Demand Forecasting Engine for a National Retail Chain
The Challenge
A national retail chain with 400+ stores was losing millions annually to stockouts and overstock situations. Their existing demand planning was based on simple moving averages and manual adjustments by category managers.
Before Saks Tech
Forecast accuracy was below 60% at the SKU-store level. Markdown waste represented 8% of revenue. Seasonal planning started too late, and promotional impact was estimated by intuition.
Our Solution
Saks Tech built a machine learning forecasting engine incorporating weather data, promotional calendars, local events, and historical sales patterns. The model generates daily SKU-store forecasts with confidence intervals and feeds directly into the replenishment system.
Results Delivered
“The forecasting engine paid for itself in the first quarter. Our category managers now spend time on strategy instead of spreadsheets.”