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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)
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.

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