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Prescriptive Control of Business Processes

Author

Listed:
  • Julian Krumeich

    (German Research Center for Artificial Intelligence (DFKI GmbH))

  • Dirk Werth

    (German Research Center for Artificial Intelligence (DFKI GmbH))

  • Peter Loos

    (German Research Center for Artificial Intelligence (DFKI GmbH))

Abstract

This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators.

Suggested Citation

  • Julian Krumeich & Dirk Werth & Peter Loos, 2016. "Prescriptive Control of Business Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 58(4), pages 261-280, August.
    • Handle: RePEc:spr:binfse:v:58:y:2016:i:4:d:10.1007_s12599-015-0412-2
    DOI: 10.1007/s12599-015-0412-2
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    References listed on IDEAS

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    1. Martin Kowalczyk & Peter Buxmann, 2014. "Big Data and Information Processing in Organizational Decision Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 267-278, October.
    2. Matthias Jarke, 2014. "Interview with Michael Feindt on “Prescriptive Big Data Analytics”," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 301-302, October.
    3. Felix Wortmann & Kristina Flüchter, 2015. "Internet of Things," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(3), pages 221-224, June.
    4. Vasant Dhar & Matthias Jarke & Jürgen Laartz, 2014. "Big Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 257-259, October.
    5. Kowalczyk, Martin & Buxmann, Peter, 2014. "Big Data and Information Processing in Organizational Decision Processes: A Multiple Case Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65730, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Jahangirian, Mohsen & Eldabi, Tillal & Naseer, Aisha & Stergioulas, Lampros K. & Young, Terry, 2010. "Simulation in manufacturing and business: A review," European Journal of Operational Research, Elsevier, vol. 203(1), pages 1-13, May.
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    Cited by:

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    2. Miguel Fernández-Cejas & Carlos J. Pérez-González & José L. Roda-García & Marcos Colebrook, 2022. "CURIE: Towards an Ontology and Enterprise Architecture of a CRM Conceptual Model," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(5), pages 615-643, October.
    3. Mathias Eggert & Jens Alberts, 2020. "Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda," Business Research, Springer;German Academic Association for Business Research, vol. 13(2), pages 685-739, July.
    4. Ulrich Leicht-Deobald & Thorsten Busch & Christoph Schank & Antoinette Weibel & Simon Schafheitle & Isabelle Wildhaber & Gabriel Kasper, 2019. "The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity," Journal of Business Ethics, Springer, vol. 160(2), pages 377-392, December.
    5. Joash Mageto, 2021. "Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    6. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    7. Samuel Bangura, 2024. "Effects Of Hr Analytics On Hrm Practices: An Integrative Review," Eurasian Journal of Business and Management, Eurasian Publications, vol. 12(2), pages 114-122.

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