期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 60, 期 19, 页码 5956-5976出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2021.1975058
关键词
Supply chain risk management; disruption risk mitigation; sourcing mitigation; contingency planning; multistage stochastic programming
资金
- Ford Motor Company [N025042]
Supply chains face various risks that can be mitigated through different strategies. The study found that relying on the optimal strategy without considering disruption risk may increase costs significantly, but investing in mitigation strategies can help reduce this increase. Additionally, using an alternative strategy optimal near the estimated values may result in minor losses, demonstrating the effectiveness of the framework.
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.
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