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In an increasingly turbulent environment, convincing methods of production planning and control are required. Discrete event simulation has proven to be a suitable tool to fulfill this demand. However, as flows of materials are often influenced by ad-hoc decisions of workers, the quality of simulation models depends on the conformance of real-world decision strategies to the ones simulated. This article proposes a method for identifying dynamic workflows caused by decisions of human workers using methods of pattern recognition. For this purpose, underlying data from production data acquisition (PDA) shall be analyzed to identify patterns and to recognize human strategies and provide them for an automated generation of simulation models.
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Reinhart et al. (2008) studied this question.
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