Predictive Maintenance vs Preventive Maintenance: When to Use Each

Key Takeaways: Most manufacturers benefit most from moving from reactive to preventive maintenance first. Predictive maintenance (PdM) delivers additional value, but only on top of a functioning preventive maintenance foundation. Fabrico gives you both: usage-based PM triggers that work like condition-based maintenance without expensive sensor infrastructure, and an AI agent that surfaces the patterns that predict failure before it happens.

Predictive maintenance vs preventive maintenance: understanding the difference is step one. Choosing the right strategy for each asset is step two.

The maintenance strategy spectrum from lowest to highest sophistication:

• Reactive (run-to-failure): Fix it when it breaks. Lowest upfront cost, highest failure consequence cost. Only appropriate for non-critical, easily-replaced equipment where failure has no downstream impact.
• Preventive (time/usage-based PM): Scheduled maintenance at fixed intervals. Prevents most failures on critical assets. This is the foundation, nothing above it works without it.
• Condition-based (CBM): Maintenance triggered by measured equipment condition. More efficient than calendar PMs, maintenance happens when needed, not on a schedule.
• Predictive (PdM): Machine learning analysis of sensor data to predict the specific time a component will fail. Most sophisticated, most expensive. Requires 12-18 months of data to build reliable models.

Predictive maintenance delivers positive ROI only when three conditions are met simultaneously:

1. High failure consequence: The asset is critical enough that unplanned failure costs significantly more than planned maintenance. For equipment with under $2,000 downtime cost per failure event, PdM investment rarely pays back.

2. Detectable degradation signature: The failure mode must produce a measurable signal before failure, vibration increasing, temperature rising, oil contamination developing. Catastrophic failures with no warning cannot benefit from PdM regardless of sensor investment.

3. Sufficient data volume: ML-based PdM requires 12-18 months of sensor data and multiple failure events to build reliable prediction models. New equipment or low-failure-frequency assets take years to generate useful models.

Before investing in PdM, confirm that PM compliance on target assets is above 80%. PdM on a machine with sporadic PM execution produces unreliable results, the data is contaminated by variable maintenance quality, not just equipment condition.

The most practical path to condition-based maintenance for most manufacturers doesn't require vibration sensors, oil analysis programs, or separate PdM software.

Fabrico's OEE monitoring provides the data foundation for condition-based decisions without additional hardware:

• OEE performance rate trends: A press running 5% slower than its ideal cycle time over 4 weeks is a condition signal. Fabrico automatically generates a CMMS inspection work order when performance drops below threshold, condition-based maintenance from OEE data alone.
• Usage-based PM triggers: Fabrico connects OEE cycle counters directly to CMMS PM scheduling. A die that reaches 10,000 press cycles gets an automated maintenance work order, regardless of whether that happens in 3 days or 3 weeks.
• Fabrico AI Agent: Analyzes historical OEE and maintenance data patterns to surface "bad actor" assets, the 20% driving 80% of your unplanned downtime, and suggests PM schedule refinements before failures occur.

This approach delivers 70-80% of the benefit of a full PdM program at 10-20% of the cost.