Savings on one side, platform and hardware on the other, payback in months. Every assumption is an input you can argue with.
Change any input and the result updates. Nothing is emailed anywhere and nothing is stored.
Payback counts the one-off hardware against the monthly net saving. Recurring platform cost is already deducted from the net, so a positive net means the system pays for itself before hardware is even considered.
ROI calculators are usually built to produce a flattering number. This one lets you drag every assumption down, and we would rather you did — a business case that survives pessimistic inputs is one you can take to a finance director.
A 12% fuel saving is a reasonable central estimate for a fleet with no current visibility, and it comes from several sources at once: reduced idling, fewer unauthorised trips, better route discipline, and the behaviour change that follows simply from drivers knowing the vehicle reports. Fleets that already run tight operations will see less. Fleets with a genuine fuel-loss problem can see considerably more, but that is a theft recovery rather than an efficiency gain, and it is worth modelling separately.
The 10% maintenance saving assumes moving from calendar-based servicing to usage-based, which requires real odometer or engine hours from CAN data. Without that input, set this to zero — you will not achieve it from position data alone, and a business case that quietly assumes it is a business case that will miss.
Insurance premium reductions, which some fleets negotiate after demonstrating driver monitoring. Recovered stolen vehicles, which are real but occasional and lumpy. Administrative time saved on timesheets and proof of delivery. Customer retention from accurate arrival information. All of these are genuine and none belongs in a headline ROI number, because none of them is predictable enough to promise.
It also excludes your own implementation effort, which is never zero — fitting, calibration, training and the first weeks of tuning alerts all consume management attention. If your business case is marginal on these numbers alone, add that cost before deciding.
Reconsider the scope rather than the vendor. A phased deployment on the subset of vehicles where the numbers are strongest usually produces a far better return than fitting everything at once, and it gives you real data to size the rest of the rollout. We would rather quote you for forty vehicles that pay back than two hundred that argue about it.
Bring the numbers you put in above and we will pressure-test them against comparable deployments. Book a call or message us on WhatsApp — including if the answer is that you should start smaller.
For a fleet with no current visibility, yes, as a central estimate — it comes from reduced idling, fewer unauthorised trips, better route discipline and the behaviour change that follows drivers knowing the vehicle reports. Tightly run fleets will see less. Fleets with an actual theft problem may see much more, but that is recovery rather than efficiency and should be modelled separately.
Because the saving comes from servicing on real usage rather than on the calendar, and that needs true odometer or engine hours from the vehicle. Position-derived distance is an approximation that diverges from the vehicle's own figure. Without CAN or a reliable odometer feed, set that input to zero rather than hoping.
On the default assumptions, hardware pays back in months rather than years, because the recurring platform cost is already covered by the recurring saving. If your own numbers produce a payback beyond eighteen months, the honest response is to narrow the scope to the vehicles with the strongest case rather than to argue the assumptions upward.
No. Some fleets negotiate premium reductions after demonstrating driver monitoring, and it can be significant, but it depends entirely on your insurer and claims history. We leave it out so the headline number does not depend on a negotiation that has not happened yet.
Usually not. A phased rollout starting with the vehicles where the case is strongest gives you real data to size the rest, and it limits the exposure if your assumptions turn out optimistic. It is also easier to run a proper calibration and training process on forty vehicles than on four hundred.
Send us what you put in. We will tell you where the assumptions are optimistic, including when that argues against buying from us.
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