Industry: Surface mining
Organization: An unnamed gold mine in Suriname
Use case: Haul-truck refuel assignment optimization
Evidence basis: Mine Tech Services case study
Disclosure: This is an independent analysis by Conscious Engines. The mine is unnamed and results are consultant and customer reported, limiting independent verification.
1. Outcome at a Glance
The operation reconfigured its fleet-management system to assign refueling based on fuel level, distance, and expected queue rather than simple routines. Across 30 haul trucks, it reported recovering 1,242 operating hours per year, supporting an additional 348,000 tonnes moved and approximately $7 million in revenue.
Key Outcomes
30
Haul trucks
Fleet in scope reported in the cited case.
About $7 million
Revenue impact
Company estimate reported in the cited case.
| Measure | Reported result | Evidence note |
|---|---|---|
| Haul trucks | 30 | Fleet in scope |
| Truck hours recovered | 1,242 per year | Modelled operating result |
| Additional material moved | 348,000 tonnes | Reported throughput impact |
| Revenue impact | About $7 million | Company estimate |
The case reframes fuel efficiency. The largest problem was not necessarily fuel burned per engine hour. It was production time lost through premature refueling, queues, and trucks running short. Optimizing the decision of when and where to refuel increased equipment availability.
The public source does not name the customer or publish detailed calculation assumptions. The revenue number should therefore be treated as a case estimate, not an independently audited return.
2. The Operational Problem
Haul trucks operate in a synchronized production system. A refuel stop removes a truck from the haul cycle. If many trucks arrive together, queues form. If a truck refuels too early, it sacrifices productive time. If it runs out or must make an emergency trip, disruption is greater.
The optimal choice depends on live fuel level, burn rate, route, assignment, distance to fuel, queue length, shift plan, and production priority. Manual rules struggle to update these variables continuously.
Fuel data can also be unreliable. Gauge calibration, telemetry gaps, inconsistent delivery records, and different fuel sources can distort decisions. An enterprise must first reconcile receipt, storage, dispensing, and equipment consumption if it wants trustworthy optimization.
3. What Was Built
The intervention used the existing fleet-management system and changed its refuel-assignment logic.
System at a Glance
Current fuel level
Estimate urgency and remaining operating range.
Distance to fuel point
Calculate travel and diversion cost.
Queue condition
Avoid nonproductive waiting.
Truck assignment
Protect current production cycle.
Burn-rate history
Predict when fuel becomes critical.
Dispatch state
Coordinate the decision across the fleet.
| Input | Decision role |
|---|---|
| Current fuel level | Estimate urgency and remaining operating range |
| Distance to fuel point | Calculate travel and diversion cost |
| Queue condition | Avoid nonproductive waiting |
| Truck assignment | Protect current production cycle |
| Burn-rate history | Predict when fuel becomes critical |
| Dispatch state | Coordinate the decision across the fleet |
This is a constrained optimization use case, not a generative AI task. A language model can support exception explanations, shift summaries, and operator queries, but the dispatch decision should be calculated from live data and explicit constraints.
A bespoke system can add anomaly detection for suspected fuel leakage, reconciliation across suppliers, driver voice capture, and a model that forecasts consumption by route, payload, grade, weather, and equipment condition.
4. How It Reached Production
The public account emphasizes configuration and training, which is important. Optimization creates value only when dispatchers and operators trust and follow the assignments.
Audit fuel telemetry. Validate gauges, timestamps, dispensers, and missing data before changing assignments.
Model the whole delay. Include travel, queue, fueling, return, and interruption to the haul cycle.
Set operational guardrails. Minimum safe fuel, emergency routing, equipment restrictions, and supervisor override should remain explicit.
Train dispatch and operators. Users need to know why assignments changed and how to report exceptions.
Verify production impact. Compare recovered hours, tonnes, fuel per tonne, queue time, runouts, and maintenance effects against normalized baselines.
The revenue estimate depends on additional tonnes being processed and sold. If the plant, market, or downstream operation is constrained, recovered truck hours may not realize the same financial value.
5. What Mining Leaders Should Take Away
This mine shows why fuel programs should connect procurement, storage, dispensing, equipment, dispatch, and production. A narrow change in dispatch logic reportedly recovered more than a thousand truck hours.
A production fuel-intelligence stack reconciles fuel receipts from different sources, detects loss and anomalies, forecasts consumption, optimizes refuel assignments, and explains exceptions through a private operations assistant. Speech-to-text can capture field events where manual entry is difficult.
The core metric is total fuel and delay cost per tonne moved, not liters alone. That keeps the system aligned with production while preserving safety and equipment constraints.
Related Conscious Engines research
- Enterprise AI model stack for this industry
- High-value workflow deep dive
- Technical implementation guide
- Why one model is not an enterprise AI strategy
- Why your evaluation set is your AI moat
Sources
- Mine Tech Services, Optimised refuel assignments case study
- The customer is unnamed and calculations are provider published. Validate fuel assumptions, production constraints, commodity prices, and the revenue bridge before reuse.