Reluctant to Decrease: Human-algorithm Collaboration in Tactical Inventory Decisions

Workshop’s Topic: We study augmented decision making when setting base stock levels in high-stakes spare parts inventory management. Such spare parts inventory management is characterized by many 0-1 decisions: the base stock level is either 0 or We partner with an OEM where state-of-the-art inventory control algorithms have been deployed, and observe that human decision makers deviate from the optimal solution by adjusting 24% of the base stock levels. We deploy a structural estimation model estimating the humans’ psychological cost for overage and underage, and find that in particular the psychological cost for underage are substantially higher than parameterized in the algorithms. Interestingly, we can show that such psychological cost for underage are particularly high in case the algorithm proposes to reduce the inventory from 1 to 0, similar to the well-known endowment effect. Leveraging our structural estimation, we run a counterfactual analysis and show that a Pareto-optimal alternative solution can be obtained that is more aligned with the humans’ perceptions of underage costs.

Time and Location: 10:00 AM (GMT+8), Room A523 (School of Management)

Language: English