Improving supply chain efficiency for a major retailer with better accuracy and key customer insights.

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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.

AI-Driven Demand Forecasting: Improving Supply Chain Efficiency in Retail

Project Scope and Technological Implementation