AI-Driven Inventory Management
A major retailer struggled with chronic overstock and stockouts across 200+ locations. We deployed an AI-powered demand-sensing engine that improved forecast accuracy by 40%, reduced stockouts by 65%, and cut carrying costs by 30%.
Key Outcomes
The Challenge
The retailer managed inventory across 200+ locations using rule-based replenishment systems that couldn’t adapt to demand volatility. Seasonal spikes, supplier disruptions, and regional demand shifts led to chronic overstock in some categories and stockouts in others. Manual forecasting consumed analyst time whilst still producing inaccurate projections, resulting in millions in carrying costs and lost sales.
Our Solution
We deployed a demand-sensing engine combining gradient-boosted models with external signals — weather, promotions, and supplier lead times — to generate location-level forecasts updated daily. An automated replenishment layer translated forecasts into purchase orders, dynamically adjusting safety stock by SKU and site. Forecast accuracy improved by 40%, stockouts dropped by 65%, and carrying costs fell by 30% within six months.
