Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

Abstract

Prescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant’s chances of taking a loan. This intervention requires time from a credit officer. Thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on a case’s outcome. These estimates are used to allocate resources to interventions to maximize a cost function. A preliminary evaluation suggests that the approach produces a higher net gain than a purely predictive (non-causal) baseline.

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

“…To verify the effectiveness and relevance of the proposed method, we experimentally investigate whether we can learn when to trigger an intervention to maximize the total gain at the run time, considering the tradeoff between intervening now or later. We compare our results to baselines that consider either predictive models without quantifying the prediction uncertainty [6,14] or only the current case's execution state scores [2,18] as state-of-the-art baselines by addressing the following research questions:…”

“…Recently, various PrPM methods have been proposed [2,6,14,18]. These methods, however, implement intervention policies based on predictions of negative outcomes without considering the uncertainty of these predictions.…”

“…In our previous work [18], we propose a PrPM method that utilizes the potential outcome method and a resource allocator technique in the outcomeoriented PPM to allocate resources to cases with max gain. That work considers only a given case's current state and triggers interventions when the prediction score and the causal effect exceed a threshold.…”

“…Once the most profitable case is selected and a free resource is available, we assign that resource to the selected case and block it for a certain time, i.e., treatment duration (T dur ). The number of available resources and the time required to perform the intervention could be identified via domain knowledge [18].…”

Learning to act: a Reinforcement Learning approach to recommend the best next activities

“…Several Prescriptive Process Monitoring techniques have been recently proposed in the literature. Focusing on the type of interventions that the approaches recommend [12], we can roughly classify existing work in Prescriptive Process Monitoring in three main groups: (i) the ones that recommend different types of interventions to prevent or mitigate the occurrence of an undesired outcome [24,8,18,17,20]; (ii) the ones that take a resource perspective and recommend a resource allocation [22,19]; (iii) the ones that provide recommendations related to the next activity to optimize a given KPI [25,9,2].…”