Abstract
Flexible manufacturing systems, as a vision of Industry 4.0, depend on the collaboration of domain experts coming from different engineering disciplines. These experts often depend on (interdisciplinary) results from previous engineering phases and require an explicit representation of knowledge on relationships between products and production systems. However, production systems engineering organizations, which are set in a multidisciplinary environment, rather than focusing on process analysis and improvement options ranging over multiple disciplines, focus mostly on one particular discipline and neglect collaborations between several workgroups. In this chapter, we investigate requirements for the product/ion (i.e., product and production process)-aware analysis of engineering processes to improve the engineering process across workgroups. We, therefore, consider the following three aspects: (1) engineering process analysis methods; (2) artifact and data modeling approaches, from business informatics and from production systems engineering; and (3) persistent representation of product/ion-aware engineering knowledge and data. We extend existing work on business process analysis methods and BPMN 2.0 to address their limited capabilities for product/ion-aware process analysis. We evaluate the resulting contributions in a case study with domain experts from a large production system engineering company. We conclude that an improved product/ion-aware knowledge representation facilitates traceable design decisions as foundation for better quality assurance in the engineering process.
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The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged.
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Kathrein, L., Lüder, A., Meixner, K., Winkler, D., Biffl, S. (2019). Product/ion-Aware Analysis of Collaborative Systems Engineering Processes. In: Biffl, S., Eckhart, M., Lüder, A., Weippl, E. (eds) Security and Quality in Cyber-Physical Systems Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-25312-7_7
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