How AI cut unplanned downtime 22% at a major mine
Illustrative case study · Client details and outcomes await verification. Two years of ignored sensor data became a three-week early-warning system for equipment failure — and a new way of running maintenance.
What changed
The starting point
Kariba Minerals ran maintenance on fixed calendars: components were replaced whether worn or not, yet catastrophic failures still arrived unannounced. A single mill gearbox failure had recently halted production for five days.
Ironically, the data to prevent this already existed — vibration, temperature and load sensors streamed readings that nobody analysed. Maintenance planning meetings worked from breakdowns, not forecasts.
What we built
HLM built a data pipeline consolidating telemetry from plant PLCs and historians into a time-series store, then trained failure-prediction models on two years of readings matched against the maintenance log.
The models score each critical asset daily. When failure probability rises, planners see the alert — with the contributing signals explained — inside the same dashboard they use for scheduling, and a work order is drafted automatically.
How the rollout ran
Phased, transparent, and designed to earn trust at every gate.
Weeks 1–3: Data audit
Sensor coverage assessed, telemetry consolidated, two years of history cleaned and matched to maintenance records.
Weeks 4–8: Model development
Failure-prediction models trained and validated per asset class; alert thresholds agreed with engineers.
Weeks 9–12: Shadow running
Models ran silently alongside normal operations; every alert reviewed with the maintenance team to build trust.
Weeks 13–16: Go-live & handover
Dashboard and work-order integration launched; planners trained; retraining pipeline automated.
Illustrative quotation · not a verified testimonial
“For the first time, the machines tell us when they need attention — before they fail. Maintenance has moved from firefighting to planning, and the numbers speak for themselves.”Engineering Manager
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