WI2023 – Track: Business Process Management

Track description

Business process management (BPM) has been established as a central management tool. With the help of BPM, companies are supported in the development, management, design, monitoring and implementation of business processes with the purpose of achieving their goals more effectively and efficiently. Over the last few decades, BPM has spread to different areas like e.g. Business Process Redesign, Process Mining, Robotic Process Automation or Predictive Process Monitoring, to name just a few. The aim of the track is to discuss these diverse facets of BPM from the different perspectives of management and technology and to work out how BPM can be used to meet current challenges like BPM in view of digitalization or the integration of AI in BPM. The track is open to papers from the entire research spectrum of BPM – both organizational and technical.  

Track Topics

Possible topics are (but not limited to):

  • business process redesign
  • explorative business process management and business process innovation
  • business process management in view of digitalization
  • business process management and co-creation
  • process mining and gamification
  • AI techniques for process mining
  • AI-based process analytics
  • event log quality 
  • pre-processing techniques for process mining
  • organizational facets related to process mining
  • organizational routines and routine dynamics

Track Chairs

Prof. Dr. Agnes Koschmider

University of Bayreuth

Agnes Koschmider is a professor of business informatics at the University of Bayreuth. Before she was Professor at Kiel University, Associate Professor at the Poznań University of Economics and Business, interim professor at the University of Cologne and Postdoctoral Researcher at the Institute of Applied Informatics and Formal Description Methods at KIT. She received her doctoral degree and venia legendi in Applied Informatics, both from KIT. The core area of her research is a data-driven analysis of processes based on AI methods. Particularly, her research group explores how to extract knowledge from raw data (like sensor data, time-series, video-based data) in the form of processes.

Prof. Dr. Maximilian Röglinger

University of Bayreuth & Queensland University of Technology

Maximilian Röglinger is a professor of Information Systems and Business Process Management at the University of Bayreuth and an Adjunct Professor in the School of Management at Queensland University of Technology. Maximilian also serves as Deputy Academic Director of the Research Center Finance & Information Management (FIM) as well as Deputy Director of Fraunhofer FIT, including the Fraunhofer Center for Process Intelligence. Maximilian’s activities in research and teaching center around customers, business processes, and digitalization.

Prof. Moe Thandar Wynn

Queensland University of Technology (QUT)

Moe Thandar Wynn is a professor of Information Systems at Queensland University of Technology (QUT), Brisbane, Australia. She leads the Business Process Management (BPM) research group and co-leads the Data for Discovery Research Theme within QUT’s Tier 1 Centre for Data Science. She is Vice-Chair and one of the steering committee members of IEEE Taskforce on Process Mining. Her ongoing research focuses on process mining, data quality and robotic process automation for the digital transformation of processes.

Dr. Christian Bartelheimer

Paderborn University

Christian Bartelheimer is a postdoctoral researcher at the department of management information systems at Paderborn University. His research focuses on the design, implementation, and evaluation of innovative digital technologies and their impact on processes and actors in socio-technical service systems. Christian is also involved at Software Innovation Campus Paderborn (SICP) and currently leads a research project that focuses on the data-driven analysis and prescription of knowledge-intensive processes.

Associate Editors

  • Carl Corea, Universität Koblenz
  • Michael Fellmann, University of Rostock 
  • Kanika Goel, Queensland University of Technology
  • Thomas Grisold, University of Liechtenstein
  • Sander Leemans, RWTH Aachen University
  • Christian Janiesch, TU Dortmund
  • Sareh Sadeghianasl, Queensland University of Technology
  • Flavia Santoro, Universidade do Estado do Rio de Janeiro 
  • Bastian Wurm, Ludwig-Maximilians-Universität München