Journal of Intelligent System of Systems Lifecycle Management
Systems of Systems Lifecycle Management is a scholarly journal, based on peer-reviewed paper, peer-reviewed presentations and peer-reviewed journal articles, dedicated to advancing the understanding, development, and application of lifecycle management principles across complex, interdependent systems. These systems, often composed of multiple subsystems with distinct lifecycles, present unique challenges that require a holistic, interdisciplinary approach to management. The journal provides a platform for research that addresses the complexities involved in overseeing the development, integration, operation, and eventual decommissioning of interconnected systems—each capable of independent function but designed to operate harmoniously within a larger framework. Our mission is to explore and promote innovative methods that support the adaptability, resilience, and sustainability of systems of systems. This includes studies on how individual subsystems can evolve and adapt without disrupting the overarching network, ensuring continuity and enhancing performance across varied domains, from transportation and energy to defense and environmental systems. We delve into the governance frameworks, design principles, and lifecycle strategies that allow these systems to remain flexible and responsive to changing demands and technological advancements. The journal emphasizes research contributions that focus on sustainable lifecycle management, ensuring minimal environmental impact and optimal resource use across the entire lifespan of each system and its components. By fostering a better understanding of how these interdependent systems interact, adapt, and coalesce into a cohesive whole, Systems of Systems Lifecycle Management seeks to drive advancements that align with global needs for efficiency, sustainability, and robust performance in increasingly interconnected and complex environments.
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.
Copyright (c) 2025 Journal of Intelligent System of Systems Lifecycle Management
AI based System of Systems Lifecycle Management of Digital Threads
Over the past two decades, our understanding of product development has changed fundamentally. Disciplines that were once clearly distinct from one another, such as mechanical engineering, electrical engineering, and software development, have now become increasingly intertwined. Products are no longer isolated objects, but rather nodes in a network of systems, data, and processes. In this environment, it is no longer enough to manage individual data sets – they must flow, remain consistent, and be available to the right people at the right time. The AI based lifecycle management system of Digital Threads (Digital Thread for short) has therefore established itself as the backbone of modern system engineering. Early approaches to product data management (PDM) focused on central storage of CAD data and the management of versions and approvals. However, with the advent of complex mechatronic systems, networked vehicles, and modular plants, it became clear that this approach was not sufficient. Companies began to realize that it was not enough to simply store geometries; they needed a system that mapped the entire lifecycle of a product, from the initial requirement through development and production to operation and decommissioning. Today, Digital Threads’ AI based lifecycle management system acts as a digital nervous system that connects disciplines such as mechanics, electronics, software, and simulation. While system engineering provides methodical approaches for structuring requirements and architectures, Digital Thread ensures that the associated data are findable, traceable, and up to date. This object oriented approach creates transparency and enables end to end traceability, which is a key requirement of modern systems engineering. Another feature of modern digital thread approaches is the seamless integration of models and product data. Changes in one discipline, for example, in design, can automatically detect and forward all affected areas such as requirements, tests, and functions. This promotes the continuous flow of information and prevents unnoticed consequences of changes elsewhere in the system. The close link between AI based lifecycle management systems for digital threads and system engineering goes beyond technical necessities and promotes cultural change in companies. Organizations must not only introduce technology, but also clearly define processes and responsibilities in order to fully exploit the effectiveness of this system. Clear, transparent handling of changes, systematic management of variants and integration of real time data from operations are key success factors for the implementation of digital threads. The concept of ”‘digital twin’, which enables a continuous connection between the models of real and virtual products, can only be realized through the consistency and transparency offered by the digital threads. This, in turn, promotes more sustainable and data driven decision making processes that can be taken into account throughout the entire product lifecycle, from development to maintenance. Overall, it is clear that AI based digital thread lifecycle management systems not only provide the technical basis for system engineering, but are also an integral element for the successful development and operation of complex, networked systems. They promote innovation, improve quality, and enable companies to make their product development more flexible and efficient.
MBSE in practice linking models, people, and methods correctly
The practical implementation of model-based systems engineering (MBSE) poses a complex challenge for many companies, as it encompasses not only technical aspects of modeling, but also organizational, methodological, and human factors. While MBSE is considered a promising approach to managing growing system complexity and improving efficiency, practice shows that success does not depend solely on technology, but significantly on the correct linking of models, people, and methods. Models are not just documents, but living artifacts that connect requirements, architecture, interfaces, and verification throughout the entire life cycle of a system. However, the use of MBSE often remains limited to communication and documentation if there is no consistent methodology and integration into existing processes. To fully exploit the potential of MBSE, modeling techniques and tools must be carefully selected and integrated, with a methodological foundation of governance, data management, and reuse of model components being essential. People are the second crucial factor. The success of MBSE depends not only on technology, but also on the acceptance, understanding, and responsibility of the people involved. Studies show that the biggest challenges lie in training and engaging the right professionals. Without clear assignment of roles and visible commitment from management, MBSE often remains fragmented and inefficient. Finally, the introduction of MBSE also requires the establishment of robust methods. A structured approach, starting with pilot projects, is essential for the successful implementation of MBSE. This includes defining model libraries, integrating with CAD/AI-based systems, and continuously adapting processes and tools. Without such a methodological foundation, models remain isolated and stakeholders are not adequately supported. In summary, MBSE in practice means more than just implementing models and tools. It requires the conscious design of interactions between models, people, and methods, with models acting as the connecting element between disciplines. When these elements are successfully integrated, MBSE can become an essential part of efficient and sustainable system development that not only manages documents but also creates and utilizes knowledge.
Digital twins across the entire life cycle vision and reality
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.
Seamless integration of CAD in AI based System of Systems Lifecycle Management of Digital Threads
The seamless integration of CAD, PDM, and AI-based system-of-systems lifecycle management for digital processes is one of the biggest challenges in modern product development. While the vision of a continuous digital development process from the concept phase to the disposal of a product is appealing, practice shows that technical and organizational integration is far more complex than expected. Attempting to link CAD systems with product data management (PDM) and AI-based lifecycle management systems often leads to challenges, as each of these systems speaks different languages, data structures, and models. This integration process requires not only a technical solution, but also a consistent definition of information models, responsibilities, and governance. The key to successful integration lies in a common understanding of the data and its meaning across all disciplines. This means that not only must fields and attribute values be aligned, but there must also be semantic clarity so that all parties involved have the same understanding and responsibility for the same data objects. Pilot projects that focus on specific value-adding processes are the more pragmatic approach to gradually building trust and understanding for larger integration measures. Furthermore, the literature shows that successful integrations of CAD, PDM and AI-based lifecycle management systems are not just a technical solution but require cultural and organizational change. Without clear governance, defined responsibilities, and systematic control of data flows, integration cannot be successful. The use of technologies such as digital twins and blockchain can significantly improve the quality and traceability of data and help to realize the ‘end-to-end’ concept. The path to functional integration is not a quick technological ‘grand slam’, but requires focused, step-by-step progress based on a clearly defined model and vision. This paper provides a realistic overview of the necessary steps and practical solutions to bridge the gap between theory and practice in seamless digital integration.
Systems thinking in the product development process: why PDP needs to be rethought
For decades, the classic product development process (PDP) was the backbone of industrial development: clearly structured, linear, with fixed handover points between mechanics, electronics, and software. However, this structure is becoming less effective in an increasingly networked world. Modern products are no longer self contained objects, but part of complex system landscapes embedded in data flows, digital services, and feedback from operations. This means that the old logic of ‘define design produce’ is no longer sufficient. PDP must be thought of as a dynamic system that integrates interactions, dependencies, and learning loops from the outset. Systems thinking means recognizing connections rather than optimizing in isolation. Today, decisions in mechanics affect software, data architectures and service processes and vise versa. A modern PDP must structurally reflect this interconnectedness. This begins with a system architecture developed at an early stage that brings together requirements, functions, data flows, and operating conditions. It serves as a common language between disciplines and forms the basis for traceability, simulation, and adaptation throughout the entire AI based Lifecycle management. Instead of rigid phases, iterative cycles are needed: short architecture and integration sprints, early testing, virtual prototypes. In this way, learning becomes part of the process and not a corrective measure. In addition, end to end data chain digital threads are crucial for making changes transparent and keeping knowledge usable across disciplinary boundaries. In systemic thinking, quality is understood as an emerging property: safety, reliability, maintainability, and data integrity arise from the interaction of components. This perspective requires not only new methods (e.g. MBSE, DSM, AI supported lifecycle management), but also a rethinking of the organization. Roles such as system architects and lifecycle owners link disciplines, while management creates the framework for interdisciplinary decisions. Those who embed systems thinking early on in the PDP reduce late waves of change, improve integration capabilities, and create sustainable knowledge continuity. This makes the PDP itself an adaptive system capable of producing products that can hold their own in networked environments. The transition from product to system is not a trend, but a necessary evolution of technical development.
Interdisciplinary collaboration in complex Systems: Tools, structures, and cultural change
Interdisciplinary collaboration in complex systems cannot be achieved through interfaces alone, but rather through the conscious merging of mindsets, models, and responsibilities. In projects where mechanics, electronics, software, and data driven approaches intertwine, the quality of this integration determines stability, efficiency, and innovative strength. This abstract outlines a practical framework with three interlinked levels: structures, tools and culture. Organizationally, resilient bridges are needed: cross departmental steering committees, jointly defined roles such as system architect or integrator, and governance that aligns decisions with the overall system rather than departmental logic. In terms of processes, adapted agile principles with short, integrated iterations, shared milestones and regular system demos in which mechanics, electronics and software become visible together have proven their worth. A shared ‘definition of done’, coordinated maturity models and systemic risk assessments create direction and commitment without unnecessary rigidity. Technically, open information models and shared data rooms are central: MBSE with SysML, parametric and associative CAD models, model based simulation chains and end to end digital thread concepts. Uniform versioning, clear binding rules and transparent change processes reduce discontinuities; open standards facilitate couplings between domains and tools. Where digital twins are linked to real world tests, early insights into interactions that would otherwise only become apparent at a late stage are gained. But the greatest leverage lies in culture. Trust, shared system responsibility and a common language prevent silos despite modern platforms. Targeted communication formats—design workshops, cross disciplinary reviews, glossaries and ‘Lunch and Learn’ sessions—build understanding; regular retrospectives and post mortems anchor learning as a routine. Leadership becomes moderation: it creates spaces where perspectives collide productively and translates between specialist logics. Case studies from industry and research show that the triad works: interdisciplinary centres with their own mandates, agile transformations with visible prototypes instead of paper milestones, and model based integration environments that link real and virtual artefacts reduce friction and accelerate decisions. When teams work together on system architectures early on, data flows remain consistent, responsibilities traceable and quality measurable. Conclusion: In complex systems, interdisciplinarity is not an option, but a necessity. Those who combine structures, methods and culture gain speed without flying blind, robustness without rigidity and innovation without chance. The decisive factor is an attitude that sees differences as a resource, lives integration as a daily practice and synchronises technical, organisational and social changes—from the initial concept to iterative prototyping to operation. This results in systems that remain sustainable, secure and connectable throughout their life cycle.
Action research in the AI based System of Systems Lifecycle Management of Digital Threads environment
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.
From part to networked system: How CAD design supports system integration
Product development is undergoing profound change: whereas the focus used to be on geometric modelling of individual components, today the focus is on networked systems. Classic CAD design is increasingly becoming a strategic integration tool that combines mechanics, electronics, software and simulation. Model-based approaches such as MBSE and semantically linked CAD models create consistent data chains that enable seamless collaboration across disciplinary boundaries. The shift from component design to systemoriented development processes is particularly evident in the growing importance of parametric and functionally linked models. Changes to a parameter automatically lead to adjustments in dependent geometries, simulations and parts lists, significantly reducing development times and error costs. At the same time, CAD is becoming increasingly linked to PDM and AI-based lifecycle management systems. This integration creates a uniform, always up-to-date database the ‘single source of truth’ across the entire product lifecycle. Modern CAD systems not only map geometries, but also manage semantic information such as functions, materials, manufacturing data and tolerances. Simulation plays a central role in this: it is no longer a downstream step, but an integral part of the design. Isogeometric analyses and automated feedback loops are increasingly blurring the line between design and verification. Another milestone is the connection between CAD and the digital twin. Here, real operating data is linked to virtual models, so that design, simulation and operation are part of a continuous cycle. Companies that implement this approach report shorter development cycles, higher product quality and better maintainability. But the transformation remains challenging: data quality, standardisation, interfaces and cultural change are key hurdles. The move towards ‘system-driven product development’ is forward-looking: CAD as a platform for interdisciplinary collaboration, automated adjustments and learning digital twins. This turns the CAD system into the control centre of networked development a place where mechanics, electronics, software and data management merge into an intelligent overall system.
Data governance in the system of systems who controls the flood of data?
In highly networked system-of-systems (SoS) architectures, the volume of data is growing faster than organisations can manage: development, production and operational data is generated in autonomous subsystems that are simultaneously linked via interfaces. The abstract outlines a governance framework that channels this flood without dampening innovation. The focus is on clear decisionmaking authority over data (sovereignty), consistent classification and access models, robust procedures for versioning and change control, and the embedding of these rules ‘by design’ in processes and tools. Roles such as data owner, steward and custodian are thus understood not as bureaucracy, but as an operating system for collaboration. Technically, the framework combines model-based systems engineering, digital thread and digital twin, so that artefacts remain traceable across lifecycle phases: from CAD design to manufacturing parameters to field data. Metadata schemas and uniform identities ensure semantic interoperability between heterogeneous sources; automated approvals and validation rules ensure quality without manual friction. Where cross-organisational work is involved, cryptographically secured protocols complement the traceability of access and changes. The balance between openness and control is crucial: rapid iterations, experimentation and domainspecific freedom remain possible, while security, compliance and consistency requirements are enforced in a machine-readable manner. Practical experience from industry projects shows that governance is most effective early in the lifecycle: when classification, rights and versioning are set automatically when an artefact is created, transparency is built in from the outset, redundancy is reduced and decision-making processes are shortened. Challenges lie in the heterogeneity of data landscapes, in scaling across many stakeholders and in cultural aspects of assuming responsibility. The outlook points to governance that is increasingly data and eventcentric: AI-supported quality assurance, synthetic data sets for secure collaboration, and architectures that manage SoS data flows in real time without undermining the autonomy of the subsystems. Overall, data governance in SoS is not a nice-to-have, but a strategic design tool: it transforms data overload into reliable decisionmaking bases and makes networked technology landscapes both more secure and faster to learn.
System of Systems Lifecycle management as a strategic success factor in mechanical engineering
In modern mechanical engineering, the understanding of success has changed fundamentally. It is no longer enough to simply deliver technically sophisticated products today, the ability to strategically support and shape the entire life cycle of a machine is crucial. System of Systems Lifecycle management (LCM) is thus becoming a key success factor that combines technology, economic efficiency and sustainability. At its core, LCM describes the holistic control of all phases of a product from the idea to development, use and maintenance to dismantling and recycling. Especially in mechanical engineering, where systems are often in use for decades, this approach opens up enormous potential for increasing efficiency, reducing costs and boosting innovation. Current research, for example by Salehi [32] and Salehi and Witte [33], shows that the integration of digital concepts such as modelbased systems engineering (MBSE), agile methods and blockchain technologies is crucial to taking LCM to a new level. By using the Munich Agile MBSE Concept (MAGIC), complex systems can be modelled virtually in early development phases and continuously monitored via digital twins. This creates a continuous data chain that seamlessly connects the flow of information between development, production and operation. Blockchain-based architectures ensure data integrity and traceability throughout the entire life cycle an essential factor for trust and efficiency in global value creation networks. For mechanical engineering, this means a profound change: processes become more transparent, feedback loops between operations and development accelerate innovation, and ecological key figures can be precisely recorded and optimised. LCM thus becomes not only a tool for sustainability, but also a driver for new business models such as ‘machine-as-a-service’ or data-based service contracts. Ultimately, System of Systems Lifecycle management is not purely an IT system, but a management philosophy. Those who succeed in combining technological tools with organisational learning and long-term thinking will position themselves as pioneers of a digital and sustainable industry. This makes LCM the key to competitiveness, innovative strength and entrepreneurial resilience in 21st-century mechanical engineering.
System of systems model instead of parts list: change in basic assumptions in engineering
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.
How companies are making their AI based System of Systems Lifecycle Management of Digital Threads landscape fit for systems of systems
3D design meets system architecture: CAD and MBSE join forces
The integration of 3D CAD design and model-based systems engineering (MBSE) marks a decisive turning point in modern product development. In view of growing system complexity, shorter development cycles, and globally distributed teams, traditional, separate disciplines are reaching their limits. Although CAD has primarily represented geometric and physical aspects to date, MBSE depicts the functional and logical architecture of a system. The convergence of these two worlds enables, for the first time, a consistent digital product description in which requirements, functions, geometries, and behavior models are linked together. This creates a new development logic: the CAD model is no longer merely a visual artifact, but an integral part of the system architecture. This fusion creates considerable added value: early consistency checks, automatic traceability of requirements, cross-domain simulations, and shortened iteration cycles. At the same time, it requires new ways of thinking, tools, and forms of organization. Research by Stechert, Salehi, Zhang, and others shows that close coupling of MBSE and CAD not only improves technical quality and efficiency, but also measurably increases interdisciplinary communication. Methods such as the Munich Agile Concept for MBSE (MAGIC) provide a tried-and-tested framework for this, enabling the agile development of complex mechatronic systems based on digital twins. At the same time, new challenges arise: different levels of abstraction, incompatible data formats, and a lack of semantic standards. Approaches such as ontologies, SysML v2 and STEP AP242 are intended to remedy this by making functional and geometric elements describable across the board. All in all, it is clear that the integration of CAD and MBSE is not just a technical advance, but marks the beginning of a new development philosophy one that focuses on transparency, traceability, and agility throughout the entire product lifecycle, thus laying the foundation for a fully digitally networked Industry 4.0.
Engineering change management in complex system networks
In modern development landscapes, products are no longer isolated artefacts, but densely networked systems comprising mechanics, electronics, software and data driven services. Changes are inevitable but their effects behave like waves in water: a local impulse can influence sensor technology, control logic, mechanical tolerances and service processes across interfaces. Engineering change management (ECM) has the task of identifying such chain reactions early on, making them manageable and keeping them traceable. The text shows why classic, paper based processes are too slow, too siloed and too bureaucratic in this reality, and outlines how modern approaches combine speed, transparency and collaboration. At its core is a systemic view of change: change propagation analysis and models such as dependency structure matrices make dependencies visible before they become costly. Digital twins, graph based analytics and AI supported pattern recognition support the evaluation by deriving probabilities and risks from historical change data and real time usage. Equally important is the organisational side: clear roles (from requester to CCB), short decision cycles, interdisciplinary reviews and a culture in which uncertainty can be openly addressed. Model Based Systems Engineering (MBSE), SysML and System Driven Product Development bridge the gap between disciplines; platform and modularity strategies limit spillover effects. Blockchain based traceability can increase auditability without slowing down collaboration. Because products are increasingly operated as services and updated throughout their lifecycle, ECM is expanding its focus from the end of development to operation, maintenance and return: over the air updates, variant statuses and field feedback are becoming part of the change dialogue. In practical terms, this means that decisions are based less on gut feeling and Excel and more on connected models, data flows and agreed governance rules. The article summarises empirical findings from industry and research: mature ECM organisations avoid duplication of work, reduce miscommunication and shorten throughput times provided that processes are lived and not just described. The end result is a model: a ‘nervous system’ for product development that quickly recognises stimuli, systematically evaluates effects, responds in a targeted manner and documents the decision making process in its entirety. ECM thus evolves from a reactive correction mechanism to a proactive design tool for complex system networks with measurable benefits in terms of quality, time and costs. The key is to give equal attention to technology and collaboration: only when models, data and people work together can changes be truly absorbed and used as a lever for innovation and lasting competitiveness.
Focus on people: roles and competencies in system lifecycle management
System Lifecycle Management (SLM) places people at the centre of the technical and organisational interaction of modern systems. While in the past the focus was primarily on methods, tools and processes, today it is clear that the success of complex systems depends crucially on the people involved – their skills, communication abilities and willingness to share responsibility. SLM describes the design, development, use and decommissioning of systems as a socio-technical process in which technology, organisation and culture are inextricably intertwined. New roles such as lifecycle managers, system architects, data stewards and change managers illustrate that system work is becoming increasingly interdisciplinary. These roles arise at the interfaces between IT, production, data management and sustainability. Their main task is not only to control technical processes, but also to promote trust, transparency and communication. Skills such as systems thinking, reflective ability and ethical awareness are just as crucial as technical know-how. Studies by INCOSE, Fraunhofer and MIT show that teams are more successful when they think systemically and develop knowledge collectively. With the advance of digitalisation and automation, the skills profile of skilled workers is also changing. However, human creativity, contextual understanding and judgement remain irreplaceable. Humans are increasingly acting as mediators between digital twins, data streams and real-world requirements. Blockchain technologies, AI-based security assessments and model-based development approaches such as the Munich Agile MBSE Concept illustrate that technological innovations only have a lasting effect if they are supported by clear roles, responsibilities and a culture of trust. SLM is therefore not a rigid process model, but a dynamic, learning system. It combines technological excellence with social competence and promotes a culture in which learning, communication and ethical behaviour are equally important alongside efficiency and quality. People are at the centre – as designers, learners and links between technology, organisation and society.
Standardisation versus flexibility: dilemma or balancing act in AI based System of Systems Lifecycle Management of Digital Threads
The tension between standardisation and flexibility is one of the key challenges of modern industrial product development, especially in the context of AI based System of Systems Lifecycle Management (SoS LM) and Digital Threads. While standardisation creates efficiency, transparency and process reliability, flexibility is crucial for enabling innovation and adaptability. This paper examines how organisations can reconcile both principles without losing the advantages of either. Standardisation forms the backbone for consistent data flows, interoperability and quality management. It creates common structures, promotes reusability and ensures global collaboration. Nevertheless, excessive standardisation can lead to rigid processes that inhibit innovation and learning. Flexibility, on the other hand, opens up scope for action to respond to new requirements, technologies and markets. It manifests itself in modular system architectures, adaptable workflows and decentralised decision making structures. Companies that design their processes to be adaptive respond more quickly to change and increase their speed of innovation. But here, too, there are risks: too much freedom can lead to inconsistencies, data chaos and a lack of governance. The central concern of this work is to show ways in which standardisation and flexibility can be understood not as opposites, but as complementary variables. The balance is achieved through modular process building blocks, adaptive workflows and ‘bound flexibility’ i.e. freedom within clear boundaries. In addition to technological expertise, the implementation of such hybrid systems requires, above all, an open corporate culture that promotes both compliance with rules and creative thinking. In this way, the AI based SoS lifecycle management approach becomes an adaptive, dynamic system that is equally focused on efficiency and innovation. The goal is to achieve a balance in which standardisation serves as the foundation and flexibility as the engine of further development a balancing act that will become a decisive competitive factor in the digitalised industry in the future.
Sustainability as a system goal: How lifecycle management supports ecological responsibility
Sustainability is no longer a secondary issue, but a central system goal of technical value creation. The focus is on a holistic view of the product life cycle from material selection, manufacturing, use, repair and upgrades to return and recycling. The approach of the AI based System of Systems Lifecycle Management of Digital Threads opens up new ways of managing environmental responsibility based on data. Instead of evaluating sustainability retrospectively, it is anchored early on in the development process through data driven methods, digital twins and networked decision making platforms. This not only allows environmental goals to be defined, but also makes them measurable and controllable. The combination of life cycle analysis (LCA), digital traceability and circular design forms the methodological backbone of this approach. ISO 14040/44 compliant LCAs provide the basis for transparency and comparability, while digital twins capture real world usage data, enabling continuous optimisation. Companies can only successfully implement sustainability as a system goal if data quality, governance and technical interoperability are guaranteed. Clear responsibilities, defined roles and the principle of ‘bounded flexibility’ i.e. binding standards with situational freedom of adaptation play a decisive role here. The aim is to understand sustainability not as an external goal, but as an integral part of technical and organisational decision making processes. The early integration of LCA findings, standardised key performance indicators (ecological, circular and maturity KPIs) and the use of digital tools creates a learning, self improving system. Research and practice show that it is precisely the linking of data, processes and responsibility that offers the greatest leverage for ecological effectiveness. In this way, sustainability becomes not just a marketing term, but a guiding principle throughout the entire life cycle from the initial idea to recycling.
From project to product: agility in the system of Systems Lifecycle Management system
The development of technical products is undergoing radical change: instead of clearly defined projects with fixed start and end points, today's market is dominated by living systems whose value only unfolds during operation, through updates and over generations. This article traces the shift ‘from project to product’ and situates it in system system of Systems Lifecycle Management (SLM) as a further development of classic PLM approaches. Agility is not understood as a set of methods, but as an attitude: iterative learning, early and continuous feedback, responsibility in interdisciplinary, self-organised teams embedded in a sustainable, modular architecture. On this basis, changes can be made in a targeted and controlled manner instead of leaving them to chance. Standards such as ISO/IEC/IEEE 15288 and current guidelines on ‘systems engineering agility’ support a view of the entire life cycle and emphasise feedback from operation and use. Technologically, agility only becomes practicable through the digital thread, end-to-end data models, MBSE and digital twins; it shortens feedback loops, makes decisions traceable and increases the ability to change without compromising quality. At the same time, limitations remain apparent: safety-critical domains, insufficiently modular architectures, cultural inertia and regulatory compliance requirements call for a conscious translation of agile principles rather than a 1:1 transfer from the software world. Practical examples such as ASEL-CM, system-driven product development, or centralised vehicle architectures show how continuous validation, platform strategies, and data-based governance make the leap possible. The common thread: stability comes from architecture, dynamism from working methods the two belong together. Those who understand SLM as a learning system closely link development, operation and service, use data as a source of knowledge and organise responsibility along the product, not along projects. In this way, the life cycle is transformed from a cost factor into a competitive advantage.
The role of simulation and co simulation in the systems engineering process
In today's system development, mechanics, electronics, software and control systems merge into a highly complex overall structure. Simulation and co simulation play a central role in this process: they make it possible to think in terms of virtual models right from the start of the development process, rather than waiting until the end to carry out tests. Simulation thus becomes a tool for understanding, decision making and risk minimisation. Co simulation extends this approach by synchronously coupling different domain models, thus creating a realistic representation of system dynamics. As a result, development is no longer based on static specifications, but on a living, evolving system model. Especially in the early concept phase, simulations provide valuable feedback on feasibility, performance and possible misconceptions. Standards such as FMI or HLA/HELICS enable the interoperability of a wide variety of tools, allowing the coupling of models from mechanics, electronics, thermodynamics and software. This creates a holistic understanding of complex interrelationships that would not be possible in individual disciplines. The integration of such models not only creates technological advantages, but also forms the basis for well founded, comprehensible decisions. Furthermore, the coupling of simulation and lifecycle management systems opens up new perspectives on sustainability. The work of Salehi and McMahon shows that the combination of parametric associative CAD systems, digital twins and AI supported lifecycle management makes ecological aspects measurable and controllable. Through the consistent digitalisation and linking of models, data and processes, sustainability becomes an integral part of systems engineering. The Munich Agile MBSE (MAGIC) concept illustrates that ecological optimisation can be achieved in iterative development cycles. Simulation thus becomes not only a technical tool, but also a strategic instrument for sustainable innovation processes. Simulation and co simulation thus mark the transition from linear development processes to dynamic, networked processes. They enable systems to be understood, optimised and designed in an ecologically responsible manner throughout their entire lifecycle.
Application of Systems Engineering in context of building 3D Environment for Autonomous Vehicle Systems in an industrial context for Central Car Computing
This paper outlines a robust framework for the creation of intelligent 3D traffic environments driven by advanced data insights. The primary objective is to enhance traffic process efficiency using virtualization and comprehensive data integration. Smart 3D environments offer a sophisticated means to replicate real-life traffic scenarios digitally, thereby supporting well-informed decision-making, optimizing traffic flows, and enabling predictive maintenance strategies. By incorporating real-time sensor data and leveraging innovative AI models, these environments build a dynamic and precise digital representation of traffic systems, enabling continuous monitoring and actionable data insights. The presented framework incorporates essential elements such as sensor networks, IoT-based systems, and AI-driven algorithms to construct an all-encompassing digital counterpart of the actual traffic environment. This digital replica facilitates the simulation of various traffic conditions, allowing for the identification and mitigation of inefficiencies before they impact real-world systems. Additionally, the implementation of these intelligent environments enables the seamless integration of novel technologies, including autonomous vehicles and connected infrastructure, into current traffic systems. Moreover, this paper emphasizes the importance of the Systems Modelling Language (SysML) for effectively organizing and managing the complex data sets required for the development and ongoing maintenance of these environments. The study’s findings illustrate that intelligent 3D traffic environments contribute significantly to improved operational efficiency, resource optimization, and adaptability within dynamic traffic ecosystems. This research provides valuable insights for engineers and scholars aiming to apply intelligent 3D modelling solutions in the domain of traffic management and urban planning.
Engineering, Model-Based Systems-Engineering, Munich Agile Concept for Systems Engineering, SysML Modelling, Systems Engineering
Application of Blockchain in case of Engineering Data Processes and Product Lifecycle Management System
In the following paper, the possibilities of the latest industry 4.0 and digitization technologies, such as the use of Blockchain (BC) in Model-Based Systems Engineering with the associated product data (PDM) and product life cycle (PLM) management information, will be considered holistically. A Blockchain Engineering approach called EngineeringBlock will be researched in Systems engineering for the management of software data and engineering data within product lifecycle management. At this point, data input refers, for example, to the uploading of software versions to a control unit in the Engineering Data like Requirements and Functions. The two most important directions of the research paper will show the use of Blockchain technology with the related PLM System. This paper will present a Framework which enables the presentation of all product-relevant requirements, functions and logical aspects of the product up to the usage phase in a holistic and consistent way.
This work will elaborate the fundamental principles of physical artificial intelligence (Physical AI ) from a scientific and systemic perspective. The aim is to create a theoretical foundation that describes the physical embodiment, sensory perception, ability to act, learning processes, and context sensitivity of intelligent systems within a coherent framework. While classical AI approaches rely on symbolic processing and data driven models, Physical AI understands intelligence as an emergent phenomenon of real interaction between body, environment, and experience. The six fundamentals presented here are embodiment, sensory perception, motor action, learning, autonomy, and context sensitivity, and form the conceptual basis for designing and evaluating physically intelligent systems. Theoretically, it is shown that these six principles do not represent loose functional modules but rather act as a closed control loop in which energy, information, control, and context are in constant interaction. This circular interaction enables a system to generate meaning not from databases, but from physical experience, a paradigm shift that understands intelligence as an embodied, material process. Physical AI understands learning not as parameter adjustment, but as a change in the structural coupling between agents and the environment. To illustrate this, the theoretical model is explained using a practical scenario: An adaptive assistant robot supports patients in a rehabilitation clinic. Thanks to its soft, sensor supported joints, it can physically sense how much weight a person can bear. It learns from resistance, movement, and body language to precisely regulate its strength and recognizes when it needs to actively intervene or hold back. This example illustrates that physical intelligence does not arise from abstract calculation, but from immediate, embodied experience. It shows how the six fundamentals interact in a real system: embodiment as a prerequisite, perception as input, movement as expression, learning as adaptation, autonomy as regulation, and context as orientation. In general, this work is intended to contribute to the theoretical and methodological foundation of a new generation of intelligent systems that act physically, learn, and act responsibly in the world. The principles formulated here are intended to serve as a guide for researchers and engineers to further develop Physical AI from an abstract idea into a practical design concept.
Integration of Vehicle Data File based on Blockchain Technologie in an industrial context
The following paper will discuss the results of dealing with future vehicle data files based on Block Chain. Future vehicles will integrate the application and adoption of different kinds of vehicle files based on Blockchain. Moreover, this paper will elaborate an industrial case of how the integration of such a technology will contribute to more vehicle data security. It is possible to ensure the communication of vehicles to the environment infrastructure which can be based on other vehicles or infrastructure equipment like traffic light or even Maut stations for exchanging information in a secure way. For that reason it is important to understand how the basic of this technology for vehicle data files can work and how the requirements for this technology can be implemented and considered in a systematic way. The atomization of such technologies will contribute to a more secure way to exchange different kinds of vehicle data in the future. The future will be based on a software defined vehicle and the secure was to exchange data files between e.g. vehicle will be one of the important aspects. This paper will contribute a literature review for Blockchain Technology and its fundamentals and functions. Based on the literature survey the requirements for an integration in the industry will be elaborated. The last section will conclude the results.
Collaboration and lifecycle management across domains and applications in a federated environment
We integrate data, applications, processes and information for optimum, cross-domain collaboration between engineers. We unify technologies and APIs from PLM and Engineering Design Tool vendors in one platform. We productize integration between engineering tools, PLM and business applications. XPLM has built up an extensive partner community over many years. We maintain OEM, technology and solution partnerships with the top providers in the PLM market. We work very closely with partners and develop future integration concepts together with them. That is how we develop industry leading integrations for the long term. Link vs. Sync: Less replication of product data. Data stays in federated applications. Link what you need: Manage cross application links that are relevant for your job and the job of colleagues involved in the project. Access securely: Explore and edit data in the federated application through system login via API and fully controlled (security, state of life and access rights).
PRODUCT LIFECYCLE MANAGEMENT AS KEY ENABLER FOR CIRCULAR ECONOMY
A Circular Economy is an alternative to a traditional linear economy (make, use, dispose) in which we keep resources in use as long as possible, extract the maximum value from them, and ultimately recycle products and materials at the end of each useful life. Sustainability is at the heart of BMW Group‘s broader strategy, encompassing all relevant aspects, from the supply chain to production and the product life cycle. Our clear claim is that the GREENEST ELECTRIC VEHICLE comes from BMW. We are reducing CO2 emissions per vehicle over the entire value chain. This is what sets the BMW Group apart.
With artificial intelligence, the second wave of digitization is picking up speed. Efficiency-enhancing solutions, new data-based business models, sustainable production – not only for corporations, but especially for SMEs. (ZVEI, AI in Industrial Automation, 12.04.2021) AI applications are now run on high-performance computers, where training, data processing and other services are bundled. Added value and applicability Extremely flexible solution through modular components, microservices and hybrid cloud system Minimal effort through reverse search from business goal to input data and concrete technical questions Fast visibility of transparent results through demonstrators and cause-and-effect analyses Easy deployment without adjustments to model or program code Easy integration into existing systems Scalability through hybrid and cloud-independent development (on-premise and cloud)
An ecological cycle: sustainable economy
In the form of future workers empowered to use knowledge and know-how to create sustainable experiences that inspire innovation. Through the use of optimized global logistics and manufacturing processes that eliminate waste in the production of sustainable experiences and enable the regeneration of natural systems. With the orchestration of value networks for greater effectiveness, productivity and agility for maximized returns and new business models. The circular economy • Implementation of deconstruction processes enables the circular economy. • Technological innovations in materials and production techniques are creating a new market for regenerated goods, which is expected to grow to $4.5 trillion by 2030. • Closed-loop recycling has the potential to significantly increase the yield and purity of precious metals, critical metals and plastics. Next steps Apply available technological solutions Use a marketplace to support the circular economy Create incentives for industry through a disassembly certification process Build mobile factories for on-site disassembly Use robots and artificial intelligence
Digital twins pave the way for a sustainable economy
Digital twins will boost sustainability and traceability in the manufacturing and process industry. The information needed for this, such as processed materials, past faults and used recipes, is already stored in a wide variety of systems. However, there is no central access point or overview of this information, which means that researching their interrelationships is only possible at great expense in terms of time and money. With an Enterprise Knowledge Graph, all relevant artifacts are linked and their dependencies and effects are made visible at an early stage. As the most native form of knowledge representation, Knowledge Graph technology makes it easy to bridge data silos, making the digital twin a reality. Increased demands for sustainability and traceability have far-reaching consequences for supply chains in the manufacturing and process industries. One example of this is the European End of Life Vehicles (ELV) Directive for the recycling of end-of-life vehicles within the European Union (EU). A second example is the deactivation of Recommendation 019 in the context of the International Material Data Systems (IMDS), which means that since March 2021, all manufactured and resold components contain an exact breakdown of their constituent parts and their respective quantities. As a digital representation of tangible and intangible business objects (e.g. products, components, assets, customers, suppliers, services or processes), digital twins are a great help in complying with these new guidelines. They also serve to capture the relationships between business objects, and thus their business context, over entire lifecycles. For example, if it becomes known that some components from a supplier exceed the maximum limit of hazardous substances, a digital twin can prevent production from having to come to a complete standstill. To do this, only the parts that contain components from the affected suppliers are identified. Depending on the material or product, this opens up new possibilities for reuse and sometimes unexpected opportunities for producers not only to comply with current guidelines, but also to expand their activities with circular business models. So what is stopping manufacturers from creating a digital twin? Consistently tracking all component- and process-relevant data, from the design to the scrapping of a vehicle, is difficult. The relevant information is stored in different data silos such as ERP systems, service applications, IoT applications, CRM systems or fleet management systems, whose structure rarely matching. A transparent and data-driven supply chain is the key to the required transparency. Linksphere offers the possibility to link all relevant artifacts of the value chain using a knowledge graph, to visualize dependencies and to provide stakeholders with the information centrally. A digital twin is thus an enterprise knowledge graph-based digital representation of the semantic context reality. This makes the supply chain transparent and enables the forward-looking control of the flow of goods and processes, compliance with guidelines and minimization of risks. We show how such a digital twin is built and used using specific customer projects in the automotive industry.
Green PLM - Extending PLM to Closed Loop Lifecyclemanagement
„DIGITAL TECHNOLOGIES ENABLING SUSTAINABILITY WITH PLM“ How can digital technologies, especially in combination with PLM, positively influence the ecological dimension of sustainability? The following contribution, from the perspective of a leading PLM vendor, will highlight the challenges that new regulations and standards, such as the Product Carbon Footprint (PCF), pose for a PLM system and the capabilities that a modern PLM system should have to meet these requirements. Using practical examples, the auditorium will be shown how to successfully transition from classic PLM to closed-loop lifecycle management and “green PLM”.