This book constitutes the proceedings of the BPM Forum held at the 24th International Conference on Business Process Management, BPM 2026, which took place in Toronto, Canada, during September/October 2026.
Contributions presented at the BPM Forum include novel applications, early-stage results, or creative perspectives on known problems. They cover a diverse and timely set of topics, reflecting the evolving socio-technical and AI-enhanced landscape of BPM. The topics explored include business process management; robotic process automation; process modeling-, execution-, monitoring-, and simulation; object-centric process mining; business intelligence; and large language models.
The 24 full papers included in this book were carefully reviewed and selected from a total of 169 submissions. The papers were organized in topical sections as follows: Foundations; Engineering; and Management.
This book constitutes the proceedings of the BPM Forum held at the 24th International Conference on Business Process Management, BPM 2026, which took place in Toronto, Canada, during September/October 2026.
Contributions presented at the BPM Forum include novel applications, early-stage results, or creative perspectives on known problems. They cover a diverse and timely set of topics, reflecting the evolving socio-technical and AI-enhanced landscape of BPM. The topics explored include business process management; robotic process automation; process modeling-, execution-, monitoring-, and simulation; object-centric process mining; business intelligence; and large language models.
The 24 full papers included in this book were carefully reviewed and selected from a total of 169 submissions. The papers were organized in topical sections as follows: Foundations; Engineering; and Management.
Fabrizio Maria Maggi
business process management business intelligence process mining process monitoring process analytics process execution digital transformation process modeling large language models robotic process automation natural language processing process simulation machine learning