What is predictive maintenance?
Unplanned machine downtime is one of the most costly disruptions in manufacturing. According to Deloitte, unplanned downtime costs manufacturing companies an average of $50 per minute, and 82% of all companies have experienced at least one unforeseen failure in the past 3 years.
Predictive maintenance uses sensor data and machine learning to continuously monitor machine condition and schedule maintenance at the optimal moment — not too early (waste) and not too late (failure).
How does it work?
Sensors continuously measure parameters such as vibrations, temperature, pressure, power consumption and sound. Machine learning models learn the normal behaviour pattern of each machine and detect subtle changes that indicate wear or an impending failure.
The system generates maintenance suggestions with a confidence score and an estimated remaining service life. Maintenance can be scheduled at a moment that the production plan allows.
What does it deliver?
Manufacturing companies report up to 50% less unplanned downtime, 25–30% lower maintenance costs and 10–20% longer machine lifespan. According to McKinsey, predictive maintenance delivers an ROI of 300–500% through avoided failures and optimised maintenance cycles.