Asset Utilisation Optimisation · IRON & STEEL · MINING
Iron-ore loading and hauling run on cycle time, not gut feel.
Excavator and haul-truck idle minutes, cycle drift and fuel inefficiency quietly drain throughput across the pit. The XMPro AO Platform fuses real-time telemetry from every mobile asset with predictive analytics so loading and hauling productivity is managed against target — shift by shift, asset by asset.
What's getting in the way today.
Iron-ore loading and hauling is where shift productivity is made or lost. Four pressures compound:
Idle time hidden in the aggregate
Excavator and truck idle accumulates across the shift without continuous telemetry — eroding throughput without ever surfacing as a single visible event.
Reactive maintenance on mobile assets
Calendar-driven servicing of haul trucks and excavators either over-services healthy assets or misses degradation already underway.
Cycle-time drift
Loading, hauling, dumping and return cycles vary by shift, route and operator. Without measurement, the variance is invisible until tons-per-hour falls.
Fuel and emissions exposure
Fuel consumption per ton and CO2 emissions are major cost and compliance lines, but rarely tracked against the asset and route that caused them.
Loading & Hauling Optimisation — how it works.
A unified picture of every excavator and haul truck — fed by on-board sensors, ranked by productivity and condition, and surfaced for shift supervisors and reliability engineers as a single operational view.
The platform ingests sensor data from excavators and haul trucks continuously — operational status, idle time, vibration, load capacity, engine status, fuel consumption and CO2 emissions. ML models analyse this telemetry to predict component failures and surface cycle-time inefficiencies, with confidence scoring and time-to-action windows. Customisable dashboards show production throughput per excavator and per truck against target, real-time site overview, cycle and idle-time analysis, fuel use per ton and operational cost per ton. Threshold breaches generate ranked recommendations — maintenance, operational adjustment or shift reallocation — feeding directly into work-request creation.
*Illustrative dashboards from the platform. Layout, signals and decision points are scoped per site.
Scope this for your operation.
Tell us about your fleet, your control maturity and the lever that matters most. We’ll map this use case to your starting point.
What this looks like in operation.
Productivity managed against target
Excavator and truck throughput is tracked shift by shift, so deviation gets a corrective action rather than a month-end post-mortem.
Lower fuel and emissions cost
Per-asset, per-route fuel and CO2 baselines turn the utility bill and the compliance number into leverable line items.
Right-sized mobile-asset maintenance
Predicted condition replaces OEM calendars, extending mobile-asset life and freeing crew time for higher-value reliability work.
Built for these industries.
Other solutions you might explore.
Not a concept. In production.
XMPro is deployed at Tier 1 global operators across asset-intensive and mission-critical industries — delivering measurable results across predictive maintenance, process optimisation and operational intelligence.
Now pushing the frontier.
MAGS agents are achieving what no other industrial platform has demonstrated — sustained autonomous operations at enterprise scale.
