EY Optimizes Retail Inventory and Demand Forecasting with AI Solutions

Ernst & Young (EY) developed an AI-powered inventory and demand forecasting platform tailored for retail clients, leveraging Microsoft Cloud for Retail and Microsoft Azure. The solution enables trusted data pipelines for standardizing, modeling, and scaling customer and inventory data with predictive analytics and machine learning. Multiple advanced forecasting models, including eight different techniques, are deployed to generate SKU- and location-level demand predictions, accommodating both repeat and new orders. Customer-specific histories and demographic data are incorporated to optimize stock levels and better anticipate demand variability, supporting scenario analysis and strategy simulation before real-world deployment. The solution uses a simulation engine for inventory improvement, reduces supply gaps, and helps prevent overstock or lost sales. Machine learning models are distributed across multiple clusters for scalable computation, supporting hundreds of thousands of SKUs and locations. By reducing human bias through automation and including external/internal factors, the platform streamlines inventory management, cuts unnecessary stock, increases forecast accuracy, and enhances overall customer experience by ensuring the right product availability. The approach is flexible and can be specialized to client needs with configurable add-ons, seamlessly integrating into existing inventory management workflows and systems.