Study on a Combined Demand Forecasting Model of the Supply Chain

Author

Hu, Hui ; Zhu, Guangyu ; Bo, Yanjun ; Shen, Jinsheng

Author_Institution

Traffic & Transp. Sch., Beijing Jiaotong Univ.

Volume

1

fYear

2006

Firstpage

251

Lastpage

255

Abstract

This paper presents a combined forecasting model for uncertain demands based on rough set (RS) and radial basic function (RBF) network. First, RBF network is introduced to work on the historical data and extrapolate future demands. Then attributes reduction of the RS is applied to analyze the datasheet and draw the kernel index set from it. The forecasting results are obtained with a combination of the above two methods. The combined model is tested with real historical data collected from a large firm in the automobile industry, and it produces more precise results than the RBF model

Keywords

automobile industry; demand forecasting; radial basis function networks; rough set theory; supply chains; RBF network; automobile industry; combined demand forecasting model; future demands; kernel index; radial basic function; rough set network; supply chain; uncertain demands; Biological system modeling; Data analysis; Demand forecasting; Economic forecasting; Euclidean distance; Neural networks; Neurons; Predictive models; Radial basis function networks; Supply chains; Enterprise Competence and Products Market Share (EC&PMS); Radical Basic Function (RBF) network; Supply chain; combined model; demand forecasts;

fLanguage

English

Publisher

ieee

Conference_Titel

Service Systems and Service Management, 2006 International Conference on

Conference_Location

Troyes

Print_ISBN

1-4244-0450-9

Electronic_ISBN

1-4244-0451-7

Type

conf

DOI

10.1109/ICSSSM.2006.320621

Filename

4114441