Unlocking the "Virtual Fleet": How Payload Precision and TKPH Governance Defer CAPEX Without Compromising Asset Health
By Dmitry Budkin, Head of Mining Automation Solutions
July 24, 2026
Let us know what you have in mind
Describe your goals, and we'll outline a clear path to achieve them
By submitting this form, you agree to the processing of your personal data.
From theoretical maximums to operational certainty — why targeting a 93–95% average payload with a tight standard deviation drives sustainable profitability in open-pit mining
In open-pit mining, the most cost-effective tonne often already sits within the existing fleet—hidden in load-to-load variability. While Fleet Management Systems (FMS) excel at route optimization, payload utilization remains the most volatile variable in the haulage cycle.
This analysis moves past generic claims to address the engineering realities of heavy equipment. Drawing on published industry case studies, technical papers, and OEM application guidelines, we explore how to maintain payload within a "High-Efficiency Corridor" of 93–95% of rated capacity. We demonstrate that integrating real-time TKPH (Tonne-Kilometre Per Hour) governance with cross-validated On-Board Weighing Systems (OBWS) is one of the most cost-effective strategies to defer multi-million-dollar CAPEX without infringing warranty limits or sacrificing tire and frame integrity.
The "Virtual Fleet" Concept: Hidden Capacity in Load Distribution
The term "Virtual Fleet" refers to the incremental production capacity that can be unlocked by reducing underloading and overloading, thereby moving the same total tonnage with fewer cycles or fewer trucks. For a fleet of 30 haul trucks averaging 85% payload compliance, correcting the distribution to a 93% mean with a standard deviation of ≤2.5% can eliminate the need for one additional truck—effectively creating a "virtual" unit without capital expenditure. This concept is grounded in statistical modeling of cycle-time and payload variance, an approach increasingly adopted in large-scale open-pit operations.
The High-Efficiency Corridor: Engineering Reality, Not a Marketing Slogan
Payload optimization is not about hitting 100% at all costs; it is about trimming the tails of the distribution curve.
Scenario Analysis
Why 93–95% and not 100%? Industry data collected from multiple large open-pit mines shows that fleets targeting an average of 93–95% with a coefficient of variation below 5% achieve the lowest cost per tonne. This range accounts for natural density fluctuation, prevents thermal overload of tires, and leaves a buffer for measurement uncertainty. The "Fill Factor" of the shovel bucket is a volumetric metric; translating it to a target payload requires real-time density inputs from the geological block model— a process we will discuss later.
Data Integrity: From Garbage In to Cross-Validated Truth
An FMS is only as good as its payload data. Hydraulic OBWS are prone to drift due to temperature, vibration, and cylinder wear. A system displaying "100%" may actually be carrying 85% or 105%, generating phantom efficiency metrics.
Best Practices for Data Reliability
- Dynamic Calibration Schedule – Combine static (weighbridge) and dynamic (tipping-point) calibration every 250–500 engine hours, as recommended in OEM OBWS maintenance guidelines. Budget 2–4 hours per truck per month.
- Cross-Validation as Golden Standard – The FMS should automatically compare OBWS readings with downstream conveyor belt scales (for ore) or weighbridge measurements (for waste, where available). A variance exceeding ±3% triggers a recalibration alert. This approach has been validated at large operations, reducing unrecorded overloads by as much as 35%.
- Realistic Accuracy Targets – Hydraulic systems in dynamic pit conditions typically maintain a standard deviation of ±2.0–2.5% of rated load, as verified by field tests and manufacturer specifications. Strain-gauge systems may achieve ±0.5–1.0%, but come with higher installation cost. Aiming for ±2.0% is practical for most existing fleets.
Integrating Human, Mechanical, and Geological Factors
A holistic approach connects four critical elements:
- TKPH Governance – Real-time TKPH monitoring integrated into the FMS is the safety net for payload optimization. When payload rises, the FMS must dynamically recommend speed limits or route changes to keep tire internal temperature within safe bounds. Without this, higher payload can destroy tire investments. Tire manufacturers provide detailed TKPH management guidelines that should be embedded in the FMS logic.
- Operator Engagement – Instead of "electronic policing," use transparent scorecards and bonus structures linked to payload accuracy and impact-free loading. Implementations of similar programs have seen operator acceptance rise to over 85% within a year after introducing gamification and peer comparisons.
- Density-Aware Targeting – The FMS must ingest the mine's block model to adjust target payloads based on material density (e.g., wet laterite vs. dry waste). This prevents volumetric overfilling and underfilling.
- Shovel-Truck Interface – Payload precision starts at the face. Training excavator operators to manage bucket fill factor and reduce hang-up time improves payload consistency by up to 10%, as demonstrated in multiple productivity studies.
Conclusion: Precision as a Strategic Asset
Mastering payload precision is a disciplined, data-driven process requiring:
The question for mine management is no longer "Do we have an FMS?" but "Is our payload data trustworthy enough to defer a multi-million-dollar truck purchase?" The Virtual Fleet is there—it just needs to be measured and managed.
- A target of 93–95% average load with a standard deviation ≤2.5%;
- Routine OBWS calibration cross-referenced against independent scales;
- Mandatory TKPH governance to protect tires and frame integrity;
- Integration of density data and operator incentives.
The question for mine management is no longer "Do we have an FMS?" but "Is our payload data trustworthy enough to defer a multi-million-dollar truck purchase?" The Virtual Fleet is there—it just needs to be measured and managed.
Don't be a stranger!
Tell us what you're working on — we'll find the best way to help.