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Paper Type
Complete
Description
Business Process Simulation (BPS) is commonly used by decision makers to evaluate the expected impact of process changes, without interfering in running systems. Though powerful, the benefits of BPS are highly dependent on the quality of the employed simulation model. Given that both manual and automated approaches have their limitations, we propose that simulation models should rather be created using a project-based approach, in which data-driven analysis techniques are applied in a manner tailored to the characteristics of the project at hand. In this work, we provide guidance for this by proposing DDPS, a project methodology for Data-Driven Process Simulation. The methodology guides users through project preparation and parameter estimation to the creation and validation of the simulation model itself, i.e., a digital twin derived from execution data. Overall, DDPS thus supports practitioners by making the proper execution of BPS projects more feasible.
Paper Number
1767
Recommended Citation
Oberle, Laura Johanna and van der Aa, Han, "DDPS: A Project Methodology for Data-Driven Process Simulation" (2023). AMCIS 2023 Proceedings. 13.
https://aisel.aisnet.org/amcis2023/sig_dsa/sig_dsa/13
DDPS: A Project Methodology for Data-Driven Process Simulation
Business Process Simulation (BPS) is commonly used by decision makers to evaluate the expected impact of process changes, without interfering in running systems. Though powerful, the benefits of BPS are highly dependent on the quality of the employed simulation model. Given that both manual and automated approaches have their limitations, we propose that simulation models should rather be created using a project-based approach, in which data-driven analysis techniques are applied in a manner tailored to the characteristics of the project at hand. In this work, we provide guidance for this by proposing DDPS, a project methodology for Data-Driven Process Simulation. The methodology guides users through project preparation and parameter estimation to the creation and validation of the simulation model itself, i.e., a digital twin derived from execution data. Overall, DDPS thus supports practitioners by making the proper execution of BPS projects more feasible.
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