Keywords
lifecycle management
MBSE
PLM
CAD/CAE
Digital Thread
Digital Twin
variant and configuration management
How to Cite
Abstract
This article highlights how action research (AR) effectively supports the establishment and further development of an AI-supported system of systems lifecycle management with digital threads. The starting point is the growing complexity of networked product and system landscapes, in which technical, organizational, and data related issues are inextricably intertwined. AR combines planned intervention with systematic observation and joint reflection in real business environments. In several industry academic collaborations, recurring cycles of diagnosis, planning, intervention, and reflection were used to integrate heterogeneous processes and data models across the entire lifecycle: from CAD CAE PDM couplings to endtoend change processes to MBSE embeddings with SysML and model driven mechatronics. The results show three core effects. First, AR promotes organizationwide learning: boundaries between mechanics, electronics, software and IT are dissolving, acceptance of new roles and data sovereignty is increasing, and understanding of quality, consistency, and traceability is growing. Second, AR facilitates gradual technical integration because social negotiation and technical architecture are designed together including links to IoT MES data, digital twin approaches, and sustainability metrics (e.g., LCA integration into development decisions). Third, measurable improvements are emerging throughout the lifecycle, such as shorter lead times, more robust change statuses, and traceable thread links across domains and organizations. At the same time, the limitations become transparent: case transferability remains limited, the balance between participation and analytical distance requires clear roles, and SoS constellations with many partners require patience and reliable governance. A practical design framework can be derived from the overall view: AR serves as a sociotechnical catalyst that combines MBSE, digital threads, and sustainability into a learning system. Early joint hypothesis formation, iterative piloting with clear maturity criteria, the coupling of qualitative reflection and quantitative indicators, and the anchoring of communities of practice are crucial to success. In this way, AR not only enables the introduction of new tools, but also the transformation of the development culture a prerequisite for ensuring SoS capability, transparency, and adaptability in the digital age.
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