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

Maximilian Nebel (), Philip Stahmann () and Christian Janiesch ()
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Maximilian Nebel: TU Dortmund University
Philip Stahmann: TU Dortmund University
Christian Janiesch: TU Dortmund University

A chapter in Digital Innovation and Organizational Transformation, 2026, pp 379-386 from Springer

Abstract: 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.

Keywords: Artificial intelligence; Anomaly detection; Bibliometric analysis; Manufacturing (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-08483-5_24

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DOI: 10.1007/978-3-032-08483-5_24

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