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A comprehensive decision-making model for risk management of supply chain

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Xia, De and Chen, Bo, Dr.. (2011) A comprehensive decision-making model for risk management of supply chain. Expert Systems with Applications, Volume 38 (Number 5). pp. 4957-4966. ISSN 09574174

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Abstract

Risk management of a supply chain (SC) has a great influence on the stability of dynamic cooperation among SC partners and hence very important for the performance of the SC operations as a whole. A suitable decision-making model is the cornerstone for the efficiency of SC risk management. We propose in this paper a decision-making model based on the internal triggering and interactive mechanisms in an SC risk system, which takes into account dual cycles, the operational process cycle (OPC) and the product life cycle (PLC). We explore the inter-relationship among the two cycles, SC organizational performance factors (OPF) and available risk operational practice (ROP), as well as the risk managerial elements in OPC and PLC. In particular, three types of relationship, bilateral, unilateral and inter-circulative ones, are analyzed and verified. We build this dynamic relation into SC risk managerial logic and design a corresponding decision-making path. Based on the analytic network process (ANP), a methodology is designed for an optimal selection of risk management methods and tools. A numerical example is provided as an operational guideline for how to apply it to tailor operational tactics in SC risk management. The results verify that this strategic decision model is a feasible access to the suitable risk operational tactics for practitioners. (C) 2010 Elsevier Ltd. All rights reserved.

Item Type: Journal Article
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
Divisions: Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Business logistics, Risk management, Decision making -- Mathematical models, Product life cycle
Journal or Publication Title: Expert Systems with Applications
Publisher: Pergamon-Elsevier Science Ltd.
ISSN: 09574174
Date: May 2011
Volume: Volume 38
Number: Number 5
Page Range: pp. 4957-4966
Identification Number: 10.1016/j.eswa.2010.09.156
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
References: Aeppel, T., White, J. B., & Power, S. (2001). Firestone quits as tire supplier to Ford - Auto Company is accused of refusing to concede to SUV safety concerns. The Wall Street Journal, May 22. Alex, R. (2007). Fuzzy point estimation and its application on fuzzy supply chain analysis. Fuzzy Sets and Systems, 158, 1571–1587. Copra, S., & Sodhi, M. S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46(1), 53–61. Childerhouse, P., Disney, S. M., & Towill, D. R. (2008). On the impact of order volatility in the European automotive sector. International Journal of Production Economics, 114(1), 2–13. Florida, R. (1996). Lean and green: The move to environmentally conscious manufacturing. California Management Review, 39, 80–105. Goh, M., Lim, J. Y. S., & Meng, F. (2007). A stochastic model for risk management in global supply chain networks. European Journal of Operational Research, 182, 164–173. Graves, S. C., & Tomlin, B. T. (2003). Process flexibility in supply chain. Management Science, 7, 907–919. Grey, W., & Shi, D. (2001). B2B risk management: Managing enterprise risks from the perspective of the extended supply chain. White Paper. Yorktown Heights, New York: IBM T.J. Watson Research Center. Ha, A. Y., Li, L., & Ng, S. (2003). Price and delivery logistics competition in a supply chain. Management Science, 49, 1139–1153. Huchzermeier, A. (2000). The real option value of operational and managerial flexibility in global supply chain networks. In M. Frenkel, U. Hommel, & M. Rudolf (Eds.), Risk management: Challenge and opportunity (pp. 181–201). Springer Verlag. Huchzermeier, A., & Cohen, M. A. (1996). Valuing operational flexibility under exchange rate uncertainty. Operations Research, 44, 100–113. Lin, K.-P., Chang, P.-T., Hung, K.-C., & Pai, P.-F. (in press). A simulation of vendor managed inventory dynamics using fuzzy arithmetic operations with genetic algorithms. Expert Systems with Applications, doi:10.1016/j.eswa.2009.08.020 (available online 23.08.09). Kleindorfer, P. R., & Partovi, F. Y. (1990). Integrating manufacturing strategy and technology choice. European Journal of Operational Research, 47, 214–224. Kouvelis, P., & Rosenblatt, M. J. (2002). A mathematical programming model for global supply chain management: Conceptual approach and managerial insights. In J. Geunes, P. M. Pardalos, & H. E. Romeijn (Eds.), Supply chain management: Models, applications, and research directions. Boston: Kluwer Academic Publishers. Nagurney, A., Cruz, J., & Matsypura, D. (2003). Dynamics of global supply chain super-networks. Mathematical and Computer Modelling, 37, 963–983. Ray, S., Li, S., & Song, Y. (2005). Tailored supply chain decision making under price sensitive stochastic demand and delivery uncertainty. Management Science, 12, 1873–1891. Riddalls, C. E., & Bennet, S. (2002). The stability of supply chains. International Journal of Production Research, 40(2), 459–475. Saaty, T. L. (1996). The analytic network process. Pittsburgh, PA: RWS Publications. Schenkerman, S. (1994). Avoiding rank reversal in AHP decision support models. European Journal of Operational Research, 74(3), 407–419. Smith, R., & Huchzermeier, A. (2003). Global supply chain and risk optimization: Real options add value for global manufacturing firms. In K. Kummar, J. Van Nunen, & L. N. Van Wassenhove (Eds.), Global supply chains. Stanford University Press. Thonemann, U. W., & Bradley, J. R. (2002). The effect of product variety on supplychain performance. European Journal of Operational Research, 143, 548–569. Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications, 9(1), 33–45. Wang, J., & Shu, Y. F. (2007). A possibilistic decision model for new product supply chain design. European Journal of Operational Research, 177, 1044–1061.
URI: http://wrap.warwick.ac.uk/id/eprint/41612

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