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Fixing Phantom Stockouts: Optimal Data‐Driven Shelf Inspection Policies
Author(s) -
Chen Li
Publication year - 2021
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.13310
Subject(s) - stockout , convexity , shrinkage , computer science , heuristic , process (computing) , mathematical optimization , operations research , operations management , economics , mathematics , artificial intelligence , machine learning , financial economics , operating system
A “phantom stockout" is a retail stockout phenomenon caused by unobserved inventory shrinkage. Unlike conventional stockouts that can be corrected by inventory replenishment, a phantom stockout persists and requires human inspection. In this study, we formulate such a problem as an infinite‐horizon Bayesian dynamic program with joint inventory inspection and replenishment decisions. This problem is challenging to solve due to non‐convexity and high dimensionality. However, we find that under the Bernoulli shrinkage process, the optimal inventory inspection policy has a simple threshold structure that depends on the number of consecutive zero‐sales periods since the last inspection, while the optimal inventory replenishment policy is the same as the optimal policy without inventory shrinkage. Our numerical studies further demonstrate that this simple and intuitive policy can be an effective heuristic for more general shrinkage processes.