A Structural Equation Model (SEM) Evaluation of Lean Practices for Small & Medium Scale Manufacturing Enterprises Within Gujarat State

DOI : 10.17577/IJERTCONV4IS10013

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A Structural Equation Model (SEM) Evaluation of Lean Practices for Small & Medium Scale Manufacturing Enterprises Within Gujarat State

Suketu Y. Jani

PhD Scholar C.U.Shah University & Assistant Professor, Indus University Ahmedabad India

Dr Tushar N. Desai Associate Professor, MED S.V.N.I.T,Surat


AbstractThe purpose of this paper is to empirically test a framework (operational performance Model) which identifies the relationships between lean practices critical success factors, organizational performance Measurement of Gujarats manufacturing firms. Specifically, this study examines the direct effects of lean practices success factors on organizational performance and whether the relationship between lean practices success factors and organizational performance measures is confirmed or not using confirmatory factor analysis. A structural equation model (SEM) is estimated using data provided by 152 manufacturing firms Small & Medium Scale Enterprises. The results show that lean practices success factors have a direct and significant impact on organizational performance of manufacturing firms.

Keywords SEM (Structural Equation Modeling), Lean practices, critical success factors, Organizational performance measures


    The research model of this study is shown in Figure 1. The model proposed that lean practices implemented by Gujarat manufacturers to improve their organizational and innovation performance. Also the improvement of innovation performance will improve the organizational performance. The lean practices, organizational performance and innovation performance are discussed in the next subsection.


      The successful implementation of lean practices has become accepted by Toyota as source of competitive advantage (Doolen and Hacker, 2005; Womack et al. 1990). There are several studies that have examined the effects of lean on performance. The results showed that lean practices might not be universally valid in all organizational contexts (Boyle et al., 2011, Cooney, 2002). Many researchers confirmed that the relationship of lean on financial performance is mixed (York and Miree ,2004; Boyd et al., 2006; Wayhan and Balderson, 2007). The study of Furlan et al. (2011) indicated that not all the plants implement lean manufacturing bundles show the improvement on operational performance. This paper investigates the relationship of lean practices on organizational performance and innovation performance, and the relationship of innovation performance on organizational performance of manufacturers in Gujarat using a structural equation model (SEM). This allows us to evaluate whether the lean practices that are effective in advanced economies like Japan are also Effective in a developing country like Gujarat. The next sections of the paper review existing literature, explain the research methodology and the data analysis. The final section examines the results and provides conclusions and suggestions for future research.

      Fig.1 Research Model

      The above research model will explore the relationship existing between critical success factors and performance measures of lean manufacturing practices cited by various authors in literature in context with small and medium scale manufacturing enterprises across the global. The theoretical framework is also derived from various study related to performance measures of lean and structured equation modeling methods has been used by various authors as discussed in next section , total seven different lean models have studied for analyzing theoretical framework

      The performance measurement model has a crucial role for continuing improvement and growth of SMEs to make efficient and effective management in the manufacturing sectors (Kennerley & Neely 2002, Garengo 2005). Kennerly & Neely (2002) defined performance measurement as the

      process of quantifying the efficiency and effectiveness of action. While this definition does not involve strategies development and improvement action, which can be carried out by the existing results of performance measurement In terms of strategy and performance measurement, the various frameworks have been introduced by many researchers such as the

      1. Balance Score Card (BSC) (Kaplan & Norton, 1996),

      2. Strategic Measurement and Reporting Technique (SMART) (Cross & Lynch, 1989),

      3. Performance Measurement Questionnaire (PMQ) (Dixon, 1990),

      4. Performance Prism (Kennerley & Neely 2002, Dixon et al. 1991),

      5. Integrated Performance Measurement System (IPMS) (Bititci, 1997),

      6. Integrated Dynamic Performance Measurement System (IDPMS) (Ghalayini, 1997)

      7. European Foundation for Quality Management (EFQM) Model (The EFQM Excellence Model 1999, EFQM, 2003).

      Fig.2 Theoretical Research linkages A Model

      Based on a review of the literature, the research model in the level of variables is shown in Figure 2. The hypotheses of this study are based on Figure 2 as following:

      H1: A lean mindset and attitude amongst all levels of employees will result in Reduction of operating costs.

      H2: A lean Soft issues & Hard Issues amongst all levels of employees will result in growth of SMEs & performance.

      H3: Lean CSFs are positively associated with Performance measures for growth in SMEs.

      Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. As there are 7 performance measures and

      11 critical factors, multiple linear regression models will be tested for assessing fit

      1. Y1=+0X1+1X2+3X3+4X4+5X5+6X6+7X7+8X


      2. Y2=+0X1+1X2+3X3+4X4+5X5+6X6+7X


      3. Y3=+0X1+1X2+3X3+4X4+5X5+6X6+7X


      4. Y4=+0X1+1X2+3X3+4X4+5X5+6X6+7X


      5. Y5=+0X1+1X2+3X3+4X4+5X5+6X6+7X


      6. Y6=+0X1+1X2+3X3+4X4+5X5+6X6+7X


      7. Y7=+0X1+1X2+3X3+4X4+5X5+6X6+7X



      Y= Performance Measures or Dependent Variables X= Critical Success Factors or Independent Variables & = Regression constants, e = error


    A survey instrument was developed in order to test the research model. The items and questions in the proposed questionnaire were adopted existing studies. The questionnaire was pre-tested with several senior executives from a manufacturing firm to ensure that the wording and format of the questions were appropriate. Data for this study were collected using a self- administered questionnaire that was distributed to 400 manufacturing firms. The sample was selected randomly from the CII. The data collections took six months and were collected from May 2015 to December 2015. The survey was completed by senior officer/Managers/Auditors/Consultants in the firms. Out of the 400 surveys sent out, 248 were returned, yielding a response rate of 38 per cent.


    To test the research hypotheses, structural equation modeling was performed using AMOS 20 software. Compared with conventional analytical techniques in the literature on lean practices, organizational performance and innovation performance such as correlation analysis, structural equation modeling (SEM) has the following advantages Anderson and Gerbing, 1988). First, it can estimate relationships among latent constructs indicated by observed variables. Second, it can measure recursive relationship between constructs. Third, it can allow for correlations among measurement errors

    organizational performance and among manufacturing firms. Previous studies have suggested that lean practices had significant positive effect on organizational performance (Rosemary 2008; Motwani 2003; Krafcik 1988; Rahmai et al. 2010). Extending, this study has empirically examined how lean practices influenced organizational performance for growth of manufacturing firms.


    Fig.3 SEM for Critical Success Factors

    Fig.4 SEM for Performance Measures

    SEM used several goodness-of-fit indices, including Chi- Square statistics divided by the degree of freedom (x2 /df) was recommended to be less than 3, Goodness-of fit (GFI), Adjusted goodness-of fit (AGFI, Comparative Fit Index (CFI), Tucker-Lewis (TLI) were recommended to be greater than 0.90; and Root Mean Square Error of Approximation (RMSEA) was recommended to be 0.05 up and acceptable up to 0.08


    Overall, the model had a very good fit with the data (x 2

    /df = 1.174 GFI 0.761, AGFI = 0.826, RMSEA = 0.087,

    TLI = 0.886, CFI = 0.776) and all of the paths were significant at the level of 0.001.


This study has provided empirical justification for the proposed research model which investigates the relations between lean practice critical success factors,

  1. Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, New York: Guilford Press.

  2. Asparouhov, T. & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397438

  3. Gatignon, H. (2010). Confirmatory Factor Analysis in Statistical analysis of management data. DOI: 10.1007/9781441912701_4

  4. Levine, T. R. (2005). Confirmatory factor analysis and scale validation in communication research. Communication Research Reports, 22(4), 335338.

  5. Guba E.G. and Lincoln Y.S. (1994), Competing paradigms in qualitative research in Handbook of Qualitative Research, Eds. N.K.Denzin & Y.S. Lincoln, Sage, Thousand Oaks, pp. 105117

  6. Leedy P. D. and Ormrod J. E. (2001), Practical Research: Planning and Design (Sevened.): Merrill Prentice Hall.

  7. Perry C. and McPhail J. (2002), Module 5, Qualitative Research Methodologies, Unit MGT8400 Research Methodology Study Book 1, The University of Southern Queensland, pp. 5.15.38

  8. IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.

  9. Anderson J. C., Gerbing D. W. and Hunter J. E. (1987). On the Assessment of

  10. Unidimensional Measurement: Internal and External Consistency, and Overall

  11. Consistency Criteria. Journal of Marketing Research, 24, 432437.

  12. Byrne B. M. (2001), Structural Equation Modelling with AMOS: Basic Concepts Applications and Programming. NJ: Lawrence Erlbaum Associates.

  13. EisenhardtK. M. and Galunic D. C. (2000), Coevolving: At Last a Way to Make Synergies Work, Harvard Business Review, 78, 91101.

  14. Hair J. F., Black W. C., Babin B. J., Anderson R. E. and Tatham R. L. (2006),

  15. Multivariate Data Analysis (Sixth Ed.), Upper Saddle River, NJ: PrenticeHall.

  16. Sharma S. (1996), Applied Multivariate Techniques. Hoboken: John Wiley & Sons

  17. Tabachnick B. G. and Fidell L. S. (2007). Using Multivariate Statistics (5th ed.). Boston:Pearson.

  18. Wolfe L. M. (2003),The Introduction of Path Analysis to the Social sciences, and Some Emergent Themes: An Annotated Bibliography, Structural Equation Modeling, 10(1), 134.

  19. Yin, R.K. (1994), Case Study Research Design and Methods, Sage Publications, London

  20. Liker, J.K. 2004. The Toyota Way. New York: Mc GrawHill.

  21. Liker, J.K. 2007. Toyota Talent: Developing Your People the Toyota Way. New York: McGrawHill.

  22. Jablanski, J.R. 1992. Implementing TQM. Competing in the Nineties Through Total Quality Management. 2nd ed. San Diego: Pfeiffer & Company.

  23. Jones, D.T. 2003. The meaning and future of lean.

  24. Jones, D.T. 2003. The beginners guide to lean.

  25. Jones, D.T. 2005. Lean Lessons for 2006. www.leanuk.org/articles.

  26. Donovan, R.M. 2005. Redesign supply chain process for breakthrough results.

    www.rmdonovan.com/articles/pdf_2005/Reading%20Supply%20Cha in%20Process.pdf., S. 2004. Journey to Lean making operational change stick. Hampshire: Pallgrave Macmillan,. Delivering and implementation on manufacturing

  27. Faull, N. & Whelan, K. 2003. Implementing operations strategy an eight factor approach. http://www.ccint.net/newsroom/ontraccnewsletter/2003vol2/pg5.htm.

  28. Howardell, D. 2004. Seven skills people need to create a lean enterprise

  29. Riek, T. 2002. A spirit of teamwork at Lockwood Security products Setup Time Blitz. http://www.ccint.net/newsroom/ontraccnewsletter/2002vol1/pg3.htm

  30. Vaughan Jones, J. 2005. TRACC Users Group 2005 Australia

  31. Womack, J.P., Jones, D.T. & Ross, D. 1991. The Machine that Changed the World. New York: Harper Perennial

  32. Wickens, P.D. 1999. Energise your enterprise. London: MacMillan Press

  33. Stone, J. & Watson, V. 1999. Measuring return on investment for training.

  34. TRACC. 2003. Staff development at PET project at Thermopac

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