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Fixing Phantom Stockouts: Optimal Data‐Driven Shelf Inspection Policies

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  • Li Chen

Abstract

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.

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  • Li Chen, 2021. "Fixing Phantom Stockouts: Optimal Data‐Driven Shelf Inspection Policies," Production and Operations Management, Production and Operations Management Society, vol. 30(3), pages 689-702, March.
    • Handle: RePEc:bla:popmgt:v:30:y:2021:i:3:p:689-702
    DOI: 10.1111/poms.13310
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    References listed on IDEAS

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    1. Kök, A. Gürhan & Shang, Kevin H., 2014. "Evaluation of cycle-count policies for supply chains with inventory inaccuracy and implications on RFID investments," European Journal of Operational Research, Elsevier, vol. 237(1), pages 91-105.
    2. Li Chen & Adam J. Mersereau, 2015. "Analytics for Operational Visibility in the Retail Store: The Cases of Censored Demand and Inventory Record Inaccuracy," International Series in Operations Research & Management Science, in: Narendra Agrawal & Stephen A. Smith (ed.), Retail Supply Chain Management, edition 2, chapter 0, pages 79-112, Springer.
    3. A. Gürhan Kök & Kevin H. Shang, 2007. "Inspection and Replenishment Policies for Systems with Inventory Record Inaccuracy," Manufacturing & Service Operations Management, INFORMS, vol. 9(2), pages 185-205, February.
    4. Adam J. Mersereau, 2015. "Demand Estimation from Censored Observations with Inventory Record Inaccuracy," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 335-349, July.
    5. Donald L. Iglehart & Richard C. Morey, 1972. "Inventory Systems with Imperfect Asset Information," Management Science, INFORMS, vol. 18(8), pages 388-394, April.
    6. Achal Bassamboo & Antonio Moreno & Ioannis Stamatopoulos, 2020. "Inventory Auditing and Replenishment Using Point‐of‐Sales Data," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1219-1231, May.
    7. Nicole DeHoratius & Adam J. Mersereau & Linus Schrage, 2008. "Retail Inventory Management When Records Are Inaccurate," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 257-277, November.
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    Cited by:

    1. Madison J. Bixler & Dorothée Honhon, 2021. "Exploring the Connections between Backrooms, Inventory Record Inaccuracies, and Waste," Sustainability, MDPI, vol. 13(17), pages 1-12, August.

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