Abstract

Manufacturing companies face a vast increase of data. Connected sen-sors 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 espe-cially machine learning and deep learning constitute instruments to leverage sta-tistical 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 pre-mature. Additionally, we find that a technical perspective on the topic prevails in literature.

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