Mitigation strategies against supply disruption risk: A case study at the Ford Motor Company

Ece Sanci, Mark S. Daskin, Young-Chae Hong, Steve Roesch, Don Zhang

Research output: Contribution to journalArticlepeer-review

5 Citations (SciVal)

Abstract

Supply chains are exposed to different risks, which can be mitigated by various strategies based on the characteristics and needs of companies. In collaboration with Ford, we develop a decision support framework to choose the best mitigation strategy against supply disruption risk, especially for companies operating with a small supplier base and low inventory levels. Our framework is based on a multistage stochastic programming model which incorporates a variety of plausible strategies, including reserving backup capacity from the primary supplier, reserving capacity from a secondary supplier, and holding backup inventory. We reflect disruption risk into the framework through decision makers’ input on the time to recover and the disruption probability. Our results demonstrate that relying on the strategy which is optimal when there is no disruption risk can increase the expected total cost substantially in the presence of disruption risk. However, this increase can be reduced significantly by investing in the mitigation strategy recommended by our framework. Our results also show that this framework removes the burden of estimating the time to recover and the disruption probability precisely since there is often a small loss associated with using another strategy that is optimal in the neighbourhood of the estimated values.

Original languageEnglish
Pages (from-to)5956-5976
Number of pages21
JournalInternational Journal of Production Research
Volume60
Issue number19
Early online date23 Sep 2021
DOIs
Publication statusE-pub ahead of print - 23 Sep 2021

Keywords

  • Supply chain risk management
  • contingency planning
  • disruption risk mitigation
  • multistage stochastic programming
  • sourcing mitigation

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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