Abstract
In this article, a method for a scalable autonomous data acquisition for an analysis and optimization of production systems based on interpretation of the material flow within small and medium-sized manufacturing enterprises is presented. The data is acquired locally and combined centrally to interpret the material flow as a basis for the optimization of the material flow as well as individual processes. When it is not completely observable for efficiency reasons or due to technical restrictions, one can also reconstruct relevant but unobservable system behavior based on system knowledge and actual measurements. A validation of the method is carried out in a company maintaining engines. The application of the model shows that with the presented method it is possible to reduce buffers in the production, optimize transportation routes and reduce waiting and therefore cycle times in job shop productions for an increasing productivity.
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