Skip to main content
Log in

Identification of Flexible Manufacturing System Dimensions and Their Interrelationship Using Total Interpretive Structural Modelling and Fuzzy MICMAC Analysis

  • Original Article
  • Published:
Global Journal of Flexible Systems Management Aims and scope Submit manuscript

Abstract

In today intense turbulent era and crumbling economy flexible practices are key to superior organizational performance. The purpose of this paper is to identify key variables of flexible manufacturing systems (FMS) through systematic literature review. Further attempt has been made to resolve debates related to relationship among various constructs of FMS and their relationship using interpretive structural modelling (ISM) and TISM analysis. The result shows that management commitment and management strategy leads to availability of skilled and trained workers and high commitment and motivation. The present study has tried to answer three questions out of six key questions of Whetten (Acad Manag Rev 14(4):490–495, 1989) from his seminal article “What constitutes a theoretical contribution?” i.e. what, how and why. The study has employed ISM, fuzzy MICMAC and TISM to develop a FMS framework and formulated strategy to implement in Indian scenario.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Ali, M., & Khan, U. W. (2012). Implementation issues of AGVs in flexible manufacturing: A review. Global Journal of Flexible systems Management, 11(1, 2), 55–62.

    Google Scholar 

  • Badr, I. (2008). An agent-based scheduling framework for flexible manufacturing systems. International Journal of Computer, Information, and Systems Science, and Engineering, 2(2), 123–129.

    Google Scholar 

  • Baer, T., & Richardson, B. (1991). Flexible manufacturing its gotten easier to change on demand. Computer World, 25(6), 59–65.

    Google Scholar 

  • Belassi, W., & Fadlalla, A. (1998). An integrative framework for FMS diffusion. Omega, 26(6), 699–713.

    Google Scholar 

  • Bolanos, R., Fontela, E., Nenclares, A., & Pastor, P. (2005). Using interpretive structural modelling in strategic decision-making groups. Managerial Decision, 43(6), 877–895.

    Google Scholar 

  • Boppana, V., & Srinivasarao, M. (2012). Selection of a flexible machining centre through a knowledge based expert system. Global Journal of Flexible Systems Management, 13(1), 3–10.

    Article  Google Scholar 

  • Borenstein, D., Becker, J., & Santos, E. (1999). A systemic and integrated approach to flexible manufacturing systems design. Integrated Manufacturing Systems, 10(1), 6–14.

    Article  Google Scholar 

  • Bruccoleri, M., Amici, M., & Perrone, G. (2003). Distributed intelligent control of exceptions in reconfigurable manufacturing systems. International Journal of Production Research, 41(7), 1393–1412.

    Article  Google Scholar 

  • Buyurgan, N., Meyyappan, L., Saygin, C., & Dagli, C. H. (2007). Real-time routing selection for automated guided vehicles in a flexible manufacturing system. Journal for Manufacturing Technology Management, 18(2), 169–181.

    Article  Google Scholar 

  • Buzacott, A. J., & Yao, D. D. (1986). Flexible manufacturing systems: A review of analytical models. The Institute of Management Science, 25, 890–907.

    Article  Google Scholar 

  • Camisón, C., & López, A. V. (2010). An examination of the relationship between manufacturing flexibility and firm performance: The mediating role of innovation. International Journal of Operations and Production Management, 30(8), 853–878.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chen, I., Gupta, A., & Chung, C. H. (1996). Employee commitment to the implementation of flexible manufacturing systems. International Journal of Operations and Production Management, 16(7), 4–13.

    Article  Google Scholar 

  • Diabat, A., Kannan, G., & Panicker, V. V. (2012). Supply chain risk management and its mitigation in a food industry. International Journal of Production Research, 50(11), 3039–3050.

    Article  Google Scholar 

  • Farris, D. R., & Sage, A. P. (1975). On the use of interpretive structural modelling for worth assessment. Computers and Electrical Engineering, 2(2/3), 149–174.

    Article  Google Scholar 

  • Gabriel, O., & Ling-y, (2012). The Effect of manufacturing flexibility on export performance in china. International Journal of Business and Social Science, 3(6), 7–13.

    Google Scholar 

  • Gerwin, D. (1993). Manufacturing flexibility: A strategic perspective. Management Science, 39(4), 395–410.

    Article  Google Scholar 

  • Gorane, S. J., & Kant, R. (2013). Supply chain management: Modelling the enablers using ISM and fuzzy MICMAC approach. International Journal of Logistics Systems and Management, 16(2), 147–166.

    Article  Google Scholar 

  • Goyal, S. K., Mehta, K., Kodali, R., & Deshmukh, S. G. (1995). Simulation for analysis of scheduling rules for a flexible manufacturing system. Integrated Manufacturing Systems, 6(5), 21–26.

    Article  Google Scholar 

  • Hallgren, M., & Olhager, J. (2009). Flexibility configurations: Empirical analysis of volume and product mix flexibility. Omega, 37(4), 746–756.

    Article  Google Scholar 

  • Jharkharia, S., & Shankar, R. (2004). IT enablement of supply chains: Modelling the enablers. International Journal of Product Performance Management, 53(8), 700–712.

    Article  Google Scholar 

  • Kandasamy, W. B. V. (2007). Elementary fuzzy matrix, theory and fuzzy models for social scientists. Los Angeles, CA/Ann Arbor, MI: Automaton/ProQuest Information and Learning (University of Microfilm International).

    Google Scholar 

  • Kannan, G., & Haq, N. A. (2007). Analysis of interactions of criteria and sub-criteria for the selection of supplier in the built-in-order supply chain environment. International Journal of Production Research, 45(17), 3831–3852.

    Article  Google Scholar 

  • Kaula, R. (1998). A modular approach towards flexible manufacturing. Integrated Manufacturing System, 9(2), 77–86.

    Article  Google Scholar 

  • Khan, J., & Haleem, A. (2013). An integrated ISM and fuzzy MICMAC approach for modelling of the enablers of technology management. Indian Journal of Applied Research, 3(7), 236–242.

    Google Scholar 

  • Lee, A., & Cheng, C. H. (1996). Metaplanning in FMS scheduling. International Journal of Operations and Production Management, 16(6), 12–24.

    Article  Google Scholar 

  • Maffei, M. J., & Meredith, J. (1994). The organizational side of flexible manufacturing technology: Guidelines for managers. International Journal of Operations and Production Management, 14(8), 17–34.

    Article  Google Scholar 

  • Mair, A. (1994). Honda’s global flexifactory network. International Journal of Operations and Production Management, 14(3), 6–23.

    Article  Google Scholar 

  • Mandal, A., & Deskmukh, S. G. (1994). Vendor selection using interpretive structural modelling (ism). International Journal of Operations and Production Management, 14(6), 52–59.

    Article  Google Scholar 

  • McDermott, K. J., & Yao, W. A. (1997). Developing a hybrid programmable logic controller platform for a flexible manufacturing system. International Journal of Flexible Manufacturing Systems, 9(4), 367–374.

    Article  Google Scholar 

  • Mehdi, K., & Kurapati, V. (1994). Flexible manufacturing systems: An overview. International Journal of Operations and Productions Management, 14(4), 26–49.

    Article  Google Scholar 

  • Meijboom, B., & Vos, B. (1997). International manufacturing and location decisions: Balancing configuration and co-ordination aspects. International Journal of Operations and Production Management, 17(8), 790–805.

    Article  Google Scholar 

  • Mohammed, A. (2013). Impact of routing and pallet flexibility on flexible manufacturing system. Global Journal of Flexible Systems Management, 13(3), 141–149.

    Google Scholar 

  • Nagalingam, S. V., & Lin, G. C. I. (1999). Latest developments in CIM. Robotics and Computer-Integrated Manufacturing, 15(6), 423–430.

    Article  Google Scholar 

  • Nandkumar, G., Shankar, M., & Gopakumaran, T. (2013). Infrastructural backbone of enabling and converging technologies for mass customization manufacturing system in automotive industries. International Journal for Quality Research, 7(1), 153–162.

    Google Scholar 

  • Narain, R., Yadav, R., & Antony, J. (2004). Productivity gains from flexible manufacturing—experiences from India. International Journal of Productivity and Performance Management, 53(2), 109–128.

    Article  Google Scholar 

  • Offodile, O. F., & Grznar, J. (1997). Part family formation for variety reduction in flexible manufacturing systems. International Journal of Operations and Production Management, 17(3), 291–304.

    Article  Google Scholar 

  • Petticrew, M. A. (2001). Systematic literature reviews from astronomy to zoology: Myths and misconceptions. British Medical Journal, 322, 98–101. Retrieved from http://www.bmj.com/cgi/contact/full/322/7278/98. Accessed 12 Feb 2012.

  • Petticrew, M. A., & Roberts, H. (2006). Systematic reviews in the social sciences. Oxford: Blackwell.

  • Prasad, U. C., & Suri, R. K. (2011). Modeling of continuity and change forces in private higher technical education using total interpretive structural modeling (TISM). Global Journal of Flexible Systems Management, 12(3-4), 31–40.

    Google Scholar 

  • Rao, K. V. S., & Deshmukh, S. G. (1994). Strategic framework for implementing flexible manufacturing systems in India. International Journal of Operations and Production Management, 14(4), 50–63.

    Article  Google Scholar 

  • Rao, V., & Parnichkun, M. (2009). Flexible manufacturing system selection using a combinatorial mathematics-based decision-making method. International Journal of Production Research, 47(24), 6981–6998.

    Article  Google Scholar 

  • Ravi, V., Shankar, R., & Tiwari, M. K. (2005). Productivity improvement of a computer hardware supply chain. International Journal of Productivity and Performance Management, 54(4), 239–255.

    Article  Google Scholar 

  • Saboohi, N. (2011). Total interpretive structural modeling of continuity and change forces in e-government. Journal of Enterprise Transformation, 1(2), 147–168.

    Article  Google Scholar 

  • Sharma, S. K., Panda, B. N., Mahapatra, S. S., & Sahu, S. (2011). Analysis of barriers for reverse logistics: An Indian perspective. International Journal of Model Optimisation, 1(2), 101–106.

    Article  Google Scholar 

  • Shayan, E., & Liu, C. (1995). Tool management in flexible manufacturing systems. Integrated Manufacturing Systems, 6(4), 26–35.

    Article  Google Scholar 

  • Singh, M. D., Shankar, R., Narain, R., & Agarwal, A. (2003). An interpretive structural modeling of knowledge management in engineering industries. Journal of Advances in Management Research, 1(1), 28–40.

    Article  Google Scholar 

  • Singh, A. K., & Sushil, (2013). Modeling enablers of TQM to improve airline performance. International Journal of Productivity and Performance Management, 62(3), 250–275.

    Article  Google Scholar 

  • Spano, M. R., O’Grady, P. J., & Young, R. E. (1993). The design of flexible manufacturing systems. Computers in Industry, 21(2), 185–198.

    Article  Google Scholar 

  • Srivastava, A. K., & Sushil, (2013). Modeling strategic performance factors for effective strategic execution. International Journal of Productivity and Performance Management, 62(6), 354–582.

    Google Scholar 

  • Sushil, (2005a). Interpretive matrix: A tool to aid interpretation of management and social research. Global Journal of Flexible Systems Management, 6(2), 27–30.

    Google Scholar 

  • Sushil, (2005b). A flexible strategy framework for managing continuity and change. International Journal of Global Business and Competitiveness, 1(1), 22–32.

    Google Scholar 

  • Sushil, (2009). Interpretive ranking process. Global Journal of Flexible Systems Management, 10(4), 1–10.

    Google Scholar 

  • Sushil, (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 13(2), 87–106.

  • Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14, 207–222.

    Article  Google Scholar 

  • Theodorou, P., & Florou, G. (2008). Manufacturing strategies and financial performance—The effect of advanced information technology: CAD/CAM systems (Vol. 36(1), pp. 107–121). Omega, Elsevier.

  • Wadhwa, S. R. (2012). Towards measuring investment in flexible foundry manufacturing. International Journal of Computer Sciences, 9(4), No 2, 137–142.

    Google Scholar 

  • Warfield, J. N. (1974). Structuring complex systems. Battelle monograph (Vol. 4). Columbus, OH: Battelle Memorial Institute.

    Google Scholar 

  • Warfield, J. N. (1994). A science of generic design: Managing complexity through systems design. Iowa: Iowa State University Press.

    Google Scholar 

  • Warfield, J. N. (1999). Twenty laws of complexity: Science applicable in organizations. Systems Research and Behavioral Science, 16(1), 3–40.

    Article  Google Scholar 

  • Wasuja, S., Sagar, M., & Sushil, (2012). Cognitive bias in salespersons in speciality drug selling of pharmaceutical industry. International Journal of Pharmaceutical and Healthcare Marketing, 6(4), 310–335.

    Article  Google Scholar 

  • Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495.

    Article  Google Scholar 

  • Yigang, X., Yifei, D., Yong, Z., & Shiming, L. (2012). Flexible Manufacturing of Continuous Process Enterprises with Large Scale and Multiple Products. Technology and Investment, 4, 45–56.

    Google Scholar 

  • Zhang, Q. (2006). Achieving flexible manufacturing competencies. International Journal of Production Research, 26(6), 580–599.

    Article  Google Scholar 

Download references

Acknowledgement

We are extremely grateful to editor-in-chief, regional editor, reviewers and Springer team for their excellent support in terms of quick and excellent response to improve the quality of manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rameshwar Dubey.

Appendix

Appendix

See Table 12.

Table 12 Initial reachability matrix

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dubey, R., Ali, S.S. Identification of Flexible Manufacturing System Dimensions and Their Interrelationship Using Total Interpretive Structural Modelling and Fuzzy MICMAC Analysis. Glob J Flex Syst Manag 15, 131–143 (2014). https://doi.org/10.1007/s40171-014-0058-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40171-014-0058-9

Keywords

Navigation