Cloud Computing Platform for Management Information System under Intelligent Background
In view of the problems of poor information flow and low decision-making efficiency in modern enterprise management, the Enterprise Management Information System (EMIS) and Decision Support System (DSS) are introduced to improve the intelligence level and information integration ability of the decision-making process, so as to promote the efficient operation and accurate decision-making of enterprises. Through systematic analysis, the role and complementarity of EMIS and DSS in enterprise management are explored, which further promotes the development of enterprise informatization. First, the architecture of EMIS is optimized by realizing efficient information sharing and real-time updating through data integration technology and cloud computing platform, providing a reliable data foundation for subsequent decision support. Secondly, an intelligent decision-making model combining OLAP (Online Analytical Processing), clustering algorithm and regression and classification algorithm is proposed, which can accurately analyze the historical data and market dynamics of enterprises and automatically generate optimized decision-making suggestions. Finally, the framework of combining EMIS and DSS is introduced to construct a comprehensive enterprise management decision-making model, which greatly improves the operational efficiency and market competitiveness of enterprises. After the decision is implemented, the sales increase from 5 million yuan before the decision to 5.5 million yuan, an increase of 500,000 yuan, and a growth rate of 10%. Profits increase from 1 million yuan to 1.1 million yuan. The combination of EMIS and DSS not only provides enterprises with more accurate decision-making basis but also provides important support for the modernization and intelligence of enterprise management.
Authors
- Feng Ge (ORCID: https://orcid.org/0000-0003-0812-1926)
Publication Details
- Journal
- Procedia Computer Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.procs.2025.05.036
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 2.28