Exploring data-driven model-based systems engineering: a systematic literature review

Digitalization is changing the way we work today, driven primarily by digital and data-driven technologies and methodologies such as Model-Based Systems Engineering (MBSE). MBSE focuses on formal models of a system to manage complexity and system properties. By analyzing and integrating data from various sources, system models, underlying development processes, and the later product can be optimized. Although the potentials are many, the research, integration, and application of data-driven methods, such as data analytics, into MBSE is rare. Hence, this study aims to define the aspects, potentials, and challenges of data-driven model-based systems engineering. In order to answer our research questions, a comprehensive systematic literature review is performed. Based on the results, a detailed initial description of data-driven model-based systems engineering with six aspects is deduced. In addition, the potentials, and challenges of data-driven model-based systems engineering are identified. The aspects, potentials, and challenges enable a closer look at the topic and at the same time reveal future demands and a new research area.