Catch me if you scan: Data-driven prescriptive modeling for smart store environments

Matthias Hauser, Christoph M. Flath and Frédéric Thiesse

European Journal of Operational Research, 2021, vol. 294, issue 3, 860-873

Abstract: The increasing adoption of omnichannel strategies in recent years has led retail companies worldwide to fundamentally rethink the future role of their network of brick-and-mortar stores. One strategic option being pursued by many retailers is the transformation of the stationary store into a “smart store,” augmented by various digital services. However, an essential prerequisite for the success of smart store services is high quality of the underlying data generated through the use of technologies for tracking products and customer behavior. As a means of investigating the use of machine learning to improve data quality, the present study considers the example of Radio Frequency Identification (RFID) as a technological infrastructure for tracking products in fashion retail. We examine electronic article surveillance and automated checkouts as practical use cases enabled by a classification model for the detection of product movements on the store floor. In order to identify an economically optimal configuration of the classifier, we develop a complementary service operations model that allows for determining the respective cost impact. In addition to the specific results for the considered use cases, the study thus points to a general and novel prescriptive analytics approach.

Keywords: Retailing; Service management; Predictive analytics; Prescriptive analytics; RFID (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:294:y:2021:i:3:p:860-873

DOI: 10.1016/j.ejor.2020.12.047

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