Online Discovery of Simulation Models for Evolving Business Processes

Francesco Vinci, Gyunam Park, Wil M. P. Van Der Aalst, Massimiliano De Leoni
August, 2025
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Abstract

Business Process Simulation (BPS) refers to techniques designed to replicate the dynamic behavior of a business process. Many approaches have been proposed to automatically discover simulation models from historical event logs, reducing the cost and time to manually design them. However, in dynamic business environments, organizations continuously refine their processes to enhance efficiency, reduce costs, and improve customer satisfaction. Existing techniques to process simulation discovery lack adaptability to real-time operational changes. In this paper, we propose a streaming process simulation discovery technique that integrates Incremental Process Discovery with Online Machine Learning methods. This technique prioritizes recent data while preserving historical information, ensuring adaptation to evolving process dynamics. Experiments conducted on four different event logs demonstrate the importance in simulation of giving more weight to recent data while retaining historical knowledge. Our technique not only produces more stable simulations but also exhibits robustness in handling concept drift, as highlighted in one of the use cases.

Type
Conference paper
Publication
Business Process Management

Practical problem

Companies use business process simulation to test what-if scenarios, anticipate bottlenecks, and plan staffing, and tools now build those simulation models automatically from historical event data. The catch is that processes keep changing: a bank automates more reviews, a hospital reorganizes a department, workloads shift. A simulation model discovered from all past data blends the old and the new behaviour and then forecasts a mixture that never existed, while a model built only from the most recent weeks throws away hard-won knowledge about resources, calendars, and rare paths. Analysts are left either re-discovering models from scratch at every change or trusting models that drift out of date.

Research questions

  1. How can a hybrid business process simulation model be updated continuously from an event stream so that it adapts to recent behaviour while retaining historical knowledge?
  2. Does prioritizing recent data while preserving historical information produce more accurate and more stable simulations than discovering from all past data or from only the latest window?

Contributions

  • Formalizes online simulation-model discovery as an update function over a sliding window of an event stream, producing a sequence of models each extending the previous one.
  • A four-step update technique: trace-fragment extraction, incremental control-flow discovery (Schuster et al.), descriptive-parameter refresh, and online updating of predictive parameters.
  • Uses Hoeffding Adaptive Trees with adaptive windowing (ADWIN) drift detection as explainable, incrementally trained models for durations, waiting times, arrivals, and branching probabilities.
  • An implementation in Python on top of the Cortado library and the River online-learning library, released on GitHub (franvinci/ProcessSimulationTool).
  • An evaluation protocol with ten temporal windows and eight simulation-quality distances (Chapela-Campa et al.) comparing single-batch, last-batch, and online discovery on four event logs.

Key findings

  • Across BPIC17W, BPIC12W, Purchase-to-Pay, and CVS pharmacy logs, the online technique is best in 24 of 32 log-metric combinations (75%), versus 8 for single-batch and 4 for last-batch discovery.
  • Temporal perspectives benefit most: average Cycle Time Distribution distance drops to 196.49 (online) from 245.87 (single batch) and 319.98 (last batch), and Relative Event Distribution to 156.16 from 195.77 and 241.06.
  • Control-flow distances stay close to single-batch results (average CFLD 0.14 vs 0.16; 3GD 0.17 vs 0.17), indicating no major control-flow drift in these logs.
  • Using only the last window is consistently worst (average CFLD 0.26, AED 2139.58 with CVS at 5711.09), showing that discarding historical data harms simulation quality.
  • On BPIC17W, after the Validate Application duration drops 38% at week 28, the online model yields lower Wasserstein distances than both baselines in all but two later weeks; removing obsolete behaviour from the incrementally updated model remains open.