  Anomaly detection (AD) is a crucial process often required in industrial
settings. Anomalies can signal underlying issues within a system, prompting
further investigation. Industrial processes aim to streamline operations as
much as possible, encompassing the production of the final product, making AD
an essential mean to reach this goal.Conventional anomaly detection
methodologies typically classify observations as either normal or anomalous
without providing insight into the reasons behind these
classifications.Consequently, in light of the emergence of Industry 5.0, a more
desirable approach involves providing interpretable outcomes, enabling users to
understand the rationale behind the results.This paper presents the first
industrial application of ExIFFI, a recently developed approach focused on the
production of fast and efficient explanations for the Extended Isolation Forest
(EIF) Anomaly detection method. ExIFFI is tested on two publicly available
industrial datasets demonstrating superior effectiveness in explanations and
computational efficiency with the respect to other state-of-the-art explainable
AD models.
