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Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain

Author

Listed:
  • David Simchi-Levi

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

  • William Schmidt

    (Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853)

  • Yehua Wei

    (The Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Peter Yun Zhang

    (Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Keith Combs

    (Ford Motor Company, Dearborn, Michigan 48126)

  • Yao Ge

    (Ford Motor Company, Dearborn, Michigan 48126)

  • Oleg Gusikhin

    (Ford Motor Company, Dearborn, Michigan 48126)

  • Michael Sanders

    (Ford Motor Company, Dearborn, Michigan 48126)

  • Don Zhang

    (Ford Motor Company, Dearborn, Michigan 48126)

Abstract

Firms are exposed to a variety of low-probability, high-impact risks that can disrupt their operations and supply chains. These risks are difficult to predict and quantify; therefore, they are difficult to manage. As a result, managers may suboptimally deploy countermeasures, leaving their firms exposed to some risks, while wasting resources to mitigate other risks that would not cause significant damage. In a three-year research engagement with Ford Motor Company, we addressed this practical need by developing a novel risk-exposure model that assesses the impact of a disruption originating anywhere in a firm’s supply chain. Our approach defers the need for a company to estimate the probability associated with any specific disruption risk until after it has learned the effect such a disruption will have on its operations. As a result, the company can make more informed decisions about where to focus its limited risk-management resources. We demonstrate how Ford applied this model to identify previously unrecognized risk exposures, evaluate predisruption risk-mitigation actions, and develop optimal postdisruption contingency plans, including circumstances in which the duration of the disruption is unknown.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:45:y:2015:i:5:p:375-390
    DOI: 10.1287/inte.2015.0804
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    References listed on IDEAS

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