Inabe Logistics Service — Japan
Four heavy diesel vehicles at Inabe Logistics Service — two 10 tonne tippers and two trucks — were measured before and after treatment. All four land between +4.9% and +7.1%, a median of +6.3%. It is the narrowest spread and the smallest median of any operator group on this site, and for a dataset of this kind that combination is a mark in its favour, not against it.
Four heavy vehicles, four results within two percentage points of each other, and not one of them large enough to raise an eyebrow. That is what an unremarkable measurement series looks like.
| Operator | Inabe Logistics Service (有限会社いなべ物流サービス), Japan |
|---|---|
| Vehicles | 4 diesel — Hino 10 t tipper (2005), Hino 10 t tipper (2002), Hino 5 t truck (2014), Hino 10 t truck (2012) |
| Method | 施工前後比較 — direct before-and-after comparison |
| Measured quantity | Fuel efficiency, km per litre |
| Result | Median +6.3%, range +4.9% to +7.1% |
| Vehicle age at measurement | Spanning 2002 to 2014 model years |
| Control group | None |
Every measurement, not just the best one
| Vehicle | Before | After | Change |
|---|---|---|---|
| Hino Dump 10t/2005 | 2.15 | 2.29 | +6.5% |
| Hino Dump 10t/2002 | 1.97 | 2.09 | +6.1% |
| Hino Truck 5ｔ/2014 | 6.39 | 6.70 | +4.9% |
| Hino Truck 10ｔ/2012 | 3.79 | 4.06 | +7.1% |
| Median across all 4 measurements | +6.3% |
Method before result
Each vehicle was measured directly before and after treatment — the method the dataset records as 施工前後比較. That is the weakest of the three methods represented in this dataset, because nothing controls for season or duty between the two readings.
What partly compensates for it here is the consistency. Four vehicles across three different classes and twelve model years produced results within 2.2 percentage points of each other. An uncontrolled method that happens to be measuring noise does not usually produce that.
It is also worth reading the absolute figures rather than only the percentages: the two 10 t tippers run at 2.15 and 1.97 km/L. At that consumption level a +6.3% improvement is a substantial quantity of diesel per year, which is why the calculated CO₂ effect here (-60 g/km at the median) is larger than at operators with much bigger percentages.
Every percentage on this page was recalculated by us from the recorded before-and-after figures rather than taken from the summary column of the source; all of them reproduce the recorded value. That is a check on transcription, not on the measurement itself — it confirms the arithmetic is right, not that the reading was.
The limits of this trial
We publish these next to the result rather than in a footnote. If a limitation would change how you read the number, you should read it at the same time as the number.
Known limitations
- A direct before-and-after comparison with no seasonal control is the weakest method in this dataset. The tight spread makes the group more interesting, but it does not make the method stronger.
- Tipper and heavy haulage duty is dominated by load weight and terrain, neither of which is recorded here.
- Four vehicles at one operator.
- This is a dataset entry, not a trial report. What we hold is the before-and-after fuel-efficiency figure per vehicle as recorded in the master test dataset — not the operator’s raw fuel logs, not the odometer readings behind them, and not a written test protocol.
- There is no control group. No comparable untreated vehicle was tracked over the same period, so seasonal, route and traffic effects cannot be separated from the treatment.
- The measurement dates and the length of each measurement period are not recorded in these entries. We therefore cannot say how far each vehicle ran between the two readings, or which months the comparison covers.
- The figures were recorded by the operator or by the local distributor, not by an independent party, and we cannot re-derive them from primary records.
Reading this evidence in context
For heavy vehicle operators this is a more relevant group than most of this section: 10 t tippers at 2 km/L are exactly the duty where a small percentage translates into a large fuel bill.
A +6.3% median with a 2.2-point spread is a modest, believable claim. We would rather point you at this group than at the operators on this site with numbers three times the size.
What would make it solid is straightforward and cheap: untreated sister vehicles running the same sites over the same weeks.
Questions operators ask about this trial
Why do you highlight the smallest result on the site?
Why is the CO₂ figure larger here than at operators with bigger percentages?
Does the age of the vehicles matter?
Provenance
- Source
- Eco-Spray / NanoEFX master test dataset, compiled 22 March 2020 — operator-named section
- Recalculation
- All percentages recomputed from the recorded before-and-after figures
- Raw records
- Held by the operator or the local distributor — not in our possession
- Operator entry
- 有限会社いなべ物流サービス — Inabe Logistics Service Ltd.
- Method as recorded
- 施工前後比較 (direct before/after)
- Name release
- Operator naming approved by ECO EFX Solutions, 15 August 2026
- Last checked
- 15 August 2026
The only baseline that matters is yours
Every trial on this site was run on someone else’s vehicles, duty cycle and baseline. A NanoEFX pilot measures yours — and we will help you design it with a control group so the result stands up.