Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach

Abstract

Productivity has often been cited as a key factor in a flexible manufacturing system (FMS) performance, and actions to increase it are said to improve profitability and the wage earning capacity of employees. Improving productivity is seen as a key issue for survival and success in the long term of a manufacturing system. The purpose of this paper is to make a model and analysis of the productivity variables of FMS. This study was performed by different approaches viz. interpretive structural modelling (ISM), structural equation modelling (SEM), graph theory and matrix approach (GTMA) and a cross-sectional survey within manufacturing firms in India. ISM has been used to develop a model of productivity variables, and then it has been analyzed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are powerful statistical techniques. CFA is carried by SEM. EFA is applied to extract the factors in FMS by the statistical package for social sciences (SPSS 20) software and confirming these factors by CFA through analysis of moment structures (AMOS 20) software. The twenty productivity variables are identified through literature and four factors extracted, which involves the productivity of FMS. The four factors are people, quality, machine and flexibility. SEM using AMOS 20 was used to perform the first order four-factor structures. GTMA is a multiple attribute decision making (MADM) methodology used to find intensity/quantification of productivity variables in an organization. The FMS productivity index has purposed to intensify the factors which affect FMS.

Key words： FMS; ISM; EFA; SEM; GTMA

Acknowledgements

The authors acknowledge the anonymous referees of this paper for their valuable suggestions, which have helped to improve the quality of this paper.

References

1 Cosmetatos G P, Eilon S. Effects of productivity definition and measurement on performance evaluation. European Journal of Operational Research, 1983, 14(1): 31–35

2 Russell R S, Taylor-Iii B W. Operations Management along the Supply Chain. 6th ed. New York: John Wiley & Sons, 2009

3 Kilic H, Okumus F. Factors influencing productivity in small island hotels: Evidence from Northern Cyprus. International Journal of Contemporary Hospitality Management, 2005, 17(4): 315–331

4 Tangen S. Understanding the concept of productivity. In: Proceedings of the 7th Asia Pacific Industrial Engineering and Management Systems Conference. Taipei, 2002

5 Jahromi M, Tavakkoli-Moghaddam R. A novel 0–1 linear integer programming model for dynamic machine-tool selection and operation allocation in a flexible manufacturing system. Journal of Manufacturing Systems, 2012, 31(2): 224–231

6 Cordero R. Changing human resources to make flexible manufacturing systems (FMS) successful. Journal of High Technology Management Research, 1997, 8(2): 263–275

7 Dai J B, Lee N K. Economic feasibility analysis of flexible material handling systems: A case study in the apparel industry. International Journal of Production Economics, 2012, 136(1): 28–36

8 Li J F, Long Y, Liu Q P. Advanced manufacturing technology, quality of product and level of performance: Empirical evidence from Chongqing. In: Proceedings of International Conference on Services Systems and Services Management. IEEE, 2005, 486– 490

9 Black S E, Lynch L M. Human-capital investments and productivity. American Economic Review, 1996, 86(2): 263–267

10 Hoffman J M, Mehra S. Management leadership and productivity improvement programs. International Journal of Applied Quality Management, 1999, 2(2): 221–232

11 Millea M, Fuess S M Jr. Does pay affect productivity or react to it?: Examination of U.S. manufacturing. The Quarterly Review of Economics and Finance, 2005, 45(4–5): 796–807

12 Kazerooni A, Chan F, Abhary K. A fuzzy integrated decision-making support system for scheduling of FMS using simulation. Computer Integrated Manufacturing Systems, 1997, 10(1): 27–34

13 Oke A. A framework for analysing manufacturing flexibility. International Journal of Operations & Production Management, 2005, 25(10): 973–996

14 Buyurgan N, Saygin C, Kilic S E. Tool allocation in flexible manufacturing systems with tool alternatives. Robotics and Computer-integrated Manufacturing, 2004, 20(4): 341–349

15 Özbayrak M, Bell R. A knowledge-based decision support system for the management of parts and tools in FMS. Decision Support Systems, 2003, 35(4): 487–515

16 Anderson E W, Fornell C, Rust R T. Customer satisfaction, productivity, and profitability: Differences between goods and services. Marketing Science, 1997, 16(2): 129–145

17 Löthgren M, Tambour M. Productivity and customer satisfaction in Swedish pharmacies: A DEA network model. European Journal of Operational Research, 1999, 115(3): 449–458

18 Sharma R K, Kumar D, Kumar P. Manufacturing excellence through TPM implementation: A practical analysis. Industrial Management & Data Systems, 2006, 106(2): 256–280

19 Youssef M A, Al-Ahmady B. Quality management practices in a Flexible Manufacturing Systems (FMS) environment. Total Quality Management, 2002, 13(6): 877–890

20 Singh B, Narain R, Yadav R C. Identifying critical barriers in the growth of Indian Micro, Small and Medium Enterprises (MSMEs). International Journal of Business Competition and Growth, 2012, 2(1): 84–105

21 Swamidass P M, Waller M A. A classification of approaches to planning and justifying new manufacturing technologies. Journal of Manufacturing Systems, 1990, 9(3): 181–193

22 Banaszak Z, Tang X Q, Wang S C, Zaremba M B. Logistics models in flexible manufacturing. Computers in Industry, 2000, 43(3): 237–248

23 Kashyap A S, Khator S K. Analysis of tool sharing in an FMS: A simulation study. Computers & Industrial Engineering, 1996, 30(1): 137–145

24 Conti G. Training, productivity and wages in Italy. Labour Economics, 2005, 12(4): 557–576

25 Barron J M, Berger M C, Black D A. Do workers pay for on-the-job training? Journal of Human Resources, 1999, 34(2): 235–252

26 Yellen J L. Efficiency wage models of unemployment. Information and Macroeconomics, 1984, 74(2): 200–205

27 Mehrabi M G, Ulsoy A G, Koren Y. Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing, 2000, 11(4): 403–419

28 Öztürk A, Kayalıgil S, Özdemirel N E. Manufacturing lead time estimation using data mining. European Journal of Operational Research, 2006, 173(2): 683–700

29 D'Souza D E, Williams F P. Toward a taxonomy of manufacturing flexibility dimensions. Journal of Operations Management, 2000, 18(5): 577–593

30 Gupta Y P, Somers T M. The measurement of manufacturing flexibility. European Journal of Operational Research, 1992, 60(2): 166–182

31 Chan F T, Chan H. A comprehensive survey and future trend of simulation study on FMS scheduling. Journal of Intelligent Manufacturing, 2004, 15(1): 87–102

32 Das S K. The measurement of flexibility in manufacturing systems. International Journal of Flexible Manufacturing Systems, 1996, 8(1): 67–93

33 Gupta Y P. Organizational issues of flexible manufacturing systems. Technovation, 1988, 8(4): 255–269

34 Martin T, Ulich E, Warnecke H J. Appropriate automation for flexible manufacturing. Automatica, 1990, 26(3): 611–616

35 Beamon B M. Performance, reliability, and performability of material handling systems. International Journal of Production Research, 1998, 36(2): 377–393

36 Kulak O. A decision support system for fuzzy multi-attribute selection of material handling equipments. Expert Systems with Applications, 2005, 29(2): 310–319

37 Jain V, Raj T. Evaluation of flexibility in FMS using SAW and WPM. Decision Science Letters, 2013, 2(4): 223–230

38 Jain V, Raj T. Ranking of flexibility in flexible manufacturing system by using a combined multiple attribute decision making method. Global Journal of Flexible Systems Management, 2013, 14(3): 125–141

39 Co H C, Patuwo B E, Hu M Y. The human factor in advanced manufacturing technology adoption: An empirical analysis. International Journal of Operations & Production Management, 1998, 18(1): 87–106

40 Stecke K E. Planning and scheduling approaches to operate a particular FMS. European Journal of Operational Research, 1992, 61(3): 273–291

41 Bengtsson J, Olhager J. Valuation of product-mix flexibility using real options. International Journal of Production Economics, 2002, 78(1): 13–28

42 Elkins D A, Huang N, Alden J M. Agile manufacturing systems in the automotive industry. International Journal of Production Economics, 2004, 91(3): 201–214

43 Silveira G D. A framework for the management of product variety. International Journal of Operations & Production Management, 1998, 18(3): 271–285

44 ElMaraghy H A. Flexible and reconfigurable manufacturing systems paradigms. International Journal of Flexible Manufacturing Systems, 2005, 17(4): 261–276

45 Azevedo S, Carvalho H, Cruz-Machado V. Using interpretive structural modelling to identify and rank performance measures: An application in the automotive supply chain. Baltic Journal of Management, 2013, 8(2): 208–230

46 Govindan K, Palaniappan M, Zhu Q, Kannan D. Analysis of third party reverse logistics provider using interpretive structural modeling. International Journal of Production Economics, 2012, 140(1): 204–211

47 Raj T, Attri R, Jain V. Modelling the factors affecting flexibility in FMS. International Journal of Industrial and Systems Engineering, 2012, 11(4): 350–374

48 Singh R K, Garg S K, Deshmukh S G, Kumar M. Modelling of critical success factors for implementation of AMTs. Journal of Modelling in Management, 2007, 2(3): 232–250

49 Jain V, Raj T. Evaluating the variables affecting flexibility in FMS by exploratory and confirmatory factor analysis. Global Journal of Flexible Systems Management, 2013, 14(4): 181–193

50 Ramanathan U, Muyldermans L. Identifying the underlying structure of demand during promotions: A structural equation modelling approach. Expert Systems with Applications, 2011, 38(5): 5544–5552

51 Su Y, Yang C. A structural equation model for analyzing the impact of ERP on SCM. Expert Systems with Applications, 2010, 37(1): 456–469

52 Lau A K, Yam R C, Tang E P. Supply chain integration and product modularity: An empirical study of product performance for selected Hong Kong manufacturing industries. International Journal of Operations & Production Management, 2010, 30(1): 20–56

53 Vázquez-Bustelo D, Avella L, Fernández E. Agility drivers, enablers and outcomes: Empirical test of an integrated agile manufacturing model. International Journal of Operations & Production Management, 2007, 27(12): 1303–1332

54 Rao R V, Padmanabhan K. Selection, identification and comparison of industrial robots using digraph and matrix methods. Robotics and Computer-integrated Manufacturing, 2006, 22(4): 373–383

55 Rao R V. A material selection model using graph theory and matrix approach. Materials Science and Engineering A, 2006, 431(1–2): 248–255

56 Grover S, Agrawal V, Khan I. Human resource performance index in TQM environment. International Journal of Management Practice, 2005, 1(2): 131–151

57 Grover S, Agrawal V P, Khan I A. A digraph approach to TQM evaluation of an industry. International Journal of Production Research, 2004, 42(19): 4031–4053

58 Wilcox J B, Bellenger D N, Rigdon E E. Assessing sample representativeness in industrial surveys. Journal of Business and Industrial Marketing, 1994, 9(2): 51–61

59 Haleem A, Sushil, Qadri M A, Kumar S. Analysis of critical success factors of world-class manufacturing practices: An application of interpretative structural modelling and interpretative ranking process. Production Planning and Control, 2012, 23(10–11): 722–734

60 Shah R, Goldstein S M. Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 2006, 24(2): 148–169

61 Rigdon E E. The equal correlation baseline model for comparative fit assessment in structural equation modeling. Structural Equation Modeling, 1998, 5(1): 63–77

62 Jöreskog K G, Sörbom D. LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Chicago: Scientific Software International, 1993

63 Jöreskog K G, Sörbom D. PRELIS 2 User’s Reference Guide: A Program for Multivariate Data Screening and Data Summarization: A Preprocessor for LISREL. 3rd ed. Chicago: Scientific Software International, 1996

64 Rao R V. Decision Making in the Manufacturing Environment: Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods: Volume 2. London: Springer,2013

65 Jurkat W B, Ryser H J. Matrix factorizations of determinants and permanents. Journal of Algebra, 1966, 3(1): 1–27

66 Sage A. Interpretive Structural Modeling: Methodology for Large-Scale Systems. New York: McGraw-Hill, 1977, 91–164

67 Warfield J N.Developing interconnection matrices in structural modeling. Systems, Man and Cybernetics, IEEE Transactions on, 1974, SMC-4(1): 81–87

68 Nunnally J C, Bernstein I H. Psychometric Theory. 3rd ed. New York: McGraw-Hill, 1994

69 Hair J F, Black W C, Babin B J, Anderson R E. Multivariate Data Analysis. 7th ed. New Jersey: Prentice Hall, 2009.

70 Anderson J C, Gerbing D W. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 1988, 103(3): 411–423

71 Byrne B M. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. 2nd ed. London: Routledge, 2009

72 Fornell C, Larcker D F. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 1981, 18(1): 39–50

73 Carmines E G, Zeller R A. Reliability and validity assessment. London: Sage Publications, Inc., 1979