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The design and management of a supply chain (SC) in a global pandemic require a different approach than those used for more spatially restricted risks, such as earthquakes. A successful SC design and management plan should consider pandemic spatiotemporal characteristics as well as its effects on production and logistical operations, and on the SC workforce at risk. In this paper, a stochastic mixed integer linear programming model is developed to maximise the conditional value at risk (CVaR) of SC profit given a set of pandemic scenarios. An exemplar SC network from the literature is utilised, along with randomly generated pessimistic and optimistic pandemic scenarios. The proposed model is demonstrated by obtaining SC designs for different cases pertaining to pandemic influence and strategic policies. The resultant SC designs are used to contrast the performance of management plans across different pandemic scenarios and for different levels of workforce at risk. Supply chains for socially critical products, such as ventilators, are studied separately to examine the impact of SC network expansion on maximizing satisfied demand. Finally, we investigate the effects of diversifying network node locations across different administrative regions on SC performance. Several managerial insights are presented for SC planners to aid in creating viable designs and management plans.

Resilient supply chain to a global pandemic