@inproceedings{63652,
  abstract     = {{In dynamic environments, product management plays a key role in aligning innovation, customer needs, and strategic decision-making. Digitalization offers significant opportunities to enhance this role by enabling data-driven insights for improved customer and product understanding—yet its successful implementation requires a fundamental transformation. Based on a systematic literature review, this study synthesizes key advantages, challenges, and design fields that shape this transformation. The results highlight performance benefits across business, product, process, and decision-making dimensions, while also uncovering barriers rooted in strategy, organization, people, and technology. To address these barriers, critical enablers and conditions for success are identified. Four overarching design fields provide orientation for structuring digitalization efforts and guiding organizational change in industrial practice. The paper provides both a conceptual foundation and a practical guide for companies seeking to digitalize their product management effectively.}},
  author       = {{Fichtler, Timm and Petzke, Lisa Irene and Grigoryan, Khoren and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the 59th Hawaii International Conference on System Sciences}},
  location     = {{Maui, Hawaii}},
  title        = {{{Enhancing Product Management Performance through Digitalization: Advantages, Challenges, Design Fields}}},
  year         = {{2026}},
}

@inproceedings{64075,
  author       = {{Humpert, Lynn and Graunke, Jannis and Cichon, Gerrit and Ammanagi, Anuradha and Schierbaum, Anja and Dumitrescu, Roman}},
  booktitle    = {{2025 IEEE International Symposium on Systems Engineering (ISSE)}},
  publisher    = {{IEEE}},
  title        = {{{Generative AI in Systems Engineering: Automated Creation of System Architectures and Early-Stage Calculation in the B2B Sector}}},
  doi          = {{10.1109/isse65546.2025.11370000}},
  year         = {{2026}},
}

@article{64223,
  abstract     = {{<jats:p>The complexity and interconnectivity of modern automotive systems are rapidly increasing, particularly with the rise of distributed and cooperative driving functions. These developments increase exposure to a range of disruptions, from technical failures to cyberattacks, and demand a shift towards resilience-by-design. This study addresses the early integration of resilience into the automotive design process by proposing a structured method for identifying gaps and eliciting resilience requirements. Building upon the concept of resilience scenarios, the approach extends traditional hazard and threat analyses as defined in ISO 26262, ISO 21448 and ISO/SAE 21434. Using a structured, graph-based modeling method, these scenarios enable the anticipation of functional degradation and its impact on driving scenarios. The methodology helps developers to specify resilience requirements at an early stage, enabling the integration of resilience properties throughout the system lifecycle. Its practical applicability is demonstrated through an example in the field of automotive cybersecurity. This study advances the field of resilience engineering by providing a concrete approach for operationalizing resilience within automotive systems engineering.</jats:p>}},
  author       = {{Mpidi Bita, Isaac and Ugur, Elif and Hovemann, Aschot and Dumitrescu, Roman}},
  issn         = {{1999-5903}},
  journal      = {{Future Internet}},
  number       = {{1}},
  publisher    = {{MDPI AG}},
  title        = {{{Resilience-by-Design: Extracting Resilience Requirements Using the Resilience Graph in the Automotive Concept Phase}}},
  doi          = {{10.3390/fi18010051}},
  volume       = {{18}},
  year         = {{2026}},
}

@inproceedings{64224,
  author       = {{Yee, Jingye and Hermelingmeier, Dominik and Thederajan, Abishai Asir A. and Low, Cheng Yee and Gossen, Alexander and Dumitrescu, Roman}},
  booktitle    = {{2025 IEEE International Symposium on Systems Engineering (ISSE)}},
  publisher    = {{IEEE}},
  title        = {{{System Architecture and Analytical Inverse Kinematics for Autonomous Docking of Passenger Boarding Bridges}}},
  doi          = {{10.1109/isse65546.2025.11370093}},
  year         = {{2026}},
}

@inproceedings{64226,
  author       = {{Hermelingmeier, Dominik and Graunke, Jannis and Menne, Leon and Schierbaum, Anja Maria and Dumitrescu, Roman}},
  booktitle    = {{2025 IEEE International Symposium on Systems Engineering (ISSE)}},
  publisher    = {{IEEE}},
  title        = {{{Process Model for the Development of Physical Prototypes in Context of Hardware Start-Ups using Maker Systems Engineering}}},
  doi          = {{10.1109/isse65546.2025.11370109}},
  year         = {{2026}},
}

@inproceedings{64225,
  author       = {{Grote, Eva-Maria and Koldewey, Christian and Voelk, Thomas Alexander and Schwarz, Stefan Eric and Dumitrescu, Roman and Albers, Albert}},
  booktitle    = {{2025 IEEE International Symposium on Systems Engineering (ISSE)}},
  publisher    = {{IEEE}},
  title        = {{{From Generic to Specific: Scalable Role Modeling for Engineering Advanced Systems}}},
  doi          = {{10.1109/isse65546.2025.11370103}},
  year         = {{2026}},
}

@inproceedings{64221,
  author       = {{Lick, Jonas and Kattenstroth, Fiona and van der Valk, Hendrik and Trienens, Malte and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{2025 Winter Simulation Conference (WSC)}},
  publisher    = {{IEEE}},
  title        = {{{Characterizing Digital Factory Twins: Deriving Archetypes for Research and Industry}}},
  doi          = {{10.1109/wsc68292.2025.11338979}},
  year         = {{2026}},
}

@inproceedings{64228,
  author       = {{Hanke, Fabian and von Heißen, Oliver and Feld, Markus and Heuwinkel, Tim and Hovemann, Aschot and Dumitrescu, Roman}},
  booktitle    = {{2025 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}},
  publisher    = {{IEEE}},
  title        = {{{Cross-View Trace Link Prediction with Multi-Feature GNNs: Creating and maintaining Traceability from Requirements to Components}}},
  doi          = {{10.1109/ictmod66732.2025.11371884}},
  year         = {{2026}},
}

@inproceedings{64227,
  author       = {{Könemann, Ulf and Niemeyer, Marcel and Schierbaum, Anja and Dumitrescu, Roman}},
  booktitle    = {{2025 IEEE 4th German Education Conference (GECon)}},
  publisher    = {{IEEE}},
  title        = {{{Status quo and challenges of professional Systems Engineering education in industrial practice}}},
  doi          = {{10.1109/gecon64629.2025.11369324}},
  year         = {{2026}},
}

@article{59512,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:p>Große Sprachmodelle wie GPT-4 bieten erhebliche Potenziale für das Systems Engineering. Prompt-Engineering ermöglicht einen flexiblen Einsatz im Anforderungsmanagement, Systementwurf und in der Integration, Verifikation und Validierung ohne aufwendiges Modelltraining. Die Formulierung von Prompts und die Anwendung fortschrittlicher Techniken erfordern jedoch tiefes Domänenwissen. Der Beitrag zeigt Potenziale und Herausforderungen dieser Technik auf und illustriert praktische Anwendungsbeispiele</jats:p>}},
  author       = {{Hovemann, Aschot and Bita, Isaac Mpidi and Aldade, Abed Alrahman and von Heißen, Oliver and Dumitrescu, Roman}},
  issn         = {{2511-0896}},
  journal      = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}},
  number       = {{s1}},
  pages        = {{101--106}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Prompt Engineering im Systems Engineering}}},
  doi          = {{10.1515/zwf-2024-0139}},
  volume       = {{120}},
  year         = {{2025}},
}

@article{59513,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:p>The increasing complexity of modern technical systems necessitates innovative approaches such as Model-Based Systems Engineering (MBSE). In this context, using Artificial Intelligence (AI) emerges as a key enabler for practical application and efficiency improvement. This article introduces a maturity model for AI-based assistance systems in MBSE. It helps companies assess their current automation level in MBSE activities, providing a foundation for strategic planning of process improvements.</jats:p>}},
  author       = {{Bernijazov, Ruslan and Dumitrescu, Roman and Hanke, Fabian and von Heißen, Oliver and Kaiser, Lydia and Tissen, Denis}},
  issn         = {{2511-0896}},
  journal      = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}},
  number       = {{s1}},
  pages        = {{96--100}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{AI-Augmented Model-Based Systems Engineering}}},
  doi          = {{10.1515/zwf-2024-0123}},
  volume       = {{120}},
  year         = {{2025}},
}

@inproceedings{59514,
  author       = {{Humpert, Lynn and Zagatta, Kristin and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the 2024 7th International Conference on Computational Intelligence and Intelligent Systems}},
  publisher    = {{ACM}},
  title        = {{{Systems Engineering and Validation: A systematic literature review}}},
  doi          = {{10.1145/3708778.3708793}},
  year         = {{2025}},
}

@inbook{60156,
  author       = {{Ködding, Patrick and Jahn, Mathis and Dumitrescu, Roman and Koldewey, Christian}},
  booktitle    = {{New Digital Work II - Digital Sovereignty of Companies and Organizations}},
  editor       = {{Schmuntzsch, Ulrike and Shajek, Alexandra and Hartmann, Ernst Andreas}},
  isbn         = {{9783031699931}},
  pages        = {{93--108}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Challenges for Scenario-Based Foresight and Potential for Digital Technologies: Insights from Practice}}},
  doi          = {{10.1007/978-3-031-69994-8}},
  year         = {{2025}},
}

@inproceedings{60160,
  author       = {{Förster, Felix and Bausen, Steffen and Bernijazov, Ruslan and Koldewey, Christian and Dumitrescu, Roman and Bursac, Nikola}},
  booktitle    = {{Stuttgarter Symposium für Produktentwicklung SSP 2025}},
  editor       = {{Hölzle, Katharina and Kreimeyer, Matthias and Roth, Daniel and Maier, Thomas and Riedel, Oliver}},
  location     = {{Stuttgart}},
  pages        = {{102--113}},
  publisher    = {{Fraunhofer IAO, Stuttgart}},
  title        = {{{Chances of smart Views: Integration of Stakeholder perspectives using videobased Views in MBSE}}},
  doi          = {{10.18419/opus-16366}},
  year         = {{2025}},
}

@inproceedings{58763,
  abstract     = {{Utilizing data is crucial for economic success, but a lack of interoperability and concerns about the misuse of ones own data are hindering the cross-organizational use of data. Dataspaces provide the infrastructure necessary to integrate heterogeneous data sources within an organization or ecosystem, enabling seamless data interaction and interoperability. In addition, data spaces strengthen data sovereignty through their decentralized nature, which enables organizations to effectively control and manage their data. However, challenges persist in managing the complexity and dynamic nature of dataspaces, requiring significant resources and technical expertise. The decentralized nature leads to a large and diverse number of stakeholders, who need to agree on the use and scope of a dataspace. Modeling is a common approach to cope with technical complexity and heterogeneous stakeholders. In this paper, we propose a version of SysML and a corresponding method that focus on the modelling of data spaces. We provide a dataspace modelling method to unify the understanding of dataspaces and scope among all stakeholders to simplify the design and development process.}},
  author       = {{Kulkarni, Pranav Jayant and Zerbin, Julian and Koldewey, Christian and Bernijazov, Ruslan and Dumitrescu, Roman}},
  booktitle    = {{2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}},
  keywords     = {{Dataspaces, Modelling, SysML, Gaia-X, System Specification}},
  location     = {{Sharjah, United Arab Emirates }},
  publisher    = {{IEEE}},
  title        = {{{Using SysML as a Modelling Language for Dataspaces}}},
  doi          = {{10.1109/ictmod63116.2024.10878227}},
  year         = {{2025}},
}

@inproceedings{64219,
  author       = {{Graunke, Jannis and Bita, Isaac Mpidi and Hermelingmeier, Dominik and Humpert, Lynn and Schierbaum, Anja and Dumitrescu, Roman}},
  booktitle    = {{2025 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)}},
  publisher    = {{IEEE}},
  title        = {{{Evolving Modularity: Rethinking Architecture in Product and Organizational Systems}}},
  doi          = {{10.1109/rasse64831.2025.11315315}},
  year         = {{2025}},
}

@inproceedings{64220,
  author       = {{Disselkamp, Jan-Philipp and Seidenberg, Tobias and Lick, Jonas and Dumitrescu, Roman}},
  booktitle    = {{2025 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)}},
  publisher    = {{IEEE}},
  title        = {{{24 Reasons for the Failure of Integrative and Integrated Product and Production System Development Methods}}},
  doi          = {{10.1109/rasse64831.2025.11315397}},
  year         = {{2025}},
}

@article{64222,
  author       = {{Ködding, Patrick and Koldewey, Christian and Dumitrescu, Roman}},
  issn         = {{0360-8581}},
  journal      = {{IEEE Engineering Management Review}},
  pages        = {{1--9}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Tailoring scenario projects with method and tool building blocks}}},
  doi          = {{10.1109/emr.2025.3646110}},
  year         = {{2025}},
}

@inproceedings{64229,
  abstract     = {{<jats:title>ABSTRACT:</jats:title><jats:p>This paper presents the MBSE-Graph-RAG framework to address key challenges in Model-Based Systems Engineering (MBSE). Traditional MBSE tools suffer from usability barriers, limited accessibility, and integration challenges. By combining knowledge graphs with Retrieval-Augmented Generation (RAG), the proposed framework enables AI-Augmented engineering through natural language interactions and automated system architecture generation. A systematic literature review establishes a solid research foundation, identifying gaps in AI-assisted MBSE. Key contributions include a structured MBSE-Graph interface, improved usability via Large Language Models (LLMs), and automated graph construction aligned with SysML. A proof-of-concept demonstrates the potential of this approach to enhance MBSE by reducing complexity, improving data accessibility, and supporting engineering collaboration.</jats:p>}},
  author       = {{Hanke, Fabian and Mpidi Bita, Isaac  and von Heißen, Oliver  and Weller, Julian and Hovemann, Aschot and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  pages        = {{439--448}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{AI-augmented systems engineering: conceptual application of retrieval-augmented generation for model-based systems engineering graph}}},
  doi          = {{10.1017/pds.2025.10058}},
  volume       = {{5}},
  year         = {{2025}},
}

@article{64253,
  abstract     = {{<jats:p>This paper presents a comprehensive design framework for Enterprise Architecture aimed at facilitating decision-driven analytics in smart factories. The motivation behind this research lies in challenges faced by manufacturing companies, such as skilled labor shortages and increasing global competition, alongside the imperative for sustainable production. This journal provides a novel approach for designing and documenting prescriptive analytics use cases in manufacturing environments. The framework addresses the need for effective integration of advanced data analytics and prescriptive analytics solutions within existing production environments, thereby enhancing operational efficiency and decision-making processes. A Design Science Research approach is used to iteratively derive a framework based on stakeholder needs and activities along the prescriptive analytics use case development cycle. The resulting framework is demonstrated and evaluated in an IoT Factory setup in a research facility. From a practical perspective, the framework supports manufacturing companies in systematically designing prescriptive analytics use cases. From a research perspective, it contributes to the body of knowledge on Enterprise Architecture Management (EAM) by operationalizing the design of prescriptive analytics use cases in manufacturing contexts. The main contributions of this study include the development of a framework that supports the planning, design, and integration of prescriptive analytics use cases. This framework fosters interdisciplinary collaboration and aids in managing the complexity of data-driven projects.</jats:p>}},
  author       = {{Weller, Julian and Dumitrescu, Roman}},
  issn         = {{2079-9292}},
  journal      = {{Electronics}},
  number       = {{21}},
  publisher    = {{MDPI AG}},
  title        = {{{Decision-Driven Analytics in Smart Factories: Enterprise Architecture Framework for Use Case Specification and Engineering (FUSE)}}},
  doi          = {{10.3390/electronics14214271}},
  volume       = {{14}},
  year         = {{2025}},
}