Advertisement

“Strukturieren, Strukturieren, Strukturieren” in the Era of Robotic Process Automation

  • Peter FettkeEmail author
  • Peter Loos
Chapter

Abstract

While, in the first machine age, physical power was automated, intellectual power is automated in the second machine age. This development has interesting implications for Jörg’s leitmotif “Strukturieren, Strukturieren, Strukturieren”. In this article, we add to this discussion in the context of structuring processes. Therefore, we first structure the discussion about robotic process automation. After that, we illustrate how robots can support the structuring of processes. Our article concludes with some general remarks on process automation.

Keywords

Process modelling Process management Process mining Artificial intelligence Machine learning 

References

  1. Becker, J. (2001). Einige Thesen zur Forschung in der Wirtschaftsinformatik. Unpublished Manuscript.Google Scholar
  2. Bostrom, N. (2016). Superintelligence: Paths, dangers, strategies. Oxford: Oxford University Press.Google Scholar
  3. Di Valentin, C., Emrich, A., Werth, D., & Loos, P. (2013). Architecture and implementation of a decision support system for software industry business models. In Proceedings of the 19th Americas Conference on Information Systems (AMCIS 2013). Chicago, lL (USA).Google Scholar
  4. Evermann, J., Rehse, J.-R., & Fettke, P. (2017). Predicting process behaviour using deep learning. Decision Support Systems, 100(8), 129–140.CrossRefGoogle Scholar
  5. Fettke, P., & Loos, P. (2003). Model driven architecture (MDA). Wirtschaftsinformatik, 45(5), 555–559.CrossRefGoogle Scholar
  6. Fettke, P., & Loos, P. (2004). Referenzmodellierungsforschung. Wirtschaftsinformatik, 46(5), 331–340.CrossRefGoogle Scholar
  7. Fettke, P., & Loos, P. (2007). Perspectives on Reference Modeling. In P. Fettke & P. Loos (Eds.), Reference modeling for business systems analysis (pp. 1–20). Hershey, PA/London: Idea Group.CrossRefGoogle Scholar
  8. Friedman, M. (1991). Old wine in new bottles. The Economic, 101(404), 33–40.Google Scholar
  9. Heinrich, B., Klier, M., & Zimmermann, S. (2015). Automated planning of process models: Design of a novel approach to construct exclusive choices. Decision Support Systems, 78(10), 1–14.CrossRefGoogle Scholar
  10. Houy, C., Fettke, P., & Loos, P. (2010). Empirical research in business process management—Analysis of an emerging field of research. Business Process Management Journal, 16(4), 619–661.CrossRefGoogle Scholar
  11. Koschmider, A., Hornung, T., & Oberweis, A. (2011). Recommendation-based editor for business process modeling. Data & Knowledge Engineering, 70(6), 483–503.CrossRefGoogle Scholar
  12. Krivograd, N., Fettke, P., & Loos, P. (2014). Development of an intelligent maturity model-tool for business process management. In Proceedings of the 47th Hawaii International Conference on System Science (HICSS). Waikoloa (HI).Google Scholar
  13. Krumeich, J., Zapp, M., Mayer, D., Werth, D., & Loos, P. (2016). Modeling complex event patterns in EPC-models and transforming them into an executable event pattern language. In V. Nissen, D. Stelzer, S. Straßburger, & D. Fischer (Eds.), Multikonferenz Wirtschaftsinformatik (MKWI) (pp. 81–92). Ilmenau: Universitätsverlag Ilmenau.Google Scholar
  14. Morana, S., Schacht, S., & Maedche, A. (2016). Exploring the design, use, and outcomes of process guidance systems - a qualitative field study. In J. Pasons, T. Tuunanen, J. Venable, B. Donnellan, M. Helfert, & J. Kenneally (Eds.), Tackling Society’s Grand Challenges with Design Science: 11th International Conference, DESRIST 2016 (pp. 81–96). St. John’s, NL (CA): Springer.Google Scholar
  15. Ng, A. (2016). What artificial intelligence can and can’t do right now. Harvard Business Review, Digital Articles.Google Scholar
  16. Rehse, J.-R., Fettke, P., & Loos, P. (2015). A graph-theoretic method for the inductive development of reference process models. Software & Systems Modeling, 16(3), 833–873.CrossRefGoogle Scholar
  17. Riefer, M., Ternis, S. F., & Thaler, T. (2016). Mining process models from natural language text: A state-of-the-art analysis. In V. Nissen, D. Stelzer, S. Straßburger, & D. Fischer (Eds.), Multikonferenz Wirtschaftsinformatik (MKWI) (pp. 1–12). Ilmenau: Universitätsverlag Ilmenau.Google Scholar
  18. Scheer, A.-W. (2000). ARIS—business process modeling (3rd ed.). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  19. Scheer, A. -W. (2017). Robotic Process Automation (RPA): Revolution der Unternehmenssoftware. IM + io, 127(3), 30–41.Google Scholar
  20. Simon, H. A. (1960). The new science of management decision. New York: Harper & Brothers.CrossRefGoogle Scholar
  21. van der Aalst, W. M. P. (2012). Process mining: Overview and opportunities. Communications of the ACM, 55(8), 76–83.CrossRefGoogle Scholar
  22. van der Aalst, W. M. P., Bichler, M., & Heinzl, A. (2018). Robotic process automation. Business and Information Systems Engineering, 60(4), 269–272.CrossRefGoogle Scholar
  23. Zapp, M., Fettke, P., & Loos, P. (2017). Towards a software prototype supporting automatic recognition of sketched business process models. In J. M. Leimeister & W. Brenner (Eds.), Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017) (pp. 1283–1286). St. Gallen.Google Scholar
  24. Zelewski, S. (1986). Das Leistungspotential der Künstlichen Intelligenz: Eine informationstechnisch-betriebswirtschaftliche Analyse. Witterschlick, Bonn: M. Wehle.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Saarland UniversitySaarbrückenGermany
  2. 2.German Research Center for Artificial Intelligence (DFKI)SaarbrückenGermany

Personalised recommendations