Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network

Quan Quan,Zhongqiang Zhang,Naeem Jan

doi:10.1155/2022/8572424

Abstract

With the rapid development of economy and information technology, traditional manufacturing industry is facing severe challenges. Enterprises need to rectify the traditional manufacturing industry and realize the transformation from traditional manufacturing industry to intelligent manufacturing industry. In order to adapt to market demand, enterprises need to constantly integrate resources to improve the competitiveness of enterprise supply chain. Based on the background of suppliers in intelligent manufacturing enterprises, the evaluation method of supplier efficiency was studied by using machine learning. In this paper, based on the traditional backpropagation (BP) neural network, combined with the improved particle swarm optimization (PSO) algorithm, and on the basis of the supplier evaluation index system, the supplier efficiency evaluation model of intelligent manufacturing enterprises based on DPMPSO-BP neural network 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, and the supplier efficiency evaluation method based on DPMPSO-BP neural network is validated and analyzed. Compared with the traditional BP neural network method, the supplier efficiency evaluation method is effective and feasible.

Highlights

Manufacturing plays a key role in the economic development of a country or region and reflects the comprehensive strength of a country or region
With the rapid development of the supply chain of intelligent manufacturing enterprises, the evaluation of supplier efficiency has been paid more and more attention by the enterprise management, and more and more researchers have joined in the research of supplier efficiency evaluation
On the basis of the research of experts and scholars, this paper constructed a set of intelligent manufacturing enterprise classification index system and evaluation index system, respectively, through the enterprise field investigation and relying on the school-enterprise cooperation project. e DPMPSO-BP neural network model was used, and the enterprise example was used for simulation verification, and the supplier efficiency evaluation model was established

Summary

Introduction

Manufacturing plays a key role in the economic development of a country or region and reflects the comprehensive strength of a country or region. E more reasonable supplier evaluation method is the combination of qualitative and quantitative methods, which can make up for the strong subjectivity of qualitative evaluation method and simplify the complex quantitative evaluation. In the mixed evaluation method, the method combining BP neural network is more advanced and has the advantages of objective, scientific, easy operation, simple calculation, and so on. Erefore, this paper proposes an improved PSO-BP model by improving particle swarm optimization (PSO) and combining it with the traditional BP neural network and applies the model to the supplier evaluation of a large battery manufacturing enterprise.

Theoretical Basis and Methods

Experiments and Results

Conclusion