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Real-time data analytics methods are key elements to overcome the currently rigid planning and improve manufacturing processes by analysing historical data, detecting patterns and deriving measures to counteract the issues. The key element to improve, assist and optimize the process flow builds a virtual representation of a product on the shop-floor called the digital twin or digital shadow. Using the collected data requires a high data quality, therefore measures to verify the correctness of the data are needed. Based on the described issues the paper presents a real-time reference architecture for the order processing. This reference architecture consists of different layers and integrates real-time data from different sources as well as measures to improve the data quality. Based on this reference architecture, deviations between plan data and feedback data can be measured in real-time and countermeasures to reschedule operations can be applied.

Towards a Data-oriented Optimization of Manufacturing Processes - A Real-Time Architecture for the Order Processing as a Basis for Data Analytics Methods