A neural network based method for part demands prediction in auto aftermarket

In the supply chain management of auto aftermarket, companies strive to manage inventory with low cost while maintaining a reasonable order fulfillment rate. To achieve this objective, a critical issue is to predict the demand for auto parts with high accuracy. Based on the factors relevant to auto aftermarket, this paper proposed an artificial neural network based method to forecast the demands of auto parts. The effectiveness of the proposed method is illustrated with a case study of an auto 4s shop in Shanghai.