Predictive Maintenance System — Heavy Equipment Operator
4.2d→0.6d
Average unplanned downtime per asset per year
83%
Of predicted failures avoided with planned intervention
31%
Reduction in total maintenance cost
£2.8M
Annual avoided production loss
The Challenge
A heavy equipment operator running 240 assets across 8 sites was managing maintenance reactively — equipment failures caused an average of 4.2 unplanned downtime days per asset per year, each costing £18,000–£45,000 in lost production and emergency repair costs. Maintenance schedules were calendar-based, not condition-based.
What We Built
We deployed a predictive maintenance system ingesting IoT sensor data from all 240 assets: vibration, temperature, pressure, oil quality, and operational load. Models predict failure probability for 12 failure modes per asset type, triggering maintenance work orders before failure with 18-day average lead time — enabling planned downtime instead of unplanned breakdowns.
“We used to fix equipment after it broke. Now we fix it before it breaks — and the production reliability numbers show exactly what that's worth.”
— VP of Asset Management
Want a Result Like This?
SmartPath AI builds and deploys production AI systems for enterprises. Schedule a strategy session to discuss your specific use case.
Schedule Strategy Session