Understanding Human Decision-Making During Supply Chains Shortages

Research conducted by MIE Associate Professor Jacqueline Griffin, MIE Professor Ozlem Ergun, and affiliate faculty member Stacy Marsella on “Agent-Based Modeling of Human Decision-makers Under Uncertain Information During Supply Chain Shortages” was published in the proceedings from the 2023 International Conference on Autonomous Agents and Multiagent Systems.


In recent years, product shortages caused by supply chain disruptions have generated problems for consumers worldwide. In supply chains, multiple decision-makers act on uncertain information they receive from others, often leading to sub-optimal decisions that propagate the effects of supply chain disruptions to other stakeholders. Therefore, understanding how humans learn to interpret information from others and how it influences their decision-making is key to alleviating supply chain shortages. In this work, we investigated how downstream supply chain echelons, health centers in pharmaceutical supply chains, interpret and use manufacturers’ estimated resupply date (ERD) information during drug shortages. We formulated a computational model of a health center based on a partially observable Markov decision process that learns a manufacturer’s information sharing tendencies through an observation function. To investigate the model and important factors influencing decisions and perceptions of ERD, we conducted a human experiment to study where subjects played the role of a health center during a drug shortage. They received ERDs from a manufacturer on a weekly basis and decided whether or not to switch to an alternative product (and pay additional costs) to avoid running out of stock. The results show that different manufacturers’ sequences of ERDs and the accuracy of ERDs could impact subjects’ decisions, beliefs, performance, and perception of the manufacturer. We also found that the subjective belief of ERDs is the best predictor of subjects’ switching decisions. Lastly, we fit the observation function’s learning rate and show that the model can predict subjects’ decisions better than other baseline models in most conditions.

Related Faculty: Jacqueline Griffin, Ozlem Ergun

Related Departments:Mechanical & Industrial Engineering