We developed an AI-driven predictive maintenance system that monitors sensor data from production equipment, predicts failures before they happen and automatically schedules maintenance windows — reducing unplanned downtime by 60% and saving an estimated €400K annually.
Real-time ingestion of temperature, vibration and pressure data from 200+ sensors into a time-series database.
ML models trained on historical failure patterns that flag equipment degradation 2–5 days before failure.
Workflow automation that creates maintenance tickets, assigns technicians and orders replacement parts via ERP integration.
Real-time equipment health overview with risk scores, predicted failure windows and maintenance history.