Simod: Automated discovery of business process simulation models

SIMOD combines process mining and machine learning techniques to automate the discovery and tuning of Business Process Simulation models from event logs extracted from enterprise information systems (ERPs, CRM, case management systems, etc.). SIMOD takes as input an event log in CSV format, a configuration file, and (optionally) a BPMN process model, and discovers a business process simulation model that can be simulated using the Prosimos simulation engine, which is embedded in Simod.

Dependencies

Required

Optional

Depending on your CPU architecture, some dependencies might not be pre-compiled for your platform. In that case, you will most likely also need the following dependencies:

Getting Started

PyPI

❗️Make sure java -version returns 1.8 and pip is installed.

Then, install Simod and run it with the following commands:

pip install simod
simod --configuration resources/config/configuration_example.yml

Use your own configuration file instead of resources/config/configuration_example.yml and specify the path to the event log in the configuration file itself. Paths are relative to the configuration file, or absolute.

PyPI project is available at https://pypi.org/project/simod/.

Docker

docker pull nokal/simod

To start a container:

docker run -it -v /path/to/resources/:/usr/src/Simod/resources -v /path/to/output:/usr/src/Simod/outputs nokal/simod bash

Use the resources directory to store event logs and configuration files. The outputs directory will contain the results of Simod.

From inside the container, you can run Simod with:

poetry run simod --configuration <path-to-config>

Docker images for different Simod versions are available at https://hub.docker.com/r/nokal/simod/tags.

Configuration file

A set of example configurations can be found in the resources folder along with a description of each element:

• Basic configuration to discover the full BPS model (here).
• Basic configuration to discover the full BPS model using fuzzy (probabilistic) resource calendars (here).
• Basic configuration to discover the full BPS model with data-aware branching rules (here).
• Basic configuration to discover the full BPS model, and evaluate it with a specified event log (here).
• Basic configuration to discover a BPS model with a provided BPMN process model as starting point (here).
• Basic configuration to discover a BPS model with no optimization process (one-shot) (here).
• Complete configuration example with all the possible parameters (here).

For developers

Testing

Use pytest to run tests on the package:

poetry run pytest

To run unit tests, execute:

poetry run pytest -m "not integration"

Coverage:

poetry run pytest -m "not integration" --cov=simod

Documentation

For more details about the installation, usage, and implementation visit the documentation here:
📖 ️ https://simod.readthedocs.io/en/latest/