Auto parts demand forecasting based on nonnegative variable weight combination model in auto aftermarket

Author

Yang Qin ; Chen Yun

Author_Institution

Inf. Sci. Sch., Nanjing Audit Univ., Nanjing, China

fYear

2012

fDate

20-22 Sept. 2012

Firstpage

817

Lastpage

822

Abstract

Accurate demand forecasting for auto parts can improve the performance of the whole auto supply chain and is very important for the management improvement for the companies in auto aftermarket who mainly forecast demands by experience. It has both economic significance and social means for the auto industry considering the important role of auto aftermarket in the whole auto industry. After exploring the complicated characteristics of the auto parts and also the strengths of some forecasting methods, ARIMA, multiple regression and Support Vector Regression are selected finally to develop a nonnegative variable weight combination model to forecast the demand of auto parts for the auto aftermarket in China. The following case study proves that this model has higher accuracy and more stability.

Keywords

automobile industry; automotive components; forecasting theory; production engineering computing; regression analysis; supply chain management; support vector machines; ARIMA; China; auto aftermarket; auto parts demand forecasting; auto supply chain; forecasting method; nonnegative variable weight combination model; performance improvement; support vector regression; Companies; Computational modeling; Data models; Demand forecasting; Predictive models; Stability analysis; ARIMA; SVR; auto aftermarket; auto parts; multiple regressions; variable weight combination;

fLanguage

English

Publisher

ieee

Conference_Titel

Management Science and Engineering (ICMSE), 2012 International Conference on

Conference_Location

Dallas, TX

ISSN

2155-1847

Print_ISBN

978-1-4673-3015-2

Type

conf

DOI

10.1109/ICMSE.2012.6414273

Filename

6414273