Demand Forecasting Engine for a Fast-Fashion Retailer's Micro-Batch Buying
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Problem
Sell-outs and markdown mountains
A 30-store fast-fashion retailer with a growing online arm was buying on instinct: hit styles sold out within days while slow movers piled into markdowns that consumed 22% of revenue. Buyers worked from month-old sales exports, had no view of size-level demand, and reordered winning styles too late to catch the trend.
Solution
Style-level demand forecasting
We built a demand forecasting engine that combines sales history, product attributes, web behaviour, and seasonality to predict style-and-size-level demand for the weeks ahead. Buyers see ranked reorder recommendations and suggested initial buy quantities in a planning dashboard, enabling smaller first orders with faster, data-backed replenishment of proven sellers.
Measurable Impact
What changed after launch
Style-level forecast accuracy improved from 61% to 82% within two buying seasons
Markdown share of revenue reduced from 22% to 14% over the same period
Stockout rate on top-50 styles dropped by 35% while overall inventory fell 18%
Full-price sell-through improved from 48% to 63% across the store network
Tech & Tools Used
What powered the build
Ready to Build your Fashion Retail Business with Predictive Modeling & Forecasting
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