In the context of cross-disciplinary and cross-company cooperation, several challenges in developing manufacturing systems are revealed through industrial use cases. To tackle these challenges, two propositions are used in parallel. First, coupling technical models representing different content areas facilitates the detection of boundary crossing consequences, either by using a posteriori or a priori connection. Second, it is necessary to enrich these coupled technical models with team and organizational models as interventions focusing on the collaboration between individuals and teams within broader organizational conditions. Accordingly, a combined interdisciplinary approach is proposed. The feasibility and benefits of the approach is proven with an industrial use case. The use case shows that inconsistencies among teams can be identified by coupling engineering models and that an integrated organizational model can release the modelling process from communication barriers.

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.