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Study Of Adoption Barriers For Flexible Manufacturing System In Industry

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  • S.H.Sundarani
  • M. N. Qureshi

Abstract

Manufacturing companies face a challenge of technological innovation in competitive markets. Rapid changes in technology produce product and process with short product life cycles, short lead times with continuously changing consumer preferences with high the uncertainty that demands enhanced manufacturing flexibility not only for productivity enhancement but for survival too. The higher manufacturing flexibility offers more spare time to feed the customers with a higher product range and variety of options. To dynamic changes and competitive market may be captured through flexibility in manufacturing. This research is focused on the study of the adoption of flexible manufacturing system in industries by studying various barriers for adoption. These study industries which are found for willing to accept modification in present manufacturing system. Mathematical modeling can be developed with the help of Multiple Criteria Decision Making (MCDM) like AHP or TOPSIS can be used by any industry by just finding the value of barriers. Once this is done, Industry can be assessed how they can adopt flexible manufacturing system, which are the most significant barriers for the adoption of FMS. The major outcome of this research is adoption assessment and intensity of barriers for successful implementation of Flexible Manufacturing System. This procedure can be used for both old as well as new industry. Key Words: Flexible manufacturing systems, Adoption Barriers. Policy

Suggested Citation

  • S.H.Sundarani & M. N. Qureshi, 2017. "Study Of Adoption Barriers For Flexible Manufacturing System In Industry," Working papers 2017-03-19, Voice of Research.
    • Handle: RePEc:vor:issues:2017-03-19
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    References listed on IDEAS

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    1. David Atkin & Azam Chaudhry & Shamyla Chaudry & Amit K. Khandelwal & Eric Verhoogen, 2017. "Organizational Barriers to Technology Adoption: Evidence from Soccer-Ball Producers in Pakistan," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(3), pages 1101-1164.
    2. Denis Borenstein, 1998. "Intelligent decision support system for flexible manufacturing system design," Annals of Operations Research, Springer, vol. 77(0), pages 129-156, January.
    3. Shang, Jen & Sueyoshi, Toshiyuki, 1995. "A unified framework for the selection of a Flexible Manufacturing System," European Journal of Operational Research, Elsevier, vol. 85(2), pages 297-315, September.
    4. Rakesh Narain & R.C. Yadav & Jiju Antony, 2004. "Productivity gains from flexible manufacturing," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 53(2), pages 109-128, March.
    5. Chan, Felix T. S. & Jiang, Bing & Tang, Nelson K. H., 2000. "The development of intelligent decision support tools to aid the design of flexible manufacturing systems," International Journal of Production Economics, Elsevier, vol. 65(1), pages 73-84, April.
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    Cited by:

    1. Hafiz Zahid Nabi & Tauseef Aized, 2020. "Performance evaluation of a carousel configured multiple products flexible manufacturing system using Petri net," Operations Management Research, Springer, vol. 13(1), pages 109-129, June.

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