Problem
Most enterprise demand forecasting still relies on rolling averages or Excel-based statistical models that cannot incorporate external signals (weather, social trends, competitor pricing) or react to real-time inventory anomalies.
Solution
An ML forecasting service trained on 2–5 years of historical demand data, enriched with external signals. The model produces SKU-level weekly forecasts with confidence intervals, fed directly into the replenishment workflow in the ERP.
Outcome
Buyers receive actionable, confidence-ranked reorder recommendations rather than raw forecast numbers. Inventory teams reduce emergency orders and overstock write-downs significantly within the first two quarters.