Mining
Mining operations run on precision and certainty. Your fleet knows no off-hours—production targets, wear rates, and fuel consumption don’t pause. Yet most mines still make haul cycle, tire, and maintenance decisions on incomplete data: last month’s reports, gut feel, and spreadsheets updated by hand. The result? Millions in annual waste hiding inside normal operations. We help mining operations see what’s actually happening—in real time, across every piece of equipment, every shift.
Mining operations span three distinct equipment ecosystems. Mobile mining equipment—the workhorses that move material—includes haul trucks, excavators, dozers, wheel loaders, and drill rigs. Underground mining equipment operates in confined, high-temperature conditions. Mill and processing equipment turns raw material into product. The equipment you choose to focus on first matters less than choosing to focus. Start with your most expensive asset class or your biggest operational headache.
Each category has its own wear profiles, failure modes, and operational pressure points. Real visibility across all three ecosystems is what transforms mining operations from reactive to strategic.
A fully loaded 400-ton haul truck can generate $50K–$150K in direct revenue per shift. Haul cycle time typically runs 20–35 minutes. A 10% improvement (2–3 minutes) across your fleet compounds into thousands of tons of additional annual throughput, with zero new equipment. Real-time visibility into cycle components—load time, travel time, dump time, return travel—reveals where those minutes are going.
A single haul truck tire costs $45K–$120K installed. A fleet of 50 trucks rotating through 600+ tires means tire costs can exceed $5M annually. Tire life is load, pressure, and road condition-dependent. Overinflation degrades the shoulder; underinflation kills sidewalls. Real tire intelligence uses pressure readings, temperature sensors, and load data to predict remaining useful life. Extend tire life by 15–25% and you’ve recovered millions.
Diesel is your second-largest variable cost after tires. A poorly maintained truck burns 12–18% more fuel than the fleet average. An operator riding the brakes on downhill haul roads burns an additional 8–15%. Real fuel intelligence isolates these factors, showing cost-per-ton-moved across operators, equipment, and routes.
In mining, availability equals production. A primary haul truck down for 24 hours isn’t a $20K repair—it’s $200K+ in lost throughput. Yet many mines track availability as a weekly or monthly calculation. Shift-by-shift visibility eliminates the 48–72 hour lag between a breakdown and management visibility.
Mining typically runs 60/40 or worse (planned/unplanned) due to extreme utilization. Unplanned maintenance is expensive: overtime, expedited parts, cascading failures. Every point you move from unplanned to planned saves 10–15% of total maintenance costs. Real condition data—vibration, temperature, pressure, fluid analysis—lets planners schedule weeks ahead, not hours before failure.
Operator skill drives 8–15% of operational cost variance across identical equipment. Load technique affects cycle time and tire wear. Braking patterns affect fuel consumption. Real data creates a performance scoreboard for objective coaching, not impressions.
Buckets, blades, teeth, and wear liners can cost $30K–$80K per machine and need replacement 3–5 times annually. Most mines change GET on calendar intervals. Real GET intelligence uses wear data and remaining-life prediction, eliminating premature replacement and preventing run-to-failure decisions. Save $200K–$400K annually across a mid-sized fleet.
Feed inconsistency from fleet scheduling creates boom-bust cycles: hours of overfeeding follow hours of underfeeding. Processing efficiency falls 8–12% due to feed inconsistency alone. Coordinating fleet scheduling with mill feed requirements—hour by hour—optimizes material flow.
Modern mining sites operate under strict environmental permits: dust suppression, fuel containment, emissions reporting, water management. Real equipment monitoring provides an auditable record of which trucks ran which routes, fuel consumption, and dust suppression application.
Most mines plan maintenance using hour-meter readings and calendar intervals. But actual engine wear is not linear to hours. A truck in high-altitude, ultra-cold conditions wears faster than one in moderate conditions. Real condition data lets planners schedule based on actual condition. Sometimes you extend intervals 20–30%; sometimes you shorten them. Both save money and prevent failures.
“Our cycle times varied by haul truck and shift. No two drivers did the job the same way. Once we had actual data, we saw that three operators were 8–12% slower than the fleet average. Coaching those operators brought the whole fleet up by 6%. That’s an extra 400,000 tons a year on a 5-million-ton mine.” — Mine Operations Director, Nevada
Operational transformation in a mining business is not a solo project. The mine manager owns production targets. The maintenance planner owns equipment reliability. The fleet coordinator owns dispatch and cycle times. None of them see the full system. The first step is gathering these people for a focused two-hour conversation: What’s actually broken? How much is it costing? What would it look like if we fixed it?
Your fleet runs every hour. Downtime is not a revenue dip—it’s a production miss that often cannot be recovered. This drives aggressive utilization, rapid wear, and constant pressure.
A haul truck in a hard-rock mine experiences 100x the wear rate of a highway truck. Suspension, tires, engines, transmissions all fail faster. Spares inventory must be deep and service capacity continuous.
On a typical haul truck, annual tire costs often exceed annual equipment depreciation. Tire management is not a maintenance item—it’s a budget line that rivals capital equipment.
A large mining operation might consume 10,000–20,000 gallons of diesel daily. A 10% efficiency improvement is 1,000–2,000 gallons per day—$200K–$400K in annual savings.
Every hour you take a primary asset out of service is an hour of lost throughput. Real data tips this balance: if you know an asset will fail in 48 hours, you schedule maintenance immediately. If it will run two more weeks, you maintain the production schedule.
Dust, fuel, water, emissions—all audited and reported. Compliance failure is not a fine; it’s a suspension of operations.
Experienced operators are in short supply. When you have real performance data, you can identify top performers, compensate them fairly, and use them as training benchmarks.
In an average week, where does time evaporate?
These time drains compound across every shift. In a typical mining operation, the annual cost of poor visibility is measured in millions—not thousands.
“What is your current equipment availability target, and what’s your actual average?”
“What percentage of your maintenance work is planned vs. unplanned?”
“How do you currently track haul cycle time, and how frequently do you analyze it?”
“What’s your current process for managing tire life, and how many tires do you replace prematurely each year?”
“How do you currently estimate actual cost-per-ton-moved (including tires, fuel, maintenance, and labor)?”
“What is your fuel consumption variance month-to-month, and what do you attribute it to?”
“How long does a typical shift handover last, and what percentage of critical information is captured in writing?”
“What’s your safety incident rate, and how much correlates to equipment condition vs. operator error?”
“How much production does your operation lose annually to unplanned downtime?”
“What’s your planned vs. actual maintenance ratio for each major equipment category?”
“How do you currently assess operator performance, and how often do operators receive formalized coaching?”
“If you could improve one operational metric today, what would generate the most value?”
A large haul truck generates $50K–$150K in throughput per shift. When it breaks down unexpectedly, you lose that production immediately. The financial impact is not the repair cost—it’s the lost throughput. A primary haul truck down for 48 hours in peak season is $200K–$400K in lost production.
Unplanned downtime is almost always preventable with condition visibility.
You budget for tires based on calendar intervals. Some tires die early due to underinflation, misalignment, or operator damage. Some could run another 200–300 hours but you’re replacing them because the calendar says so. Tire cost variance across your fleet can easily be 25–40%.
You’re paying for tires you’re not getting value from, and occasionally stuck with surprise failures because a tire died ahead of schedule. Cost: $50K–$100K per truck per year.
Your operation consumes 5,000–20,000+ gallons of diesel daily. Fuel cost per ton varies by operator, truck condition, and haul road quality. No single factor is dramatic; together, they add 8–15% to total fuel cost. Over a year, that’s $150K–$500K in unnecessary fuel spend.
Your best operators run 8–12% faster cycles, burn less fuel, and have fewer tire failures. If this performance difference was distributed across the fleet, you’d generate 5–8% more throughput or reduce fuel cost by $200K–$400K annually. Yet operator performance is assessed casually. Performance variance persists because it’s not measured.
You replace teeth, blades, and wear liners on calendar or hour schedules. Some operators are harder on GET than others but you don’t have visibility into actual wear rates. Real GET management eliminates premature replacement and prevents run-to-failure decisions. Cost: $200K–$500K annually.
Fleet scheduling that doesn’t coordinate with mill feed plans creates boom-bust cycles. Processing efficiency falls 8–12% due to feed inconsistency alone. Coordinating fleet scheduling with mill feed requirements is often treated as a logistics problem, not an operational integration opportunity.
Regulators want proof: which trucks ran dust-suppressed haul roads, fuel consumption, water use, emissions. Most mines have records but they’re scattered. Pulling an audit trail takes weeks. Real equipment intelligence provides a unified, auditable record.
Your maintenance team works on today’s breakdown and tomorrow’s emergency. Long-term planned maintenance gets pushed. Backlog grows. When condition data lets you plan 2–4 weeks ahead instead of 2–4 days, maintenance capacity becomes available for planned work. Backlog shrinks. Quality improves. Cost goes down.
“Our tire program was costing $8.2M a year. We thought that was just mining. After nine months of real pressure and load data, we realized we were replacing 120 tires annually that still had 15–25% life remaining. Fixing both sides brought tire cost down to $6.8M. That’s $1.4M a year.” — Maintenance Director, Peru
Large manufacturers offer proprietary systems tied to their equipment. Good at tracking component hours and alerting on service intervals. The limitation: they don’t integrate across mixed-brand fleets, don’t correlate operational data into actionable intelligence, and are optimized around manufacturer service intervals.
Optimize which truck loads which bucket and delivers where. Improve haul cycle efficiency by reducing empty miles. But they optimize logistics, not condition. A dispatch system doesn’t tell you why Truck 47 is running 3 minutes slower than average.
Some mines implement specialized tire monitoring with periodic pressure checks and wear measurements. Better than calendar-based replacement, but still episodic and don’t integrate with fleet data.
Most mines still document shift events on paper or unstructured text. Valuable qualitative information, but not actionable at scale. Aggregating data across months is manual and slow. Trends are invisible.
IoT sensors, fluid analysis, and vibration monitoring create visibility into asset health: engine temperature trending, fuel consumption per hour, pressure and temperature on critical systems, wear rates on high-cost consumables.
Equipment telematics, GPS, and dispatch systems track how the asset is actually being used: cycle time components, payload consistency, haul road surface, idle time, operator behavior.
Work orders, service history, and parts inventory provide the record of what was done, when, and the outcome. Did that compressor overhaul extend engine life? Did tire rotation improve performance?
Mine planning systems and cost accounting connect equipment performance to business outcomes: cost-per-ton-moved, cost-per-liter, availability correlation to throughput, maintenance cost trends.
When these data streams come together, you see the complete picture. A haul truck consuming 12% more fuel isn’t random—it correlates with maintenance history, operator assignment, and tire condition. When you see all of this together, the diagnosis is obvious. When you see it separately, it stays invisible.
Every shift, see the actual cost to move one ton of material, broken into components: fuel, tire amortization, maintenance, and labor.
Your mine plan budgeted $12.50 per ton. Actual is $14.80. Once you see the real number, you can target it. Is the problem fuel? Tires? Labor utilization? Mine planning becomes data-driven. Capital budgeting decisions are made on real cost data, not averages. Target: reduce cost-per-ton-moved by 8–12% within 12 months.
Monitor every tire continuously—pressure, temperature, load history—and predict remaining useful life to extend tire lifespan by 15–20%.
A single haul truck tire that lasts 10% longer saves $4,500–$12,000 per tire. Across a fleet, that’s $2.7M–$7.2M annually. Tire replacement becomes predictive instead of reactive. Emergency replacements drop 40–60%. Target: extend average tire life from 1,800 hours to 2,100+ hours.
Move your planned vs. unplanned maintenance ratio from 60/40 to 85/15 using condition data to schedule maintenance weeks ahead.
Unplanned maintenance is 3–4x more expensive than planned. Reducing unplanned downtime by 40% also improves equipment availability, directly increasing production throughput. Fleet availability improves from 82% to 90%+. Maintenance costs flatten or decrease despite intense utilization. Target: increase mean time between failures by 25–35%.
Replace 45-minute verbal shift handovers with a 15-minute data-driven review where everyone sees the same dashboard.
Incoming shift supervisors see equipment status, active work orders, tire condition flags, incident reports, fuel consumption trends, and production vs. target. Misunderstandings and missed information drop 70–80%. Shift-to-shift operational continuity improves. Safety incidents driven by miscommunication decrease.
“Unplanned downtime is not a maintenance issue. It’s a production issue. A 48-hour breakdown on a primary haul truck is $200K–$400K in lost throughput. Prevention costs thousands. That math is simple.”
You don't need to transform your entire operation in 90 days. You need a clear entry point, early wins, and momentum.
Goal: Align on priorities, establish baselines, and create initial visibility.
Outcome: Aligned team with a financial target (e.g., reduce tire cost by 12%), a baseline measurement, and weekly review cadence building organizational credibility.
Goal: Deploy targeted sensors, execute quick-win interventions, and build momentum.
Outcome: Proven improvements with measurable financial impact. Team adopting new protocols. Internal credibility building for expansion.
Goal: Expand to additional problem areas and build predictive capabilities.
Outcome: Comprehensive visibility across multiple problem areas. Predictive intelligence letting you act before problems occur. Compounding financial returns.
For 35+ years, equipment intelligence has been the bottleneck. Data existed but was scattered, delayed, and siloed. That constraint is gone. Real-time, integrated visibility into equipment condition, operational performance, and financial outcomes is now achievable. Not months away. Not after a massive capital project. Now.
Your team is ready. Your operation is ready. The question is not whether this is possible—it’s whether you’re going to be the operation that moves first.
EquipmentFX: Real-time visibility for equipment-driven businesses. Built by operators, for operators.