Research On Evaluation Method Of Auto Parts Supplier Based On Rough Set Of Composite Relationship

Under the impetus of a comprehensive opening pattern,the establishment of a standardized and information-based automotive logistics supply chain management system will further stimulate the vitality of domestic auto companies.With the entry of foreign excellent investment companies into the Chinese market,it will have greater impact on domestic industries,especially the automotive industry,facing greater opportunities and challenges.Due to the relatively late development of China's auto industry supply chain management and imperfect systems,the adoption rate of the automotive industry standard certification is dropping behind and the market competitiveness and recognition are insufficient.This dissertation takes auto parts suppliers as the research object,establishing the supplier evaluation index system based on the global logistics management/logistics assessment system and other industry standards as the reference basis,which uses the data mining method to obtain the supplier review certification decision rules and the auto parts supply.Firstly,it introduces the supply chain elements and assessment process covered by the logistics supply chain management certification system,which uses the evaluation tools to evaluate the evaluation subjects and obtains the evaluation original data.In order to ensure the comprehensiveness and accuracy of the evaluation classification method,the index system of the evaluation method of auto parts suppliers is established by the evaluation elements of the logistics operation evaluation system.Secondly,based on the variable precision rough set model,the superiority relationship,the equivalence relation,and the adjustment of the approximate set classification precision and the dynamic updation of the research object are constructed,and the rough set model of the composite relationship is constructed.Then,the attribute reduction is carried out on the evaluation information system,and the data dimension is reduced.Then the variable antiinterference ability is improved by the variable precision threshold.The feasibility of the method for processing information inconsistency is verified by the example.The approximate set is used as the input of the decision extraction algorithm and the basis for obtaining supplier classification.In addition,according to the retained approximate set calculation process,the research object is dynamically updated,and the effectiveness of the instance verification update method is designed.Finally,the supplier classification evaluation system is designed by the auto parts supplier evaluation classification method.According to the current review data of the logistics supply chain management certification,the decision rules between the evaluation indicators and the certification results are mined.The supplier is found through the classification rules.The stage and bottlenecks are continuously improved against industry best practices.It provides technical support to help companies conduct supply chain management and improve the standard certification pass rate of the domestic automotive industry.