When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints

TL;DR

This paper introduces a novel prescriptive process monitoring approach that optimally decides when to intervene in ongoing cases by considering uncertainty, resource constraints, and potential benefits, outperforming existing methods.

Contribution

It presents a new intervention policy that accounts for uncertainty and resource limitations, improving decision-making in prescriptive process monitoring.

Findings

Outperforms existing baselines in total gain.

Effectively balances intervention timing and resource constraints.

Demonstrates practical applicability on real-life event logs.

Abstract

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.