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A Holistic Framework for AI Systems in Industrial Applications

In: Innovation Through Information Systems

Author

Listed:
  • Can Kaymakci

    (Fraunhofer Institute for Manufacturing Engineering and Automation IPA
    University of Stuttgart)

  • Simon Wenninger

    (University of Applied Sciences Augsburg
    Project Group Business and Information Systems Engineering of the Fraunhofer FIT)

  • Alexander Sauer

    (Fraunhofer Institute for Manufacturing Engineering and Automation IPA
    University of Stuttgart)

Abstract

Although several promising use cases for artificial intelligence (AI) for manufacturing companies have been identified, these are not yet widely used. Existing literature covers a variety of frameworks, methods and processes related to AI systems. However, the application of AI systems in manufacturing companies lacks a uniform understanding of components and functionalities as well as a structured process that supports developers and project managers in planning, implementing, and optimizing AI systems. To close this gap, we develop a generic conceptual model of an AI system for the application in manufacturing systems and a four-phase model to guide developers and project managers through the realization of AI systems.

Suggested Citation

  • Can Kaymakci & Simon Wenninger & Alexander Sauer, 2021. "A Holistic Framework for AI Systems in Industrial Applications," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 78-93, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86797-3_6
    DOI: 10.1007/978-3-030-86797-3_6
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    Citations

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    Cited by:

    1. Simon Wenninger & Christian Wiethe, 2021. "Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 223-242, June.
    2. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).

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