mlsanityData to 7 Oct 2026

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

arXiv 2105.07111 ↗First posted 15 May 2021cs.LG cs.AIcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 15 May 2021.

Builds on

9 of 31 references.

References · 9

  1. 2016Generalized Random Forestscited by 309
  2. 2018Orthogonal Random Forest for Causal Inferencecited by 51
  3. 2018Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoringcited by 43
  4. 2020Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confoundingcited by 25
  5. 2020Prescriptive Business Process Monitoring for Recommending Next Best Actionscited by 22
  6. 2021A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequencescited by 17
  7. 2018Alarm-Based Prescriptive Process Monitoringcited by 16
  8. 2020Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logscited by 15
  9. 2021Case Level Counterfactual Reasoning in Process Miningcited by 4

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Cited by year

Citing papers per year: 2021: 2; 2022: 6; 2023: 4; 2024: 3; 2025: 2.

17 papers.

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  1. 2021Prescriptive Process Monitoring: Quo Vadis?cited by 15
  2. 2021Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approachcited by 12
  3. 2022When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraintscited by 11
  4. 2024How well can a large language model explain business processes as perceived by users?cited by 8
  5. 2022Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patternscited by 7
  6. 2023Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learningcited by 6
  7. 2022Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processescited by 5
  8. 2024Machine learning in business process management: A systematic literature reviewcited by 5
  9. 2022Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directionscited by 4
  10. 2024Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processescited by 4
  11. 2022Explainable Predictive Process Monitoring: A User Evaluationcited by 3
  12. 2022Goal-Oriented Next Best Activity Recommendation using Reinforcement Learningcited by 3
5 more
  1. 2023A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areascited by 2
  2. 2023Timing Process Interventions with Causal Inference and Reinforcement Learningcited by 1
  3. 2023Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidentscited by 1
  4. 2025An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoringcited by 1
  5. 2025SCOPE: Sequential Causal Optimization of Process Interventionscited by 0