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Minimizing supply chain risk factors using interpretive structural modeling (ISM)

Author Affiliations

  • 1Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
  • 2Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
  • 3Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
  • 4Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

Res. J. Management Sci., Volume 8, Issue (1), Pages 1-14, January,6 (2019)

Abstract

Singling out of supply chain risks is the prior stage in the risk management process. To understand and manage risk of supply chain is a significant concern of business and a compounded problem. There exists a variety of standard for risk minimizing in supply chain management. Interpretive Structural Modeling(ISM) tactic initiates with an identification of variables, which is applicable to the problem or an issue. In this research, these variables were taken under a company as risk factors whereas Structural Self-Interaction Matrix (SSIM) is converted into a Reachability Matrix (RM) and its transitivity has also been seasoned. Once transitivity has been checked, a contextually applicable subordinate relation is being chosen. Having decided the contextual relation, a Structural Self-Interaction Matrix (SSIM) is established based on pair wise comparison of variables. In this paper the elements (also referred as variables) for the implementation of RM in a warehouse has been analyzed to find an ISM which indicates the interrelationships of the elements and also their levels. These elements have also been categorized according to their driving power and dependency. This research work has been done with twenty factors, also the percentage of the drivers, linkages, autonomous along with the independent variables have been found.

References

  1. Raj T., Shankar R. and Suhaib M. (2007)., An ISM approach for modeling the enablers of flexible manufacturing system: The case for India., International Journal of Production Research, 46(24), 1-30.
  2. Rajesh A, Dev N. and Sharma V. (2013)., Interpretive Structure Modeling (ISM) approach: An Overview, Research Journal of Management Sciences, 2(2), 3-8.
  3. Chopra S. and Sodhi M.S. (2004)., Supply-chain breakdown., MIT Sloan management review, 46(1), 53-61.
  4. Svensson G. (2000)., A conceptual framework for the analysis of vulnerability in supply chains., International Journal of Physical Distribution & Logistics Management, 30(9), 731-750.
  5. Jüttner U., Peck H. and Christopher M. (2003)., Supply chain risk management: outlining an agenda for future research., International Journal of Logistics: Research and Applications, 6(4), 197-210.
  6. Zsidisin G.A., Ellram L.M., Carter J.R. and Cavinato J.L. (2004)., An analysis of supply risk assessment techniques., International Journal of Physical Distribution & Logistics Management, 34(5), 397-413.
  7. Pfohl H.C., Gallus P. and Köhler H. (2008)., Risk Management in Supply Chain Status Quo and Challenges from Industry, Trade and Service Provider Perspective (No. 36171)., Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)., 95-147.
  8. Hauser L.M. (2003)., Risk-adjusted supply chain management., Supply Chain Management Review, 7( 6), 64-71.
  9. Norrman A. and Lindroth R. (2004)., Categorization of supply chain risk and risk management., Supply chain risk, 15(2), 14-27.
  10. Jüttner U. (2005)., Supply chain risk management: Understanding the business requirements from a practitioner perspective., The international journal of logistics management, 16(1), 120-141.
  11. Faisal M.N., Banwet D.K. and Shankar R. (2007)., Management of risk in supply chains: SCOR approach and analytic network process., In Supply Chain Forum: An International Journal, 8(2), 66-79.
  12. Franck C. (2007)., Framework for supply chain risk management., Supply Chain Forum: An International Journal, 8(2), 2-13.
  13. Pfohl H.C., Gallus P. and Köhler H. (2008)., Konzeption des Supply Chain Risiko managements (No. 36170)., Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), 7-94.
  14. Pfohl H.C., Gallus P. and Thomas D. (2011)., Interpretive structural modeling of supply chain risks., International Journal of physical distribution & logistics management, 41(9), 839-859.
  15. Austin L.M. and Burns J.R. (1985)., Management Science: An Aid for Managerial Decision Making., Macmillan, New York, NY.
  16. Warfield J.N. (1994)., A Science of Generic Design: Managing Complexity through Systems Design., 2nd ed., Iowa State University Press, Ames, IA.
  17. Malone D.W. (1975)., An introduction to the application of interpretive structural modeling., Proceedings of the IEEE, 63(3), 397-404.
  18. Warfield J.N. and Fitz R. (1977)., Societal Systems: Planning Policy, Complexity., IEEE Transactions on Systems, Man, and Cybernetics, 7(10), 759-760.
  19. Mandal A. and Deshmukh S.G. (1994)., Vendor selection using interpretive structural modelling (ISM)., International Journal of Operations & Production Management, 14(6), 52-59.
  20. Saxena J.P. and Vrat P. (1990)., Impact of indirect relationships in classification of variables-a micmac analysis for energy conservation., Systems Research, 7(4), 245-253.