Due to demanding and unstable business environments, companies must be able to quickly react to disturbances from outside sources. Many supply chain (SC) planning models exist which involve proactive measures to mitigate effects due to SC uncertainty and therefore a certain level of prior investment. However, a reactive supply chain may be able to avoid these upfront costs. In this research, supply uncertainty with regards to lead time is investigated. The lead time distribution, for each supplier in a multi-echelon SC, is dependent upon the current level of bottleneck within the supplier (light, normal or congested). For each of these three levels, two distributions of lead time are investigated, beta and normal. The two types of distributions are considered separately and chance constrained programming is used to solve for the optimal supplier set while minimizing cost to the entire SC. The result is a SC which, by re-optimizing at each echelon, can exhibit lower overall cost.
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