Preventive vs Predictive Maintenance: Which to Use (and When)
Key takeaways
- Reactive = fix on failure. Preventive = fix on a schedule. Predictive = fix when the data says so.
- Predictive cuts both breakdowns and the wasted work of over-maintaining.
- It is not all-or-nothing: match the strategy to each asset's criticality and failure pattern.
- Predictive maintenance pays back fastest on critical, expensive-to-fail equipment.
"Should we do preventive or predictive maintenance?" is the wrong question. The right one is: which assets deserve which strategy? Get that mix right and you stop both surprise breakdowns and the money wasted servicing things that were fine.
The four maintenance strategies
| Strategy | Trigger | Best for | Watch out for |
|---|---|---|---|
| Reactive (run to failure) | It breaks | Cheap, non-critical, redundant assets | Surprise downtime, secondary damage, rush costs |
| Preventive (PM) | Time or usage interval | Known wear-out patterns, regulatory items | Over-maintaining; intervals rarely match real wear |
| Predictive (PdM) | Condition data / early warning | Critical, costly-to-fail assets | Needs sensors, data and skills to act on it |
| Prescriptive | Condition data + recommended action | Mature programs with good data | Highest setup; depends on data quality |
Preventive vs predictive, head to head
Preventive is simple and predictable: service every X hours or months. The catch is that fixed intervals almost never match how a machine actually wears, so you either act too early (wasting parts and labour, and introducing maintenance-induced failures) or too late (it fails anyway).
Predictive watches the asset's real condition (vibration, temperature, current, machine signals) and flags a developing fault before it becomes a breakdown. You act once, at the right time. Industry studies (e.g. Deloitte) put the reduction in downtime at roughly 30 to 50 percent, with 20 to 40 percent longer machine life. Those are benchmarks, not promises, but the direction is consistent.
Model the payback of monitoring and a closed-loop CMMS against your current losses.
How to choose (a simple rule of thumb)
- Rank assets by criticality (what does a failure cost in downtime, safety, quality?).
- Critical and costly-to-fail assets: predictive / condition-based monitoring.
- Predictable wear-out or regulatory items: preventive on a sensible interval.
- Cheap, non-critical, redundant assets: reactive is fine, do not over-invest.
- Track the result with MTBF and MTTR to see whether the strategy is working.
Why most plants stall at preventive
The barrier to predictive maintenance is rarely the sensors. It is turning the data into the right action fast enough. A vibration alarm that nobody triages, or a stop with no clear cause, just becomes noise. That is the gap to close.
This is where the partner we recommend, , fits: it reads OEE and stops straight from the PLC, uses computer vision to show the true cause of micro-stops, and closes the loop from signal to an auto-routed work order. It is EU-built with EU data residency and holds ISO 27001 / 20000-1 / 9001 (which supports audit-readiness). Fabrico is a partner we recommend; the tools here are free regardless.
Is condition-based maintenance the same as predictive?
Closely related. Condition-based maintenance acts on a current measurement crossing a threshold; predictive maintenance goes a step further and forecasts when failure will occur. Both rely on monitoring the asset's condition.
Where does TPM fit?
Total Productive Maintenance is broader: it involves operators in routine care and targets the six big losses. It complements whichever technical strategy you use per asset.
What is the fastest first step?
Stop over-maintaining low-risk assets and redirect that effort to monitoring your few critical ones. Then measure MTBF/MTTR to confirm it worked.