This is like having a smart weather forecast, but for your store’s inventory. It looks at your past sales, seasons, promotions, and other patterns to predict how many units of each product you’ll need in the future so you don’t run out or overstock.
Reduces stockouts and overstock by predicting demand more accurately than manual planning or simple spreadsheets, especially for online and omnichannel retail where demand is volatile and SKU counts are high.
Proprietary demand history and retail operations data (SKUs, channels, promotions, seasonality) combined with integration into ordering, replenishment, and supply-chain workflows can create a sticky system that’s hard to replace.
Classical-ML (Scikit/XGBoost)
Time-Series DB
Medium (Integration logic)
Data cleanliness and granularity (per-SKU, per-location histories, promotion flags) and the need to retrain models frequently as demand patterns shift.
Early Majority
Positioned as an online/ML-centric forecasting approach for retailers that may offer more flexible, SKU-level models and faster iteration than legacy planning suites, at potentially lower implementation cost.
3 use cases in this application