备份
供应链
随机规划
业务
多样性(控制论)
风险管理
供应链风险管理
风险分析(工程)
运筹学
运营管理
计算机科学
供应链管理
经济
数学优化
工程类
财务
数学
服务管理
营销
数据库
人工智能
作者
Ece Sancı,Mark S. Daskin,Young-Chae Hong,Steve Roesch,Don C. Zhang
标识
DOI:10.1080/00207543.2021.1975058
摘要
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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