A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains

Marina Meireles Pereira and Enzo Morosini Frazzon

International Journal of Information Management, 2021, vol. 57, issue C

Abstract: The integration of selling and fulfillment processes triggered by omni-channels is transforming the retailer’s operations management. In this context, there is a lack of research regarding the connection between digital and physical worlds in retail supply chains. This paper aims to propose a data-driven approach that combines machine-learning demand forecasting and operational planning simulation-based optimization to adaptively synchronize demand and supply in omni-channel retail supply chains. The findings are substantiated through the application of the approach in an omni-channel retail supply chain. The combination of clustering and neural networks improved demand forecast, supporting an assertive identification of demand volume and location. Simulation-based optimization allowed for the definition of which facility would serve identified demands most effectively. The approach reduced fulfillment lead time, mitigated backorders arising from incompatible product´s supply and demand, and lowered operational costs, which are key performance indicators in today’s competitive retail markets.

Keywords: Simulation-based optimization; Machine learning; Omni-channel retail supply chains; Data-driven (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:ininma:v:57:y:2021:i:c:s026840122030205x

DOI: 10.1016/j.ijinfomgt.2020.102165

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