Open AccessPerformance Evaluation of Enterprise Supply Chain Management Based on the Discrete Hopfield Neural Network
Author(s) -
Miaoling Bu
Publication year - 2021
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2021/3250700
Subject(s) - computer science , artificial neural network , supply chain , value (mathematics) , index (typography) , supply chain management , order (exchange) , evaluation methods , performance improvement , core (optical fiber) , process management , business , artificial intelligence , operations management , machine learning , reliability engineering , marketing , telecommunications , finance , world wide web , economics , engineering
In order to make up for the shortcomings of current performance evaluation methods, this paper proposes a new method of enterprise performance evaluation, discusses the construction principle of the evaluation index, and proposes a method of enterprise supply chain overall performance evaluation based on the discrete Hopfield neural network (DHNN) algorithm. Enterprise supply chain (SC) is an important way for enterprises to conduct business with other strategic partners in the market, and the improvement of SC performance is an important way to improve the core competitiveness of enterprises, so it is of great value to study the performance evaluation and index design of the enterprise SC. This method calculates the level value of the overall performance of the SC. This level value is a value between 0 and 1. The higher the value, the higher the overall performance level of the SC. Therefore, when evaluating the overall performance of the SC, appropriate index weights must be selected according to the characteristics of the industry, which helps to objectively evaluate the overall performance of the SC.