Post-script - Retail forecasting : Research and Practice

Fildes, Robert and Kolassa, Stephan and Ma, Shaohui (2022) Post-script - Retail forecasting : Research and Practice. International Journal of Forecasting, 38 (4). pp. 1319-1324. ISSN 0169-2070

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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.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Forecasting
Additional Information:
This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 38, 4, 2022 DOI: 10.1016/j.ijforecast.2021.09.012
Uncontrolled Keywords:
?? covid-19disruptionstructural changeinstabilityomni-retailingonline retailmachine learningbusiness and international management ??
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Deposited On:
08 Nov 2021 12:45
Last Modified:
05 May 2024 00:32