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

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外文摘要：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。

外文关键词：

ARIMASVRauto aftermarketauto partsmultiple regressionsvariable weight combination

作者：

Yang, Qin、Chen, Yun

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作者单位：

Information Science School, Nanjing Audit University, China, 210093

会议名称：

International Conference on Management Science & Engineering;ICMSE

会议地点：

Dallas, TX(US);Dallas, TX(US)

会议母体文献：

2012 International Conference on Management Science & Engineering.

页码：

p.817-822

出版时间：

2012

DOI：

10.1109/ICMSE.2012.6414273