Center for Applied Research on Supply Chain Services at Fraunhofer IIS»Process intelligence« enables companies to embed data-driven and predictive process analyses as well as operational and tactical excellence into their business processes. The data collected during process execution (IoT, Industry 4.0) is collated and analysed using a combination of management and data analytics methods. This enables process owners to recognise the causes of weaknesses, anticipate future developments, make their processes more efficient and effective and thus achieve continuously improved results.
In addition to internal company processes, process-aware models can also be used in supply chain management, for example, to proactively analyse and improve the flow of goods and information between manufacturers, suppliers and retailers.
Optimising processes with data analysis and AI
The »Process Intelligence« group develops methods for the predictive analysis of processes in order to optimise workflows in terms of time, quality and resource conservation. By analysing process data with the help of machine learning and process mining, we can gain insights into the process and its structure and predict future developments such as delays or errors. We combine this knowledge with specific process knowledge in production, logistics and transport as well as with classic methods of business process management, benchmarking and root cause analysis in order to provide customised recommendations for improving processes. These recommendations can be used both for controlling and implementing the process in the form of monitoring and suggestion systems.