  Predictive process monitoring is a family of techniques to analyze events
produced during the execution of a business process in order to predict the
future state or the final outcome of running process instances. Existing
techniques in this field are able to predict, at each step of a process
instance, the likelihood that it will lead to an undesired outcome.These
techniques, however, focus on generating predictions and do not prescribe when
and how process workers should intervene to decrease the cost of undesired
outcomes. This paper proposes a framework for prescriptive process monitoring,
which extends predictive monitoring with the ability to generate alarms that
trigger interventions to prevent an undesired outcome or mitigate its effect.
The framework incorporates a parameterized cost model to assess the
cost-benefit trade-off of generating alarms. We show how to optimize the
generation of alarms given an event log of past process executions and a set of
cost model parameters. The proposed approaches are empirically evaluated using
a range of real-life event logs. The experimental results show that the net
cost of undesired outcomes can be minimized by changing the threshold for
generating alarms, as the process instance progresses. Moreover, introducing
delays for triggering alarms, instead of triggering them as soon as the
probability of an undesired outcome exceeds a threshold, leads to lower net
costs.
