AI-driven demand forecasting and inventory optimization system for global supply chain management and distribution
A global consumer goods company struggled with demand planning across multiple product lines and geographic regions. Their traditional forecasting methods couldn't handle complex seasonal patterns, promotional impacts, and supply chain disruptions, resulting in excess inventory, stockouts, and high carrying costs across their distribution network.
We developed an integrated demand planning platform using ensemble forecasting methods that combined statistical models, machine learning algorithms, and external data sources. The system incorporated promotional calendars, economic indicators, weather patterns, and social trends to generate accurate demand forecasts across different time horizons and geographic markets.
The AI-powered demand planning system reduced overall inventory levels by 26% while maintaining service levels above 95%. Forecast accuracy improved to 89% across all product categories and regions. The optimized inventory management resulted in 19% cost savings through reduced carrying costs, improved cash flow, and minimized waste from expired or obsolete products.