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 from 312 to 81 each quarter.
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.
Approach
We created a demand forecast using past sales data, promotional schedules, search and social trends, and seasonal patterns. The model produced weekly SKU-level forecasts with confidence ranges. These forecasts directly guided safety stock levels and automated purchase orders.
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 of every 100.
How we dug into the data to spot what’s coming next for the business
Over three months, we saw that early demand changes were slipping past us. So, we pulled in search volume and social engagement data to spot those shifts 4 to 6 weeks before orders showed up. That tweak boosted our 8-week forecast accuracy by 14 points.
How We Used Automation to Fix Inventory Shortages for Good
We plugged the demand forecasts straight into the ERP’s replenishment system. That way, anytime inventory dropped below the safety stock, it automatically triggered purchase orders. No more manual hassle. Inventory stayed tight and matched what we planned to sell.
Results
- $2.8M Annual inventory carrying cost reduction
- 312 to 81 Quarterly stockout incidents
- $3.6M Inventory balance freed
- 54 to 82 of 100 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%.