Reactive vs predictive maintenance

The difference between fixing what's broken and fixing what's about to break.

Every maintenance strategy is a bet on when to act. Reactive maintenance bets the failure will be cheap; predictive maintenance bets you can see it coming. The difference in outcomes is measured in downtime hours and emergency spend.

Reactive teams fix equipment after it fails. It's simple — no planning, no monitoring, no upfront cost. But unplanned failures are the most expensive kind: they happen at the worst time, cause secondary damage, and force overtime, expedited parts and missed orders.

Reactive isn't always wrong — a cheap, redundant, non-critical asset can sensibly run to failure. The problem is when it's the default for everything.

Predictive maintenance uses condition data — vibration, temperature, current, ultrasound — to fix equipment just before it would fail. You intervene early enough to plan the work, but not so early that you're replacing healthy parts.

Done well, it's the cheapest point on the maintenance curve: fewer failures than reactive, less wasted effort than over-servicing on a fixed schedule.

Real-world programmes are hybrid: preventive tasks on a calendar for predictable wear, condition monitoring on critical assets, run-to-failure where the maths supports it. The goal isn't a pure strategy — it's matching the approach to the consequence of each failure.

You don't switch overnight. Rank your assets by the cost of failure, put continuous monitoring on the top tier, and let the caught failures fund the expansion. Most teams see the first save inside a quarter.

Preparing…