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- W3035200126 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." @default.
- W3035200126 created "2020-06-19" @default.
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- W3035200126 date "2021-04-01" @default.
- W3035200126 modified "2023-10-16" @default.
- W3035200126 title "A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains" @default.
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- W3035200126 doi "https://doi.org/10.1016/j.ijinfomgt.2020.102165" @default.
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