RetailIQ - Demand Forecasting Engine
87% MAPE
Forecast Accuracy
34%
Stockout Reduction
The Problem
The client was losing an estimated 18% of potential revenue to stockouts and overstock simultaneously - a classic inventory visibility problem at scale. Their existing system relied on 30-day moving averages with no seasonality or promotions modelling.
Our Approach
We built a forecasting pipeline using gradient boosting models trained on 4 years of historical sales data, promotions, regional events, and weather signals. The output fed an optimization layer that generated weekly purchase orders per SKU per location.
Results
Forecast accuracy improved from 61% to 87% MAPE. Stockout events reduced by 34% in the first retail quarter. Inventory holding costs decreased by 22%.
Client
RetailIQ
E-Commerce
Tech Stack
Service
AI & ML SolutionsNext Step
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