Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-21T04:43:11.505Z Has data issue: false hasContentIssue false

EVALUATING MBSE POTENTIAL USING PRODUCT AND DEVELOPMENT CHARACTERISTICS – A STATISTICAL INVESTIGATION

Published online by Cambridge University Press:  11 June 2020

M. Schöberl*
Affiliation:
Technical University of Munich, Germany
E. Rebentisch
Affiliation:
Massachusetts Institute of Technology, United States of America
J. Trauer
Affiliation:
Technical University of Munich, Germany
M. Mörtl
Affiliation:
Technical University of Munich, Germany
J. Fottner
Affiliation:
Technical University of Munich, Germany

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

As model-based systems engineering (MBSE) is evolving, the need for evaluating MBSE approaches grows. Literature shows that there is an untested assertion in the MBSE community that complexity drives the adoption of MBSE. To assess this assertion and support the evaluation of MBSE, a principal component analysis was carried out on eight product and development characteristics using data collected in an MBSE course, resulting in organizational complexity, product complexity and inertia. To conclude, the method developed in this paper enables organisations to evaluate their MBSE adoption potential.

Type
Article
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), 2020. Published by Cambridge University Press

References

Field, A. (2009), Discovering Statistics Using SPSS, Thousand Oaks, CA, sage, 9781847879073.Google Scholar
Friedenthal, S., Moore, A. and Steiner, R. (2015), A practical guide to SysML: the systems modeling language, Burlington, MA, Morgan Kaufmann. 0128008008Google Scholar
Huldt, T. and Stenius, I. (2019), “State-of-practice survey of model-based systems engineering”, Systems Engineering, Vol. 22, pp. 134145. https://doi.org/10.1002/sys.21466CrossRefGoogle Scholar
Hutcheson, G.D. and Sofroniou, N. (1999), The multivariate social scientist: Introductory statistics using generalized linear models, Thousand Oaks, CA, Sage. 0761952012CrossRefGoogle Scholar
INCOSE (2007), Systems engineering vision 2020, San Diego, CA, INCOSE. Available at: http://www.ccose.org/media/upload/SEVision2020_20071003_v2_03.pdfGoogle Scholar
INCOSE (2014), A world in motion: systems engineering vision 2025, San Diego, CA, INCOSE. Available at: https://www.incose.org/docs/default-source/aboutse/se-vision-2025.pdfGoogle Scholar
Madni, A.M. and Purohit, S. (2019), “Economic Analysis of Model-Based Systems Engineering”, Systems, Vol. 7, pp. 118. https://doi.org/10.3390/systems7010012Google Scholar
Madni, A.M. and Sievers, M. (2018), “Model-based systems engineering: Motivation, current status, and research opportunities”, Systems Engineering, Vol. 21, pp. 172190. https://doi.org/10.1002/sys.21438CrossRefGoogle Scholar
Reichwein, A. and Paredis, C. (2011), “Overview of architecture frameworks and modeling languages for model-based systems engineering”, Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, 2011, New York, NY, 1-9.Google Scholar
Scheeren, I. and Pereira, C.E. (2014), “Combining Model-Based Systems Engineering, Simulation and Domain Engineering in the Development of Industrial Automation Systems: Industrial Case Study”, IEEE 17th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing, IEEE, 2014. pp. 40-47. https://doi.org/10.1109/ISORC.2014.64CrossRefGoogle Scholar
Sheard, S. et al. (2015), “A complexity primer for systems engineers”, INCOSE Complex Systems Working Group White Paper, Vol. 1, pp. 110.Google Scholar