TL;DR (60 seconds):
Your freight forwarding business tracks vehicle utilisation like most do: time on the road, miles covered, loads per week. The dashboard shows 85% utilisation and you assume that means 85% efficiency.
Your freight forwarding business tracks vehicle utilisation like most do: time on the road, miles covered, loads per week. The dashboard shows 85% utilisation and you assume that means 85% efficiency. According to SupplyChainBrain's analysis of fleet metrics, these common measurements can reward activity over value, hiding the real cost drivers that determine whether your fleet makes money.
The problem is that freight forwarder fleet utilisation, measured traditionally, tells you almost nothing about profitability.
A truck running empty miles to collect the next load shows as "utilised" while burning fuel and driver hours for zero revenue. A vehicle sitting idle because the previous delivery ran late gets marked as underutilised, even though the delay cost more than the downtime. Locus research on the empty-mile problem shows these non-revenue miles constitute a significant portion of total truck operations, directly eroding margins whilst appearing as productive utilisation.
This article examines what freight forwarders actually need to track instead of basic utilisation metrics, why the usual measurements hide profit leaks, and how to identify the leading indicators that predict whether your fleet delivers genuine returns. We will show you the cost calculation that separates activity from value, and when technology can help measure what matters.
The number you already trust
Revenue per truck per month.
Most freight forwarders track this figure obsessively, and for good reason. It captures what matters most: whether each vehicle is bringing in enough money to justify its cost. You can calculate it from your existing invoicing system, it updates monthly without additional data collection, and it directly connects to your cash flow.
Revenue per truck works because it reflects genuine productivity under normal conditions. When a truck generates $8,000 monthly revenue against $6,500 in operating costs, you know that vehicle is profitable. When another truck only brings in $4,200, you have a problem to solve. The metric cuts through complexity and gives you a single number that connects vehicle performance to business results.
Why it works most of the time
Revenue per truck aligns with fleet utilisation when your operations follow predictable patterns. According to SupplyChainBrain's analysis of fleet metrics, revenue-based measures accurately reflect asset productivity when route density remains stable and customer mix stays consistent.
The alignment holds when trucks serve similar routes with comparable margins. A vehicle doing five local deliveries at 80% utilisation generates similar revenue to another doing three longer hauls at 75% utilisation, assuming standard pricing. Both show healthy revenue per truck, both represent efficient fleet use.
Revenue per truck also works when empty miles stay within normal ranges. Most established freight forwarders operate with 15-25% empty miles built into their pricing models. As long as actual empty running stays within these parameters, revenue per truck accurately reflects how well you are using your assets.
The metric succeeds because it captures the cumulative effect of good utilisation. High-utilising trucks typically serve more customers, cover more profitable routes, and maintain steadier loading patterns. Revenue per truck rises accordingly. When utilisation drops due to poor scheduling or route planning, monthly revenue falls in proportion.
Your invoicing system calculates this figure automatically. Your drivers understand it connects to their work. Your accountant can verify it against cash receipts. Revenue per truck has earned your trust because it measures what you can control and directly affects what you care about: whether each vehicle pays its way.
Where the two disagree
The divergence appears when loaded miles climb while actual fleet utilisation drops. This happens in freight forwarding when trucks spend more time moving cargo but less time generating margin per hour of operation.
Revenue per loaded mile and fleet utilisation point in opposite directions when cargo mix shifts toward low-margin, time-intensive loads. A truck hauling premium electronics 200 miles in four hours generates different economics than the same truck moving bulk commodities 300 miles in eight hours. The first scenario shows lower loaded miles but higher fleet utilisation. The second shows the reverse.
The mechanism behind the gap
Revenue per loaded mile aggregates away the time dimension entirely. According to SupplyChainBrain's fleet analysis, common fleet metrics reward activity over value creation, masking the relationship between asset deployment and profitability.
The substitution fails because it treats all loaded miles as equivalent. A 100-mile delivery requiring four hours of driver time (including loading, traffic delays, and customer wait time) generates different asset returns than a 100-mile run completed in two hours. Revenue per loaded mile cannot distinguish between these scenarios.
Loading and unloading time creates the largest distortion. When a truck spends three hours at a customer site for a 50-mile delivery, the loaded miles metric credits the full revenue to those 50 miles. Fleet utilisation correctly attributes the revenue to five total hours of asset deployment. The per-mile figure appears strong while the per-hour return remains poor.
Empty miles compound the problem. The Empty-Mile Problem research shows that empty miles constitute a significant portion of total truck miles, directly impacting utilisation calculations while remaining invisible in per-loaded-mile metrics. A truck completing a 200-mile loaded delivery followed by a 150-mile empty return leg shows identical loaded-mile performance to a truck making the same delivery with a 50-mile empty return. Fleet utilisation correctly penalises the longer empty leg.
Route density affects the divergence. High-density routes with multiple stops can generate impressive loaded-mile figures while trucks sit in traffic or wait at congested facilities. The loaded miles accumulate, but the time-based utilisation metric captures the reduced asset productivity.
Seasonal cargo shifts amplify the gap. During peak seasons, freight forwarders often accept lower-margin loads to maintain loaded-mile targets. Revenue per loaded mile may hold steady or improve slightly, while fleet utilisation declines as trucks spend more time handling time-intensive shipments. The loaded-mile metric rewards volume maintenance while the utilisation metric reveals deteriorating asset returns.
Contract structures create additional distortion. Customers paying flat rates per loaded mile regardless of actual service requirements can inflate the per-mile metric while utilisation-based analysis reveals unprofitable time allocation. A contract paying $2.50 per loaded mile looks identical whether the delivery takes two hours or six hours, but only fleet utilisation captures the difference in asset productivity.
How long the gap can hide
The divergence can persist for months before detection in freight forwarding operations. MLDeep's freight KPI analysis distinguishes between lagging and leading indicators, noting that traditional metrics often mask emerging problems until they become severe.
Monthly reporting cycles create natural lag. Most freight forwarders review performance monthly, allowing three to four weeks for problems to compound before measurement. If loaded-mile performance remains stable while utilisation deteriorates, the divergence grows unnoticed until the next reporting period.
Driver behaviour
Which one to act on
Track margin per truck per week. Everything else is secondary.
Utilisation only matters when your trucks earn more than they cost to run. A truck running 90% of available hours at $50 per hour loses money if its total operating cost is $60 per hour. The SupplyChainBrain analysis confirms this trap: common fleet metrics reward activity over value, driving decisions that increase costs faster than revenue.
The decision rule is simple. Calculate weekly margin per truck by subtracting all direct costs from revenue earned by that specific vehicle. Direct costs include fuel, driver wages, maintenance, insurance allocation, and financing. If this number trends down while utilisation climbs, you have a utilisation problem masquerading as efficiency.
Use utilisation as your primary metric only when two conditions hold. First, your margin per truck stays stable or improves as utilisation increases. Second, you have genuine excess capacity that customer demand can fill at current rates. Most freight forwarders discover they fail the first test within weeks of proper tracking.
Once you switch to margin-first measurement, three operational changes happen immediately. Dispatchers stop accepting loads that keep trucks busy but generate losses. Sales teams justify rate increases using truck-level profitability, not industry averages. Route planning prioritises profitable runs over maximum mileage coverage.
The weekly tracking frequency matters because it captures both high-margin days and the inevitable empty miles between jobs. According to Locus research, empty miles constitute 20-25% of total truck miles in most freight operations. Monthly or quarterly margin calculations smooth out these costs, hiding problems until they compound.
Your current systems probably cannot produce truck-level weekly margins without manual work. Most freight management software tracks revenue and broad cost categories but cannot allocate specific expenses to individual vehicles by time period. This gap forces the choice between approximate margins calculated manually or precise utilisation from existing reports.
The approximate margin wins because direction matters more than precision. A margin estimate that shows truck 12 lost $200 last week drives different decisions than knowing truck 12 ran 87% utilised. The ATRI operational cost data provides industry benchmarks for cost allocation when your own data lacks detail.
Start with one truck
Next Steps
The clearest sign your freight forwarding operation needs better fleet utilisation tracking is when drivers consistently return with empty vehicles while you're outsourcing loads to third parties.
Start by measuring what matters. For two weeks, track every vehicle's revenue per mile alongside traditional utilisation percentages. According to MLDeep's analysis of freight KPIs, most forwarding operations discover their highest-utilised vehicles generate the lowest margins. The empty-mile problem alone accounts for significant operational costs that utilisation rates completely miss.
Look for three warning signs in your own numbers:
- Vehicles showing 85%+ utilisation but carrying low-value cargo or frequent partial loads
- Regular use of subcontractors while your own fleet records high empty-mile percentages
- Route planning decisions made on vehicle availability rather than total trip profitability
If you recognise these patterns, the problem is costing you. A forwarding operation with 20 vehicles typically loses $2,000-4,000 monthly to poor load matching and route optimisation that high utilisation rates mask completely.
The fix requires knowing which loads your vehicles should never carry, not just how often they move.
We help freight forwarders identify exactly where manual route planning and load assignment decisions cost the most. Our free 20-minute diagnosis shows whether tracking different metrics would improve your margins and, if so, by how much.
About AutoSpark
AutoSpark helps established small and mid-sized businesses find the one place AI or automation is genuinely worth applying, then builds and deploys it. The method is plain: interview the people doing the work, find where work repeatedly gets stuck, rank the problems by what they cost, and only build when the maths shows a clear payback.
AutoSpark is led by Patrick Nesbitt, a CA(SA), CFA and former private-equity investor, so AI is treated as an investment rather than a trend. Not an AI audit. Not a transformation programme. A short, evidence led diagnosis of where the money is leaking and what fixing it returns.
Start here: autospark.ai