  Predictive business process monitoring (PBPM) techniques predict future
process behaviour based on historical event log data to improve operational
business processes. Concerning the next activity prediction, recent PBPM
techniques use state-of-the-art deep neural networks (DNNs) to learn predictive
models for producing more accurate predictions in running process instances.
Even though organisations measure process performance by key performance
indicators (KPIs), the DNN`s learning procedure is not directly affected by
them. Therefore, the resulting next most likely activity predictions can be
less beneficial in practice. Prescriptive business process monitoring (PrBPM)
approaches assess predictions regarding their impact on the process performance
(typically measured by KPIs) to prevent undesired process activities by raising
alarms or recommending actions. However, none of these approaches recommends
actual process activities as actions that are optimised according to a given
KPI. We present a PrBPM technique that transforms the next most likely
activities into the next best actions regarding a given KPI. Thereby, our
technique uses business process simulation to ensure the control-flow
conformance of the recommended actions. Based on our evaluation with two
real-life event logs, we show that our technique`s next best actions can
outperform next activity predictions regarding the optimisation of a KPI and
the distance from the actual process instances.

Artificial Intelligence;Software Engineering