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The data-driven newsvendor with censored demand observations

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  • Sachs, Anna-Lena
  • Minner, Stefan

Abstract

Motivated by data from a large European retail chain, we tackle the newsvendor problem with censored demand observations by a distribution-free model based on a data-driven approach. The model estimates the optimal inventory levels as a linear function of exogenous variables, e.g., price or temperature. To improve the forecast accuracy, we simultaneously estimate unobservable lost sales, determine the coefficients of the exogenous variables which drive demand, and calculate the optimal order quantity. Since demand exceeding supply cannot be recorded, we use the timing of (hourly) sales occurrences to establish (daily) sales patterns. These sales patterns allow conclusions on the amount of unsatisfied demand and thus the true customer demand. To determine the coefficients of the inventory function, we formulate a Linear Programming model that balances inventory holding and penalty costs based on the censored demand observations. In a numerical study with data generated from the normal and the negative binomial distribution, we compare our model with other parametric and non-parametric estimation approaches. We evaluate the performance in terms of inventory and service level for (non-)price-dependent demands and different censoring levels. We find that the data-driven newsvendor model copes especially well with highly censored data and price-dependent demand. In most settings with price-dependent demand, it achieves similar or higher service levels by holding lower inventories than other benchmark approaches from the literature. Finally, we show that the non-parametric approaches are better than the parametric ones based on real data with several exogenous variables where the true demand distribution is unknown.

Suggested Citation

  • Sachs, Anna-Lena & Minner, Stefan, 2014. "The data-driven newsvendor with censored demand observations," International Journal of Production Economics, Elsevier, vol. 149(C), pages 28-36.
  • Handle: RePEc:eee:proeco:v:149:y:2014:i:c:p:28-36
    DOI: 10.1016/j.ijpe.2013.04.039
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    References listed on IDEAS

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    2. Albiński, Szymon & Fontaine, Pirmin & Minner, Stefan, 2018. "Performance analysis of a hybrid bike sharing system: A service-level-based approach under censored demand observations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 59-69.
    3. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    4. Serrano, Breno & Minner, Stefan & Schiffer, Maximilian & Vidal, Thibaut, 2024. "Bilevel optimization for feature selection in the data-driven newsvendor problem," European Journal of Operational Research, Elsevier, vol. 315(2), pages 703-714.
    5. Berndt Jesenko & Christian Schlögl, 2021. "The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6785-6801, August.
    6. Özgün Turgut & Florian Taube & Stefan Minner, 2018. "Data-driven retail inventory management with backroom effect," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 945-968, October.
    7. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2021. "Distributional regression for demand forecasting in e-grocery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 831-842.
    8. Huber, Jakob & Müller, Sebastian & Fleischmann, Moritz & Stuckenschmidt, Heiner, 2019. "A data-driven newsvendor problem: From data to decision," European Journal of Operational Research, Elsevier, vol. 278(3), pages 904-915.
    9. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    10. 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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