Journal of Supply Chain Management Systems

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Modeling the Enablers of Flexible Manufacturing Systems Using Interpretive Structural Modeling


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1 2 Uranium Road, Vulcania Ext 2, Brakpan, 1541, South Africa
     

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The objective of the study is to identify the interrelationship between enablers of flexible manufacturing system (FMS) whose richness plays a vital role in successful FMS implementation. The present study employed ISM methodology. ISM is an interactive planning methodology whereby a set of directly and indirectly related variables are structured into a comprehensive systematic model.

Variables such as FMS Workforce Commitment, FMS Workforce Motivation, Adaptation, Relationship management between in-house team&vendor, Skills of FMS workforce, Experience of FMS workforce, Cross Training, Continuous Experimentation, Cross functional cooperation, Team Building, and Job Rotation have been identified and categorised under enablers which will help managers and decision makers in enhancing productivity and profitability. Finally, the paper interprets FMS enablers in terms of driving and dependence powers that have been carried out.


Keywords

Flexible Manufacturing Systems (FMS), Enablers, Measures, ISM, MICMAC.
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  • Adler, P. S., Goldoftas, B., & Levine, D. I. (1999). Flexibility versus efficiency? A case study of model changeovers in the Toyota production system. Organization science, 10(1), 43-68.

  • Adler, P. S. (1988). Managing flexible automation. California Management Review, (Spring), 34-56.

  • Attri, R., Dev, N., & Sharma, V. (2013). Interpretive structural modeling (ISM) approach: An overview. Research Journal of Management Sciences, 2(2), 3-8

  • Avlonitis, G. J., & Parkinson, S. T. (1986). The adoption of flexible manufacturing systems in British and German companies. Industrial Marketing Management, 15(2), 97-108.

  • Belassi, W., & Fadlalla, A. (1998). Flexible manufacturing is gotten easier to change on demand. Omega, 26(6), 699-713.

  • Boer, H. (1994). Flexible manufacturing systems. new wave manufacturing strategies, Storey, John, Paul Chapman Publishing Ltd, London.

  • Browne, J., Dubois, D., Rathmill, K., Sethi, S. P., & Stecke, K. E. (1984). Classification of flexible manufacturing systems. The FMS Magazine, 2(2), 114-117.

  • Buzacott, J. A., & Shanthikumar, J. G. (1993). Stochastic models of manufacturing systems. 4, Englewood Cliffs, NJ: Prentice Hall.

  • Dubey, R., & Ali, S. S. (2014). Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modeling and fuzzy MICMAC analysis. Global Journal of Flexible Systems Management, 15(2), 131-143.

  • Graham, M. B., & Rosenthal, S. R. (1986). Flexible manufacturing systems require flexible people. Human Systems Management, 6(3), 211-222.

  • Gunasekaran, A., Patel, C., & Gaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87, 333-347. doi: 10.1016/j.ijpe.2003.08.003

  • 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, 1-22

  • Kim, M. H., & Kim, Y. D. (1994). Simulation-based realtime scheduling in a flexible manufacturing system. Journal of Manufacturing Systems, 13(2), 85-93.

  • Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., & Van Brussel, H. (1999). Reconfigurable manufacturing systems. CIRP Annals-Manufacturing Technology, 48(2), 527-540.

  • Kostal, P., & Velisek, K. (2010). Flexible manufacturing system. World Academy of Science, Engineering and Technology, 77, 825-829.

  • Lightfoot, H., Baines, T., & Smart, P., (2013). The servitization of manufacturing: A systematic literature review of interdependent trends. International Journal of Operations & Production Management, 33(11/12), 1408-1434

  • Liu, V. B., Wires, D. L., Lamping, M. J., Fischer, A. M., & Miller, G. L. (2003). U.S. Patent No. 6, 574, 520. Washington, DC: U.S. Patent and Trademark Office.

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

  • Masuyama, A. (1995). Idea and practice of flexible manufacturing systems of Toyota. Manufacturing Research and Technology, 23, 305-316, Doi: 10.1016/ S1572-4417(06)80015-X

  • Mizoguchi, K., Momoi, S., & Nakamura, Y. (1994). U.S. Patent No. 5, 310, 396. Washington, DC: U.S. Patent and Trademark Office.

  • 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.

  • Parker, R. P., & Wirth, A. (1999). Manufacturing flexibility: Measures and relationships. European Journal of Operational Research, 118(3), 429-449.

  • Petri, C. (1962). Kommunikation mit Automaten, (Ph.D Disseratation), University of Bonn, West Germany.

  • Petri, C. (1976). General net theory. In: Procedings of the joint IBM/University of Newcastle upon Tyne seminar on computation system design, Springer, Berlin, 131-169.

  • Pittaway, L., Robertson, M., Munir, K., Denyer, D., & Neely, A. (2004). Networking and innovation: A systematic review of the evidence. International Journal of Management Reviews, 5(3-4), 137-168.

  • Sarin, S. C., & Chen, C. S. (1987). The machine loading and tool allocation problem in a flexible manufacturing system. International Journal of Production Research, 25(7), 1081-1094.

  • Sawik, T. (1990). Modelling and scheduling of a flexible manufacturing system. European Journal of Operational Research, 45(2), 177-190.

  • Shang, J., & Sueyoshi, T. (1995). A unified framework for the selection of a flexible manufacturing system. European Journal of Operational Research, 85(2), 297-315.

  • Shanker, K., & Tzen, Y. J. J. (1985). A loading and dispatching problem in a random flexible manufacturing system. International Journal of Production Research, 23(3), 579-595.

  • Shivanand, H. K. (2006). Flexible manufacturing system. New Age International.

  • Slack, N. (1983). Flexibility as a manufacturing objective. International Journal of Operations & Production Management, 3(3), 4-13.

  • Son, Y. K., & Park, C. S. (1987). Economic measure of productivity, quality and flexibility in advanced manufacturing systems. Journal of Manufacturing systems, 6(3), 193-207.

  • Stecke, K. E. (1986). A hierarchical approach to solving machine grouping and loading problems of flexible manufacturing systems. European Journal of Operational Research, 24(3), 369-378.

  • Stecke, K. E., & Solberg, J. J. (1981). Loading and control policies for a flexible manufacturing system. The International Journal of Production Reserach, 19(5), 481-490.

  • Suri, R., & Whitney, C. K. (1984). Decision support requirements in flexible manufacturing. Journal of Manufacturing Systems, 3(1), 61-69.

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

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

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

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

  • Tamimi, A., Abidi, M. H. Mian S. H., & Aalam, J. (2012). Analysis of performance measures of flexible manufacturing system. Journal of King Saud University- Engineering Sciences, 24(2), 115-129.

  • Tetzlaff, U. A. (1990). Flexible manufacturing systems (pp. 5-11). Physica-Verlag HD.

  • 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(3), 207-222 Van Aken, J. E. (2005). Management research as a design science: Articulating the research products of mode 2 knowledge production in management. British journal of management, 16(1), 19-36.

  • Warfield, J. N. (1974). Structuring complex systems. Battele monograph. Columbus, O.H: Battele Memorial Ins. 4.

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

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

  • Wilhelm, W. E., & Shin, H. M. (1985). Effectiveness of alternate operations in a flexible manufacturing system. International Journal of Production Research, 23(1), 65-79.

  • Zhou, M. C., & Venkatesh, K. (1998). Modeling, Simulation, and control of flexible manufacturing systems: A Petri Net Approach. World Scientific, Singapore.


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  • Modeling the Enablers of Flexible Manufacturing Systems Using Interpretive Structural Modeling

Abstract Views: 362  |  PDF Views: 2

Authors

Surajit Bag
2 Uranium Road, Vulcania Ext 2, Brakpan, 1541, South Africa

Abstract


The objective of the study is to identify the interrelationship between enablers of flexible manufacturing system (FMS) whose richness plays a vital role in successful FMS implementation. The present study employed ISM methodology. ISM is an interactive planning methodology whereby a set of directly and indirectly related variables are structured into a comprehensive systematic model.

Variables such as FMS Workforce Commitment, FMS Workforce Motivation, Adaptation, Relationship management between in-house team&vendor, Skills of FMS workforce, Experience of FMS workforce, Cross Training, Continuous Experimentation, Cross functional cooperation, Team Building, and Job Rotation have been identified and categorised under enablers which will help managers and decision makers in enhancing productivity and profitability. Finally, the paper interprets FMS enablers in terms of driving and dependence powers that have been carried out.


Keywords


Flexible Manufacturing Systems (FMS), Enablers, Measures, ISM, MICMAC.

References