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Development and Future Research Directions of AI-Based Anomaly Detection in Smart Manufacturing: A Bibliometric Analysis

In: Digital Innovation and Organizational Transformation

Author

Listed:
  • Maximilian Nebel

    (TU Dortmund University)

  • Philip Stahmann

    (TU Dortmund University)

  • Christian Janiesch

    (TU Dortmund University)

Abstract

Manufacturing companies face a vast increase of data. Connected sensors turn physically isolated objects into nodes in data communication networks. This development enables but also forces companies to harness their data to gain a competitive edge. In this regard, anomaly detection enables seamless processes, so that production failures can be avoided. Artificial intelligence (AI) and especially machine learning and deep learning constitute instruments to leverage statistical complexity necessary to identify anomalies in these vast amounts of data. AI-based anomaly detection has therefore been subject to an intensive academic discourse in Information Systems. This short paper provides preliminary results from a bibliometric analysis highlighting the development over time of scientific contributions in this field. Our findings show that the academic discourse has gained momentum but is still premature. Additionally, we find that a technical perspective on the topic prevails in literature.

Suggested Citation

  • Maximilian Nebel & Philip Stahmann & Christian Janiesch, 2026. "Development and Future Research Directions of AI-Based Anomaly Detection in Smart Manufacturing: A Bibliometric Analysis," Lecture Notes in Information Systems and Organization, in: Christoph M. Flath & Gunther Gust & Frédéric Thiesse & Axel Winkelmann (ed.), Digital Innovation and Organizational Transformation, pages 379-386, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08483-5_24
    DOI: 10.1007/978-3-032-08483-5_24
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