From Just-in-Time, to Just-in-Case, to Just-in-Worst-Case: Simple Models of a Global Supply Chain under Uncertain Aggregate Shocks
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41308_2021_148_ReferencePDF.pdf
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14.48 MB
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2579f02b8355c0a0d72394acef0c37e1
Author(s)
Jiang, Bomin
Rigobon, Daniel
Rigobon, Roberto
Date Issued
November 15, 2021
Publisher
Palgrave Macmillan UK
Citation
Jiang, Bomin, Rigobon, Daniel and Rigobon, Roberto. 2021. "From Just-in-Time, to Just-in-Case, to Just-in-Worst-Case: Simple Models of a Global Supply Chain under Uncertain Aggregate Shocks."
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Author's final manuscript
Abstract
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Sloan School of Management
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1057/s41308-021-00148-2
