Predicting Demand for Direct Store Delivery

This project tackles longstanding inefficiencies in direct store delivery (DSD) and retail assortment planning by embedding artificial intelligence into commercial software tools. Rather than relying on intuition and personal experience, the solution introduces intelligent, data-driven decision-making to improve product delivery accuracy, reduce stock imbalances, and enhance in-store execution. The system integrates two AI modules: a predictive forecasting engine that analyzes, among other things, historical delivery patterns to recommend optimal product quantities by point of sale, and an assortment optimization engine that tailors product mixes based on store-specific characteristics. Together, these modules boost customer satisfaction, reduce operational costs caused by overstock (e.g., food waste due to expiration) and stockouts (e.g., missed sales opportunities), and enable retail suppliers to better align with evolving consumer needs.