# Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

arXiv: https://arxiv.org/abs/2105.07111 · HTML: https://mlsanity.com/paper/2105.07111/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 15 May 2021.

## Builds on (most cited first, 9 of 9)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Orthogonal Random Forest for Causal Inference](https://mlsanity.com/paper/1806.03467/) (2018)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding](https://mlsanity.com/paper/2003.01747/) (2020)
- [Prescriptive Business Process Monitoring for Recommending Next Best Actions](https://mlsanity.com/paper/2008.08693/) (2020)
- [A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences](https://mlsanity.com/paper/2102.07298/) (2021)
- [Alarm-Based Prescriptive Process Monitoring](https://mlsanity.com/paper/1803.08706/) (2018)
- [Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs](https://mlsanity.com/paper/2009.01561/) (2020)
- [Case Level Counterfactual Reasoning in Process Mining](https://mlsanity.com/paper/2102.13490/) (2021)

## Built on by (most cited first, 17 of 17)

- [Prescriptive Process Monitoring: Quo Vadis?](https://mlsanity.com/paper/2112.01769/) (2021)
- [Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach](https://mlsanity.com/paper/2109.02894/) (2021)
- [When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints](https://mlsanity.com/paper/2206.07745/) (2022)
- [How well can a large language model explain business processes as perceived by users?](https://mlsanity.com/paper/2401.12846/) (2024)
- [Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns](https://mlsanity.com/paper/2211.04880/) (2022)
- [Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning](https://mlsanity.com/paper/2303.03572/) (2023)
- [Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions](https://mlsanity.com/paper/2205.05446/) (2022)
- [Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes](https://mlsanity.com/paper/2212.03710/) (2022)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes](https://mlsanity.com/paper/2403.09232/) (2024)
- [Explainable Predictive Process Monitoring: A User Evaluation](https://mlsanity.com/paper/2202.07760/) (2022)
- [Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning](https://mlsanity.com/paper/2205.03219/) (2022)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Timing Process Interventions with Causal Inference and Reinforcement Learning](https://mlsanity.com/paper/2306.04299/) (2023)
- [Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents](https://mlsanity.com/paper/2310.00981/) (2023)
- [An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring](https://mlsanity.com/paper/2501.10543/) (2025)
- [SCOPE: Sequential Causal Optimization of Process Interventions](https://mlsanity.com/paper/2512.17629/) (2025)

## Abstract (arXiv)

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.