TL;DR
This paper introduces a prescriptive process monitoring method that recommends optimized next actions aligned with KPIs, using simulation to ensure process conformance, thereby improving process performance over traditional activity prediction methods.
Contribution
It proposes a novel prescriptive monitoring approach that transforms activity predictions into KPI-optimized actions using process simulation, filling a gap in existing predictive methods.
Findings
Next best actions outperform activity predictions in KPI optimization.
The approach maintains process conformance through simulation.
Evaluation on real logs shows improved process performance.
Abstract
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…
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