A Python Extension to Simulate Petri nets in Process Mining

TL;DR

This paper introduces a Python library that enables simulation of business processes using event logs and discrete event simulation, allowing for forward-looking 'what-if' analyses based on historical process data.

Contribution

The paper presents a novel Python extension that generates simulated event logs from real process data, facilitating process simulation and analysis.

Findings

Supports process simulation based on historical event logs

Allows user-defined activity durations and arrival rates

Enables forward-looking process analysis and 'what-if' scenarios

Abstract

The capability of process mining techniques in providing extensive knowledge and insights into business processes has been widely acknowledged. Process mining techniques support discovering process models as well as analyzing process performance and bottlenecks in the past executions of processes. However, process mining tends to be "backward-looking" rather than "forward-looking" techniques like simulation. For example, process improvement also requires "what-if" analyses. In this paper, we present a Python library that uses an event log to directly generate a simulated event log, with additional options for end-users to specify the duration of activities and the arrival rate. Since the generated simulation model is supported by historical data (event data)and it is based on the Discrete Event Simulation (DES) technique, the generated event data is similar to the behavior of the real process.