How companies are making their AI based System of Systems Lifecycle Management of Digital Threads landscape fit for systems of systems

The transition from traditional parts list thinking to model based systems engineering (MBSE) marks a profound paradigm shift in engineering practice. While the parts list has served as the backbone of industrial development for decades, it is losing importance in an increasingly networked, software driven world. Modern products are no longer linear assemblies, but complex systems in which mechanics, electronics, software and data are dynamically interdependent. This is where the System of systems model comes in: it depicts the product as a living, digital image with all its relationships, functions and interactions. The focus thus shifts from documenting individual components to a holistic understanding of the system.The classic bill of materials describes what a product is; the System of systems model shows how it works and why it was designed that way. This shift from static to dynamic thinking changes not only methods, but also role models: engineers become system architects who must model and understand interdisciplinary relationships. MBSE creates a common language that unites mechanics, electronics, software and management based on standards such as SysML v2, ISO/IEC 15288 and the INCOSE guidelines.The advantages are obvious: changes can be tracked in real time, requirements can be directly linked to functions and tests, and consistency between disciplines is maintained. This makes the System of systems model the sole ‘source of truth’ for all technical decisions. In industries such as aviation and automotive engineering, model based approaches are already proving to shorten development times, reduce errors and promote agility.In the long term, the System of systems model grows beyond its original function: it forms the basis of the digital twin, linking real operating data with simulations and thus creating the basis for learning, adaptive systems. In conjunction with artificial intelligence, this results in the cognitive twin a model that not only describes, but also learns and optimises. The shift from the bill of materials to the System of systems model is therefore not a passing fad, but a necessary evolution that is permanently changing technology, organisation and thinking in engineering.

• Publication year

  2025

• Venue

  Journal of Intelligent System of Systems Lifecycle Management

• Publication date

  2025-11-12

• Fields of study

  Not labeled

• Identifiers
  DOI 10.71015/9801rt83
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

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• No concepts are published for this paper.

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