Business Process Simulation (BPS) provides a controlled environment to evaluate alternative process designs and perform “what-if” analyses. While several approaches exist for deriving simulation models directly from event logs, existing solutions are often limited in accessibility, configurability, and interpretability. We present ProSiT: Process Simulation Tool, a user-friendly environment for data–driven simulation. ProSiT supports (i) the ingestion of event logs and process models, (ii) automated discovery of control-flow, timing, resource, and trace attribute parameters, (iii) interactive configuration of alternative service scenarios, (iv) visualization of accuracy metrics, and (v) generation and statistical analysis of simulated event logs. ProSiT integrates state-of-the-art machine learning methods for parameter discovery in an explainable whitebox form, contrasting with existing deep learning–based approaches that act as black boxes. By combining simulation accuracy with transparency, ProSiT empowers service architects and process analysts to experiment with alternative designs, evaluate service-level impacts, and explore optimization opportunities through an intuitive graphical interface.
Before changing how a service is staffed or how work is routed, analysts want to try the change in a simulation rather than on live operations. Building such simulation models by hand is slow and error-prone, so recent tools learn them from event logs, but the most accurate learners are deep neural networks whose decision logic nobody can read or edit. That defeats the purpose of what-if analysis: an architect who cannot see why the model routes a case to a particular resource cannot credibly modify that behavior to test an alternative. Analysts are thus left choosing between manually built models they understand and automatically discovered ones they cannot inspect, and most research prototypes are hard to install and configure in the first place.
ProSiT (Process Simulation Tool), a web-based client-server application with a Flask backend, HTML/CSS/JavaScript frontend, SQLite persistence, and Docker distribution, with source code on GitHub.