Classification-based model selection in retail demand forecasting

Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R (2022)
International Journal of Forecasting 38(1): 209-223.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Ulrich, Matthias; Jahnke, HermannUniBi; Langrock, RolandUniBi; Pesch, Robert; Senge, Robin
Abstract / Bemerkung
Retailers supply a wide range of stock keeping units (SKUs), which may differ for example in terms of demand quantity, demand frequency, demand regularity, and demand variation. Given this diversity in demand patterns, it is unlikely that any single model for demand forecasting can yield the highest forecasting accuracy across all SKUs. To save costs through improved forecasting, there is thus a need to match any given demand pattern to its most appropriate prediction model. To this end, we propose an automated model selection framework for retail demand forecasting. Specifically, we consider model selection as a classification problem, where classes correspond to the different models available for forecasting. We first build labeled training data based on the models' performances in previous demand periods with similar demand characteristics. For future data, we then automatically select the most promising model via classification based on the labeled training data. The performance is measured by economic profitability, taking into account asymmetric shortage and inventory costs. In an exploratory case study using data from an e-grocery retailer, we compare our approach to established benchmarks. We find promising results, but also that no single approach clearly outperforms its competitors, underlying the need for case-specific solutions. (C) 2021 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Stichworte
Forecasting; Inventory; e-commerce; Retailing; Model selection
Erscheinungsjahr
2022
Zeitschriftentitel
International Journal of Forecasting
Band
38
Ausgabe
1
Seite(n)
209-223
ISSN
0169-2070
eISSN
1872-8200
Page URI
https://pub.uni-bielefeld.de/record/2960422

Zitieren

Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Classification-based model selection in retail demand forecasting. International Journal of Forecasting . 2022;38(1):209-223.
Ulrich, M., Jahnke, H., Langrock, R., Pesch, R., & Senge, R. (2022). Classification-based model selection in retail demand forecasting. International Journal of Forecasting , 38(1), 209-223. https://doi.org/10.1016/j.ijforecast.2021.05.010
Ulrich, Matthias, Jahnke, Hermann, Langrock, Roland, Pesch, Robert, and Senge, Robin. 2022. “Classification-based model selection in retail demand forecasting”. International Journal of Forecasting 38 (1): 209-223.
Ulrich, M., Jahnke, H., Langrock, R., Pesch, R., and Senge, R. (2022). Classification-based model selection in retail demand forecasting. International Journal of Forecasting 38, 209-223.
Ulrich, M., et al., 2022. Classification-based model selection in retail demand forecasting. International Journal of Forecasting , 38(1), p 209-223.
M. Ulrich, et al., “Classification-based model selection in retail demand forecasting”, International Journal of Forecasting , vol. 38, 2022, pp. 209-223.
Ulrich, M., Jahnke, H., Langrock, R., Pesch, R., Senge, R.: Classification-based model selection in retail demand forecasting. International Journal of Forecasting . 38, 209-223 (2022).
Ulrich, Matthias, Jahnke, Hermann, Langrock, Roland, Pesch, Robert, and Senge, Robin. “Classification-based model selection in retail demand forecasting”. International Journal of Forecasting 38.1 (2022): 209-223.
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