# Prescriptive Process Monitoring: Quo Vadis?

arXiv: https://arxiv.org/abs/2112.01769 · HTML: https://mlsanity.com/paper/2112.01769/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Prescriptive Business Process Monitoring for Recommending Next Best Actions](https://mlsanity.com/paper/2008.08693/) (2020)
- [Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring](https://mlsanity.com/paper/1905.09568/) (2019)
- [Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction](https://mlsanity.com/paper/2105.07111/) (2021)
- [Alarm-Based Prescriptive Process Monitoring](https://mlsanity.com/paper/1803.08706/) (2018)
- [DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach](https://mlsanity.com/paper/2107.08697/) (2021)
- [Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach](https://mlsanity.com/paper/2109.02894/) (2021)
- [DeepProcess: Supporting business process execution using a MANN-based recommender system](https://mlsanity.com/paper/1802.00938/) (2018)
- [What if Process Predictions are not followed by Good Recommendations? (Technical Report)](https://mlsanity.com/paper/1905.10173/) (2019)
- [A Comparative Evaluation of Log-Based Process Performance Analysis Techniques](https://mlsanity.com/paper/1804.03965/) (2018)
- [Automatic Resource Allocation in Business Processes: A Systematic Literature Survey](https://mlsanity.com/paper/2107.07264/) (2021)

## Built on by (most cited first, 15 of 15)

- [Machine Unlearning: A Comprehensive Survey](https://mlsanity.com/paper/2405.07406/) (2024)
- [Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns](https://mlsanity.com/paper/2211.04880/) (2022)
- [Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration](https://mlsanity.com/paper/2212.06564/) (2022)
- [Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning](https://mlsanity.com/paper/2303.03572/) (2023)
- [Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes](https://mlsanity.com/paper/2212.03710/) (2022)
- [Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches](https://mlsanity.com/paper/2303.09209/) (2023)
- [Learning policies for resource allocation in business processes](https://mlsanity.com/paper/2304.09970/) (2023)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach](https://mlsanity.com/paper/2307.06564/) (2023)
- [SimBank: from Simulation to Solution in Prescriptive Process Monitoring](https://mlsanity.com/paper/2506.14772/) (2025)
- [Recent Advances in Data-Driven Business Process Management](https://mlsanity.com/paper/2406.01786/) (2024)
- [Learning to act: a Reinforcement Learning approach to recommend the best next activities](https://mlsanity.com/paper/2203.15398/) (2022)
- [A Rollout-Based Algorithm and Reward Function for Resource Allocation in Business Processes](https://mlsanity.com/paper/2504.11250/) (2025)
- [SCOPE: Sequential Causal Optimization of Process Interventions](https://mlsanity.com/paper/2512.17629/) (2025)
- [Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planning](https://mlsanity.com/paper/2607.16738/) (2026)

## Abstract (arXiv)

Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This paper studies existing methods in this field via a Systematic Literature Review (SLR). In order to structure the field, the paper proposes a framework for characterizing prescriptive process monitoring methods according to their performance objective, performance metrics, intervention types, modeling techniques, data inputs, and intervention policies. The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods. The paper highlights the need to validate existing and new methods in real-world settings, to extend the types of interventions beyond those related to the temporal and cost perspectives, and to design policies that take into account causality and second-order effects.