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AI-based analysis and linking of technical and organisational data using graph models as a basis for decision-making in systems engineering

Published online by Cambridge University Press:  16 May 2024

Sebastian Katzung ,
Hüseyin Cinkaya ,
Umut Volkan Kizgin ,
Alexander Savinov ,
Julian Baschin  and
Thomas Vietor
Sebastian Katzung
Affiliation:
ENBACE GmbH, Germany
Hüseyin Cinkaya
Affiliation:
ENBACE GmbH, Germany
Umut Volkan Kizgin*
Affiliation:
Technische Universität Braunschweig, Germany
Alexander Savinov
Affiliation:
Siemens Mobility GmbH, Germany
Julian Baschin
Affiliation:
Technische Universität Braunschweig, Germany
Thomas Vietor
Affiliation:
Technische Universität Braunschweig, Germany
*
u.kizgin@tu-braunschweig.de

Abstract

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The increased complexity of development projects surpass the capabilities of existing methods. While Model Based Systems Engineering pursues technically holistic approaches to realize complex products, aspects of organization as well as risk management, are still considered separately. The identification and management of risks are crucial in order to take suitable measures to minimize adverse effects on the project or the organization. To counter this, a new graph-based method and tool using AI, tailored to the needs of complex development projects and organizations is introduced here.

Keywords

risk managementmodel-based systems engineering (MBSE)graph theoryartificial intelligence (AI)product lifecycle management (PLM)

Information

Type
Systems Engineering and Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2625 - 2634
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Abdoun, N. and Chami, M. (2022). Automatic text classification of pdf documents using nlp techniques. Incose International Symposium, 32(1), 1320-1331. https://doi.org/10.1002/iis2.12997CrossRefGoogle Scholar
Bajaj, M., Bckhaus, J., Waikar, M., Zwemer, D. Schreiber, C. (2017), Graph-Based Digital Blueprint for Model Based Engineering of Complex Systems, 27th Annual INCOSE International Symposium (IS 2017) Jul 15-20, 2017, Adelaide, Australia, https://doi.org/10.1002/j.2334-5837.2017.00351.xCrossRefGoogle Scholar
Boehm, B. (2006), "Some Future Trends and Implications for Systems and Software Engineering Processes, Systems Engineering", Vol. 9, No. 1, Wiley InterScience, https://doi.org/10.1002/sys.20044CrossRefGoogle Scholar
Bosch, J. (2014) Continuous Software Engineering: An Introduction. In: Bosch, J. (eds) Continuous Software Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-11283-1_1CrossRefGoogle Scholar
Charfi, A., Kochbati, T. and Mraidha, C. (2023). From research to practice: does ai promote or prevent the use of an mbse tool?, https://doi.org/10.5121/csit.2023.131202CrossRefGoogle Scholar
Chucholowski, N. and Lindemann, U. (2015), An Initial Metamodel to Evaluate Potentials for Graphbased Analyses of Product Development Projects, 7th International Dependency and Structure Modeling Conference, Texas, USACrossRefGoogle Scholar
Delligatti, L. (2013), SysML Distilled: A Brief Guide to the Systems Modeling Language. United States: U.S. Corporate and government Sales, 2013.Google Scholar
Estefan, J. A. (2008), Survey of model-based systems engineering (MBSE) methodologies, INCOSE MBSE Initiative.Google Scholar
Filomonov, M., Oraifige, I., Vijay, V. (2020), "A novel graph-based modelling approach for reducing complexity in modelbased systems engineering environment", International Journal of System of Systems Engineering, http://dx.doi.org/10.1504/IJSSE.2020.109142CrossRefGoogle Scholar
Filomonov, M., Raju, P. and Chapman, C. (2016), "Graph-based modelling of systems interaction in model-based systems engineering environment", 7th International Systems & Concurrent Engineering for Space Applications Conference, Madrid, SpainGoogle Scholar
Forsgren, N., Humble, J. and Kim, G. (2018), Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations, IT Revolution Press.Google Scholar
Hagedorn, T., Bone, M., Kruse, B., Grosse, I., & Blackburn, M. (2020). Knowledge representation with ontologies and semantic web technologies to promote augmented and artificial intelligence in systems engineering. Insight, 23(1), 15-20. https://doi.org/10.1002/inst.12279CrossRefGoogle Scholar
Haraty, R. and Hu, G. (2018), "Software process models: a review and analysis", International Journal of Engineering & Technology, 7 (2.28) 325-331, https://doi.org/10.14419/ijet.v7i2.29.13206Google Scholar
Harrison, W. (2016), "The Role of Graph Theory in System of Systems Engineering", https://doi.org/10.1109/ACCESS.2016.2559450Google Scholar
Huth, T. and Vietor, T. (2020), Systems Engineering in der Produktentwicklung: Verständnis, Theorie und Praxis aus ingenieurswissenschaftlicher Sicht. 125–130 (2020), https://doi.org/10.1007/s11612-020-00505-1CrossRefGoogle Scholar
INCOSE Technical Operations (2007), Systems Engineering Vision 2020, INCOSE-TP-2004-004-02Google Scholar
ISO (2017), ISO 12207:2017 Systems and software engineering -Software life cycle processesGoogle Scholar
ISO (2023), ISO 15288:2023, Systems and software engineering - System life cycle processesGoogle Scholar
Mayvan, B. and Rasoolzagedan, A. (2017), "Design pattern detection based on the graph theory", Knowledge-Based Systems 120 (2017) 211–225, http://dx.doi.org/10.1016/j.knosys.2017.01.007CrossRefGoogle Scholar
Peterson, T. (2015), "Understanding Systems through Graph Theory and Dynamic Visualization", Ground Vehicle Systems Engineering and Technology SymposiumGoogle Scholar
Peterson, T. (2016), "Exploring System Space with Graph Theory ", 26th Annual INCOSE International Symposium, Edinburgh, Scotland, UK, https://doi.org/10.1002/j.2334-5837.2016.00215.xCrossRefGoogle Scholar
Project Management Institute (2017). A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Sixth EditionGoogle Scholar
Sargut, G. and McGrath, R. G. (2011), "Learning to Live with Complexity", Harvard Business Review, 89, 68-76.Google ScholarPubMed
Schneider, B., Riedel, O., & Bauer, W. (2022), "Review: model-based systems engineering and artificial intelligence for engineering of sustainable systems", 37-59. https://doi.org/10.30844/wagb_2022_3CrossRefGoogle Scholar
VDI (2021), VDI/VDE 2206: Entwicklung mechatronischer und cyber-physischer Systeme, Verein Deutsche Ingenieure, Verband der Elektrotechnik Elektronik InformationstechnikGoogle Scholar