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Modeling a severe supply chain disruption and post-disaster decision making with application to the Japanese earthquake and tsunami

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  • Cameron A. MacKenzie
  • Kash Barker
  • Joost R. Santos

Abstract

Modern supply chains are increasingly vulnerable to disruptions, and a disruption in one part of the world can cause supply difficulties for companies around the globe. This article develops a model of severe supply chain disruptions in which several suppliers suffer from disabled production facilities and firms that purchase goods from those suppliers may consequently suffer a supply shortage. Suppliers and firms can choose disruption management strategies to maintain operations. A supplier with a disabled facility may choose to move production to an alternate facility, and a firm encountering a supply shortage may be able to use inventory or buy supplies from an alternate supplier. The supplier’s and firm’s optimal decisions are expressed in terms of model parameters such as the cost of each strategy, the chances of losing business, and the probability of facilities reopening. The model is applied to a simulation based on the 2011 Japanese earthquake and tsunami, which closed several facilities of key suppliers in the automobile industry and caused supply difficulties for both Japanese and U.S. automakers.

Suggested Citation

  • Cameron A. MacKenzie & Kash Barker & Joost R. Santos, 2014. "Modeling a severe supply chain disruption and post-disaster decision making with application to the Japanese earthquake and tsunami," IISE Transactions, Taylor & Francis Journals, vol. 46(12), pages 1243-1260, December.
    • Handle: RePEc:taf:uiiexx:v:46:y:2014:i:12:p:1243-1260
    DOI: 10.1080/0740817X.2013.876241
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    Cited by:

    1. Sawik, Tadeusz, 2021. "On the risk-averse selection of resilient multi-tier supply portfolio," Omega, Elsevier, vol. 101(C).
    2. Li, Yuhong & Zobel, Christopher W. & Seref, Onur & Chatfield, Dean, 2020. "Network characteristics and supply chain resilience under conditions of risk propagation," International Journal of Production Economics, Elsevier, vol. 223(C).
    3. Ni, Ni & Howell, Brendan J. & Sharkey, Thomas C., 2018. "Modeling the impact of unmet demand in supply chain resiliency planning," Omega, Elsevier, vol. 81(C), pages 1-16.
    4. Sawik, Tadeusz, 2019. "Disruption mitigation and recovery in supply chains using portfolio approach," Omega, Elsevier, vol. 84(C), pages 232-248.
    5. Singh, Nitya P. & Hong, Paul C., 2020. "Impact of strategic and operational risk management practices on firm performance: An empirical investigation," European Management Journal, Elsevier, vol. 38(5), pages 723-735.
    6. Ivanov, Dmitry & Pavlov, Alexander & Dolgui, Alexandre & Pavlov, Dmitry & Sokolov, Boris, 2016. "Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 7-24.
    7. Sherwin, Michael D. & Medal, Hugh & Lapp, Steven A., 2016. "Proactive cost-effective identification and mitigation of supply delay risks in a low volume high value supply chain using fault-tree analysis," International Journal of Production Economics, Elsevier, vol. 175(C), pages 153-163.

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