Keywords
lifecycle management
MBSE
PLM
CAD/CAE
Digital Thread
Digital Twin
variant and configuration management
How to Cite
Abstract
The concept of a digital twin has evolved significantly in recent years. Whereas in the past, a digital twin was often understood to be nothing more than a static 3D model, today the term encompasses much more. A digital twin is no longer just a model created once, but a living, evolving digital twin of a physical system that remains connected to it throughout its entire life cycle – from development to production, operation to decommissioning. The true value of the digital twin lies not only in its creation, but in its ability to continuously integrate data and adapt to the real world. A key aspect that is often underestimated is the feedback of operational data in the development process. Only through the targeted use of these data can the next product generation be optimized, which is the long term strategic benefit of a digital twin. However, studies show that many digital twins today fail due to a fundamental problem: systematic feedback of data from operations into design is often not given sufficient consideration. This leads to fragmentation of data and, thus, to suboptimal use of digital twins. Furthermore, the digital twin is defined not only by data and models, but also by clear processes and responsibilities. It is not enough to simply collect data; it must also be regulated who monitors the quality of these data, who changes them, and who is responsible for its use. This organizational dimension is crucial to the success of the digital twin, especially in complex, regulated environments such as aviation or shipping, where trust in data and models is of paramount importance. The architecture of a digital twin must be dynamic in order to keep pace with the evolution of the physical system. This means that the model must not only reflect the current state, but also enable forecasts and optimizations. Research shows that the integration of data and models needs to be further improved, especially with regard to the application of artificial intelligence for analysis and decision making, in order to create a truly dynamic digital twin. Another important point is the interoperability of digital twins in different phases and domains. The example of the University of Cambridge’s National Digital Twin program impressively demonstrates that effective communication between different areas – such as development, production, and operations – requires not only a technical infrastructure, but also clear governance, standards, and responsibilities. In summary, it is clear that the digital twin is more than just a technological solution throughout its life cycle. It is a combination of modeling, data management, life cycle thinking, and organizational responsibility. Only when these dimensions are properly linked can the digital twin, as a living, evolving system, contribute to improving products, making processes more efficient, and enabling informed decisions to be made.
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