Every manufacturer I talk to is being pitched predictive maintenance. Sensors, AI, real-time monitoring—it all sounds great. And sometimes it is great. But I’ve also seen plenty of operations spend six figures on predictive systems for equipment that was perfectly fine with scheduled preventive maintenance.

The truth is, predictive maintenance isn’t automatically better. It’s a different tool for different situations. Let’s break down when each approach actually makes sense.

What We’re Actually Comparing

First, definitions, because people use these terms inconsistently.

Preventive maintenance is scheduled upkeep based on time or usage. Change the oil every 500 hours. Replace bearings every 12 months. Inspect belts quarterly. It’s calendar-driven or meter-driven, not condition-driven.

Predictive maintenance uses condition monitoring to predict when something will fail. Vibration sensors on motors, thermal imaging on electrical panels, oil analysis on hydraulics. You maintain based on actual equipment condition, not a predetermined schedule.

Both are proactive. Both aim to prevent unplanned downtime. The difference is how you decide when to act.

When Preventive Maintenance Wins

Let’s start with where the simpler approach makes sense, because it’s more common than vendors want to admit.

Low-cost, high-volume consumables. If you’re replacing air filters or lubricants, the cost of monitoring exceeds the cost of just changing them on schedule. There’s no ROI case for sensors on a $15 air filter.

Equipment with predictable wear patterns. Some things just wear consistently. Conveyor belts, for instance, tend to degrade at known rates based on usage. You don’t need fancy monitoring—you need a maintenance schedule based on running hours.

Critical equipment with low replacement cost. If downtime is catastrophic but the part is cheap, just replace it preventively before it fails. The insurance value of scheduled replacement beats the optimisation value of running it longer.

Simple mechanical systems. A basic pump with well-understood failure modes doesn’t need IoT sensors. A skilled maintenance tech doing regular inspections will catch issues just fine.

Operations without maintenance data infrastructure. If you don’t have systems to collect, analyse, and act on sensor data, you’re not ready for predictive maintenance. Get good at preventive first.

When Predictive Maintenance Justifies the Cost

Now the other side. When does the complexity of predictive monitoring actually pay off?

Expensive, critical equipment. If downtime costs you $10,000 an hour and the equipment itself is worth $500K+, the business case for predictive monitoring writes itself. Think large compressors, turbines, production line motors.

Equipment with variable operating conditions. If your machinery runs at different loads, speeds, or environments, preventive schedules get tricky. Sensors can track actual wear instead of guessing based on averages.

Long lead time components. If replacement parts take 8 weeks to source, you need advance warning of failures. Predictive monitoring gives you that runway. Preventive maintenance on a fixed schedule might catch issues too late or waste parts replaced too early.

High energy consumption equipment. Motors and drives that are degrading often show it in power consumption or heat before they fail. Catching this early saves energy costs, not just maintenance costs. The International Energy Agency estimates that optimised motor systems can reduce industrial energy use by 20-30%.

Complex systems where failure modes vary. If equipment can fail in multiple ways—bearing failure, alignment issues, electrical problems—condition monitoring can differentiate between them. Preventive schedules just mark time; they don’t tell you what’s actually wrong.

The Hybrid Approach (What Most Plants Should Do)

Here’s what I see working in practice: most manufacturers run a hybrid system.

Critical, expensive equipment gets predictive monitoring. Standard production machinery stays on preventive schedules with periodic condition checks. Low-value consumables get replaced on fixed intervals.

You don’t need to pick one strategy for the whole plant. Segment your equipment based on criticality and economics, then assign maintenance strategies accordingly.

A practical framework:

• Category A: High cost of downtime + expensive equipment = full predictive monitoring
• Category B: Moderate criticality = preventive maintenance with quarterly condition assessments
• Category C: Low criticality or cheap components = time-based preventive maintenance
• Category D: Consumables = run to failure or fixed replacement intervals

The Real Barriers to Predictive Maintenance

Let’s talk about why predictive maintenance projects fail, because the technology is only part of it.

Data literacy. Your team needs to interpret what the sensors are telling you. If a vibration reading spikes, what does that mean? Is it urgent? Can it wait a week? Without expertise to translate data into decisions, you’ve just got expensive noise.

Integration challenges. Sensors are the easy part. Getting that data into a system that triggers work orders, manages parts inventory, and tracks outcomes? That’s the hard part. Most plants underestimate this.

Change management. Your maintenance crew has been doing preventive rounds for 20 years. Now you’re telling them to trust what a computer says? That transition takes training, communication, and patience.

False positives. Early predictive systems often cry wolf. Alerts that aren’t real problems train people to ignore alerts, which defeats the whole purpose. Calibration and threshold tuning take time.

Making the Decision

If you’re evaluating whether to move from preventive to predictive for specific equipment, here’s a simple framework:

1. Calculate downtime cost per hour for that equipment
2. Estimate current maintenance cost (parts + labour + production loss from scheduled shutdowns)
3. Price out predictive monitoring (sensors + software + integration + training)
4. Estimate maintenance cost reduction (fewer emergency repairs, optimised part replacement, reduced downtime)

If the payback is under 18 months, it’s probably worth it. If it’s over 3 years, you’re likely better off sticking with preventive.

But don’t forget qualitative factors. If you’ve got a skilled team that knows your equipment inside out, their preventive inspection protocols might be catching 90% of what sensors would catch. If you’ve got high turnover and junior staff, predictive systems can codify expertise you don’t have in-house.

The Bottom Line

Predictive maintenance isn’t a replacement for preventive maintenance. It’s an upgrade for situations where the economics justify it.

Start with a solid preventive maintenance program. If you can’t maintain equipment on a schedule, you’re definitely not ready for condition-based monitoring.

Then selectively add predictive capabilities where they make financial sense. Critical equipment, expensive downtime, variable conditions—that’s your sweet spot.

And be realistic about implementation. The sensors are the cheap part. The infrastructure, training, and cultural change are where the real investment lives.

Done right, predictive maintenance is brilliant. Done wrong, it’s an expensive distraction from maintenance fundamentals that weren’t working anyway.