Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain

David Simchi-Levi (), William Schmidt (), Yehua Wei (), Peter Yun Zhang (), Keith Combs (), Yao Ge (), Oleg Gusikhin (), Michael Sanders () and Don Zhang ()
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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

Interfaces, 2015, vol. 45, issue 5, 375-390

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.

Keywords: risk management; automotive; manufacturing industries; disruption; risk-exposure index (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (54)

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