Aftermarket demands forecasting with a Regression-Bayesian-BPNN model

Author

Chen, Yun ; Liu, Ping ; Yu, Li

Author_Institution

Sch. of Publics Econ. & Adm., Shanghai Univ. of Finance & Econ., Shanghai, China

fYear

2010

fDate

15-16 Nov. 2010

Firstpage

52

Lastpage

55

Abstract

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.

Keywords

automobile industry; automotive components; backpropagation; cost reduction; demand forecasting; neural nets; regression analysis; supply chain management; BPNN; China; auto spare parts; automobile industry; automotive aftermarket; backpropagation neural network; cost reduction; demands forecasting; optimizing; regression Bayesian model; supply chain operation; Accuracy; Artificial neural networks; Bayesian methods; Demand forecasting; Marketing and sales; Predictive models; Automotive Aftermarket; Demand Forecasting; Neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems and Knowledge Engineering (ISKE), 2010 International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4244-6791-4

Type

conf

DOI

10.1109/ISKE.2010.5680793

Filename

5680793