Business Process Simulation

Business Process Simulation (BPS) is the practice of generating synthetic event-log data from a specified or learned process model, for what-if analysis, design-time comparison, and (more recently) methodology-level evaluation in predictive process monitoring.

Two design philosophies

Calibrated / ecological-validity BPS

Tools such as Simod (Camargo et al.), the BPSim/WfMC-standard work (Fracca et al. bpsimpy), SimPT and PMSD (Pourbafrani et al.), Grüger et al.'s SAMPLe, and classical DES tools (Arena, CPN Tools) aim to reproduce the behaviour of a specific observed process. Control flow is either discovered from event logs or hand-built from domain knowledge; activity-duration and arrival distributions are calibrated to data. The target deliverable is realistic simulated logs for answering what-if questions about that process (staffing changes, reroutings, SLA impact).

Parametric / internal-validity BPS

SynBPS (Riess 2024) takes the opposite stance: the researcher is not trying to reproduce a specific process but to generate controlled synthetic event logs where data-generating-process factors can be independently manipulated. Control flow is a Markov chain of configurable order; entropy of the transition matrix, state-space size, activity-duration distributions (exponential / hypoexponential), arrival rate (Poisson) and stability (drift) are all user-specified. The target deliverable is factorial designs for PPM-method robustness assessment — analogous to controlled benchmarks in classical ML regularisation research (Lasso, LARS, elastic net).

Why this matters for PPM evaluation

PPM papers typically evaluate methods on 3–9 public event logs (BPIC, Sepsis, Helpdesk, Hospital Billing, etc.). Each event log is one realisation of one process from one organisation. Comparative claims across methods are valid, but claims about which process characteristics drive performance cannot be causally tested without control variables (sources/2024-riess-synbps-simulation-framework). SynBPS provides the infrastructure for this second type of question.

Separately, simulation can be used to evaluate prescriptive process monitoring interventions (e.g. queue prioritisation) where counterfactual deployment on real systems is infeasible — see Paper III of sources/2023-riess-phd-thesis-ppm (with Scholderer), which uses agent-based calibrated simulation to test predicted-loyalty queue prioritisation.

Method page: methods/process-simulation — discrete-event simulation fundamentals from sources/2018-dumas-fundamentals-of-bpm §7.3.
Concepts: concepts/predictive-process-monitoring, concepts/concept-drift, concepts/behavioral-variability.
Author: entities/mike-riess.