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A Holistic Framework for AI Systems in Industrial Applications

: Kaymakci, Can; Wenninger, Simon; Sauer, Alexander


Ahlemann, Frederik:
Innovation Through Information Systems. Vol.II: A Collection of Latest Research on Technology Issues
Cham: Springer Nature, 2021 (Lecture Notes in Information Systems and Organisation 47)
ISBN: 978-3-030-86796-6 (Print)
ISBN: 978-3-030-86797-3 (Online)
ISBN: 978-3-030-86798-0
Internationale Tagung Wirtschaftsinformatik (WI) <16, 2021, Online>
Conference Paper
Fraunhofer IPA ()
Fraunhofer FIT ()
Energiespeicher; Energiedatenerfassung; Datenmanagement; Fertigungssystem; maschinelles Lernen; Künstliche Intelligenz

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.