Modern enterprises are large complex systems operating in dynamic environments and are therefore required to respond quickly to a variety of change drivers. Moreover, they are systems of systems wherein understanding is only available in localized contexts and is partial and uncertain. Given that the overall system behaviour is hard to know a-priori and that conventional techniques for systemwide analysis either lack rigour or are defeated by the scale of the problem, the current practice often exclusively relies on human expertise for adaptation. This chapter outlines the concept of model-driven adaptive enterprise that leverages principles from modeling, artificial intelligence, control theory, and information systems design leading to a knowledge-guided simulation-aided data-driven model-based evidence-backed approach to impart adaptability to enterprises. At the heart of a model-driven adaptive enterprise lies a digital twin (i.e., a simulatable digital replica of the enterprise). The authors discuss how the digital twin can be used to analyze, control, adapt, transform, and design enterprises.
|Title of host publication||Advanced Digital Architectures for Model-Driven Adaptive Enterprises|
|Editors||Vinay Kulkarni, Sreedhar Reddy, Tony Clark|
|Number of pages||14|
|ISBN (Print)||9781799801085, 9781799801092|
|Publication status||Published - 2020|
|Name||Advanced Digital Architectures for Model-Driven Adaptive Enterprises|