Why Generic Demand Forecasting Fails in Fashion Retail

Most AI forecasting tools are built for widgets, not wardrobes. They treat a t-shirt like a toaster. But in fashion, demand isn't just a number—it's a complex function of seasonality, size curves, and trend velocity.

The Seasonality Trap

Generic models look at historical data to predict the future. If you sold 100 units in November, they predict 100 in December. But in fashion, December is a completely different beast. Holiday parties, gift-giving, and clearance sales distort the data. A generic model sees a spike; a fashion-specific model sees a "Holiday Event".

The Size Curve Problem

Selling out of Mediums while XLs gather dust isn't a win. It's a failure of allocation. Generic tools forecast at the SKU level (e.g., "Blue Shirt"). They don't understand that the demand for "Blue Shirt - Small" is correlated but distinct from "Blue Shirt - Large".

Trend Velocity

Fashion moves faster than data. By the time a generic model realizes "Leopard Print" is trending based on sales data, the trend is over. Our specialized models ingest social signals (Instagram, TikTok) to predict micro-trends before they hit the sales reports.

The Solution: Vertical AI

You don't need more data. You need smarter models. Models that understand that a "Coat" sells in Winter and a "Swimsuit" sells in Summer. Models that know a size run isn't just a ratio, but a probability distribution.