A Tutorial on Data-Driven Petri Net Model Extraction and Simulation for Digital Twins in Smart Manufacturing
Khodadadi, Atieh
1; Zare, Ashkan; Jungmann, Michelle
1; GÖtz, Manuel
1; Lazarova-Molnar, Sanja
1
1 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)
1; Zare, Ashkan; Jungmann, Michelle
1; GÖtz, Manuel
1; Lazarova-Molnar, Sanja
11 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)
Abstract:
The adoption of data-driven Digital Twins in smart manufacturing systems necessitates robust, data-driven
modeling techniques. Stochastic Petri nets offer a formal framework for capturing concurrency and
synchronization in discrete-event systems, making them well-suited for modeling smart manufacturing
systems. This tutorial provides a hands-on introduction to extracting stochastic Petri net models from
system event logs. Using our Python-based stochastic Petri nets library (PySPN), participants will learn
how to: (1) define ground-truth models using the Petri Net Markup Language (PNML), (2) generate event
logs by simulating these models, and (3) apply process mining techniques to automatically reconstruct
stochastic Petri net models from real or simulated data. Two case studies illustrate the end-to-end workflow,
from data generation to model validation, within the context of developing Digital Twins. By the end of the
tutorial, participants will gain practical skills in data-driven stochastic Petri nets modeling for Digital Twins
applications in manufacturing.
modeling techniques. Stochastic Petri nets offer a formal framework for capturing concurrency and
synchronization in discrete-event systems, making them well-suited for modeling smart manufacturing
systems. This tutorial provides a hands-on introduction to extracting stochastic Petri net models from
system event logs. Using our Python-based stochastic Petri nets library (PySPN), participants will learn
how to: (1) define ground-truth models using the Petri Net Markup Language (PNML), (2) generate event
logs by simulating these models, and (3) apply process mining techniques to automatically reconstruct
stochastic Petri net models from real or simulated data. Two case studies illustrate the end-to-end workflow,
from data generation to model validation, within the context of developing Digital Twins. By the end of the
tutorial, participants will gain practical skills in data-driven stochastic Petri nets modeling for Digital Twins
applications in manufacturing.
| Zugehörige Institution(en) am KIT | Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB) |
| Publikationstyp | Proceedingsbeitrag |
| Publikationsdatum | 07.12.2025 |
| Sprache | Englisch |
| Identifikator | ISBN: 979-8-3315-8726-0 KITopen-ID: 1000189930 |
| Erschienen in | Proceedings of the 2025 Winter Simulation Conference. Ed.: E. Azar |
| Veranstaltung | Winter Simulation Conference (WSC 2025), Seattle, WA, USA, 07.12.2025 – 10.12.2025 |
| Verlag | Institute of Electrical and Electronics Engineers (IEEE) |
| Seiten | 1212–1226 |
| Nachgewiesen in | OpenAlex |