Alfresa — A・L Plus — Japan
Two Alfresa Group delivery trucks were compared month against the same month a year later — October and November 2016 versus October and November 2017. Fuel efficiency improved between 8.4% and 20.0% depending on vehicle and month. These figures appear identically in three independent records, which is why we treat this as our most reliable Japanese dataset.
| Operator | Alfresa Group / A・L Plus Co., Ltd., Japan — pharmaceutical logistics |
|---|---|
| Fleet context | More than 50 vehicles, predominantly refrigerated |
| Vehicles measured | 2 (Isuzu Elf 2 t; 5 t truck) |
| Baseline | October and November 2016 |
| Follow-up | October and November 2017 |
| Measured quantity | Fuel efficiency in km/L |
| Monthly distance | ≈1,500 km (2 t) and ≈2,000 km (5 t) |
| Corroboration | Same values in the field-study table, the 2020 master dataset and the published case study |
Every measurement, not just the best one
| Vehicle | Month | 2016 (km/L) | 2017 (km/L) | Change |
|---|---|---|---|---|
| Isuzu Elf 2 t | October | 6.57 | 7.12 | +8.4% |
| Isuzu Elf 2 t | November | 5.75 | 6.62 | +15.1% |
| 5 t truck | October | 5.30 | 6.36 | +20.0% |
| 5 t truck | November | 5.35 | 5.82 | +8.8% |
Method before result
Both vehicles ran their normal pharmaceutical delivery duty, roughly 1,500 km and 2,000 km per month respectively. Fuel efficiency was recorded in km/L from the operator’s own records.
The comparison is deliberately built as a year-on-year match of the same calendar months. October 2016 is compared with October 2017, November with November. That removes most of the seasonal variation — temperature, daylight, traffic patterns, seasonal fuel — that makes a spring-versus-summer comparison hard to interpret.
We hold the same four figures in three places that do not depend on each other: the Japanese field-study summary, the 2020 master test dataset, and the operator case study published separately. They agree to the decimal.
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
- Only two vehicles were measured, out of a fleet of more than fifty.
- There is no control group — no untreated sister vehicle was tracked over the same year.
- A full year separates baseline and follow-up. Vehicle wear, tyre and maintenance changes and route changes over twelve months are not accounted for.
- The underlying raw records sit with the operator; what we hold are the summarised monthly figures.
- A separate published figure of roughly 95 USD saved per truck per month is not reproducible from the data we hold, so we do not repeat it here until the calculation basis is provided.
Reading this evidence in context
For a cold-chain operator, the refrigeration load runs on top of the traction load, so the engine works harder for the same distance than in a comparable dry-freight vehicle — which is part of why fuel efficiency matters so directly in this sector.
The strength here is not the size of the numbers but their corroboration and the clean year-on-year structure. When three separate records agree on the same four measurements, transcription error becomes an unlikely explanation.
The weakness is sample size. Two vehicles show a direction, not a fleet result.
Questions operators ask about this trial
Why compare October with October instead of before and after?
What makes this dataset more reliable than the others?
Does it apply to refrigerated fleets generally?
Provenance
- Operator
- Alfresa Group / A・L Plus Co., Ltd., Japan
- Sources
- Japanese field-study summary; Eco-Spray master test dataset (2020); published Alplus case study
- Measurement periods
- October and November 2016 (baseline); October and November 2017
- Raw records
- Held by the operator — not in our possession
- 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.