Partner Selection Of Supply Chain Management Based On Support Vector Machine

According the business performance of enterprise in quality,price,time,flexible and creative area are depended on supplier network, partnership and effective supplying are becoming more and more important. In the process of selecting partners, since the unbalance of competition position and profit, lack of credit and some other factors will cause the evaluation index values do not correspond with the actual. In order to improve the efficiency of businesses to choose partners and reduce the cost of their choice, this paper uses least squares support vector machine (LS-SVM) algorithm to select partners. The experimental result shows that LS-SVM not only improves training efficiency, but also possesses higher accuracy.This paper studies the supply chain management theory. On the basis of supply chain partners choose to do a detailed discussion and research, and the current supply chain partner selection method described. Further study of the statistical theory of machine learning problems, focusing on the statistical theory based on support vector machine classification. Through this theoretical study, improve and establish a process of supply chain partners, and the use of the partners evaluation. Finally, using the least squares support vector machines as a supply chain partner selection method chosen by experiment using least squares support vector machine accuracy and efficiency.