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In a leading global beverage company, weekly demand forecasting felt like a spreadsheet battlefield. Demand planners stitched data from SAP, Databricks, and scattered Excel files. The manual cycle was slow, clunky, and error-prone. Forecast accuracy hovered around 70%. A 30% SKU-level error causes c

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