Digital Twins of Business Processes: A Research Manifesto
Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay.
Authors
- Francesco Leotta (ORCID: https://orcid.org/0000-0001-9216-8502)
- Juergen Mangler (ORCID: https://orcid.org/0000-0002-6332-5801)
- Wolfgang Groher (ORCID: https://orcid.org/0009-0004-3498-3436)
- Harry H. Beyel (ORCID: https://orcid.org/0000-0002-6541-3848)
- Yannis Bertrand (ORCID: https://orcid.org/0000-0002-6407-7221)
- Uwe V. Riss (ORCID: https://orcid.org/0000-0003-0123-272X)
- Fabrizio Fornari (ORCID: https://orcid.org/0000-0002-3620-1723)
- Óscar Pastor (ORCID: https://orcid.org/0000-0002-1320-8471)
- Agnes Koschmider (ORCID: https://orcid.org/0000-0001-8206-7636)
- Marco Franceschetti (ORCID: https://orcid.org/0000-0001-7030-282X)
- Giovanni Lugaresi (ORCID: https://orcid.org/0000-0001-9625-6622)
- Ronny Seiger (ORCID: https://orcid.org/0000-0003-1675-2592)
- Joscha Grüger (ORCID: https://orcid.org/0000-0001-7538-1248)
- Massimo Callisto De Donato (ORCID: https://orcid.org/0000-0001-6745-4785)
- Estefanía Serral (ORCID: https://orcid.org/0000-0001-7579-910X)
- Massimo Mecella (ORCID: https://orcid.org/0000-0002-9730-8882)
- Ivan Compagnucci (ORCID: https://orcid.org/0000-0002-1991-0579)
- Pedro Valderas (ORCID: https://orcid.org/0000-0002-4156-0675)
- Lukas Malburg (ORCID: https://orcid.org/0000-0002-6866-0799)
- Victoria Torres (ORCID: https://orcid.org/0000-0002-2039-2174)
- Chiao-Yun Li
- Emre Kilic
- Emilio Carrión (ORCID: https://orcid.org/0000-0002-7026-0495)
Institutions
- Università di Camerino (IT)
- TU Dortmund University (DE)
- University of St.Gallen (CH)
- German Research Centre for Artificial Intelligence (DE)
- Fraunhofer Institute for Applied Information Technology (DE)
- OST - Ostschweizer Fachhochschule (CH)
- Merck, S.L.U. (Spain) (ES)
- University of Applied Sciences St. Gallen (CH)
- Gran Sasso Science Institute (IT)
- Isotopen Technologien München (Germany) (DE)
- University of Bayreuth (DE)
- Universitat Politècnica de València (ES)
- Technical University of Munich (DE)
- Sapienza University of Rome (IT)
- RWTH Aachen University (DE)
- Universität Trier (DE)
- Sirris (BE)
- KU Leuven (BE)
Publication Details
- Journal
- Internet of Things
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1016/j.iot.2024.101477
- Citations
- 12
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 6.34