Title :
A neural network based method for part demands prediction in auto aftermarket
Author :
Yu, Li ; Chen, Yun
Author_Institution :
Acad. of Modern Service Ind., Shanghai Univ. of Finance & Econ., Shanghai, China
Abstract :
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.
Keywords :
automotive components; demand forecasting; inventory management; neural nets; supply chain management; Shanghai; auto aftermarket; demand forecasting; inventory management; neural network; part demand prediction; supply chain management; Artificial neural networks; Automotive engineering; Forecasting; Manganese; Marketing and sales; Prediction methods; Supply chain management; demand prediction; neural network; supply chain management;
Conference_Titel :
Software Engineering and Service Sciences (ICSESS), 2010 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6054-0
DOI :
10.1109/ICSESS.2010.5552264
