Abstract
The One-of-a-kind production is characterized by manual and specialized manufacturing processes. Human adaptability is essential here. As a result, production data acquisition is mainly manual. In many cases, the recorded data are inaccurate or even subject to errors. Consequently, the opportunities for process optimization are impeded. This article therefore analyzes data quality problems and their causes. Subsequently, methods for the evaluation of process data such as process mining and performance indicators are considered and applied to production data sets. The comparison enables the derivation of organizational measures. The result is a practice-oriented method for continuously checking the data quality of manual production data acquisition.
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