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

Sarah Yini Gao (), David Simchi-Levi (), Chung-Piaw Teo () and Zhenzhen Yan ()
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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

Operations Research, 2019, vol. 67, issue 3, 831-852

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.

Keywords: supply chain risk management; distributionally robust optimization; time to survive; sensitivity analysis; completely positive programming (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (20)

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