Resilient supply chain to a global pandemic

Abstract

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.

Keywords:

• Supply chain resilience
• operational research
• pandemic
• COVID-19
• conditional value at risk

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mohamed R. Salama

Mohamed R. Salama is a Ph.D. candidate in Industrial Engineering at the University of Missouri, Columbia, MO. He received his B.Sc. and M.Sc. degrees in Mechanical Design and Production Engineering from Cairo University, Giza, Egypt, in 2013 and 2017, respectively. Mohamed's main research interest is the applications of operations research in supply chain, logistics and transportation systems. His research works have been published in international journals, such as Computers and Industrial Engineering (CAIE) and Transportation Research Part C (TRC).

Ronald G. McGarvey

Ronald G. McGarvey is an associate professor of Industrial Engineering and Public Affairs at the University of Missouri, Columbia, MO. He received his B.S. in Applied Mathematics from Indiana University of Pennsylvania, and his M.S. and Ph.D. in Industrial Engineering and Operations Research from Pennsylvania State University. Prior to joining the faculty at the University of Missouri in 2013, he spent 11 years as a member of the research staff at RAND Corporation. His primary research interests are in applied optimisation, in particular its applications to public policy and resource management.