In manufacturing companies, many types of data are collected, produced and often only partially stored – whether by ERP systems, sensor and machine data, order entry, permanent data streams or external data interfaces. However, the digital systems only partially or not interlock. Thus, it is often not possible to derive valuable information from the existing data.

By merging all the data into one central system, short-term changes in production can be analysed and processed in real time. Future events can be derived from the collected data pools and predictions can be made as accurately. With modern data and visual analytics methods and machine learning techniques, valuable information is derived from this data. As a result, correlations, correlations and patterns can be identified, which can be used for error and cause analysis as well as for continuous quality monitoring and improvement.

With its individual software solutions, the unit Logistics Informatics directly supports the technical experts who do not have to be IT specialists to make their own data usable. They can independently combine the entire knowledge of one or more machines into a common knowledge base and gain better understanding through data analytics. Thus, cause-and-effect relationships due to anomalies and patterns can be identified, for example, via the machine state. This reduces failures, improves quality, optimizes maintenance intervals and thus increases efficiency.