Process-aware Learning

Processes in companies are diverse and complex. The production of different products, inter-or intra-company logistic processes, or other serial event sequences within companies have one thing in common: they can be traced on the basis of a documentation of the individual process steps. Usually, companies have domain experts for each department’s processes who use their experience and knowledge to plan and control these steps. However, with increasing complexity and diversity of processes, efficient planning and control is becoming more difficult or even impossible for human decision makers. In the the focus of Process-aware Learning, information documented on the data side, which is contained in the flow and execution of any process, should be integrated into AI-enhanced models. This should be accomplished in a way that is useful and as interpretable as possible for non-expert users. These models are used to identify important factors influencing the process, various process key figures, or anomalies in the process, and, based on these insights, to make forecasts or recommendations for action tailored to the process flows.