Weekly demand forecasts at 94 percent accuracy
An online retailer with about 3,000 products
The problem
Where it hurt.
Popular items sold out while slow ones filled the warehouse. Stock decisions came from last year's spreadsheet and gut feel.
What we built
The system.
- 01Cleaned two years of sales history
- 02Trained models per product group using sales, season and promotions
- 03Tested them against past quarters before anyone relied on them
- 04Produced a weekly reorder list and low stock alerts
The result
94%accuracy on weekly demand
31%fewer stockouts
8wkfrom audit to live
Representative engagement. Client name withheld under NDA. Figures are typical of results at this type of company.
What it means for you
Then came the growth.
Getting the hours back was the first win. It also freed the team to look outward: to take on more work, answer more customers and try things they had not had time for. That is how a single automation becomes a growth plan.
Running something similar? See how we approach AI for e-commerce and retail or read about our ai forecasting service.
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