Fleet vehicle procurement has traditionally focused on upfront costs, fuel efficiency and availability. Purchase price, discounts and delivery timelines often dominate decision-making, particularly in large or decentralised organisations. While these factors remain important, they only tell part of the story. Increasingly, fleet performance is being shaped by what happens after vehicles are delivered — and that is where make-and-model repair data becomes critical.
According to Shaun Janks, Co-Founder and Chief DingGo at DingGo, many fleets continue to make buying decisions without fully understanding how different vehicles perform in real-world conditions.
“Two vehicles can look almost identical on paper,” Janks says, “but once they’re deployed, their repair profiles can be very different. If procurement decisions don’t consider that, fleets can unintentionally lock in higher costs for years.”
Make-and-model repair data provides visibility into how often vehicles are damaged, how severe that damage tends to be and what it costs to rectify. Yet this information is rarely part of structured procurement discussions.
“Purchase price is only the starting point,” Janks explains. “The more important question is how the vehicle performs across its entire lifecycle — including repairs and downtime.”
In many organisations, repair and accident data sits with fleet or insurance teams, while procurement operates separately. This functional separation can create blind spots.
“If procurement doesn’t have access to repair trends by make and model, decisions are being made with incomplete information,” Janks says.
Repair data often reveals patterns that are not immediately obvious. Some vehicles may be competitively priced upfront but attract higher repair costs due to parts pricing, labour complexity or limited availability. Others may prove more resilient to minor impacts, reducing both repair frequency and downtime.
“When fleets compare repair performance across similar segments, the differences can be significant,” Janks says. “It’s not always intuitive.”
The implications extend beyond direct repair invoices. Each incident can remove a vehicle from service, requiring temporary replacements, rescheduling or reduced productivity.
“Downtime is one of the most underestimated costs in fleet operations,” Janks notes. “Repair data helps quantify the operational impact, not just the invoice value.”
Make-and-model insights can also inform suitability assessments. If a particular vehicle attracts frequent minor damage in a specific operating environment, it may indicate a mismatch between vehicle design and task requirements.
“Repair data shows how a vehicle behaves in the real world,” Janks says. “That’s valuable when matching assets to operational roles.”
As vehicle prices rise and replacement cycles lengthen, the financial consequences of procurement decisions are amplified. Vehicles retained for five or more years accumulate repair events over time, making small differences in performance increasingly material.
“If you’re holding assets longer, repair patterns matter more,” Janks explains. “Minor differences compound over the life of the vehicle.”
There are also insurance considerations. Vehicles with higher repair frequency or severity can influence claims experience and, ultimately, premium outcomes.
“Better repair data leads to better conversations with insurers,” Janks says. “When you understand what’s driving incidents and costs, you’re in a stronger position to manage risk.”
Importantly, Janks emphasises that integrating repair data does not mean procurement becomes solely cost-focused.
“It’s not about replacing safety, specification or driver acceptance criteria,” he says. “Repair insights add another layer of intelligence to the decision.”
Digitisation is essential to unlocking this value. Fleets relying on manual or fragmented reporting struggle to build consistent datasets. Centralised digital incident management systems enable analysis by make and model across the fleet.
“Once you’re capturing repair information consistently, it becomes a powerful decision-making tool,” Janks says.
More mature fleets are already incorporating repair data into vehicle evaluations and procurement reviews. Rather than relying purely on historical preference or supplier relationships, they are using evidence to guide selection.
“Mature fleets don’t just ask what a vehicle costs to buy,” Janks concludes. “They ask what it will cost to own, operate and repair over its entire life.”
As fleet budgets tighten and operational scrutiny increases, procurement decisions carry greater long-term consequences. Ignoring make-and-model repair data is no longer just a missed detail — it is a missed opportunity to strengthen resilience, control lifecycle costs and improve overall fleet performance.
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