From Just-in-Time, to Just-in-Case, to Just-in-Worst-Case: Simple Models of a Global Supply Chain under Uncertain Aggregate Shocks

COVID-19 highlighted the weaknesses in the supply chain. Many have argued that a more resilient or robust supply chain is needed. But what does a robust supply chain mean? And how do firms’ decisions change when taken that approach? This paper studies a very stylized model of a supply chain, where we study how the decision of a multinational corporation changes in the presence of uncertainty. The two standard theories of supply chain are just-in-time and just-in-case. Just-in-time argues in favor of pursuing efficiency, while just-in-case studies how such decision changes when the firm faces idiosyncratic risk. We find that a robust supply chain is very different specially in the presence of systemic shocks. In this case, firms need to concentrate on the worst-case. This strategy implies a supply chain where the allocation of resources and capabilities does not correspond to the standard theories studied in economics, but follow a heuristic behavioral rule called “probability matching.” It has been found in nature and in experimental research that subjects appeal to probability matching when seeking survival. We find that a robust supply chain will reproduce this behavioral outcome. In fact, a multinational optimizing under uncertainty follows a probability matching which leads to an allocation that is suboptimal from the individual producer point of view, but rules out the possibility of supply disruptions.