This framework categorizes use cases in data-driven model-based systems engineering, suggesting pathways for economic efficiency and model optimisation.
The integration of Model-Based Systems Engineering (MBSE) and data analytics (DA) has introduced a novel approach, Data-Driven Model-Based Systems Engineering (DDMBSE), which combines structured system modelling with data-driven insights. DDMBSE offers the potential for improvements in model optimisation, economic efficiency and the implementation of dynamic system updates based on real-time data. However, the diverse applications of DDMBSE lack a structured overview of its use cases. This paper addresses this gap by proposing a comprehensive framework for the categorisation and description of DDMBSE use cases. It provides users with a structure to navigate within DDMBSE landscape, consolidate knowledge, and identify underexplored areas for future research. This contribution establishes a foundation for advancing the implementation of DDMBSE across industries and fostering its adoption.
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Tissen et al. (2025) studied this question.
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