They Knew What They Were Charging. They Didn’t Know What It Cost.

A growing transport operation was moving more trucks, more loads and more kilometres every month.

• Revenue was being recorded.
• Fuel slips were being collected.
• Weighbridge tickets were filed.
• Proofs of delivery were being captured.

But when management asked a simple question — “Are we actually making money on this route?” — there was no reliable answer.

The problem wasn’t a lack of data

The operation had plenty of information.

• Fuel transactions told one story.
• Weighbridge tickets told another.
• Proofs of delivery confirmed loads.
• Route distances were known.
• Truck costs existed somewhere in the business.

But these records lived in separate places and were rarely brought together at the level where the business actually made money: the trip.

Without that connection, management could see revenue and expenses in aggregate, but couldn’t reliably see the economics of an individual load, route or truck.

That made every rate decision harder than it needed to be.

• A new contract came with a rate per tonne.
• A client asked for a reduction.
• A route changed.
• Fuel prices moved.
• A truck’s consumption deteriorated.

The business had to make decisions using averages and assumptions rather than a clear understanding of what the work should actually cost.

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The missing piece was the economic baseline

We approached the problem from a different angle.

Before asking the system to calculate profitability, we needed to establish what profitable operation should look like.

That meant building a structured Vehicle Cost Schedule.

The VCS became the economic baseline for the fleet — defining the expected cost of operating each vehicle across the variables that actually drive transport economics.

Depending on the operation, this included:

• Fuel
• Tyres
• Maintenance
• Driver costs
• Insurance and tracking
• Tolls
• Depreciation
• Administration and overhead
• Route distance
• Expected utilisation
• Trip time

The objective wasn’t simply to produce a cost-per-kilometre number.
It was to create a calibrated model against which actual operational performance could be measured.

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From records to a cost model

The operation already generated the raw ingredients.
We connected them around the trip.

That created a much clearer chain:

For the first time, operational activity could be interpreted in financial terms.

What the system could now ask

Instead of looking at fuel costs at month-end, management could ask:

• What should this trip have cost?
• What did it actually cost?
• Why was there a difference?
• Which trucks consistently run above their expected cost?
• Which routes generate the strongest margins?
• Which clients are commercially attractive?
• Where is turnaround time eroding profitability?
• What rate do we actually need to make this contract worthwhile?

Those are very different questions from:
“How much did we spend on fuel this month?”

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Expected vs actual

This is where the model becomes operationally useful.

Suppose a truck completes a defined route.
The system establishes an expected cost using the vehicle’s cost profile, route distance and operating assumptions.
It can then compare that baseline against actual performance.

Trip Economics	Expected	Actual	Variance
Fuel	R6,700	R7,120	+R420
Tyres	R850	R850	—
Maintenance	R610	R610	—
Driver	R670	R670	—
Tolls	R450	R450	—
Other	R388	R521	+R133
Total Cost	R9,668	R10,221	+R553

The important number isn’t simply the R553.
It’s the question that follows:

• Was the truck consuming more fuel?
• Did it spend too long waiting?
• Was the route longer than expected?
• Was the truck poorly matched to the route?
• Was there an operational exception?

The system moves the conversation from reporting costs to explaining costs.

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The view changes from fleet to exceptions

A fleet average can hide a lot.

One truck can perform exceptionally well while another quietly destroys margin.
One route can look profitable in aggregate while a particular truck-route combination consistently underperforms.

So instead of giving management another spreadsheet full of numbers, the system surfaces the areas that deserve attention.

The objective isn’t to overwhelm management with data.
It’s to show them where the economics are moving away from expectation.

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From operational data to commercial decisions

Once the cost model exists, it can support decisions far beyond month-end reporting.

• Before accepting a new contract, management can model:

… to understand the likely contribution.

• During a contract, actual performance can be compared against the original assumptions.
• At renewal, historical route and truck performance can inform the next rate negotiation.

The business moves from:

to:

That is a very different commercial position.

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What changed

Before	After
Costs scattered across records	Operational costs connected to trips
Revenue visible in aggregate	Revenue linked to loads and routes
No consistent cost baseline	Vehicle Cost Schedule
Month-end discovery	Ongoing variance visibility
Fleet averages	Truck, route and trip economics
Rate decisions based on assumptions	Rate decisions supported by operating data
Problems discovered late	Exceptions identified earlier
Data as records	Data as decision intelligence

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The outcome wasn’t another dashboard

The real outcome was a different way of understanding the fleet.

The business already had the records.
What it was missing was the model that connected those records to the economics of the operation.

• The Vehicle Cost Schedule established the baseline.
• The operational system connected the data.
• The cost engine translated activity into expected and actual cost.

And the resulting visibility allowed management to see where performance was moving away from expectation.

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PrimeChain Perspective

A transport business can move thousands of tonnes and generate millions in revenue while still not knowing which work is actually profitable.

The problem isn’t always bad accounting.
Sometimes the accounting is perfectly correct.

The problem is that accounting tells you what happened financially.
Operational intelligence helps you understand why it happened operationally.

That’s where PrimeChain works.

We connect the operational activity — trucks, routes, loads, fuel, time and deliveries — to the economic model behind the business.