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Post-script—Retail forecasting: Research and practice

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  • Fildes, Robert
  • Kolassa, Stephan
  • Ma, Shaohui

Abstract

This note updates the 2019 review article “Retail forecasting: Research and practice” in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.

Suggested Citation

  • Fildes, Robert & Kolassa, Stephan & Ma, Shaohui, 2022. "Post-script—Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1319-1324.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:4:p:1319-1324
    DOI: 10.1016/j.ijforecast.2021.09.012
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    6. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
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    8. Ma, Shaohui & Fildes, Robert, 2021. "Retail sales forecasting with meta-learning," European Journal of Operational Research, Elsevier, vol. 288(1), pages 111-128.
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    18. Sebastian Steinker & Kai Hoberg & Ulrich W. Thonemann, 2017. "The Value of Weather Information for E-Commerce Operations," Production and Operations Management, Production and Operations Management Society, vol. 26(10), pages 1854-1874, October.
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    24. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
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    Cited by:

    1. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
    2. Elisabeth Obermair & Andreas Holzapfel & Heinrich Kuhn, 2023. "Operational planning for public holidays in grocery retailing - managing the grocery retail rush," Operations Management Research, Springer, vol. 16(2), pages 931-948, June.
    3. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
    4. Fernando, Angeline Gautami & Aw, Eugene Cheng-Xi, 2023. "What do consumers want? A methodological framework to identify determinant product attributes from consumers’ online questions," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    5. Hunneman, Auke & Bijmolt, Tammo H.A. & Elhorst, J. Paul, 2023. "Evaluating store location and department composition based on spatial heterogeneity in sales potential," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    6. Ma, Shaohui & Fildes, Robert, 2022. "The performance of the global bottom-up approach in the M5 accuracy competition: A robustness check," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1492-1499.
    7. Kolassa, Stephan, 2022. "Commentary on the M5 forecasting competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1562-1568.
    8. Theodorou, Evangelos & Wang, Shengjie & Kang, Yanfei & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2022. "Exploring the representativeness of the M5 competition data," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1500-1506.

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