Case study
Predicting equipment failure before it stops the site
Unscheduled downtime is the most expensive thing that happens on a mine site. The operator wanted to stop reacting to failures and start scheduling around them.
- Client
- A mining and heavy-equipment operator
- Sector
- Mining · Industrial
- Role
- Direct engagement
- Period
- Not disclosed
- unscheduled downtime
- −23%unscheduled downtime
- maintenance cost
- −12%maintenance cost
The problem
Heavy equipment fails, and when it fails unexpectedly the cost is not the part — it is the shift that stops. The operator was maintaining reactively and wanted to know which components were approaching failure while there was still time to schedule around them.
What we did
Two complementary approaches. On components with enough historical failure data, we modelled remaining useful life directly — how much service life is left in this specific part, given how it has been used.
On components without that history, supervised learning has nothing to learn from. So we used unsupervised methods to estimate stress levels from operating data instead, which flags parts behaving unlike their peers without needing to have seen them fail.
The result
Unscheduled downtime fell by over 23%, and maintenance costs by over 12% once preventive scheduling was in place.
The second-order effect the operator valued more: work could be planned. Knowing a component has weeks rather than days left is what lets a maintenance team order parts and book a window instead of scrambling.
Tell us what decision you are trying to get right.
Not a discovery call about our capabilities. A conversation about the specific thing you need to predict, and whether the data you have can support it.