Demand driven manufacturing and decision making in Fashion

We are solving demand forecasting in the fashion supply chain. You may not know this but clothing production accounts for as much CO2 emissions annually as aviation and global shipping, combined.

What does that mean? 15% of clothes created, never get sold. That's $400bn that ends up straight in landfill, every year. And, At the same time, brands lose out on $600bn of revenue on high demand products that prematurely sell out. The fashion industry is broken, and it is losing $1trn annually from poor demand forecasting.

That said, demand forecasting and supply chain optimisation is a hard problem to solve - closer to F1 aerodynamics than secondary school maths. However today, it's done by Merchandising teams, who have little to no mathematical training. And as such, fall back on extremely manual, and gut based forecasting. Applying instinct, not analytics.

It doesn't have to be like this. Brands sit on a wealth of data with untapped predictive capacity. As an industry, fashion only trails manufacturing in the number of data points recorded each day.

Our project is aiming to build on an idea inspired by the research conducted by two professors of demand forecasting that sit on our board. We have strong theoretical grounding to believe that by enabling brands to preview (pre-sell) new products to consumers ahead of the main launch, they can improve demand forecasts by 50-70%.

This enables brands to increase price on high demand products to better match fixed supply with larger than expected demand. Improving efficiency and returns. While also alerting downstream departments for replenishment from the supply chain, marketing and in-store/online merchandising to boost low demand products. This is one step closer to enabling brands to react to demand, and improve returns, efficiency and the sustainability of the fashion industry.