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Data-Driven Order Policies with Censored Demand and Substitution in Retailing

In: Retail Analytics

Author

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  • Anna-Lena Sachs

    (University of Cologne)

Abstract

We extend the data-driven inventory model with censored demand to the two-product case with stockout-based substitution. If one product stocks out, a fraction of the demand that cannot be satisfied is shifted to the substitute. As a result, sales of the substitute are inflated by the additional demand. The amount of substituted demand as well as unobservable lost sales are estimated based on the timing of stockout events. Similarly to the model in Chap. 4, we establish sales patterns based on the hourly sales observations before a stockout occurs. Our numerical study and data from a large European retail chain shows that the data-driven model achieves higher average profits than an existing approach from the literature. Investigating the trade-off between learning about substitution behavior from highly censored data versus learning about demand from little censored data, we find that more learning about substitution yields slightly better results in terms of profits.

Suggested Citation

  • Anna-Lena Sachs, 2015. "Data-Driven Order Policies with Censored Demand and Substitution in Retailing," Lecture Notes in Economics and Mathematical Systems, in: Retail Analytics, edition 127, chapter 0, pages 57-78, Springer.
    • Handle: RePEc:spr:lnechp:978-3-319-13305-8_5
    DOI: 10.1007/978-3-319-13305-8_5
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    Cited by:

    1. Huber, Jakob & Stuckenschmidt, Heiner, 2021. "Intraday shelf replenishment decision support for perishable goods," International Journal of Production Economics, Elsevier, vol. 231(C).

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