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Demand Estimation and Ordering Under Censoring: Stock-Out Timing Is (Almost) All You Need

Author

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  • Aditya Jain

    (Indian School of Business, Hyderabad 500 032, India)

  • Nils Rudi

    (INSEAD, 138676 Singapore)

  • Tong Wang

    (NUS Business School, National University of Singapore, 119245 Singapore)

Abstract

Retailers facing uncertain demand can use observed sales to update demand estimates. However, such learning is limited by the amount of inventory carried; when demand exceeds inventory (i.e., when a stock-out event occurs), a retailer in general cannot observe actual demand. We propose using observations on the timing of sales occurrences in a Bayesian fashion to learn about demand, and we analyze this learning method for a multiperiod newsvendor setting. We find that, as previously shown with the use of only stock-out event observations, the optimal order quantity with timing observations is greater than the optimal order quantity with full demand observations. We prove this result using a novel methodology from the statistics literature on comparison of experiments. Although the optimal over-ordering with timing observations tends to be less than that with only stock-out event observations in most cases, we do observe cases where the opposite is true. Such cases correspond to high demand uncertainty and low margins, where marginal learning from timing observations is significantly higher than using only a stock-out event. In an extensive numerical study we find that, on average and with respect to uncensored demand observations, the use of timing observations eliminates 76.1% of the loss in expected profit from using only stock-out event observations. We show that, for Poisson and normal demand with unknown mean, the proposed learning method is tractable as well as intuitively appealing: the information contained in the timing of sales occurrences is fully captured by a single number—the timing of stock-out. We also investigate checkpoint models in which the newsvendor can make observations only at predetermined times in a period, and illustrate its convergence to the models with timing and stock-out event observations.

Suggested Citation

  • Aditya Jain & Nils Rudi & Tong Wang, 2015. "Demand Estimation and Ordering Under Censoring: Stock-Out Timing Is (Almost) All You Need," Operations Research, INFORMS, vol. 63(1), pages 134-150, February.
  • Handle: RePEc:inm:oropre:v:63:y:2015:i:1:p:134-150
    DOI: 10.1287/opre.2014.1326
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    References listed on IDEAS

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    5. Zizhuo Wang & Chaolin Yang & Hongsong Yuan & Yaowu Zhang, 2021. "Aggregation Bias in Estimating Log‐Log Demand Function," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 3906-3922, November.
    6. Li, Xishu & Yin, Ying & Manrique, David Vergara & Bäck, Thomas, 2021. "Lifecycle forecast for consumer technology products with limited sales data," International Journal of Production Economics, Elsevier, vol. 239(C).
    7. Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Incorporating Search and Sales Information in Demand Estimation," Cowles Foundation Discussion Papers 2313R1, Cowles Foundation for Research in Economics, Yale University, revised Mar 2023.
    8. Joonkyum Lee & Vishal Gaur & Suresh Muthulingam & Gary F. Swisher, 2016. "Stockout-Based Substitution and Inventory Planning in Textbook Retailing," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 104-121, February.
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    10. Georgia Perakis & Melvyn Sim & Qinshen Tang & Peng Xiong, 2023. "Robust Pricing and Production with Information Partitioning and Adaptation," Management Science, INFORMS, vol. 69(3), pages 1398-1419, March.
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    13. Li Chen & Adam J.Mersereau & Zhe (Frank) Wang, 2017. "Optimal Merchandise Testing with Limited Inventory," Operations Research, INFORMS, vol. 65(4), pages 968-991, August.
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    15. Alain Bensoussan & Pengfei Guo, 2015. "Technical Note—Managing Nonperishable Inventories with Learning About Demand Arrival Rate Through Stockout Times," Operations Research, INFORMS, vol. 63(3), pages 602-609, June.
    16. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.

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