Research On Supplier Evaluation System Of Company B

With the development of my country's market economy,the auto parts manufacturing industry is facing severe challenges in an increasingly complex international situation.For auto parts manufacturing companies,the comprehensive quality of raw materials provided by suppliers has a decisive effect on product production and customer satisfaction.Therefore,determining the best supplier will have an important impact on the operation and management of auto parts manufacturing companies.This article takes the implementation of B company's supplier evaluation as the research background,and deeply analyzes the main problems in the B company's supplier evaluation system: on the one hand,the selection of evaluation indicators is relatively single,and the price and quality are the main considerations to make the evaluation When ignoring other aspects of the supplier's capabilities,the selected supplier cannot meet the core needs of the enterprise;on the other hand,because the conceptual boundary of qualitative indicators is not clear,and considering the subjectivity of the comprehensive knowledge of group experts,the current simple Evaluation methods have limitations in solving the problems of ambiguity and uncertainty.In order to solve the above problems,this article established a multi-level supplier evaluation index system,and used the analytic hierarchy process to calculate the weight.And the fuzzy comprehensive evaluation method is selected to complete the construction of the evaluation model of this article.Taking the selection of raw material suppliers to be purchased for the current project of Company B as an example,comprehensive evaluation is carried out and the optimal supplier is determined.Through the research in this article,it can provide analysis and judgment methods for company B to select suppliers,and also provide a quantitative basis for other similar supplier selection issues,and can also be used as a data basis to provide support for relevant corporate managers to make accurate decisions.