Predictive analytics for inventory forecasting and logistics optimization in large-scale operational settings with complex multi-echelon demand patterns.
An inventory and logistics platform that forecasts demand, recommends reorder points, and optimizes distribution across a multi-echelon network. The system combines a modern web application for day-to-day operations with a predictive analytics layer that learns from historical movement data to reduce stockouts and carrying cost simultaneously.
Large operations carry thousands of SKUs across many locations. Simple reorder rules either over-stock safe items or under-stock volatile ones. Demand correlates across locations in non-obvious ways, and lead times vary. Teams end up firefighting rather than planning. A learned forecasting layer, coupled with clear operational tooling, turns this into a tractable problem.