Finance leaders for shippers, third-party logistics providers and carriers that operate heavy-duty fleets are being asked to defend EBITDA, justify capital-allocation decisions, and protect shareholder returns using data that was never designed to answer those questions.
For organizations that own or lease tractors and trailers, whether private fleets, dedicated contract operations or asset-based logistics providers, equipment investment represents one of the largest capital commitments on the balance sheet. Yet most finance teams are still managing it with historical ledgers rather than forward-looking forecasts. Supply chain leaders have invested heavily in visibility into where freight is; far fewer have the same visibility into what the assets moving it cost. The intent to model risk and plan strategically is there — what’s missing is the financial intelligence infrastructure to do it.
A board asking about next year's capital budget, residual value exposure or capital replacement timing for the fleet that anchors the network is asking a forecasting question. Most finance functions supporting logistics operations can only answer it with a rearview mirror. Maintenance invoices, fuel receipts and lease schedules arrive in disconnected systems, get reconciled weeks after the fact, and land in a spreadsheet built to close the books, not forecast what happens next quarter. In a supply chain where capacity, service levels and cost-to-serve are managed lane by lane, that gap between the question being asked and the tools available to answer it is now a financial weakness.
Why the Data Keeps Failing
The board rarely asks about cost-per-mile directly, but it’s a granular number that eventually rolls up into landed cost, lane profitability, and the EBITDA and capital allocation figures that the board does track. According to the American Transportation Research Institute's 2026 operational cost analysis, the average cost to run a truck reached a record $2.336 per mile in 2025, up 3.4%. Every major cost category increased, and maintenance and repair alone climbed 8.6%.
A meaningful share of that increase traces back to government mandates rather than routine wear and tear. Emissions and safety requirements phasing in over the next several years are pushing up the price of new equipment and the parts needed to keep older units on the road, while tariff pressure on components adds to the effect. Supply chain teams already manage tariff and regulatory volatility in sourcing; the same forces are now showing up in the cost of the trucks that move the freight. The chief financial officer needs to know how much of this year's rising transportation expense is structural cost tied to policy change versus ordinary volatility, because that distinction shapes how they explain the number and how the network plans around it.
The volatility quickly becomes a financial planning problem. A spike in transmission or engine repairs on a handful of aging tractors can distort an entire quarter's operating expense line, and while those units are down, freight might have to move on costlier outside capacity. Without a system that isolates which specific assets are driving the variance, and rolls that detail into a clean per-vehicle profit and loss view, the CFO is left explaining variances after the fact, rather than anticipating them before they reach the income statement.
Even fleets with solid internal reporting often lack a reliable external reference point. A 2026 Fleet Benchmark Report aggregating anonymized data from 1.2 million vehicles and roughly $7 billion in service spend found that many organizations still struggle with basic maintenance coordination and capacity planning, even as both operating costs and equipment age continue to rise. Without benchmarking against a dataset of that scale, a supply chain finance team has no way to know whether its cost-per-mile, downtime rate or repair frequency reflects sound operations or simply industry-wide inflation. That distinction matters enormously in a boardroom, where the CFO needs to explain not just that transportation costs rose, but whether the network performed better or worse than its peers.
From Ledger to Model
Total cost of ownership (TCO) calculations frequently fail for a related reason. Replacement decisions are often made too late because capital expenditure is visible and immediate, while variable costs, including maintenance, are scattered across many small invoices that may never get aggregated into a single per-vehicle view. A tractor can quietly cross into a cost escalation zone for years before anyone connects the dots, because the data needed to see it lives in separate systems, from maintenance and telematics to transportation management and finance, and none of them is designed to model total lifecycle economics or support a broader total value of ownership (TVO) strategy. The same per-vehicle view that makes a confusing expense line explainable to the board is what catches this shift while there’s still time to act, before it forces an unplanned capacity or capital decision.
Even broader use of artificial intelligence hasn’t solved the problem. A recent industry survey on use of AI revealed a critical disconnect in TCO modeling for Class 8 assets, the workhorses of long-haul and regional freight. Thirty-two percent of respondents still perform TCO modeling manually, while 29% don’t perform TCO modeling at all. Across remaining responses, adoption of AI-driven TCO modeling averages just 12.1%, signaling that most organizations aren’t yet using analytics in one of the most impactful areas of asset management. That gap is a real opportunity for supply chain organizations to realize cost savings and improve lifecycle decisions through more sophisticated, AI-enabled modeling.
This is a data architecture problem. Supply chain organizations today aren’t short on information. They’re lacking a layer that turns operational data, telematics, work orders, invoices and utilization records into a single forward-looking financial model that a CFO can actually stand behind with their insights in front of a board.
Building the Missing Layer
Closing this gap requires treating financial intelligence as infrastructure rather than an afterthought bolted onto operations. Predictive modeling should be built on real per-vehicle cost trends and profit and loss detail instead of fleet-wide averages. Mandate-driven costs need to be separated from ordinary wear so that finance can explain them at the level the board tracks. External benchmarking should come from internal reporting, not an annual PDF, and TCO/TVO calculations should update continuously as maintenance and utilization data changes.
Finance and supply chain teams already have the analytical instinct to ask the right strategic questions. What they need is a financial intelligence layer that converts data into forward-looking, boardroom-ready insights, enabling CFOs to answer strategic questions before the next capital cycle, insurance renewal, network redesign or asset replacement decision forces the issue.
The supply chain organizations that close this gap first will make capital allocation decisions for the network with greater confidence, defend those decisions with data that withstand boardroom scrutiny, and shift finance from explaining yesterday's results to forecasting tomorrow's outcomes.
Katerina Jones is chief marketing officer at Fleet Advantage.
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