Fashion Retail: Forecasting Demand for New Items

Abstract

Fashion merchandising is one of the most complicated problems in forecasting, given the transient nature of trends in colours, prints, cuts, patterns, and materials in fashion, the economies of scale achievable only in bulk production, as well as geographical variations in consumption. Retailers that serve a large customer base spend a lot of money and resources to stay prepared for meeting changing fashion demands, and incur huge losses in unsold inventory and liquidation costs [2]. This problem has been addressed by analysts and statisticians as well as ML researchers in a conventional fashion - of building models that forecast for future demand given a particular item of fashion with historical data on its sales. To our knowledge, none of these models have generalized well to predict future demand at an abstracted level for a new design/style of fashion article. To address this problem, we present a study of large scale fashion sales data and directly infer which clothing/footwear attributes and merchandising factors drove demand for those items. We then build generalised models to forecast demand given new item attributes, and demonstrate robust performance by experimenting with different neural architectures, ML methods, and loss functions.