The Role of AI-Driven Predictive Analytics in Enhancing U.S. Retail Supply Chain Operations
Keywords:
Predictive Analytics, Retail Supply Chain OperationsAbstract
The introduction section of this essay provides an essential foundation for understanding the focus of the research, which centers on the role of AI-driven predictive analytics in enhancing U.S. retail supply chain operations. It outlines the significance of leveraging AI and predictive analytics in the retail sector, particularly in the context of supply chain optimization and customer satisfaction. The section also sets the stage for the subsequent discussions by highlighting the research objectives and the key areas that will be explored in the essay.
In the retail sector, the implementation of AI-based methods has demonstrated significant potential in addressing challenges such as optimizing store layout, managing out-of-stock situations, and streamlining the picking process. For instance, the use of AI-driven substitution recommendation engines and demand transference models has enabled retailers to better satisfy customer needs and enhance operational efficiency. Furthermore, AI methods have been instrumental in maximizing pickers' efficiency while minimizing disruptions to store operations, particularly in the context of the growing trend of using stores as local fulfillment centers [1].
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