Forecasting Seasonal Footwear Demand Using Machine Learning

Abstract

The fashion industry has been facing many challenges when it comes to forecasting demand for new products. The macroeconomic shifts in the industry have contributed to short product lifecycles and the obsolescence of the retail calendar, and consequently an increase in demand variability. This project tackles this problem from a demand forecasting perspective by recommending two frameworks leveraging machine learning techniques that help fashion retailers in forecasting demand for new products. The point-of-sale (POS) data of a leading U.S.-based footwear retailer was analyzed to identify significant predictor variables influencing demand for footwear products. These variables were then used to build two models, a general model and a three-step model, utilizing product, calendar and price attributes for predicting demand. Clustering and classification were used under the three-step model to identify look-alike products. Regression trees, random forests, k-nearest neighbors, linear regression and neural networks were used in building the prediction models. The results show that the two forecasting models based on machine learning techniques achieve better forecast accuracy compared to the company’s current performance. In addition, the proposed methodology offers visibility into the underlying factors that impact demand, with insights into the importance of the different predictor variables and their influence on forecast accuracy. Finally, the project results demonstrate the value of forecast customization based on product characteristics.