Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

TL;DR

This paper presents a prescriptive process monitoring approach that uses causal inference with orthogonal random forests to decide when to trigger interventions for reducing cycle time in business processes, balancing costs and benefits.

Contribution

It introduces a novel method combining causal effect estimation with process monitoring to optimize intervention timing for cycle time reduction.

Findings

Effective in real-life process logs

Improves cycle time reduction strategies

Balances intervention costs and benefits

Abstract

Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of a case, for example, using a faster shipping service in an order-to-delivery process or giving a phone call to a customer to obtain missing information rather than waiting passively. Each of these interventions comes with a cost. This paper tackles the problem of determining if and when to trigger a time-reducing intervention in a way that maximizes the total net gain. The paper proposes a prescriptive process monitoring method that uses orthogonal random forest models to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process. Based on this causal effect estimate, the method triggers interventions according to a user-defined policy. The method is evaluated on two real-life logs.