Fast fashion sales forecasting with limited data and time

Fast fashion is a commonly adopted strategy in fashion retailing. Under fast fashion, operational decisions have to be made with a tight schedule and the corresponding forecasting method has to be completed with very limited data within a limited time duration. Motivated by fast fashion business practices, in this paper, an intelligent forecasting algorithm, which combines tools such as the extreme learning machine and the grey model, is developed. Our real data analysis demonstrates that this newly derived algorithm can generate reasonably good forecasting under the given time and data constraints. Further analysis with an artificial dataset shows that the proposed algorithm performs especially well when either (i) the demand trend slope is large, or (ii) the seasonal cycle's variance is large. These two features fit the fast fashion demand pattern very well because the trend factor is significant and the seasonal cycle is usually highly variable in fast fashion. The results from this paper lay the foundation which can help to achieve real time sales forecasting for fast fashion operations in the future. Some managerial implications are also discussed.