How Does Artificial Intelligence Support Machine Maintenance?

Using CMMS Data for Faster and More Effective Inspections

Maintenance is one of the key challenges in manufacturing companies. Every unplanned breakdown means costly downtime, loss of productivity, and additional service expenses. Many plants today use CMMS (Computerized Maintenance Management Systems), which collect vast amounts of data on machine operations. However, this data is often used only for reporting.

With artificial intelligence (AI), you can take it a step further and turn CMMS data into a practical tool for predicting failures and planning smarter inspections.

How Does AI Use CMMS Data?

A CMMS records information such as:

• history of failures and repairs,
• inspection times and service response times,
• parts used for repairs,
• maintenance costs.

Based on this data, AI models can:

• detect patterns of recurring failures,
• predict when a machine will require intervention,
• suggest the optimal moment for inspection,
• create rankings of the most failure-prone equipment.

Better Prevention = Less Downtime

Traditional preventive maintenance relies on time-based schedules – for example, inspections every three months. This approach often means:

• inspections too frequent (unnecessary costs),
• inspections too infrequent (higher risk of breakdowns).

AI allows inspections to be aligned with the real condition of the machine, not just the calendar. Thanks to this:

• technicians focus on actual risks,
• service time is reduced,
• the risk of unplanned downtime decreases.

Business Benefits

• Reduced downtime costs – fewer breakdowns at critical moments.
• Optimized maintenance resources – technicians perform fewer but more targeted interventions.
• Improved spare parts planning – AI can forecast demand for replacement parts.
• Easy-to-calculate ROI – simply compare downtime costs before and after implementation.

A Small Step, a Big Impact

Importantly, starting with AI in preventive maintenance doesn’t require immediate investment in expensive IoT sensors. Even existing CMMS data is an excellent starting point for building predictive models. Later, the system can be expanded with additional data sources (e.g., vibration, temperature, humidity).

Summary

Artificial intelligence and CMMS form a powerful duo that enables a shift from reactive maintenance to predictive maintenance. By analyzing historical data, companies can not only forecast failures but also plan inspections better and shorten their duration. It’s an ideal direction for companies that want to start with small AI projects and quickly prove return on investment.

‍

PS: We encourage you to follow our blog, where we cover topics related to AI automation.

‍