In the fashion market, the lack of historical sales data for new products imposes the use of methods based on Stock Keeping Unit (SKU) attributes. Recent works suggest the use of functional data analysis to assign the most accurate sales profiles to each item. An application of siamese neural networks is proposed to perform long-term sales forecasting for new products. A comparative study using benchmark models is conducted on data from a European fashion retailer. This shows that the proposed application can produce valuable item level sales forecasts.
An intelligent fashion replenishment system based on data analytics and experts judgement Retail stock allocation is crucial but challenging. The authors developed an innovative solution, successfully tested in the context of high-end fashion: collaboration between articial intelligence and human intuition. Each week, stores are assigned a budget based on current stock levels versus potential sales, and offered to "spend" this budget with an initial data-driven recommendation on which SKU/sizes order and release. Each store manager is then given a time window, so she can modify the proposal while respecting budget constraints; and finally the articial intelligence optimally allocates available stock to requests based on the expected likelihood of sale minus cost of logistics, subject to management-dened constraints. Our test showed how this system outperformed the control group of stores, relying on a traditional headoce-driven allocation without direct human input. The retailer boosted sales, demand cover, and stock rotation performance: an estimated 1M EUR margin/month positive impact. Moreover, the new system improved store managers morale through non-monetary incentive-driven empowerment.
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