Your fuel spend is up. Your margins are tighter. And when you pull the data, nothing jumps out as the obvious cause.
That's not a reporting problem. That's what empty miles look like in a system that wasn't built to isolate them.
Deadhead has always been part of fleet operations. Trucks run empty. That's not new, and no one expects it to go to zero.
What changed is the math underneath it.
When fuel costs increase, they don't distinguish between loaded miles and empty ones. The cost applies equally to both. But only one of those miles generates revenue.
If your empty mile percentage is holding steady at, say, 18%, you might not flag it as a problem. The number looks the same as last quarter.
What doesn't look the same is margin.
The same 18% is now producing a different financial outcome. And because contracted rates don't always reprice at the same pace as fuel, that gap sits in your P&L without a clean label on it.
This is the part most technology teams run into.
Empty mile impact doesn't appear as a discrete line item in most TMS or ERP configurations. It's distributed. It shows up as fuel variance in one report, utilization rate in another, and tighter margin per load in a third.
Your ELD is capturing the miles. Your TMS has the rate confirmations. Your ERP has the fuel data. But none of those systems are joining the picture together and telling you what your empty miles cost this month, by lane, by terminal, by driver.
So the data exists. The visibility doesn't.
When dispatch makes a load matching decision, they're working with what's in front of them: available trucks, available loads, rate, timing.
They're not typically working with a live calculation of what an empty repositioning move costs the business relative to the margin on the next load. That data isn't surfaced at the point of decision.
Which means the same pattern runs. Week after week. Across hundreds of trucks.
And the financial outcome of those individual decisions doesn't aggregate anywhere visible until it shows up as a margin problem you're trying to explain in a quarterly review.
Take a 100-truck fleet running an average of 2,000 miles per truck per week, with 20% empty miles and an average revenue per mile of $2.50.
That's roughly 2 million empty miles per year. Against $2.50 per loaded mile, the annual revenue not captured from those miles is in the range of $5 million.
That number doesn't mean you recover all of it. Some deadhead is unavoidable given lane structure and customer locations.
But if you can't see the number at all, you can't make decisions around it. And you can't set a benchmark to improve against.
Carriers that manage empty miles well aren't doing something exotic. They're doing three things:
They know where empty miles are occurring, by lane, by driver, by terminal. Not in aggregate. In specific.
They know how often. Patterns in deadhead often point to structural issues in how loads are being assigned, or gaps in the load matching logic.
And they know the cost. Not fuel cost in isolation, but the full impact on margin relative to the loads being run.
That's not a manual analysis. That's a data architecture question. What systems are feeding into where, at what frequency, and is the output surfaced in a way that supports a decision, not a retrospective report.
Magnus connects the operational data your fleet is already generating: routing decisions, load assignments, miles run, and gives your team a view of where empty miles are occurring and what they're costing.
It's not a separate analytics layer bolted onto your existing stack. It's built to work with the data flows you have.
If you want to know where the margin is going, that starts with being able to see where the miles are going.
Book a demo or call 877-381-4632 to speak with a transportation technology expert.