AI Demand Forecasting Cuts Inventory Costs by $12M

The Challenge

RetailMax's supply chain team was caught in an impossible balancing act. Their legacy forecasting system—based on simple moving averages and buyer intuition—consistently missed the mark, leading to a costly cycle of overstock and stockouts.

With 500 stores carrying 45,000 unique SKUs, the complexity was overwhelming. Regional variations, seasonal patterns, and unpredictable demand drivers made accurate forecasting seem impossible.

Our Approach

We built a hierarchical forecasting system that generates predictions at multiple levels—from corporate aggregates down to individual SKU-store combinations—ensuring coherent planning across the organization. Machine learning models learn from diverse data sources to capture demand drivers that traditional methods miss.

"We've been in retail for 40 years and never had this kind of visibility into demand. It's transformed how we think about inventory."

Results & Impact

Within one year, RetailMax reduced inventory holding costs by $12M while improving in-stock rates from 91% to 97%. The freed-up capital has funded the opening of 25 new stores.