Demand Forecasting Methods and the Potential of Machine Learning in the FMCG Retail Industry

Thomas Aichner () and Valentin Santa ()
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Thomas Aichner: South Tyrol Business School
Valentin Santa: SPAR International

A chapter in Serving the Customer, 2023, pp 215-252 from Springer

Abstract: Abstract Forecasting of future demand and sales is a key issue for companies. The purpose of this chapter is to investigate opportunities and challenges of machine learning forecasting methods in contrast to traditional forecasting methods, specifically in the supermarkets and grocery stores industry. The main two goals of this research are to close a gap in research about the potential of machine learning forecasting methods and to inform retail professionals about which steps are necessary to make their businesses ready to take the potential of machine learning forecasting methods into serious consideration. Based on semi-structured expert interviews, three main contributions are derived: (a) an examination of the current applications of demand forecasting used in practice as well as an assessment of the ability and necessity to implement machine learning forecasting methods, (b) an outline of three major steps to be considered prior to the implementation of machine learning forecasting methods which are related to the staff, the data, and the ERP system, and (c) the insight that the forecasting process starts with the individual customer, which opens the opportunity to influence the demand with customised offers.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-658-39072-3_8

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DOI: 10.1007/978-3-658-39072-3_8

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