Research on Enterprise Management Process Reengineering Strategies for Integrating Innovation Ecosystems in the Age of Artificial Intelligence

被引：0

作者：

Huang Y. ^{[1
]}

机构：

[1] School of Management, Guangdong University of Science and Technology, Guangdong, Dongguan

来源：

Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期

关键词：

Cluster analysis; Innovation ecosystem; Petri net; Process reengineering;

D O I：

10.2478/amns-2024-1803

中图分类号：

学科分类号：

摘要：

Innovation is the inexhaustible driving force for an enterprise to realize sustainable development, and enterprise management process reengineering within the innovation ecosystem is the key know-how for the growth of enterprise benefits. This paper focuses on the realization of enterprise management process reengineering within the innovation ecosystem. The study starts with the construction of an embedded innovation ecosystem model, and it is then analyzed for threshold effects. The hexadecimal structure of fuzzy Petri nets is used to establish a diagnostic model of enterprise management processes, and problems in enterprise management processes are supervised through fault tree analysis so as to improve and optimize the management processes and process reengineering of the enterprise. The research on enterprise management process reengineering in this paper employs F-cluster analysis and fuzzy C-mean cluster analysis to fully demonstrate the relationship between the activities of every management process in the enterprise. Through the study, it is concluded that the open innovation and shared cognitive capital variables designed in this paper in constructing the embedded innovation ecosystem model have successfully passed the reliability and validity test and play an important role in enterprise innovation. In the case study of enterprise management process reengineering, the management process of enterprise A that implemented process reengineering realized intelligent transformation. The resulting total share of production cost of enterprise A decreased to 91.2%, and the overall cost of energy saving was about 10% or so. © 2024 Yamin Huang., published by Sciendo.

引用

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