What Is a Loaded-Mile Percentage — and Why It's the Most Important Number in Bulk Dispatch

If you run a bulk fleet — frac sand, crude oil, refined fuel, water, or any commodity that moves in tanks and hoppers — one number determines more about your profitability than any other metric you track. It isn't your fuel cost per mile, your driver turnover rate, or your on-time delivery percentage, though those matter too. It's your loaded-mile percentage.

Understanding exactly what it is, what a good number looks like, and how the best fleets close the gap is the difference between a marginal operation and a consistently profitable one.

What loaded-mile percentage actually means

Loaded-mile percentage is exactly what it sounds like: of every mile your trucks drove this week, how many were driven with freight on board? The rest — the miles driven empty between drop-offs and pick-ups, repositioning trucks, or returning to the yard — are deadhead miles. They cost you in fuel and maintenance and earned you nothing.

The formula is simple:

In a perfect world, you'd run 100% loaded. In practice, that's impossible — trucks have to reposition between loads, return to the yard, and navigate the real-world constraints of customer time windows, driver hours, and load availability. The question isn't whether you'll run some empty miles. It's how many.

What does a normal number look like?

After analyzing bulk fleet operations across frac sand, fuel, water, and crude commodities, the range is consistent:

Fleet type	Typical loaded-mile %	Best-in-class %
Frac sand (Permian Basin)	38–44%	47–53%
Refined fuel / petroleum	40–46%	48–54%
Water hauling (oilfield)	36–42%	44–50%
Crude oil	38–44%	46–52%

Most bulk fleets run somewhere between 38 and 44% loaded. The best-run fleets — those actively managing their dispatch plans with optimization tools and tight load matching — run 47 to 53%. That gap of roughly 8 to 12 percentage points is worth understanding in real dollar terms, because it's larger than most owners realize.

What the gap actually costs you

Here's where the math gets concrete. Let's take a real example: a 75-truck frac sand fleet operating in the Permian Basin. The fleet ran manually planned dispatch — an experienced dispatcher building tomorrow's board by hand each afternoon. Not unusual, not badly run. Just typical.

When the same historical dispatch data was run through Nivio Dispatch's Proprietary Algorithm — the same loads, the same drivers, the same HOS constraints, the same customer windows — the optimal plan came out differently. Not dramatically, not miraculously. Just tighter, in ways that compound across hundreds of loads per day.

Metric	Manual plan	Optimized plan	Change
Loaded-mile %	43.25%	47.84%	+4.59 pts
Total miles (index)	baseline	–9.6%	↓ 9.6%
Revenue per hour	baseline	+$15.47/hr	↑ $15.47
Annual impact (fuel + maintenance)	—	+$599,000	+$599K/yr

Four and a half percentage points of loaded miles. Nine point six percent fewer total miles driven. Five hundred ninety-nine thousand dollars recovered in fuel and maintenance savings per year.

Nothing about the operation changed. Same trucks. Same drivers. Same loads. Same customers. Just a smarter plan.

Why the gap exists — and why it persists

This isn't a story about bad dispatchers. The dispatcher building that manual plan is almost certainly experienced, skilled, and working hard. The problem is structural: bulk dispatch is a combinatorial optimization problem that humans are not well-equipped to solve at scale.

Consider what a dispatcher is managing in a single afternoon plan for 75 trucks:

• Which driver gets which load (commodity certification, equipment type, customer approval)
• HOS clock status for every driver — who has how many hours left, when each resets
• Customer time windows — when each site opens and closes, detention risk
• Washout requirements and cross-contamination rules (especially critical for food-grade and chemical)
• Geographic clustering — how to sequence loads so repositioning is minimized
• Unplanned events — a driver calling out sick, a load being delayed, a site shutting down

A 75-truck fleet with these constraints involves millions of possible plan combinations. The human brain pattern-matches brilliantly on the constraints it knows well. But it can't evaluate 34 million combinations in seven minutes. The best dispatchers find a good plan. Optimization finds the best available plan, consistently, every time.

The hidden cost: the plan lives in one person's head

There's a second cost to manually planned dispatch that doesn't show up in any fuel or maintenance report: the plan lives in one person's head.

Your best dispatcher knows which customer prefers which driver, which driver struggles with which commodity, which lanes run efficiently and which ones hemorrhage deadhead. That tribal knowledge took years to accumulate. It also means that when that person takes a vacation, gets sick, or leaves, the fleet wobbles. A new hire takes months to reach the same planning quality — not because they're less skilled, but because the knowledge isn't written down anywhere.

When the rules live in a system instead of a person's memory, two things happen: the plan is better every day, and the institutional knowledge is preserved regardless of who's building it.

What the best fleets do differently

The fleets running 47–53% loaded aren't necessarily bigger or better-resourced than average. They've just solved the optimization problem. Their dispatchers aren't less important — they're actually more effective, because they spend their afternoons managing exceptions and customer relationships instead of rebuilding the same spreadsheet.

The shift looks like this:

How to find your number

The first step is knowing where you actually stand. Most operators can quote a loaded-mile percentage — but it's often a rough estimate based on a subset of data or a few weeks of records. Getting a precise number requires pulling actual trip data: completed loads, total miles, driver-level breakdown.

The more important number isn't just your current loaded-mile %. It's the gap between your current performance and what the same fleet, with the same loads, could achieve with an optimized plan. That gap — not your current number — is what tells you what you're leaving on the road.

For a 120-truck refined fuel fleet, the same analysis showed a path to approximately $1.48 million per year in fuel and maintenance savings. The fleet was running 46.92% loaded. Optimized, that became 52.79% — and the $1.48M came entirely from the miles they stopped driving empty.

Different fleets, different commodities, different starting points. The math always works the same way.

What to do with this information

If your fleet is running between 38 and 44% loaded, you're in normal territory for a manually planned operation — but you're also in the range where optimization has the most to offer. Every percentage point of loaded miles you recover is leverage across every truck you run.

The most useful first step is getting your actual number — not an estimate, but a precise analysis of a real day of dispatch data. At Nivio, we run this for free: send one day of completed loads and the drivers who worked them, and you'll have the analysis back in 24 hours. You'll see your current loaded-mile %, your deadhead, and what an optimized plan for that same day would have looked like.

It won't tell you everything about your operation. But it will tell you what the gap costs — and whether it's worth closing.

For the 75-truck sand fleet, it was $599,000 worth of reasons.