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Disruption Risk Mitigation in Supply Chains: The Risk Exposure Index Revisited

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  • Sarah Yini Gao

    (Lee Kong Chian School of Business, Singapore Management University, Singapore 188065)

  • David Simchi-Levi

    (Institute for Data, Systems, and Society, Department of Civil and Environmental Engineering, and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Chung-Piaw Teo

    (Institute of Operations Research and NUS Business School, National University of Singapore, Singapore 119077)

  • Zhenzhen Yan

    (School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 639798)

Abstract

In recent years, supply chains are more prone to disruptions. The impact on performance depends on the system's ability to discover and then recover after the disruption has occurred. In this paper, we proposed a new method to integrate probabilistic assessment of disruption risks into the Risk Exposure Index (REI) approach proposed previously by Simchi-Levi et al. and measure supply chain resiliency by analyzing the worst-case CVaR (WCVaR) of total lost sales under disruptions. We show that the optimal strategic inventory positioning strategy in this model can be fully characterized by a conic program. The optimal primal and dual solutions to the conic program can be used to shed light on comparative statics in the supply chain risk mitigation problem. This information can help supply chain risk managers focus their mitigation efforts on critical suppliers and/or installations that will have greater impact on the performance of the supply chain when disrupted.

Suggested Citation

  • Sarah Yini Gao & David Simchi-Levi & Chung-Piaw Teo & Zhenzhen Yan, 2019. "Disruption Risk Mitigation in Supply Chains: The Risk Exposure Index Revisited," Operations Research, INFORMS, vol. 67(3), pages 831-852, May.
  • Handle: RePEc:inm:oropre:v:67:y:2019:i:3:p:831-852
    DOI: 10.1287/opre.2018.1776
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    References listed on IDEAS

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    1. Robert R. Meyer & Michael H. Rothkopf & Stephen A. Smith, 1979. "Reliability and Inventory in a Production-Storage System," Management Science, INFORMS, vol. 25(8), pages 799-807, August.
    2. David Simchi‐Levi & He Wang & Yehua Wei, 2018. "Increasing Supply Chain Robustness through Process Flexibility and Inventory," Production and Operations Management, Production and Operations Management Society, vol. 27(8), pages 1476-1491, August.
    3. Antonio Arreola‐Risa & Gregory A. DeCroix, 1998. "Inventory management under random supply disruptions and partial backorders," Naval Research Logistics (NRL), John Wiley & Sons, vol. 45(7), pages 687-703, October.
    4. Tingting Yan & Thomas Y. Choi & Yusoon Kim & Yang Yang, 2015. "A Theory of the Nexus Supplier: A Critical Supplier From A Network Perspective," Journal of Supply Chain Management, Institute for Supply Management, vol. 51(1), pages 52-66, January.
    5. Jing-Sheng Song & Paul H. Zipkin, 1996. "Inventory Control with Information About Supply Conditions," Management Science, INFORMS, vol. 42(10), pages 1409-1419, October.
    6. Shushang Zhu & Masao Fukushima, 2009. "Worst-Case Conditional Value-at-Risk with Application to Robust Portfolio Management," Operations Research, INFORMS, vol. 57(5), pages 1155-1168, October.
    7. Karthik Natarajan & Chung Piaw Teo & Zhichao Zheng, 2011. "Mixed 0-1 Linear Programs Under Objective Uncertainty: A Completely Positive Representation," Operations Research, INFORMS, vol. 59(3), pages 713-728, June.
    8. Gurnani, Haresh, 1996. "Optimal ordering policies in inventory systems with random demand and random deal offerings," European Journal of Operational Research, Elsevier, vol. 95(2), pages 299-312, December.
    9. David Simchi-Levi & William Schmidt & Yehua Wei & Peter Yun Zhang & Keith Combs & Yao Ge & Oleg Gusikhin & Michael Sanders & Don Zhang, 2015. "Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain," Interfaces, INFORMS, vol. 45(5), pages 375-390, October.
    10. Anders Levermann, 2014. "Climate economics: Make supply chains climate-smart," Nature, Nature, vol. 506(7486), pages 27-29, February.
    11. Gregory A. DeCroix, 2013. "Inventory Management for an Assembly System Subject to Supply Disruptions," Management Science, INFORMS, vol. 59(9), pages 2079-2092, September.
    12. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    15. Mohammadi, Mehrdad & Dehghan, Milad & Pirayesh, Amir & Dolgui, Alexandre, 2022. "Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic," Omega, Elsevier, vol. 113(C).
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