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it - Information Technology

Methods and Applications of Informatics and Information Technology

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Volume 60, Issue 3

Issues

Business process management for Industry 4.0 – Three application cases in the DFKI-Smart-Lego-Factory

Jana-Rebecca Rehse
  • Corresponding author
  • Institute for Information Systems, German Research Center for Artificial Intelligence (DFKI), Stuhlsatzenhausweg 3, Geb. D3 2, 66123, Saarbrücken, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Sharam Dadashnia
  • Institute for Information Systems, German Research Center for Artificial Intelligence (DFKI), Stuhlsatzenhausweg 3, Geb. D3 2, 66123, Saarbrücken, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Peter Fettke
  • Institute for Information Systems, German Research Center for Artificial Intelligence (DFKI), Stuhlsatzenhausweg 3, Geb. D3 2, 66123, Saarbrücken, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-06-28 | DOI: https://doi.org/10.1515/itit-2018-0006

Abstract

The advent of Industry 4.0 is expected to dramatically change the manufacturing industry as we know it today. Highly standardized, rigid manufacturing processes need to become self-organizing and decentralized. This flexibility leads to new challenges to the management of smart factories in general and production planning and control in particular. In this contribution, we illustrate how established techniques from Business Process Management (BPM) hold great potential to conquer challenges in Industry 4.0. Therefore, we show three application cases based on the DFKI-Smart-Lego-Factory, a fully automated “smart factory” built out of LEGO® bricks, which demonstrates the potentials of BPM methodology for Industry 4.0 in an innovative, yet easily accessible way. For each application case (model-based management, process mining, prediction of manufacturing processes) in a smart factory, we describe the specific challenges of Industry 4.0, how BPM can be used to address these challenges, and, their realization within the DFKI-Smart-Lego-Factory.

Keywords: Industry 4.0; Business Process Management; Smart Factories; Process Mining; Process Prediction

ACM CCS: Applied computingEnterprise ComputingBusiness Process Management

References

  • 1.

    W. M. van der Aalst, Process mining: data science in action, 2, Springer, 2016.Google Scholar

  • 2.

    Joerg Evermann, Jana-Rebecca Rehse and Peter Fettke, A Deep Learning Approach for Predicting Process Behaviour at Runtime, in: Proceedings of the 1st International Workshop on Runtime Analysis of Process-Aware Information Systems. International Workshop on Runtime Analysis of Process-Aware Information Systems (PRAISE-2016), located at International Conference on Business Process Management, September 18–22, Rio de Janeiro, Brazil (Marlon Dumas and Marcelo Fantinato, eds), Springer, 2016.Google Scholar

  • 3.

    Joerg Evermann, Jana-Rebecca Rehse and Peter Fettke, Predicting process behaviour using deep learning, Decision Support Systems 100 (2017), 129–140, Special Issue on Smart Business Process Management.Web of ScienceCrossrefGoogle Scholar

  • 4.

    Peter Fettke and Peter Loos, Referenzmodellierungsforschung, Wirtschaftsinformatik 46 (2004), 331–340.CrossrefGoogle Scholar

  • 5.

    Constantin Houy, Peter Fettke and Peter Loos, Empirical Research in Business Process Management – Analysis of an emerging field of research, Business Process Management Journal (BPMJ) 16 (2010), 619–661.CrossrefGoogle Scholar

  • 6.

    Constantin Houy, Peter Fettke, Peter Loos, Wil van der Aalst and John Krogstie, BPM-in-the-Large – Towards a higher level of abstraction in Business Process Management, in: E-Government and E-Services EGES/Global Information Systems Processes GISP 2010. World Computer Congress (WCC-2010), September 20–23, Brisbane, Australia, (Marijn Janssen, Winfried Lamersdorf, Jan Pries-Heje and Michael Rosemann, eds), 334, pp. 237–248, Springer, Berlin, 2010.Google Scholar

  • 7.

    Christian Janiesch, Agnes Koschmider, Massimo Mecella, Barbara Weber, Andrea Burattin, Claudio Di Ciccio andet al., The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges, CoRR, arXiv:1709.03628 (2017).Google Scholar

  • 8.

    Heiner Lasi, Peter Fettke, Hans-Georg Kemper, Thomas Feld and Michael Hoffmann, Industry 4.0, Business & Information Systems Engineering 6 (2014), 239–242.CrossrefWeb of ScienceGoogle Scholar

  • 9.

    Jana-Rebecca Rehse, Peter Fettke and Peter Loos, A graph-theoretic method for the inductive development of reference process models, Software & Systems Modeling 16 (2017), 833–873.Web of ScienceCrossrefGoogle Scholar

  • 10.

    August-Wilhelm Scheer, ARIS - Business Process Modeling, 3ed, Springer, Berlin et al., 2000.Google Scholar

  • 11.

    Konsta Mikael Sirvio, Intelligent Systems in Maintenance Planning and Management, pp. 221–245, Springer International Publishing, Cham, 2015.Google Scholar

  • 12.

    Verband Deutscher Maschinen- und Anlagenbauer (VDMA) - Forum Industrie 4.0: Leitfaden Industrie 4.0. Frankfurt am Main, 2015.

About the article

Jana-Rebecca Rehse

Jana-Rebecca Rehse is a Researcher at the Institute for Information Systems (IWi) at the German Research Center for Artificial Intelligence (DFKI). She holds a Bachelor and a Master Degree in Information Systems from Saarland University. In 2014, she was a visiting research scholar at Stevens Institute of Technology in Hoboken, NJ. Her research interests include Business Process Management, Reference Modeling, Process Mining, Process Prediction, and Design Science.

Sharam Dadashnia

Sharam Dadashnia is a Researcher at the Institute for Information Systems (IWi) at the German Research Center for Artificial Intelligence (DFKI). His research interest is primary in the field of business process management, especially in process mining and the application in domains such as Industry 4.0 and the automated testing of software usability. Prior to that, he worked in software development at SAP SE. In addition to his scientific activities, Sharam Dadashnia is managing director of a software development company iSol UG he co-founded.

Peter Fettke

Prof. Dr Peter Fettke is Professor of Business Informatics at Saarland University and Principal Researcher, Research Fellow and Research Group Leader at the German Research Center for Artificial Intelligence (DFKI). Prof. Fettke and his 30-strong research group focus on the interface between the topics of business process management and artificial intelligence (AI). He is the author of more than 100 publications and belongs to the top 10 most cited scientists at the DFKI.


Received: 2018-01-26

Revised: 2018-03-27

Accepted: 2018-04-25

Published Online: 2018-06-28

Published in Print: 2018-07-01


Citation Information: it - Information Technology, Volume 60, Issue 3, Pages 133–141, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2018-0006.

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