.358 (0.000) 4.556 (0.000)

9.395 (0.000) 9.131 (0.000) 12.977 (0.000) 8.398 (0.000) 7.939 (0.000) 11.407 (0.000) 10.685 (0.000) 14.248 (0.000) 10.976 (0.000) 13.054 (0.000)

4.514 (0.000) 4.040 (0.000) 4.680 (0.000) 4.747 (0.000) 3.939 (0.000) 4.961 (0.000) 4.950 (0.000) 5.147 (0.000) 4.661 (0.000) 5.310 (0.000)

introductory phase. The values of t (III/II) in Table VIII also show that there is significant improvement in manufacturing performance during the stabilising and maturity phases. It is, thus, concluded that organisations starts reaping of JIT benefits since the implementation of JIT. Acceptance of H3 H3 is accepted fully in the present context, as the manufacturing performance enhancement in all the three phases is highly significant.

Conclusions For achievement of vital benefits to meet the challenges of global competition, the present study highlights the contributions made by various JIT implementation initiatives in the Indian industry. A pragmatic analysis has been applied in this study so as to recognise the role of JIT success factors in attaining convincing manufacturing performance enhancements in the Indian manufacturing organisations. For the purpose, categories of various JIT success factor and manufacturing performance factors have been established in the study. To support how critical JIT success factors and key manufacturing performance enhancement parameters are related to each other, empirical evidence has been presented in the study. The research reveals that the JIT initiatives have great influence in affecting manufacturing performance improvements, improvement of culture of an organisation, involvement of employees, quality, etc. This authenticates that JIT initiatives have extremely high potential in recognising overall competencies of the organisation. The research also acknowledged the fact that the top management can play a major role towards achievement of improvements in manufacturing performance by providing competent framework for JIT implementation, implementing an efficacious reward and recognition system in the organisation and providing resources for coping up

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with change in the organisation. The abovementioned factors augur the usefulness of treating manufacturing performance as a multi-dimensional concept. The study revealed that cogent initiatives of JIT can significantly contribute towards achievement of enhancements of strategic manufacturing performance so as to compete in the highly dynamic global scenario. This interdependence can assist in understanding the effect of various success factors of JIT towards attainment of organisation objectives of sustainability and growth. To achieve the desired output, JIT initiatives need to be managed carefully in the organisation. The manufacturing managers must be acquainted with the existing interdependencies within the various JIT initiatives so as to be able to manage the strategic JIT initiatives effectively towards attaining world class manufacturing. It is, therefore, concluded that to implement JIT programme successfully in the organisation, it is essential for the manufacturing managers to have an in-depth understanding of the working and synergy of the different features of JIT, so that true potential of the JIT concept can be fulfilled. Further, the present research affirms the fact that implementation of JIT programme does not bring about immediate results. It requires systematic planning and a focused plan of JIT implementation, assisted adequately by top management through adequate enhancement in culture of organisation over adequate time to attain the true results from the holistic JIT implementation programme.

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About the authors Gurinder Singh holds a Bachelor’s Degree in Mechanical Engineering from Guru Nanak Dev Engineering College, Bidar, Karnataka, India, and a Master’s Degree in Production Engineering from Guru Nanak Dev Engineering College, Ludhiana, Punjab, India. Presently, he is working as a Senior Lecturer in the Mechanical Engineering Department at Government Polytechnic College, Mohali, Khunimajara, Punjab, India. His main research area is JIT manufacturing. Gurinder Singh is the corresponding author and can be contacted at: [email protected] Inderpreet Singh Ahuja holds a Bachelor’s Degree in Mechanical Engineering and Master’s Degree in Industrial Engineering from Thapar Institute of Engineering and Technology, Patiala, Punjab, India, and a PhD from Punjabi University, Patiala, Punjab, India. Presently, he is working as a Professor in Mechanical Engineering at University College of Engineering, Punjabi University, Patiala, Punjab, India. His main research areas are TQM, JIT manufacturing and TPM.

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VOL. 8 NO. 3 2014

An evaluation of just in time (JIT) implementation on manufacturing performance in Indian industry

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