The ISO 55000 standard on asset management makes a point that’s hard to argue with. There is an optimum level of preventative maintenance for any asset. Too little and the cost of failures is too high. Too much and you’re wasting money on inspections that don’t find anything.
The problem is working out where that optimum actually is.
The Challenge
Most organisations stick to fixed schedules. Inspect every six months. Replace every five years. These schedules are usually based on manufacturer recommendations or historical habit. They’re comfortable. They’re defensible. They’re also almost certainly not optimal.
The problem is that equipment doesn’t fail on a fixed schedule. It fails based on condition, usage, and a dozen other variables that a calendar can’t capture.
A Better Approach
We’ve been working with asset-intensive industries on a data-driven approach to maintenance. Instead of fixed schedules, we use statistical techniques to understand when equipment is actually likely to fail.
Bayesian Belief Networks allow us to calculate the probability of failure based on real measurements. If vibration is present, for example, the probability of failure increases significantly. By combining multiple measurements, we can build a much more accurate picture of risk.
Weibull Analysis helps us understand where equipment is in its lifecycle. Is it in the burn-in phase, where failures are more likely but decreasing? Is it in normal operation, where failures are random and unpredictable? Or is it entering the wear-out phase, where failures will increase over time?
By recording and analysing failure data, we can determine which phase equipment is in and predict what’s likely to happen next.
What This Enables
This approach enables a fundamentally different way of managing maintenance.
Inspect what needs inspecting. Instead of inspecting everything on a fixed schedule, we can prioritise the items most likely to fail.
Replace what needs replacing. Instead of replacing everything at a fixed age, we can identify the optimal economic point for refurbishment or replacement.
Schedule dynamically. Under this paradigm, priorities and frequencies change over time. This requires continuous scheduling and rescheduling, which is very different from a static preventative maintenance schedule.
Manage breakdowns efficiently. When breakdowns do occur, we can allocate them to the most appropriate team based on location, capability, and cost.
The Results
Based on projects with SP-Ausnet, Origin Energy, City West Water, and Hitachi Rail, there is the potential to reduce costs by around 20% without any impact on equipment reliability.
That’s not about doing less maintenance. It’s about doing the right maintenance.
Why It Matters
For asset-intensive industries, maintenance is one of the largest controllable costs. Getting it wrong means either spending too much or dealing with preventable failures. Getting it right means lower costs, higher reliability, and better use of limited resources.
The tools exist. The data exists. What’s often missing is the will to move beyond the calendar.
