A Study of Supplier Selection Method Based on SVM for Weighting Expert Evaluation

• Published in: Discrete dynamics in nature and society Vol. 2021; pp. 1 - 11
• Main Authors: Zhao, Li, Qi, Wenjing, Zhu, Meihong
• Format: Journal Article
• Language: English
• Published: New York Hindawi 21.12.2021; John Wiley & Sons, Inc; Wiley
• Subjects: Artificial intelligence; Classification; Component and supplier management; Confidence intervals; Credibility; Decision making; Evaluation; Machine learning; Methods; Suppliers; Supply chains; Support vector machines; Vendor relations; China
• ISSN: 1026-0226; 1607-887X
• DOI: 10.1155/2021/8056209

How to choose suppliers scientifically is an important part of strategic decision-making management of enterprises. Expert evaluation is subjective and uncontrollable; sometimes, there exists biased evaluation, which will lead to controversial or unfair results in supplier selection. To tackle this problem, this paper proposes a novel method that employs machine learning to learn the credibility of expert from historical data, which is converted to weights in evaluation process. We first use the Support Vector Machine (SVM) classifier to classify the historical evaluation data of experts and calculate the experts’ evaluation credibility, then determine the weights of the evaluation experts, finally assemble the weighted evaluation results, and get a preference order of choosing suppliers. The main contribution of this method is that it overcomes the shortcomings of multiple conversions and large loss on evaluation information, maintains the initial evaluation information to the maximum extent, and improves the credibility of evaluation results and the fairness and scientificity of supplier selection. The results show that it is feasible to classify the past evaluation data of the evaluation experts by the SVM classification model, and the expert weights determined on the basis of the evaluation credibility of experts are adjustable.