Evaluating advanced product return forecasting algorithms - a (meta-)review integrating consumer returns research

David Karl

International Journal of Business Forecasting and Marketing Intelligence, 2024, vol. 9, issue 2, 213-241

Abstract: While e-commerce has recently experienced substantial growth rates, retailers face increasing consumer returns. Machine learning techniques opened up opportunities for improved consumer returns forecasting. In the past, returns forecasting was analysed predominantly from a broader reverse logistics and closed-loop supply chain perspective. This paper extends this view by reviewing the state of research on current algorithms for forecasting returns in e-commerce in particular and integrating it into the body of knowledge regarding forecasting product returns extracted from previous reviews. Methodologically, four reviews were synthesised first. Subsequently, a systematic literature review was conducted, analysing 28 additional publications related to consumer returns and enriching the literature on product returns. Thus, this comprehensive review is the first to analyse current forecasting issues while integrating the e-commerce perspective and emphasising relevant developments regarding advanced algorithms and metrics for their assessment in returns forecasting.

Keywords: forecasting; product returns; consumer returns; literature review; performance indicators; machine learning; evaluation. (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbfmi:v:9:y:2024:i:2:p:213-241

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