Lecture: 'Short-Term Simulation for Online Business Process Optimization'
Business Process Management (BPM) is a discipline focused on identifying and optimizing business processes to enhance organizational performance. As a core component of BPM, Business Process Simulation (BPS) enables organizations to forecast performance and analyze the impact of changes prior to their implementation. BPS works by using a process model enhanced with parameters such as activity durations and resource availability, which a simulation engine interprets to estimate key performance metrics. When combined with optimization algorithms, BPS has the potential to systematically explore various configurations to identify those that minimize, for example, cost and cycle time.
Traditionally, BPS has been limited to tactical decision-making, aiming to identify process redesigns (e.g., restructuring the staff roster) that will improve process performance in a long-term timeframe. However, BPS holds significant potential for operational management, where the goal is to identify interventions (e.g., making an activity optional or reallocating an employee to a bottleneck activity) to address short-term transient changes. In this talk, I will discuss the viability of applying BPS in operational settings, critical challenges associated with it, and potential methods to address them. Finally, I will outline how combining predictive algorithms with BPS-based optimization strategies enables a shift from reactive to proactive process optimization.
Bio
David Chapela-Campa is an Assistant Professor of Information Systems at the University of Tartu. He obtained his PhD in Computer Science from the University of Santiago de Compostela in 2021, and held a Postdoctoral Research Fellow position at the University of Tartu (2021 - 2024). He has participated in 5+ national and international research projects, and has authored 20+ publications in high-impact journals and conferences on process mining and business process management (e.g., IEEE TSC, Inf. Sys., BPM, CAiSE, ICPM). His research currently focuses on data-driven techniques for the improvement and optimization of business processes. A primary area of his work is data-driven business process simulation, where he addresses simulation quality and reliability, as well as the use of short-term simulation to support operational decision-making. His recent research also explores the adoption of Agentic AI within BPM, focusing on the design of agent-based AI systems that autonomously execute, manage, and improve organizational processes.
Business Process Management (BPM) is a discipline focused on identifying and optimizing business processes to enhance organizational performance. As a core component of BPM, Business Process Simulation (BPS) enables organizations to forecast performance and analyze the impact of changes prior to their implementation. BPS works by using a process model enhanced with parameters such as activity durations and resource availability, which a simulation engine interprets to estimate key performance metrics. When combined with optimization algorithms, BPS has the potential to systematically explore various configurations to identify those that minimize, for example, cost and cycle time.
Traditionally, BPS has been limited to tactical decision-making, aiming to identify process redesigns (e.g., restructuring the staff roster) that will improve process performance in a long-term timeframe. However, BPS holds significant potential for operational management, where the goal is to identify interventions (e.g., making an activity optional or reallocating an employee to a bottleneck activity) to address short-term transient changes. In this talk, I will discuss the viability of applying BPS in operational settings, critical challenges associated with it, and potential methods to address them. Finally, I will outline how combining predictive algorithms with BPS-based optimization strategies enables a shift from reactive to proactive process optimization.
Bio
David Chapela-Campa is an Assistant Professor of Information Systems at the University of Tartu. He obtained his PhD in Computer Science from the University of Santiago de Compostela in 2021, and held a Postdoctoral Research Fellow position at the University of Tartu (2021 - 2024). He has participated in 5+ national and international research projects, and has authored 20+ publications in high-impact journals and conferences on process mining and business process management (e.g., IEEE TSC, Inf. Sys., BPM, CAiSE, ICPM). His research currently focuses on data-driven techniques for the improvement and optimization of business processes. A primary area of his work is data-driven business process simulation, where he addresses simulation quality and reliability, as well as the use of short-term simulation to support operational decision-making. His recent research also explores the adoption of Agentic AI within BPM, focusing on the design of agent-based AI systems that autonomously execute, manage, and improve organizational processes.
On-site event
Wednesday, May 13, 2026
1778630400000
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