{"title":"Utilizing Enterprise Economic Benefit Evaluation Methods in Edge Intelligent Neural Network Applications","authors":"Ling Yang, Vinh Phuc Dung","doi":"10.4018/ijisscm.348338","DOIUrl":null,"url":null,"abstract":"The core of enterprise economic benefit evaluation lies in the development of a quantitative identification model. The Back Propagation (BP) neural network possesses robust parallel computing, adaptive learning, and error correction capabilities, which can effectively reveal the economic benefits of enterprises and their relationship with influencing factors. This study establishes an economic benefit evaluation model for express delivery enterprises based on the BP neural network. The model takes the annual profit rate of enterprises as the quantitative index of economic benefits and selects 13 factors, both external and internal, influencing the annual profit rate of express delivery enterprises as inputs for the BP neural network model. The economic benefit evaluation model based on BP neural network meets the requirement of objective mean square error in the 300th training cycle. The research results demonstrate that the BP model significantly saves computing time and enables rapid, comprehensive, and objective evaluation of the economic benefits of industrial enterprises.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Systems and Supply Chain Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijisscm.348338","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
The core of enterprise economic benefit evaluation lies in the development of a quantitative identification model. The Back Propagation (BP) neural network possesses robust parallel computing, adaptive learning, and error correction capabilities, which can effectively reveal the economic benefits of enterprises and their relationship with influencing factors. This study establishes an economic benefit evaluation model for express delivery enterprises based on the BP neural network. The model takes the annual profit rate of enterprises as the quantitative index of economic benefits and selects 13 factors, both external and internal, influencing the annual profit rate of express delivery enterprises as inputs for the BP neural network model. The economic benefit evaluation model based on BP neural network meets the requirement of objective mean square error in the 300th training cycle. The research results demonstrate that the BP model significantly saves computing time and enables rapid, comprehensive, and objective evaluation of the economic benefits of industrial enterprises.
企业经济效益评价的核心在于建立定量识别模型。反向传播(BP)神经网络具有鲁棒并行计算、自适应学习和纠错能力,能够有效揭示企业经济效益及其与影响因素的关系。本研究建立了基于 BP 神经网络的快递企业经济效益评价模型。该模型以企业年利润率作为经济效益的量化指标,选取影响快递企业年利润率的 13 个外部和内部因素作为 BP 神经网络模型的输入。基于 BP 神经网络的经济效益评价模型在第 300 次训练循环中达到了客观均方误差的要求。研究结果表明,BP 模型大大节省了计算时间,能够快速、全面、客观地评价工业企业的经济效益。
期刊介绍:
The International Journal of Information Systems and Supply Chain Management (IJISSCM) provides a practical and comprehensive forum for exchanging novel research ideas or down-to-earth practices which bridge the latest information technology and supply chain management. IJISSCM encourages submissions on how various information systems improve supply chain management, as well as how the advancement of supply chain management tools affects the information systems growth. The aim of this journal is to bring together the expertise of people who have worked with supply chain management across the world for people in the field of information systems.