Truck utilisation rate measures the percentage of available hours or miles during which an asset is actively deployed. It is the metric fleets reach for first when they want to know how their equipment is performing, and as a gauge of operational efficiency it does the job. The trouble starts when fleets treat it as a gauge of financial performance, because the two can move in opposite directions.
A fleet running at 90% utilisation can still be generating negative operating margin, and in 2026 a significant number of carriers and shippers are learning this in the most expensive way possible.
According to ACT Research's 2025 trucking industry forecast, weak profitability, elevated costs, and sharply reduced Class 8 production drove a steady contraction in the highway tractor fleet through 2025, a trend ACT expects to intensify in 2026, with capacity still exceeding freight demand at year-end despite the contraction.
That dynamic, fleets shrinking because they cannot sustain profitability even when their equipment is fully deployed, is the truck utilisation trap in its most visible form. The trucks are moving freight every available hour, and the business is still losing money.
Supply chain data analytics applied to fleet financial performance reveals what utilisation rates alone cannot: where high utilisation is masking low profitability, and which specific cost drivers are responsible.
Why Utilisation and Profitability Diverge
The intuition behind utilisation as a performance metric is sound. An idle asset generates cost without revenue, so the higher the utilisation rate, the more revenue the asset earns against its fixed cost base. In a simple operating environment, that logic holds. In the real world of freight in 2026, it breaks down for four compounding reasons.
Revenue Per Mile Is Not Uniform
A truck running at 90% utilisation across a lane mix of low-rated backhaul moves, heavily discounted spot loads, and underpriced contract lanes posts a strong activity number while earning a weak revenue per mile. The headline rate flatters a lane mix that is quietly underpaid.
Logistics analytics solutions that measure revenue per mile by lane, load type, and customer, rather than across the fleet as a whole, show which portions of a high-utilisation fleet are actually generating margin and which are subsidising it.
Empty Miles Are Hidden Inside Utilisation Calculations
Most utilisation calculations count loaded miles as a percentage of total available miles. They do not separately attribute the empty miles run between loads, which represent real cost with no offsetting revenue. A fleet with a 90% utilisation rate and a 22% empty mile ratio is running at an effective revenue utilisation well below what the headline number suggests.
Real-time data analytics applied to routing and dispatch data surfaces empty mile patterns by lane, driver, and load type. Once empty mile concentration is visible at the lane level, the corrective action gets specific: backhaul matching, load sequencing optimization, or lane renegotiation with customers whose freight patterns consistently generate empty return moves.
Fuel and Maintenance Costs Are Not Distributed Evenly Across the Fleet
In a freight market where contract rates are under pressure and shippers negotiate aggressively, carriers operating without lane-level profitability visibility are especially exposed to rate erosion that never shows up clearly in aggregate financial reporting.
According to Arrive Logistics' 2025-to-2026 truckload freight forecast, asset carriers continue to face profitability pressure as shippers push for lower rates, with some carriers forced to exit the market or scale back fleet investment, gradually reducing available capacity. The carriers who survive the cycle are the ones who can see lane-level margin clearly enough to know which rate concessions they can absorb and which ones they cannot.
The same pattern shows up across big data analytics in retail and logistics alike: organizations managing by aggregate metrics make worse pricing decisions than those managing with granular, lane-level financial data.
The Data Infrastructure That Closes the Gap
Most fleets manage by utilisation rate rather than lane-level or asset-level profitability for a practical reason: the financial picture is scattered across systems that were never built to talk to each other. Revenue data lives in the TMS. Fuel cost lives in the fuel card system. Maintenance cost lives in the fleet management system. Driver performance lives in the ELD or telematics platform.
None of these are integrated by default, and pulling them together by hand to build a per-asset, per-lane profitability view is usually too labor-intensive to sustain.
Data engineering that unifies these sources into a single analytical environment is the foundational investment that makes freight profitability analytics operational. Once integrated, the platform surfaces:
- Revenue per mile by lane, load type, and customer
- Empty mile percentage by lane and driver
- Fuel cost per mile by asset and route
- Maintenance cost per mile by asset age and route type
- Net margin per lane and per asset after full cost attribution
These are the metrics that expose the trap. A lane running at 95% asset utilisation with a 24% empty mile ratio, a below-contract revenue per mile, and an aging asset carrying above-average maintenance cost may be the worst-performing lane in the fleet. The utilisation report would never tell you that.
The Network View: Where Profitability Decisions Actually Get Made
The step that separates logistics analytics programs that improve operational efficiency from those that improve financial performance is the network view. Individual lane and asset profitability data becomes most valuable once it is aggregated into a network profitability model, one that lets fleet managers and logistics leaders ask and answer questions that were previously out of reach.
Business intelligence in supply chain, applied at the network level, shows which customers contribute the highest margin, which lanes are structurally unprofitable regardless of rate and should be exited or repriced, which asset types are most efficiently deployed on which route profiles, and where network reconfiguration would lift margin without sacrificing volume.
For carriers and shippers managing complex freight networks in 2026, this is the analytical capability that turns high asset utilisation from a performance metric into a strategic lever.
For more on how analytics exposes cost leakage across freight and delivery networks, see Transportation Analytics: How Data Exposes Cost Leakage in Freight and Delivery Networks, which covers the broader cost visibility framework in detail.
Frequently Asked Questions
Why can high truck utilisation still produce low profitability?
Because utilisation measures how much an asset runs, while profitability depends on what each of those miles earns and costs. Revenue per mile quality, empty mile concentration, asset-level maintenance costs, and rate erosion on specific lanes can combine to produce negative margin on a fully utilised asset that looks healthy in the operational report.
What is the empty mile problem and how does analytics address it?
Empty miles are the miles driven between loads with no offsetting revenue, and they often hide inside high utilisation rates that count loaded miles without attributing the cost of repositioning. Analytics that tracks empty mile percentage by lane and driver at the load level surfaces which lanes and dispatch patterns generate the most empty mile exposure, enabling targeted backhaul matching and route optimization.
What data sources are required to build a fleet profitability analytics program?
The core sources are the TMS for revenue and load data, fuel card systems for fuel cost by asset, fleet management systems for maintenance cost and downtime, telematics platforms for route and driving behavior, and payroll or driver management systems for driver cost attribution. These need to be integrated into a single analytical environment to support lane-level and asset-level profitability calculations.
How does lane-level profitability analytics change rate negotiation with customers?
When carriers can see fully attributed margin on each lane (fuel cost, empty mile cost, asset wear, driver cost), they can make rate decisions on actual profitability instead of competitive instinct. Lanes that stay unprofitable at any reasonable rate become candidates for repricing or exit, replacing volume commitments made without financial visibility.
Can this analytics approach work for shippers as well as carriers?
Yes. For shippers, the network profitability view is especially valuable for transportation spend management. Understanding which carriers deliver the lowest total cost per lane once service quality, accessorial charges, and claims costs are factored in produces procurement decisions that optimize total landed cost instead of headline rate.
Stop Managing Your Fleet by the Wrong Number
Utilisation tells you the trucks are busy. Whether that activity is paying for itself is a separate question, and it is the one that decides which carriers are still standing at the end of 2026. The fleets and logistics networks holding margin are the ones that stopped ranking equipment by how hard it runs and started ranking it by what each lane, asset, and customer actually contributes after every cost is attributed.
Your TMS, fuel systems, and fleet management platform already hold the numbers required to build that view. They are simply sitting in separate places. Pulling them into one financial picture is the work that turns utilisation from a vanity metric into a decision-making tool.
Book a 1:1 strategy call with Ascend Analytics to map out what freight profitability analytics could surface across your network.
