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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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.

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:

/dk/atira/pure/subjectarea/asjc/1400/1403

Subjects:

?? covid-19disruptionstructural changeinstabilityomni-retailingonline retailmachine learningbusiness and international management ??

Departments:

Lancaster University Management School > Management Science

ID Code:

161956

Deposited By:

ep_importer_pure

Deposited On:

08 Nov 2021 12:45

Refereed?:

Yes

Published?:

Published

Last Modified:

27 Mar 2024 00:59

URI:

https://eprints.lancs.ac.uk/id/eprint/161956