# Prescriptive Business Process Monitoring for Recommending Next Best Actions

arXiv: https://arxiv.org/abs/2008.08693 · HTML: https://mlsanity.com/paper/2008.08693/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 22 of 22)

- [Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction](https://mlsanity.com/paper/2105.07111/) (2021)
- [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)
- [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)
- [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)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [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)
- [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)
- [Recent Advances in Data-Driven Business Process Management](https://mlsanity.com/paper/2406.01786/) (2024)
- [An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring](https://mlsanity.com/paper/2502.10573/) (2025)
- [Learning to act: a Reinforcement Learning approach to recommend the best next activities](https://mlsanity.com/paper/2203.15398/) (2022)
- [Timing Process Interventions with Causal Inference and Reinforcement Learning](https://mlsanity.com/paper/2306.04299/) (2023)
- [An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring](https://mlsanity.com/paper/2501.10543/) (2025)
- [A Systematic Literature Review on Process-Aware Recommender Systems](https://mlsanity.com/paper/2103.16654/) (2021)
- [Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring](https://mlsanity.com/paper/2211.16106/) (2022)
- [A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches)](https://mlsanity.com/paper/2407.11043/) (2024)
- [SCOPE: Sequential Causal Optimization of Process Interventions](https://mlsanity.com/paper/2512.17629/) (2025)

## Abstract (arXiv)

Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in running process instances. Even though organisations measure process performance by key performance indicators (KPIs), the DNN`s learning procedure is not directly affected by them. Therefore, the resulting next most likely activity predictions can be less beneficial in practice. Prescriptive business process monitoring (PrBPM) approaches assess predictions regarding their impact on the process performance (typically measured by KPIs) to prevent undesired process activities by raising alarms or recommending actions. However, none of these approaches recommends actual process activities as actions that are optimised according to a given KPI. We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI. Thereby, our technique uses business process simulation to ensure the control-flow conformance of the recommended actions. Based on our evaluation with two real-life event logs, we show that our technique`s next best actions can outperform next activity predictions regarding the optimisation of a KPI and the distance from the actual process instances.