by Berend Booms, Associate Editor | Future of Assets

A logistics operator I visited a couple of years ago made a procurement decision that looked impeccable on paper. They selected a fleet of vehicles through a competitive tender, reviewed all of the bids, chose the lowest-cost option, and felt confident they had managed their capital budget responsibly. Fast forward to today, and maintenance costs alone have far exceeded the original purchase price. There could be many underlying reasons for this: fuel performance could be below expectations, unplanned downtime could have disrupted delivery schedules, socio-economic volatility could have driven up costs significantly, and residual value at disposal might not have matched initial projections. The point is that there are many numbers to consider in logistics. At the start of the project, the purchase price had been the right number; unfortunately, every other number had been ignored. 

This is not an unusual story. Across logistics operations, procurement decisions are still frequently anchored to acquisition cost. It is a number that is easy to compare and easy to justify – but it is not at all the same as Total Cost of Ownership (TCO). TCO is harder to calculate and requires a level of cross-functional coordination that many organizations find difficult to sustain. What makes this gap so consequential in logistics is that few industries are as asset-intensive, or as operationally dependent on those assets performing reliably over time. 

What Makes Logistics Different 

In most industries, an asset underperforming creates a fairly local problem, with most of the consequence contained to the specific department or site it operates in. In logistics, it creates a chain reaction: A grounded vehicle delays a delivery, which breaches a service level agreement, which triggers a penalty and damages a customer relationship, which diminishes customer retention rates, which eats into profits, which endangers business continuity. The asset failure is the starting point in all of this, not the endpoint. This systemic exposure is what makes TCO thinking so relevant here. The question is never simply what an asset costs to own; the question is what it costs the organization when that asset does not perform the way it should. 

The Cost Layers Beneath the Price Tag 

Acquisition costs are the most visible part of the equation, covering the purchase or lease price, financing, delivery, commissioning, and any customization required. For most organizations, these costs are reasonably well managed. They are the costs that appear in the capital expenditure budget. 

Operating costs are where the picture starts to change. Fuel consumption, operator labor, lubricants, tires, and repairs accumulate continuously. In fuel-intensive fleet operations, small differences in vehicle efficiency between makes and models can translate into very large differences in total spend over a multi-year period. A vehicle that costs ten percent more to acquire but delivers eight percent better fuel economy will almost always represent better value. Yet this calculation rarely features prominently in procurement decisions. 

Maintenance and repair costs follow a similar pattern. Scheduled servicing can be modeled with reasonable accuracy, but unplanned maintenance is where many organizations absorb costs they did not anticipate. Downtime during peak periods is more than a maintenance cost, as it represents a real risk to revenue. Organizations that factor reliability track records into procurement decisions are making a more informed choice than those that do not – and will often come out benefiting in more ways than one. 

I’ve also noticed that compliance costs are frequently underestimated: insurance premiums, regulatory certifications, safety inspections, and mandatory upgrades to meet emissions standards. In cross-border logistics especially, these vary significantly by market and regularly surface as surprises for organizations that did not model them at the outset. And then there are always end-of-life costs at the end of the road (pun intended!): residual value, disposal, and decommissioning requirements all influence the true cost of ownership in ways that are invisible at the point of purchase. 

The Cost That Most Models Miss 

Across all of these layers, there is a dimension that does not always appear neatly in a TCO model but that shapes the numbers behind every other line: downtime and the opportunity cost it creates. In logistics, availability is everything. Unplanned downtime at the wrong moment can erode the margin on an entire delivery run, trigger subcontractor costs, and introduce penalties that dwarf the cost of the maintenance event itself. This is why asset reliability should be treated as a procurement input rather than an operational variable. When choosing between two assets of similar acquisition cost, the one with a stronger reliability track record may represent substantially better value once the full cost of unavailability is factored in. 

Building a Model That Actually Gets Used 

The technical elements of a TCO model are rarely the barrier. A well-constructed spreadsheet built around the cost layers above can capture the essential picture. The more common challenge is organizational: getting the right inputs from the right people at the right stage of procurement. 

It is equally important to set the appropriate time horizon. Fleet assets and heavy equipment typically warrant five to ten years. Technology assets depreciate faster, so it makes more sense to look at them over a period of three to five years. The data inputs required will typically involve finance, operations, maintenance, and procurement working from shared assumptions. Where uncertainty is high, sensitivity analysis around key variables gives a clearer picture of where the model is most exposed. Most importantly, the analysis needs to be embedded in the decision-making process early. A TCO model completed after procurement has concluded, or commissioned as a justification exercise, adds nothing. 

Changing How Asset Decisions Get Made

The thing that interests me most about total cost of ownership is that it is not really a financial calculation. It’s more of a way of thinking about asset decisions that takes the full operational context seriously. In logistics, where assets work at the center of everything and where the consequences of underperformance cascade quickly, the gap between a good asset decision and a poor one compounds over years. 

Respecting the true value of an asset means looking beyond its purchase price and considering the factors that will shape its performance over time. It starts with understanding how the asset will operate in the real world, continues through decisions that balance operational, maintenance, and financial priorities, and relies on continuously capturing lifecycle data to validate assumptions and improve future choices. Viewed this way, total cost of ownership becomes a discipline of understanding what an asset is likely to cost before those costs arrive. 

As telematics and connected assets become more widely adopted across logistics fleets and facilities, the data needed to build accurate TCO models is becoming easier to gather. Fuel consumption, maintenance histories, utilization rates, and failure frequencies are increasingly available in real time. In theory, that should make better asset decisions easier. Whether it does depends largely on whether organizations are willing to look beyond the number on the purchase order and pay equal attention to the thousands of numbers that follow it. Those are the numbers that ultimately determine what an asset costs.