# Prescriptive process analytics and digital twins From predicting process problems to recommending actions and testing them in simulation models and digital twins of organizations before they reach the real process. - Type: Research line - URL: https://www.gyunam.com/research-line/prescriptive-process-analytics/ ## Practical problem Insights do not improve a process; actions do. Most process mining stops at dashboards and leaves the translation into decisions to people, so problems that are detected are not necessarily resolved, and interventions are rarely tested before they touch operations. Three gaps follow. Predictions are produced for single cases, while managers decide for whole processes with shared resources, so a prediction has to reach a scheduler or an allocation algorithm to be of use. Actions interact: reprioritising orders and reallocating staff compete for the same people and the same time, and an update to the information system changes which objects and steps are affected before anyone sees the effect. And whether an action helped is only known afterwards, which makes simulation models and digital twins of organizations necessary to test interventions first. Action-oriented process mining, the loop from monitored constraints to executed actions, was the topic of my doctoral thesis at RWTH Aachen University (2019 to 2024, Best Dissertation Award Runner-Up at BPM 2024). The predictive groundwork goes back to my master's thesis at POSTECH on predicting process performance with deep neural networks. ## Research questions The line has three pillars. **Predictive process monitoring that decisions can use.** A prediction is only valuable if something can be done with it. - How can performance be predicted for a whole process rather than for one running case, and how can prediction be kept tractable on large event logs? - When a prediction is uncertain, how should the downstream allocation treat it, and how can the decision logic behind a prediction be explained to the people who act on it? **From insights to actions.** Monitoring results have to become management actions. - How can a stream of constraint violations be turned into a stream of management actions, and how are conflicting actions planned? - Which recommended actions are effective, compliant with the rules of the domain, fair towards the people in the process, and realistic given the information available at decision time? **Simulation and digital twins of organizations.** Interventions should be tested before they reach operations. - How can simulation models be discovered that stay faithful as the process evolves, remain transparent and configurable for what-if questions, and connect to the real ERP system? - How can a digital twin of an organization expose the control parameters of its information system, so that actions and system updates can be analysed for their structural, operational and performance impact before and after they happen? ## Contributions The contributions so far fall into six groups, each linked to the publication in which it appears. **Action-oriented process mining.** - A general framework with a constraint monitor that turns an event stream into a constraint instance stream and an action engine that turns it into transactions, formalized over multiple object classes with process-level valves, instance-level adaptors and alerts, and implemented as a ProM plug-in ([BPM Workshops 2020](/publication/2021-bpm/)). - A cube-based action engine with constraint cubes, OLAP operations and transaction mappings, evaluated by driving a real SAP ERP system with simulated logs through a gateway ([Progress in Artificial Intelligence, 2022](/publication/2022-prai/)). - A pattern-based action engine that mines temporal patterns of process problems with Allen's interval relations, maps them to actions through action graphs and schedules conflict-free action plans by makespan-minimizing scheduling ([Computers in Industry, 2023](/publication/2023-comind-pattern-based-action-engine/)). - Operational Problem Graphs and the ProAct monitoring engine for operational problems in object-centric processes ([Computers in Industry, 2025](/publication/2024-comind/)). **Digital twins of organizations.** - The Digital Twin Interface Model: an object-centric Petri net extended with valves and guards, with an operational view and a control view, realized in the dtween web service ([ICPM 2021](/publication/2021-icpm/)). - Analysis of process-aware information system updates as configuration changes of the digital twin, with structural, operational and performance impact types ([RCIS 2022](/publication/2022-rcis/)), extended into a four-step impact analysis method with prior and posterior analyses and the ProAct Impact Analysis prototype ([Information Systems, 2026](/publication/2026-is-processawareimpact/)). **Predictive process monitoring.** - Process-level performance prediction with annotated transition systems, two process representation matrices and CNN, LSTM and LRCN models adapted from traffic forecasting ([Decision Support Systems, 2020](/publication/2020-dss/)). - Next best actions instead of next most likely events, a research-in-progress technique whose usefulness was evaluated with process participants ([Wirtschaftsinformatik 2020](/publication/2020-wi/)). - Explainable predictive decision mining: decision models at the decision points of a Petri net with local and global SHAP explanations, in the eXdpn web application ([ICSOC Workshops 2022](/publication/2022-icsoc-workshop/)). - Variant-preserving event log sampling with unique, logarithmic and division strategies that cuts feature extraction and training time while preserving accuracy ([ICPM Workshops 2021](/publication/2021-ml4pm/), [Journal of Intelligent Information Systems, 2023](/publication/2023-sampling-jiis/)). - Object-centric process mining to improve predictive process monitoring: process executions kept as event graphs, as graph neural network input or as graph embeddings, for next activity, next timestamp and remaining time prediction ([Engineering Applications of Artificial Intelligence, 2023](/publication/2023-eaai/), [ECIS 2024](/publication/2024-ecis/)). - A pipeline that converts customer-support email threads into explainable ticket-escalation predictions with large language models and TreeSHAP, and withholds a prediction when a missing feature carries high impact ([BPM 2026](/publication/2026-bpm-ticketescalation/)). **Prediction-based resource allocation.** - Resource allocation as non-clairvoyant online job shop scheduling: a multi-task LSTM predicts processing time and next activity, and a minimum cost maximum flow algorithm assigns resources, including pseudo-assignments for predicted work items ([ICPM 2019](/publication/2019-icpm/)). - An uncertainty-aware version with Bayesian neural networks and Monte Carlo dropout that discards unreliable predictions, evaluated on a real tertiary-hospital emergency department log ([IEEE Access, 2023](/publication/2023-access/)). **Prescriptive recommendations.** - NeSyPA, which combines a Transformer prescriptor with Declare constraints compiled into automata and distils feasibility-masked, outcome-weighted targets into the model, with the Effective Compliance Rate metric ([BPM 2026, Best Paper Award](/publication/2026-bpm-nesyprescriptive/)). - Fair-LTN: counterfactual event-log generation by causal discovery and a Logic Tensor Network framework that optimizes KPI accuracy and counterfactual fairness jointly, with the CF-MSE and CF-RECC metrics ([BPM 2026](/publication/2026-bpm-fairness/)). - Behavioural recommendations mined from event logs as soft constraints for AI planning next to hard rules, solved by integer linear programming ([CAiSE Forum 2024](/publication/2024-caise-f/)). - Educational recommendations: behavioural features from campus management data and RuleFit rules translated into guidance on course load and sequencing, on 3,190 bachelor students ([Journal of Intelligent Information Systems, 2024](/publication/2024-jiis-learningrecommendation/)), and rules extracted from event data for study planning ([ICPM Workshops 2023, Best Paper Award](/publication/2023-icpm-edupm/)). **Business process simulation.** - A framework that integrates a simulation engine with a live ERP system, with CPN Tools driving SAP ERP through Remote Function Calls ([BPMDS 2021](/publication/2021-bpmds/)). - Online discovery of simulation models over a sliding window of an event stream, with incremental control-flow discovery and Hoeffding Adaptive Trees for predictive parameters ([BPM 2025](/publication/2025-bpm-sim/)). - Decision-aware simulation models that combine a Petri net with probabilistic decision trees, discovered by alignment and per-leaf distribution fitting, with three what-if operations on the trees ([ICSOC 2025](/publication/2025-icsoc-whiteboxsim/)), and the ProSiT tool for interactive, transparent simulation ([ICSOC Demo 2025](/publication/2025-icsoc-prosit/)).