Abstract
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.
Highlights
Process mining is a family of techniques to discover, monitor, and improve business processes by extracting knowledge from logs of process executions recorded by information systems [1]
3.2 Single-alarm cost model An alarm-based prescriptive process monitoring system is a monitoring system that can raise an alarm in relation to a running case of a business process in order to indicate that the case is likely to lead to some undesired outcome
These alarms are handled by process workers who intervene in the process instance by performing an action, thereby preventing the undesired outcome or mitigating its effect
Summary
Process mining is a family of techniques to discover, monitor, and improve business processes by extracting knowledge from logs of process executions ( called event logs) recorded by information systems [1]. A naive approach to turn a predictive process monitoring technique into a prescriptive one is by triggering an alarm whenever the probability that a case will lead to an undesired outcome is above a fixed threshold (e.g., 90%) This alarm leads to an intervention, such as calling the customer and offering a discount. An alarm of type A may lead to one type of intervention (calling the customer) whereas an alarm of type B may lead to a different type of intervention (offering a discount) This naive approach may be far from optimal, as it does not take into account the cost induced by each intervention (e.g., the time spent by workers in the intervention or forgone revenue) nor its effect (e.g., preventing the undesired outcome altogether or only partially mitigating it).
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