A Python Extension to Simulate Petri nets in Process Mining
This is an incremental tool for process analysts to simulate business processes for improvement scenarios.
The paper addresses the limitation of process mining being backward-looking by introducing a Python library that generates simulated event logs from historical data, enabling forward-looking 'what-if' analyses with options for activity duration and arrival rate specification.
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.