Major Global Convenience Store Retailer (unnamed)

Convenience retail operates on thin margins where out-of-stock events translate directly to lost sales and customer defection. This retailer — one of the largest in the US convenience-retailing industry, with more than 80,000 stores across 20 countries — faced a structural data problem: store-level purchase records and daily ordering data existed in silos, making centralized inventory optimization across more than 10,000 actively managed stores impractical. Demand patterns in convenience retail are highly localized and volatile, shifting with weather, local events, and neighborhood demographics. Without a unified forecasting model, replenishment decisions relied on manual judgment, leaving the retailer exposed to chronic stock-outs, excess shrinkage, and lost revenue from high-velocity SKUs going unreplenished.

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 promotional uplift — 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 — converting the same underlying transaction data into a customer-facing revenue channel.

The most significant operational outcome was an 8% reduction in out-of-stock situations, achieved by connecting AI-generated forecasts directly to the ordering workflow rather than routing predictions through manual review. A 3% revenue increase followed from improved store assortment decisions driven by the Store AI and Personalization AI components. The 14-day demand forecast horizon gave supply chain planners meaningful lead time to adjust replenishment before gaps materialized across more than 10,000 stores. Centralizing 20 million daily SKU-store calculations on a cloud-native platform also replaced fragmented, store-level manual processes with a consistent, data-driven replenishment cadence across the global network.

• 8% fewer out-of-stock situations via forecasting linked directly to ordering
• 3% revenue increase from improved assortment and personalization recommendations
• 14-day forward predictions enabling proactive supply chain adjustments at scale

• 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 — incorporating public API data (weather, local events) alongside historical sales meaningfully improves SKU-ranking accuracy at the store level.
• A unified transaction data layer lets demand forecasting and customer personalization be built in parallel, compressing time-to-value across both operational and customer-experience use cases.
• 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.