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Predictive process monitoring is a family of techniques to analyze eventsproduced during the execution of a business process in order to predict thefuture state or the final outcome of running process instances. Existingtechniques in this field are able to predict, at each step of a processinstance, the likelihood that it will lead to an undesired outcome.Thesetechniques, however, focus on generating predictions and do not prescribe whenand how process workers should intervene to decrease the cost of undesiredoutcomes. This paper proposes a framework for prescriptive process monitoring,which extends predictive monitoring with the ability to generate alarms thattrigger interventions to prevent an undesired outcome or mitigate its effect.The framework incorporates a parameterized cost model to assess thecost-benefit trade-off of generating alarms. We show how to optimize thegeneration of alarms given an event log of past process executions and a set ofcost model parameters. The proposed approaches are empirically evaluated usinga range of real-life event logs. The experimental results show that the netcost of undesired outcomes can be minimized by changing the threshold forgenerating alarms, as the process instance progresses. Moreover, introducingdelays for triggering alarms, instead of triggering them as soon as theprobability of an undesired outcome exceeds a threshold, leads to lower netcosts.

Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring