Abstract
The usage of simulation applications for the planning and the designing of processes in many fields of production technology facilitated the formation of large data pools. With the help of these data pools, the simulated processes can be analyzed with regard to different objective criteria. The considered use cases have their origin in questions arising in various fields of production technology, e.g. manufacturing procedures to the logistics of production plants.
The deployed simulation applications commonly focus on the object of investigation. However, simulating and analyzing a process necessitates the usage of various applications, which requires the interchange of data between these applications. The problem of data interchange can be solved by using either a uniform data format or an integration system. Both of these approaches have in common that they store the data, which are interchanged between the deployed applications. The data’s storage is necessary with regard to their analysis, which, in turn, is required to obtain an added value of the interchange of data between various applications that is e.g. the determining of optimization potentials. The examination of material flows within a production plant might serve as an example of analyzing gathered data from an appropriate simulated process to determine, for instance, bottle necks in these material flows.
The efforts undertaken to support such analysis tools for simulated processes within the field of production engineering are still at the initial stage. A new and contrasting way of implementing the analyses aforementioned consists in focusing on concepts and methods belonging to the subject area of Business Intelligence, which address the gathering of information taken from company processes in order to gain knowledge about these.
This paper focusses on the approach mentioned above. With the help of a concrete use case taken from the field of factory planning, requirements on a data-based support for the analysis of the considered planning process are formulated. In a further step, a design for the realization of these requirements is presented. Furthermore, expected challenges are pointed out and discussed.
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Reinhard, R., Büscher, C., Meisen, T., Schilberg, D., Jeschke, S. (2012). Virtual Production Intelligence – A Contribution to the Digital Factory. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33509-9_70
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DOI: https://doi.org/10.1007/978-3-642-33509-9_70
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