Aftermarket Demands Forecasting with a Regression-Bayesian-BPNN Model

摘要：

The rapid development of automobile industry in China promotes the stable growth of the automotive aftermarket. For optimizing supply chain operations and reducing costs, it is critical for a company to forecast the demands for auto spare parts in the future. This paper proposes an improved Regression-Bayesian-BBNN (RBBPNN) based model to realize the demands forecasting. Compared with a classic ARMA model, the proposed RBBPNN model has higher accuracy and better robustness. These advantages are illustrated through the case study with the real sales data of a 4s shop in Shanghai.

作者: Yun Chen Ping Liu Li Yu

作者单位: School of Publics Economics & Administration Shanghai University of Finance & Economics Shanghai, Ch School of information management and engineering Shanghai University of Finance & Economics Shanghai Academy of Modern Service Industries Shanghai University of Finance & Economics Shanghai, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 52-55

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）