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.
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
