We are solving demand forecasting & decision optimisation in fashion, through the untapped power of pre-selling data. The fashion industry operates a business model that has remained largely unchanged for the last century. Large amounts of stock are manufactured in low-cost geographies with long lead times (6-9 months). This time lag means brands take huge forecasting risk at time of ordering and merchandisers make billion-dollar decisions on the basis of little more than secondary school maths and gut based forecasting. Applying instinct, not analytics. This model has led to an industry that destroys 15% of products that get made due to insufficient demand. In total, inaccurate demand forecasts result in supply-demand mismatches that cost the industry $1trn/yr. The cost to the environment is enormous. The fashion industry is set to reach 26% of global CO2 emissions by 2050\. The solution requires an accurate picture of product demand. There is a gap in the market for a solution that provides an: 1. Initial read on demand for new products: ideally from real transactions 2. Accurate mapping of this early demand signal to a lifetime demand forecast 3. Optimisation of this demand forecast to produce actionable recommendations In June2022 we launched our new software product for pre-selling, enabling brands to test demand prior to the main launch by pre-selling a portion of inventory directly to consumers. This project delivers the 2 remaining missing links (points 2 and 3 above). Academic research demonstrates that this pre-selling data is more predictive of lifetime demand than anything currently on the market, generating forecasts that are 50-70% more accurate.We aim to unlock pre-selling data's predictive capacity, building on an idea inspired by the research conducted by two professors of demand forecasting that sit on our board. Transforming decision making from intuition to rigorous optimisation, maximising profit & minimising waste.

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.