Demand Forecasting Engine
Case Study

The challenge

A growing e-retailer was managing inventory manually, resulting in persistent stockouts on bestsellers and over-purchasing on slow movers. The operations team spent 3 days per week manually adjusting purchase orders based on gut feel and outdated spreadsheets.

What we built

• ML forecasting at SKU × location level, incorporating historical sales, seasonality, promotions, weather signals, and trend data
• Automated purchase order generation triggered when projected stock falls below dynamically calculated reorder points
• Supplier lead time modelling to account for varying delivery windows by product category and origin
• New product forecasting using attribute similarity to comparable existing SKUs
• Weekly model retraining as new sales data accumulates

The results

The solution delivered measurable results within weeks of go-live. The client achieved 30% Stockout reduction and 2.1× Forecast accuracy, with full ROI achieved within 6 months Full ROI achieved.