A seasonal discrete grey forecasting model for fashion retailing

In the fashion retail industry, level of forecasting accuracy plays a crucial role in retailers' profit. In order to avoid stock-out and maintain a high inventory fill rate, fashion retailers require specific and accurate sales forecasting systems. One of the key factors of an effective forecasting system is the availability of long and comprehensive historical data. However, in the fashion retail industry, the sales data stored in the point-of-sales (POS) systems are always not comprehensive and scattered due to various reasons. This paper presents a new seasonal discrete grey forecasting model based on cycle truncation accumulation with amendable items to improve sales forecasting accuracy. The proposed forecasting model is to overcome two important problems: seasonality and limited data. Although there are several works suitable with one of them, there is no previous research effort that overcome both problems in the context of grey models. The proposed algorithms are validated using real POS data of three fashion retailers selling high-ended, medium and basic fashion items. It was found that the proposed model is practical for fashion retail sales forecasting with short historical data and outperforms other state-of-art forecasting techniques.