Toward an Adaptive Enterprise Modelling Platform

Abstract

For the past three decades, enterprise modelling (EM) has been emerging as a significant yet complex paradigm to tackle holistic systematic enterprise analysis and design. With a high fluctuation in the global economy, industrial stability and technology shift, the necessity of such paradigms becomes crucial in determining the decisions that an enterprise can make for surviving in such a highly dynamic business ecosystem. EM practices have focused for a long time, on the design-time of enterprise systems. Recently, there has been a rapid development in data analytics, machine learning and intelligent systems from which an EM platform can benefit. EM needs to cope with the new changes in both business and technology; it should also help architects to determine optimum decisions and reduce complexity in technical infrastructure. In this paper, the author discusses several challenges facing enterprise modelling practices and offers an architectural notion for future development focusing on the requirements of a platform that can be called intelligent and adaptive.

Digital Twins of an Organization for Enterprise Modeling

“…As Grieves and Vickers described it, they show the differences between actual and imagined behavior indicating undesirable behavior or inadequate models [16]. Regarding the latter case, Fayoumi already referred to the role of future adaptive enterprise modeling platforms [13]. Moreover, we have to keep in mind that deviations often have a plausible cause: models do not meet the demands of the situation that people face in their work.…”

“…Closing the gap between Enterprise Models and real-time business process execution is still a challenge [13]. Data and process mining may appear as a way to close the gap.…”