Construction of enterprise business management analysis framework based on big data technology

数字公司 企业数据管理 利润(经济学) 计算机科学 业务 业务流程管理 技术管理 过程管理 知识管理 企业信息系统 业务流程 营销 经济 在制品 微观经济学
作者
Jinqian Peng,Liyuan Bao
出处
期刊:Heliyon [Elsevier BV]
卷期号:9 (6): e17144-e17144 被引量:16
标识
DOI:10.1016/j.heliyon.2023.e17144
摘要

With the development of science and technology, people have added a new concept big data, which is the most concerned topic at present, and has also brought great changes to the business management environment of enterprises. At present, most of the business administration work of enterprises is mainly based on human resources, and the enterprise activities are managed through the professional knowledge of relevant management personnel. However, due to human subjective factors, the management effect is unstable. Therefore, this paper designed an enterprise business management system based on intelligent data technology, and constructs an enterprise business management analysis framework. The system can help managers to make the best plan when implementing management measures, improve the efficiency of production management, sales management, financial management, personnel organization structure management, etc., so as to make business management more scientific. The experimental results showed that the improved C4.5 algorithm in the business management system proposed in this paper reduced the fuel consumption cost of shipping company A by 220.21 yuan at least and 11050.12 yuan at most, which reduced the fuel consumption cost of the company's five voyages by 13349.09 yuan in total. This indicates that the improved C4.5 algorithm has higher accuracy and better time efficiency compared to traditional C4.5 algorithms. At the same time, the optimized ship speed management effectively reduces the fuel consumption cost of flights and improves the company's operating profit. The article proves the feasibility of improved algorithms based on decision trees in enterprise business management systems, and has a good decision support effect.
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