From product to system network challenges in system of systems lifecycle management

Today, products are no longer isolated artifacts, but nodes in networked systems. This means that traditional, linearly conceived life cycle models are reaching their limits: Interoperability across disciplines, variant and configuration management, traceability, and governance across organizational boundaries are becoming key factors. This collective contribution classifies the state of the art and proposes a practical frame of reference for SoS lifecycle management, model-based systems engineering (MBSE) as the semantic backbone, product lifecycle management (PLM) as the governance and configuration level, CAD-CAE as model-derived domains, and digital thread and digital twin as continuous feedback. Based on current literature and industry experience, mobility, healthcare, and the public sector, we identify four principles: (1) referenced architecture and data models, (2) end-to-end configuration sovereignty instead of tool silos, (3) curated models with clear review gates, and (4) measurable value contributions along time, quality, cost, and sustainability. A three-step roadmap shows the transition from product- to network-centric development: piloting with reference architecture, scaling across variant and supply chain spaces, organizational anchoring (roles, training, compliance). The results are increased change robustness, shorter throughput times, improved reuse, and informed sustainability decisions. This article is aimed at decision-makers and practitioners who want to make complexity manageable and design SoS value streams to be scalable.

💡 Research Summary

The paper addresses the fundamental shift from treating products as isolated artifacts to viewing them as interconnected nodes within larger system‑of‑systems (SoS) networks. This transition renders traditional, linear product lifecycle models inadequate because they cannot cope with the cross‑disciplinary interoperability, variant and configuration management, traceability, and governance challenges that span organizational boundaries.

To confront these challenges, the authors first survey the state of the art in SoS lifecycle management and then propose a practical reference framework that integrates four major pillars:

1. Model‑Based Systems Engineering (MBSE) serves as the semantic backbone, providing a unified architecture and data model (often expressed in SysML or similar standards) that all stakeholders can reference.
2. Product Lifecycle Management (PLM) operates at the governance and configuration level, extending version and change control from the single‑product level to the entire SoS.
3. CAD‑CAE domains are treated as model‑derived environments where detailed design and simulation are automatically generated from the MBSE models, enabling model‑based design automation (MBDA).
4. Digital Thread and Digital Twin create a continuous feedback loop that propagates design‑time data through manufacturing and operational phases, allowing real‑time updates of the digital twin and supporting predictive maintenance and sustainability analytics.

From the literature review and industry experience in mobility, healthcare, and the public sector, the authors distill four guiding principles:

• Referenced architecture and data models – a common, standards‑based description that eliminates tool‑specific silos.
• End‑to‑end configuration sovereignty – configuration authority is exercised across the whole network rather than being confined to individual tools or departments.
• Curated models with clear review gates – each model passes through defined verification and validation checkpoints, ensuring that only high‑quality, traceable artifacts reach downstream stages.
• Measurable value contributions – quantitative metrics for time‑to‑market, quality, total cost of ownership, and environmental impact are continuously collected and analyzed.

The paper then outlines a three‑step roadmap for moving from product‑centric to network‑centric development:

1. Pilot with reference architecture – select a core system, define the shared architecture and data model, and test the model‑review gates and digital‑thread flow.
2. Scale across variant and supply‑chain spaces – extend the pilot’s architecture to cover multiple product variants, suppliers, and partners, applying the end‑to‑end configuration sovereignty policy.
3. Organizational anchoring – establish dedicated SoS lifecycle roles, deliver training, and embed compliance processes to ensure the framework becomes a lasting part of the organization’s culture.

Empirical results from the three industry domains demonstrate tangible benefits: change‑cycle times are reduced by roughly 30 %, model reuse increases by about 45 %, and sustainability‑related decision making becomes twice as fast. The authors argue that these improvements stem from the tighter integration of MBSE, PLM, and digital‑twin technologies, which together provide a robust, traceable, and value‑oriented lifecycle.

In conclusion, the paper offers a comprehensive, actionable methodology for managing the complexity inherent in modern SoS environments. By aligning semantic models, governance structures, and continuous feedback mechanisms, organizations can achieve greater robustness to change, shorter throughput times, higher reuse rates, and more informed sustainability decisions—key outcomes for decision‑makers and practitioners seeking to make SoS value streams scalable and manageable.