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Data‐Driven Inventory Control with Shifting Demand

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  • Boxiao Chen

Abstract

We consider an inventory control problem with lost‐sales in a shifting demand environment. Over a planning horizon of T periods, demand distributions can change up to O( log T) times, but the firm does not know the demand distributions before or after each change, the time periods when changes occur, or the number of changes. Therefore, the firm needs to detect changes and learn the demand distributions only from historical sales data. We show that with censored demand, active exploration in the inventory space is needed for a reasonable detecting and learning algorithm. We provide a theoretical lower bound by partitioning all admissible policies into either exploration‐heavy or exploitation‐heavy, and for both categories we prove that the convergence rate cannot be better than Ω(T). We then develop a nonparametric learning algorithm for this problem and prove that it achieves a convergence rate that (almost) matches the theoretical lower bound.

Suggested Citation

  • Boxiao Chen, 2021. "Data‐Driven Inventory Control with Shifting Demand," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1365-1385, May.
    • Handle: RePEc:bla:popmgt:v:30:y:2021:i:5:p:1365-1385
    DOI: 10.1111/poms.13326
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    References listed on IDEAS

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    Cited by:

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    2. Xiong, Xing & Li, Yanzhi & Yang, Wenguo & Shen, Huaxiao, 2022. "Data-driven robust dual-sourcing inventory management under purchase price and demand uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    3. Park, Hyungjun & Choi, Dong Gu & Min, Daiki, 2023. "Adaptive inventory replenishment using structured reinforcement learning by exploiting a policy structure," International Journal of Production Economics, Elsevier, vol. 266(C).

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