Process mining is increasingly being used to gain insights into processes based on operational data.
Recently, approaches have been researched as to how these findings can be automatically transferred into process-regulating actions during system operation to correct deviations between the actual and target process in real time. However, the implementation of such action-oriented process mining mechanisms requires sufficient testing of the implemented actions in the application to prevent undesirable side effects in the real system. This article explains how discrete-event simulation in production and logistics can be used to mitigate risks in the context of implementing action-oriented process mining through the use of an emulation model. For this purpose, we present simulation-enhanced action-oriented process mining as well as a proof-of-concept implementation based on a use case.