Utilising novel data in demand forecasting to solve Fashion product launches

我们正在解决需求预测和决策优化的时尚，通过预售数据的未开发的力量。时尚产业的商业模式在上个世纪基本没有改变。大量库存在低成本地区生产，交货时间长（6-9个月）。这种时间差意味着品牌在订货时要承担巨大的预测风险，而商家仅根据中学数学和直觉预测就能做出价值数十亿美元的决定。运用直觉，而不是分析。这种模式导致了一个行业，因为需求不足而摧毁了15%的产品。总而言之，不准确的需求预测导致供需不匹配，导致该行业每年损失1万亿美元。对环境造成的损失是巨大的。到2050年，时尚行业的二氧化碳排放量将达到全球的26%。解决方案需要对产品需求有一个准确的了解。提供以下功能的解决方案在市场上存在空白：1。对新产品需求的初步解读：最好是来自实际交易。将这一早期需求信号准确地映射为一生的需求预测。2022年6月，我们推出了新的软件产品进行预售，使品牌能够在主要发布之前通过直接向消费者预售部分库存来测试需求。这个项目提供了2个剩余的缺失环节（点2和3以上）。学术研究表明，这种预售数据比目前市场上的任何数据都更能预测终身需求，预测的准确率提高了50-70%。我们的目标是释放预售数据的预测能力，这一想法的灵感来自于我们董事会中两位需求预测教授的研究。将决策从直觉转变为严格的优化，实现利润最大化和浪费最小化。

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 transactions2. Accurate mapping of this early demand signal to a lifetime demand forecast3. Optimisation of this demand forecast to produce actionable recommendationsIn 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.