The present study focuses on the analysis, development, and implementation of a structured, data‑driven methodology for monitoring, classifying, and optimizing failures and micro‑stoppages within the NEF engine machining and assembly processes at the FPT Industrial Turin Plant. Conducted within the Plant Technical Support (PTS) department, the work addresses the need for a reliable, scalable, and technically rigorous system capable of transforming raw operational inputs into actionable insights for maintenance, process engineering, and asset‑management decision‑making. After introducing the industrial context, the production architecture, and the organizational structures—together with the operating logic of the Machining and Assembly areas—the thesis presents the theoretical framework underpinning the study.