How to Calculate Haul Truck Cycle Time and Find Delays

How to Calculate Haul Truck Cycle Time and Find Hidden Delays

The spreadsheet said 18.4 minutes. The GPS data said 26.

I spent two days convinced our dispatch system was broken before I realized the model itself was the problem. We had built a “perfect” theoretical haul truck cycle time and completely ignored the seven-plus minutes of queue time, spot time, and TKPH delay that were eating our production alive.

That gap between theory and reality? It was costing us north of $400K per month in lost throughput on a single loader fleet.

By the end of this guide, you will know exactly how to calculate haul truck cycle time from scratch, identify the hidden delays inflating your actuals, and close the gap between what your model predicts and what your trucks actually do.

What You Need Before You Start

You cannot fix what you cannot measure.

Before touching a single formula, make sure you have GPS or dispatch data covering at least two weeks of haul cycles. One week is usually too noisy to provide a reliable baseline.

You will also need truck and loader specifications, including payload capacity, bucket size, and bucket cycle time. Add your haul road profiles, distances, grades, and speed limits for each segment. Finally, define a clear production target in tonnes per hour or tonnes per shift.

Stop or go test: Can you state your target production rate and current fleet size in one sentence? If not, get that locked down first.

Mining teams working across multiple loaders, haul routes, and dump points should also confirm that their open-pit mining data uses consistent naming and time classifications. Otherwise, the calculation may combine unrelated operating conditions.

Step 1: Calculate the Theoretical Haul Truck Cycle Time

The formula is straightforward. The discipline is not.

Start by dividing the complete haul cycle into six measurable components.

Loader Spot Time

This is the time from arrival at the loading face to the first bucket. Industry benchmarks put this at around 45 seconds for a well-configured shovel.

Load Time

Calculate load time using:

Load Time = Bucket Cycle Time × Number of Passes

If your excavator runs a 32-second bucket cycle and you need five passes, the load time is 160 seconds.

Loaded Travel Time

Calculate loaded travel time by dividing distance by average loaded speed, adjusted for road grade and operating conditions.

Dump Spot Time

This is the time required for the truck to position and begin dumping. It usually ranges from 30 to 60 seconds.

Dump Time

Dump time typically ranges from 45 to 70 seconds, depending on the material and truck-body style.

Return Travel Time

Calculate return travel time by dividing the return distance by the average empty speed.

Once every component has been measured, calculate the total:

Theoretical Cycle Time = Loader Spot Time + Load Time + Loaded Travel Time + Dump Spot Time + Dump Time + Return Travel Time

Research data puts the baseline cycle time without delays at 19.765 minutes for a typical open-pit operation. That becomes your theoretical haul truck cycle time.

Visual checkpoint: Your calculation should now contain six clearly defined time values. If any component is blank or estimated as “about the same as last year,” stop and get real data.

Verification: Compare your total with existing dispatch reports. If you are within 5% of the loaded cycle time, excluding wait states, your base model is solid.

If your current system can only show total cycle time without explaining each stage, review the difference between fleet management software and mine operations intelligence.

Step 2: Add the Hidden Delays

This is where most models fall apart. Honestly, this is where the real money is.

Your theoretical number is a fantasy until you account for the delays that occur during actual operations.

Queue Time at the Loader

This is the single biggest hidden loss I see on most sites. Trucks arrive, sit, and wait.

In open-pit operations, queue time averages 3.2 minutes per cycle. That is not a rounding error. On a 20-minute cycle, it represents a 16% hit to throughput.

Queueing is also a major source of haul truck idle time. It should be tracked separately from productive loading, travel, and dumping activities.

TKPH Delay

If your trucks are operating close to the tire manufacturer’s TKPH, or tonne-kilometres per hour, limit, you need to slow them down or add wait time to protect the tires.

This delay averages 4.2 minutes per cycle in operations I have reviewed. Most planning models ignore it entirely. Then everyone is surprised when tires fail and maintenance costs increase.

The connection between operating conditions, waiting, and fuel performance should also be considered when reviewing mining fleet fuel consumption.

Recharge Time for Electric Fleets

For battery-electric trucks, recharge time adds roughly 4.2 minutes per cycle.

Here is the part that catches people: recharge travel time, including travel to and from the charging station, can double that if the infrastructure layout was not planned with cycle time in mind.

Battery net usage of 85 kWh per cycle against an effective capacity of 80 kWh means you are recharging every 3.2 cycles. That math compounds quickly.

The adjusted formula is:

Actual Cycle Time = Theoretical Cycle Time + Queue Time + TKPH Delay + Spot Delays + Recharge Time

In practice, this means applying a delay factor of 1.2 to 1.4 to your theoretical number. If your model says 19.765 minutes, the real cycle is likely between 25 and 28 minutes.

Visual checkpoint: Your updated model should now contain eight to ten time components, not six. If it still looks clean, you are probably missing something.

Verification: Pull five random actual cycles from your dispatch data. Compare each component with your model. If three or more are off by over 15%, your delay assumptions need recalibration.

Step 3: Calculate Fleet Size and Find the Bottleneck

Now that you have a realistic cycle time, calculate the nominal number of trucks:

Trucks Required = Total Cycle Time ÷ Load Time

If your adjusted cycle is 26 minutes and the load time is 4.5 minutes, you need 5.8 trucks. You will run six.

But here is the nuance. If you run six trucks and your loader can serve only 5.2 cycles in that period, you have created queue time.

More trucks do not always mean more production. More trucks often mean more waiting.

Check your loader idle time. If the digger is sitting empty between trucks, the fleet is under-trucked and you are losing shovel utilization. If trucks are stacking up, the fleet is over-trucked and you are burning fuel and operator hours in a queue.

The fill factor matters here too. Running a 110% fill factor might hit payload targets, but it adds pass time and can mask the real bottleneck.

This is where AI-driven mine dispatch can help continuously rebalance truck assignments as loader availability, haul-road conditions, and production priorities change.

Visual checkpoint: Plot a histogram of your cycle times. You should see a roughly normal distribution centered near your modeled cycle time.

A long tail to the right represents delay events. A bimodal distribution, or two distinct peaks, may indicate that two haul routes or operating conditions have been mixed into the same dataset.

Verification: Create a scatter plot of cycle time against payload. If there is a visible correlation, payload variability is affecting the cycle and needs to be controlled separately.

Common Cycle Time Problems and Practical Fixes

The effective operating hours on most sites are only around 10.2 per shift once you subtract all the small delays and breakdowns.

That number alone should tell you why theoretical models built around a full 12-hour shift are often wrong.

Unplanned failures can distort cycle-time calculations even further. Mines should separate normal operating delays from the impact of unplanned equipment downtime.

Close the Gap Between Your Model and Reality

If you are tired of cycle-time models that do not match what is happening on the ground, AIM by HonestDig gives you real-time operational tracking across every truck, loader, and haul route.

It does not just show you what happened. It shows you where the time went and what to do about it, using AI-driven process control and analytics that adapt as conditions change.

HonestDig’s Guaranteed Production Throughput solution helps mining teams identify bottlenecks, improve fleet balance, and respond earlier when actual performance moves away from the production plan.

Teams reviewing their current technology can also use the mine dispatch software evaluation checklist to assess whether their system provides real-time decision support or only historical reporting.

For a broader comparison, see how AI mining dispatch differs from traditional fleet management.

Frequently Asked Questions

How long does it take to build and validate a haul truck cycle-time model?

Plan for six to eight weeks in total, including data collection, modeling, validation, optimization, and implementation. Rushing the validation phase is the most common mistake. If you skip it, you may build decisions around a model that is 20% to 40% away from reality and only find out after production targets are missed.

Why does my calculated fleet size never match actual performance?

The nominal number of trucks assumes perfect scheduling and zero variability. Real operations have queue time, changing site conditions, breakdowns, and shift-change gaps. Use the adjusted cycle time with delays and validate it against actual effective operating hours rather than scheduled hours.

Can I reduce haul truck cycle time without buying more trucks?

Yes. Start with spot time at the loader and dump. Then address queue time through better fleet and dispatch scheduling. Road maintenance and grade optimization can also reduce loaded travel time. These are often faster operational improvements than adding more trucks.

How do I account for electric truck recharge time?

Add both recharge time and recharge travel time to the model. If trucks need to recharge frequently, charging-station placement relative to the active loading face becomes a major cycle-time factor.

What is the difference between theoretical and actual haul truck cycle time?

Theoretical cycle time includes the planned loading, travel, dumping, and return stages. Actual cycle time also includes queueing, spotting delays, road restrictions, TKPH controls, breakdowns, shift changes, and other operating interruptions.

How can dispatch software help reduce haul truck cycle time?

Modern dispatch software can use current equipment locations, loader availability, queue conditions, and production priorities to update assignments. This helps reduce unnecessary waiting and improves fleet balance. However, the results depend on the quality of the data and whether the system can respond to changing mine conditions in real time.

What Is Your Next Move?

If the gap between your theoretical and actual haul truck cycle time is wider than 20%, that is no longer only a modeling problem. It is an operational visibility problem.

See how AIM by HonestDig helps mine teams understand where productive time is being lost and how to respond before those delays affect the entire shift.