Research On Supplier Efficiency Evaluation Of Intelligent Manufacturing Enterprise Based On DPMPSO-BP Neural Network

With the rapid development of economy and information technology,it brings severe challenges to traditional manufacturing industry.Enterprises need to rectify the face of traditional manufacturing industry and realize the transformation from traditional manufacturing industry to intelligent manufacturing industry.In order to meet the market demand,enterprises need to continuously integrate resources to enhance the competitiveness of the supply chain.As the source of supply chain,supplier is an important part of enterprise management,and plays an extremely important role in the core competitiveness of enterprises.Based on the background of intelligent manufacturing enterprise supplier,this thesis uses the evaluation method of machine learning to conduct in-depth research on supplier efficiency evaluation.Firstly,this thesis introduces the research background and significance,and summarizes the evaluation index system and methods of suppliers at home and abroad.Then,it introduces the theoretical basis of this thesis,briefly describes the definition of intelligent manufacturing,supplier classification,incentive mechanism,supplier efficiency and the definition of supplier efficiency evaluation.On the basis of the existing relevant literature,this thesis summarizes the five principles of supplier evaluation index system construction: relative independence,scientific conciseness,flexible operation,expandability,combination of qualitative and quantitative,combining with the existing literature on supplier evaluation index system and the characteristics of suppliers of intelligent manufacturing enterprises,constructs supplier classification and evaluation index system of intelligent manufacturing enterprises,and points out that Each index in the standard system shall be explained in detail.Secondly,the efficiency evaluation of suppliers is based on the efficiency suppliers.Compared with Bayes,decision tree and support vector machine,Bayes is selected to classify many suppliers and select efficiency suppliers for evaluation.Supplier evaluation is a complex non-linear mapping problem.Compared with the domestic and foreign supplier evaluation methods,on the basis of traditional BP neural network,combined with improved PSO algorithm,and on the basis of supplier evaluation index system,a DPMPSO-BP neural network supplier efficiency evaluation model for intelligent manufacturing enterprises is constructed.Through the collected sample data,the network is trained and simulated,and the results are analyzed.Finally,the designed model is applied to a large battery manufacturing enterprise,which proves that DPMPSO-BP neural network based supplier efficiency evaluation method of intelligent manufacturing enterprise is an effective and feasible method,and the traditional BP neural network method is improved to some extent.