A Greedy Aggregation-decomposition Method for Intermittent Demand Forecasting in Fashion Retailing

Abstract

In this study, we solve a real-world intermittent demand forecasting problem for a fashion retailer in Singapore, where it has been operating retail stores and a warehouse for several decades. The demand for each stock keeping unit (SKU) at each store on each day needs to be determined to develop an effective and efficient inventory and logistics system for the retailer. The SKU-store-day demand is highly intermittent. In order to solve this challenging intermittent demand forecasting problem, we propose a greedy aggregation–decomposition method. It involves a new hierarchical forecasting structure and utilizes both aggregate and disaggregate forecasts, which differs from the classical bottom-up and top-down approach. The method is investigated on the real-world SKU-store-day demand database from this retailer in Singapore, and significantly outperforms other widely used intermittent demand forecasting methods. The proposed method also serves as a general self-improvement procedure for any intermittent time series forecasting method taking dual source of variations into account. Moreover, we introduce a revised mean absolute scaled error (RMASE) as a new accuracy measure for intermittent demand forecasting. It is a relative error measure, scale-independent, and compares with the error of zero forecasts.