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Scientific Programming
Volume 2016 (2016), Article ID 3717650, 13 pages
Research Article

Exploring an Interactive Value-Adding Data-Driven Model of Consumer Electronics Supply Chain Based on Least Squares Support Vector Machine

1School of Management, Shandong University, Jinan 250100, China
2Research Center for Value Co-Creation Network, Shandong University, Jinan 250100, China
3School of Mathematics, Shandong University, Jinan 250100, China

Received 18 May 2016; Accepted 17 July 2016

Academic Editor: Junhu Ruan

Copyright © 2016 Xiao-le Wan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The differences in supply chains and their competitiveness depend on the differences in supply chain value creation systems. On the basis of the theory of value cocreation, this study investigates the interactive value creation of consumer electronics supply chains from the perspective of the interaction and added value created by the main value creation bodies in supply chains. Least squares support vector machine (LS-SVM) is innovatively introduced into the study on consumer electronics supply chains. A data-driven model is also established, the parameters of the method and kernel functions are optimized and selected, and an LS-SVM algorithm of consumer electronics supply chains is proposed to deal with the limited number of samples. Then, an empirical analysis of the top 10 smartphone supply chains in the Chinese market is conducted, and the LS-SVM model and other forecasting methods are compared. Results suggest that the LS-SVM model achieves a good predictive accuracy. This study also analyzes the value-adding structure of supply chains from the perspective of interaction and enriches the theory of value creation among supply chains. This study is conducive to helping consumer electronics enterprises to conduct market analyses and determine value growth points accurately.