Mid-Market E-Commerce Brand · Retail & E-Commerce · Austin, TX · 10 weeks
Demand forecasting AI reduced overstock by $2
A mid-market e-commerce company with 4,200 SKUs struggled to manage inventory using a 90-day rolling average, which ignored trends, promotions, and seasonality. We built a demand forecasting model that lowered overstock costs by $2.8 million per year and reduced stockouts by 74%.
Challenge
The brand held $11M in inventory on $38M annual sales. Their 90-day average replenishment ignored SKU trends, causing overstock on slow sellers and stockouts on fast movers during peaks. We replaced this with a demand signal that separated growth from decline, improving inventory allocation and availability.
Outcome
We cut inventory carrying costs by $2.8 million a year. Stockouts fell sharply, from 312 to 81 each quarter. We lowered inventory from $11 million to $7.4 million without causing more stockouts. Forecast accuracy for an 8-week horizon jumped from 54% to 82%.
Results
- $2.8M Annual inventory carrying cost reduction
- 312 to 81 Quarterly stockout incidents
- $3.6M Inventory balance freed
- 54% to 82% 8-week forecast accuracy
We had $11 million stuck in inventory but still ran out of our best sellers during peak times. Thinklytics built a forecasting model that actually delivers. In the first quarter, we freed up $3.6 million in working capital and cut stockouts by 74%.