When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints
This work addresses a gap in prescriptive process monitoring for business operations by optimizing intervention timing under uncertainty and limited resources, representing an incremental improvement.
The paper tackles the problem of when to trigger interventions in business processes by considering prediction uncertainty and resource constraints, and shows that the proposed method outperforms existing baselines in total gain on a real-life event log.
Prescriptive process monitoring approaches leverage historical data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process's performance. A centerpiece of a prescriptive process monitoring method is its intervention policy: a decision function determining if and when to trigger an intervention on an ongoing case. Previous proposals in this field rely on intervention policies that consider only the current state of a given case. These approaches do not consider the tradeoff between triggering an intervention in the current state, given the level of uncertainty of the underlying predictive models, versus delaying the intervention to a later state. Moreover, they assume that a resource is always available to perform an intervention (infinite capacity). This paper addresses these gaps by introducing a prescriptive process monitoring method that filters and ranks ongoing cases based on prediction scores, prediction uncertainty, and causal effect of the intervention, and triggers interventions to maximize a gain function, considering the available resources. The proposal is evaluated using a real-life event log. The results show that the proposed method outperforms existing baselines regarding total gain.