Modeling Hierarchical Relationships Among Enablers of Supply Chain Coordination in Flexible Environment

  • Dhanya Jothimani
  • Ravi Shankar
  • Surendra S. Yadav
Chapter
First Online:
Part of the Flexible Systems Management book series (FLEXSYS)

Abstract

Coordination among the supply chain (SC) members provides great benefits in meeting the customers’ needs. It helps to reduce the bullwhip effect and improve the SC profitability. Though there are many coordination mechanisms, also known as enablers, available to improve the SC coordination, but understanding the hierarchical relationship among them would provide great insights to the organizations. In this chapter, the hierarchical relationship among these elements using interpretive structural modeling (ISM) is presented. Dependence and driving power, obtained from MICMAC analysis, are taken as a base to categorize the enablers into four clusters. The enablers with high driving power and low dependence should be the focus of the management.

Keywords

Coordination mechanism Enablers Interpretive structural modeling Supply chain coordination 
This is a preview of subscription content, log in to check access.

References

  1. Arshinder, Kanda A, Deshmukh SG (2008) Supply chain coordination: perspectives, empirical studies and research directions. Int J Prod Econ 115(2):316–335CrossRefGoogle Scholar
  2. Chander M, Jain SK, Shankar R (2013) Modeling of information security management parameters in Indian Organizations using ISM and MICMAC approach. J Model Manage 8(2):171–189CrossRefGoogle Scholar
  3. Charan P, Shankar R, Baisya RK (2008) Analysis of interactions among the variables of supply chain performance measurement system implementation. Bus Process Manage J 14(4):512–529CrossRefGoogle Scholar
  4. Christopher M, McKinnon A, Sharp J, Wilding R, Peck H, Chapman P, Juttner U, Bolumole Y (2002) Supply chain vulnerability. Report for Department of Transport, Local Government and the Regions, Cranfield University, CranfieldGoogle Scholar
  5. Hahn CK, Duplaga EA, Hartley JL (2000) Supply chain synchronization: lessons from Hyundai Motor Company. Interfaces 30(4):32–45CrossRefGoogle Scholar
  6. Henriott L (1999) Transforming supply chains into e-chains. Supply Chain Manage Rev (Global Supplement), Spring:15–18Google Scholar
  7. Hill RM, Omar M (2006) Another look at the single-vendor single-buyer integrated production inventory problem. Int J Prod Res 44(4):791–800CrossRefGoogle Scholar
  8. Huan SH, Sheoran SK, Wang G (2004) A review and analysis of supply chain operations reference (SCOR) model. Supply Chain Manage Int J 9(1):23–29CrossRefGoogle Scholar
  9. Jharkharia S, Shankar R (2005) IT-enablement of supply chains: understanding the barriers. J Enterp Inf Manage 18(1):11–27CrossRefGoogle Scholar
  10. Joshi R, Banwet DK, Shankar R (2009) Indian cold chain: modeling the inhibitors. Br Food J 111(11):1260–1283CrossRefGoogle Scholar
  11. Kumar R, Singh RK, Shankar R (2013) Study on coordination issues for flexibility in supply chain of SMEs: a case study. Glob J Flex Syst Manage 14(2):81–92CrossRefGoogle Scholar
  12. Larsen TS (2000) European logistics beyond 2000. Int J Phys Distrib Logist Manage 30(5):377–387CrossRefGoogle Scholar
  13. Lee HL, Whang S (2000) Information sharing in a supply chain. Int J Manuf Technol Manage 1(1):79–93Google Scholar
  14. Lee HL, Padmanabham V, Whang S (1997a) The bullwhip effect in supply chains. Sloan Manage Rev 38(3):93–102Google Scholar
  15. Lee HL, Padmanabhan P, Whang S (1997b) Information distortion in a supply chain: the bullwhip effect. Manage Sci 43(4):546–558CrossRefGoogle Scholar
  16. Mandal A, Deshmukh SG (1994) Vendor selection using interpretive structural modeling (ISM). Int J Oper Prod Manage 14(6):52–59CrossRefGoogle Scholar
  17. Mentzer JT, DeWitt W, Keebler JS, Min S, Nix NW, Smith CD, Zacharia ZG (2001) Defining supply chain management. J Bus Logist 22(2):1–25CrossRefGoogle Scholar
  18. Narus JA, Anderson JC (1996) Rethinking distribution: adaptive channels. Harv Bus Rev 74(4):112–120Google Scholar
  19. Raj T, Shankar R, Suhaib M (2008) An ISM approach for modelling the enablers of flexible manufacturing system: the case for India. Int J Prod Res 46(24):6883–6912CrossRefGoogle Scholar
  20. Ramesh A, Banwet DK, Shankar R (2010) Modeling the barriers of supply chain collaboration. J Model Manage 5(2):176–193CrossRefGoogle Scholar
  21. Ravi V, Shankar R (2005) Analysis of interactions among the barriers of reverse logistics. Technol Forecast Soc Change 72(8):1011–1029CrossRefGoogle Scholar
  22. Saxena JP, Sushil, Vrat, P (2006) Policy and strategy formulation—an application of flexible systems methodology. GIFT Publishing, New DelhiGoogle Scholar
  23. Singh D, Agrawal DP (2003) CRM practices in Indian industries. Int J CRM 5(3):241–257Google Scholar
  24. Singh MD, Kant R (2007) Knowledge management barriers: an interpretive structural modeling approach. In proceedings of the international conference on industrial engineering and engineering management (IEEM) 2007 in Singapore, IEEE, 2091–2095Google Scholar
  25. Singh RK, Acharya P (2013) Supply chain flexibility: a frame work of research dimensions. Glob J Flex Syst Manage 14(3):157–166CrossRefGoogle Scholar
  26. Souter G (2000) Risks from supply chain also demand attention. Bus Insur 34(20):26–28Google Scholar
  27. Stank TP, Crum MR, Arango M (1999) Benefits of inter-firm coordination in food industry in supply chains. J Bus Logist 20(2):21–41Google Scholar
  28. Tsay AA, Lovejoy WS (1999) Quantity flexibility contracts and supply chain performance. Manuf Serv Oper Manage 1(2):89–111Google Scholar
  29. Warfield JW (1974) Developing interconnected matrices in structural modeling. IEEE Trans Syst Man Cybern 4(1):81–87CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Dhanya Jothimani
    • 1
  • Ravi Shankar
    • 1
  • Surendra S. Yadav
    • 1
  1. 1.Department of Management StudiesIndian Institute of Technology DelhiNew DelhiIndia

Personalised recommendations