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Building the Enterprise Retail AI Supply Chain Decision Engine using the concepts of "Data Science" in IT by Balaji.T (BT) This video of mine outlines the comprehensive Data Science Life Cycle required to build a scalable inventory and demand-forecasting system for the enterprise retail sector. It emphasizes that achieving operational excellence requires more than just accurate predictions; it necessitates the integration of predictive modelling with prescriptive optimization to manage stock levels effectively. I detailed a multi-step framework ranging from business understanding and data engineering to the deployment of automated replenishment engines. By utilizing a case study involving bottled water distribution, the source illustrates how machine learning and traditional economic formulas work together to minimize costs. Ultimately, this video serves as a strategic guide for IT leaders to transform fragmented data into a unified decision platform that enhances supply chain resilience.