Abstract
Integration has formed the core of logistics and supply chain literature since the 1980s, but empirical research indicates that it does not always lead to better performance. This research effort endeavors to apply information theory to begin to assess the cost of complexity in supply chains. Extant research into supply chain complexity remains in the formative stages, so the purpose of this research is to explore the utility of entropy as a means of comparing alternative supply chain strategies and structures. Simulation experiments revealed that when compared to the bullwhip effect index, an index based upon weighted entropy offered superior assessments of supply chain performance when comparing the effects of safety stock and information sharing, but exhibited performance equally poor to the bullwhip effect index’s for predicting the effect of changing the number of echelons in a supply chain. This research lays the foundation for future research that will expand the weighted entropy formulation to account for differences in the number of supply chain players as well as the effect of different demand scenarios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alchian, A. A., & Demsetz, H. (1972). Production, information costs, and economic organization. American Economic Review, 62(5), 777–795.
Dejonckheere, J., Disney, S., Lambrecht, M., & Towill, D. (2002). Transfer function analysis of forecasting induced bullwhip in supply chains. International Journal of Production Economics, 78(2), 133–144.
Dejonckheere, J., Disney, S., Lambrecht, M., & Towill, D. (2004). The impact of information enrichment on the bullwhip effect in supply chains: A control engineering perspective. European Journal of Operational Research, 153(3), 727–750.
Disney, S. M., & Towill, D. R. (2003). Vendor-managed inventory and bullwhip reduction in a two-level supply chain. International Journal of Operations & Production Management, 23(6), 625–651.
Drucker, P. F. (1962). The Economy’s Dark Continent. Fortune, 5(4), 103, 265, 266, 268, 270.
Fabbe-Costes, N., & Jahre, M. (2008). Supply chain integration and performance: A review of the evidence. International Journal of Logistics Management, 19(2), 130–154.
Forrester, J. W. (1961). Industrial dynamics. Cambridge: MIT Press.
Fransoo, J. C., & Wouters, M. J. F. (2000). Measuring the bullwhip effect in the supply chain. Supply Chain Management: An International Journal, 5(2), 78–89.
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The Bullwhip effect. Management Science, 43(4), 546–558.
Magill, M. J. P., & Quinzii, M. (2002). Theory of incomplete markets (Vol. 1). Cambridge: The MIT Press.
Manuj, I., & Mentzer, J. T. (2008). Global supply chain risk management strategies. International Journal of Physical Distribution & Logistics Management, 38(3), 192–223.
Narasimhan, R., & Kim, S. (2002). Effect of supply chain integration on the relationship between diversification and performance: Evidence from Japanese and Korean firms. Journal of Operations Management, 20(3), 303–323.
Perona, M., & Miragliotta, G. (2004). Complexity management and supply chain performance assessment. A field study and a conceptual framework* 1. International Journal of Production Economics, 90(1), 103–115.
Roman, S. (1992). Coding and information theory. New York: Springer.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(379–423), 623–656.
Steckel, J. H., Gupta, S., & Banerji, A. (2004). Supply chain decision making: Will shorter cycle times and shared point-of-sale information necessarily help? Management Science, 50(4), 458–464.
Sterman, J. D. (1989). Misperceptions of feedback in dynamic decision making experiment. Organizational Behavior and Human Decision Processes, 43(3), 301–335.
Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. New York: Irwin/McGraw-Hill.
Torres, O. C., & Maltz, A. B. (2010). Understanding the financial consequences of the Bullwhip effect in a multi-echelon supply chain. Journal of Business Logistics, 31(1), 23–41.
U.S. Census Bureau (2002). Economic census: Semiconductor and related device manufacturing. Washington D.C.
Venkateswaran, J., & Son, Y. (2004). Impact of modelling approximations in supply chain analysis – An experimental study. International Journal of Production Research, 42(15), 2971–2992.
Venkateswaran, J., & Son, Y. (2007). Effect of information update frequency on the stability of production-inventory control systems. International Journal of Production Economics, 106(1), 171–190.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gravier, M.J., Kelly, B.P. (2012). Measuring the Cost of Complexity in Supply Chains: Comparison of Weighted Entropy and the Bullwhip Effect Index. In: Jodlbauer, H., Olhager, J., Schonberger, R. (eds) Modelling Value. Contributions to Management Science. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2747-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-7908-2747-7_13
Published:
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2746-0
Online ISBN: 978-3-7908-2747-7
eBook Packages: Business and EconomicsBusiness and Management (R0)