Networked Business Models, Interorganizational Business Processes and Cooperative Information Systems: A Holistic Point of View
Paul Grefen, School of Industrial Engineering, Eindhoven University of Technology / Eviden Digital Transformation Consulting, Netherlands
Machine Learning and Generative AI in BPM: Recent Developments and Emerging Challenges
Barbara Weber, University of St.Gallen, Switzerland
Networked Business Models, Interorganizational Business Processes and Cooperative Information Systems: A Holistic Point of View
Paul Grefen
School of Industrial Engineering, Eindhoven University of Technology / Eviden Digital Transformation Consulting
Netherlands
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Brief Bio
Paul Grefen is a senior full professor in digital business architecture at Eindhoven University of Technology and a principal architect at Eviden Digital Transformation Consulting. He received his Ph.D. in computer science in 1992 from the University of Twente. He has been a visiting researcher at Stanford University, IBM Almaden Research Center, Penn State University and KU Leuven. He has been involved in many European and national research projects, mostly in collaboration with industry. He has authored and edited books on workflow management, electronic business, service-dominant business engineering, information systems, blockchain and business process management. His current work covers digital business transformation, architectural design of business systems, inter-organizational business process management and service-oriented business engineering.
Abstract
The modern economy is heavily networked. Values to customers are often delivered not by a single organization but by a network of organizations. How these values are composed from contributions by these organizations is defined in networked, collaborative business models. To operationalize these business models, interorganizational business processes need to be designed and executed. To provide automated support to the execution of these processes, networks of cooperative information systems are deployed. In research, these three layers are often considered in isolation. In order to streamline effective value delivery down to digital function execution, an integrative approach is required, however. Therefore, this presentation takes the holistic point of view and shows how the elements in these layers are related, and how this relation can be made explicit in concepts, models and system architectures.
Machine Learning and Generative AI in BPM: Recent Developments and Emerging Challenges
Barbara Weber
University of St.Gallen
Switzerland
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Brief Bio
Barbara Weber is Full Professor for Software Systems Programming and Development and Director at the Institute of Computer Science at the University of St. Gallen (HSG), Switzerland since 2019. Since February 2024 she is additionally Vice-President for Studies and Teaching at HSG. Before joining HSG, Barbara held a full professorship at the Technical University of Denmark and led the Section for Software and Process Engineering for 3 years. Before moving to Denmark, Barbara worked for over 15 years for the University of Innsbruck where she started her research career and obtained her doctorate and habilitation degrees. Barbara’s research interests include human and cognitive aspects in software and process engineering, process modeling and mining. Together with her team, she focusses on the development and evaluation of software artifacts. This includes topics in the areas of source code analysis, the Internet of Things, and process mining to study and build event-driven software systems that adapt based on the user’s behavior and context. On these and other topics, Barbara published around 200 peer-reviewed papers and articles in scientific journals. Barbara is part of the BPM and CAiSE Steering Committee and served as PC chair for BPM 2013, CAiSE 2019, EASE 2021, ICPM 2022 and was general chair of BPM 2015.
Abstract
This keynote presentation explores the integration of Machine Learning and Generative AI within Business Process Management (BPM), focusing on recent developments and emerging challenges. Generative AI holds potential for improving automation efficiency and democratizing process design and -analysis. In addition, it facilitates innovative problem-solving, enables conversational BPM, and allows to tap into novel data sources. With the third wave of BPM emphasizing data-led processes, AI technologies, including machine learning and natural language processing, are driving significant advancements in AI-augmented BPM including process automation and process mining. Attendees will gain insights into the transformative impact of AI on BPM, recent developments, and key challenges.