Major Global Convenience Retailer Achieves 3% Revenue Growth and 8% Fewer Stock-Outs with AI-Powered Demand Forecasting

The retailer — a premier name in the US convenience-retailing industry, with more than 80,000 stores across 20 countries — lacked a unified view of store data: store-level purchase records and daily ordering data existed in silos, which hindered effective inventory management across more than 10,000 stores. Demand patterns in convenience retail are highly localized and volatile. The retailer needed to reduce out-of-stock situations and shrinkage.

LTIMindtree deployed a four-component machine learning and predictive analytics solution on Azure Databricks and Azure Data Lake Gen2, engineered to process 20 million SKU-store combinations daily. The Ordering AI applied advanced ML algorithms — incorporating historical sales, shrinkage, seasonal variation, and promotions — to generate daily store-level forecasts wired directly into the ordering system, eliminating the gap between prediction and replenishment action. A Store AI component used linear regression against public API data to rank top-selling SKUs by location across all 10,000+ stores, supporting data-driven assortment decisions. Basket item analysis mined transaction data for co-purchase associations, while a Personalization AI layer applied collaborative filtering to surface tailored offers and product recommendations on the retailer's mobile app.

An 8% reduction in out-of-stock situations was achieved by connecting AI-generated forecasts directly to the ordering workflow. A 3% revenue increase followed from better store assortment recommendations. The 14-day demand forecast horizon supported supply chain and inventory optimization across more than 10,000 stores.

• 8% fewer out-of-stock situations via forecasting linked directly to ordering
• 3% revenue increase from better store assortment recommendations
• 14-day forward predictions supporting an optimized supply chain and inventory

• Forecast-to-order integration is the critical link: an AI model feeding a dashboard produces insights; one wired directly to the ordering system produces measurable stock-out reduction.
• Hyper-local demand signals matter in convenience retail — using public API data to rank top-selling SKUs for each store supports assortment decisions at the store level.
• A unified transaction data layer can support both demand forecasting and customer personalization.
• Cloud-native infrastructure (Azure Databricks + Data Lake Gen2) is a prerequisite, not an afterthought, when processing tens of millions of SKU-location combinations daily at enterprise scale.