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Inconsistency Management in Heterogeneous Models - An Approach for the Identification of Model Dependencies and Potential Inconsistencies

Part of: Systems Engineering

Published online by Cambridge University Press:  26 July 2019

Niklas Kattner ,
Harald Bauer ,
Mohammad R. Basirati ,
Minjie Zou ,
Felix Brandl ,
Birgit Vogel-Heuser ,
Markus Böhm ,
Helmut Krcmar ,
Gunther Reinhart  and
Udo Lindemann

Abstract

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In today's engineering projects, interdisciplinary work leads to an increase in interfaces between different departments and domains. As each stakeholder pursues different goals and tasks, a heterogeneous model landscape is required. In each domain, a variety of different model and software implementations provide the essential basis for efficient work. On the interfaces, the risk of model inconsistencies increases. To handle occurring inconsistencies, various approaches have been presented. For model-based systems engineering projects, rule-based methods are considered as the most suitable technique. However, said approaches require a high manual effort in identifying model dependencies and establishing consistency rules. Unfortunately, in particular these steps are not well described and supported. Therefore, this paper presents an easily applicable approach for the identification of model dependencies in interdisciplinary projects. The method is supported by a software implementation and is directly integrated in engineering workflows. A first industrial case study has shown positive effects of the approach and revealed further research goals.

Keywords

Inconsistency ManagementProduct-Service Systems (PSS)Case studyDesign learning
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 3661 - 3670
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) 2019

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