An international retail chain developed an AI-powered demand forecasting system to enhance supply chain efficiency, focusing on ultra-fresh product categories to reduce waste. The system integrated various data sources, addressed regional and seasonal variations, and achieved a significant error reduction from 37% to 26%. The approach utilized multiple models, including H2O AutoML, Facebook Prophet, and XGBoost, highlighting the importance of data engineering over model complexity. The project provided valuable insights into customer behavior and regional differences, offering actionable business strategies.
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Project Scope and Technological ImplementationKey Challenges AddressedImplementation ApproachResults and Performance MetricsMore Ways a Data-First Approach Pays OffWhat We Learned31 Impressions