When a container gives poor results, two explanations compete: the container itself or the vehicle it rides in. Reading the same data from two directions separates them. The table below is an example; each cell shows the highest internal temperature measured for that container in that vehicle, with the run count in brackets.
| Container | Vehicle 1 | Vehicle 2 | Vehicle 3 |
|---|---|---|---|
| K-01 | … °C (…) | … °C (…) | … °C (…) |
| K-02 | … °C (…) | … °C (…) | … °C (…) |
| K-03 | … °C (…) | … °C (…) | … °C (…) |
K-02 runs high in all three vehicles, so the problem sits with the container. Vehicle 2 runs high whichever container rides in it, so the problem sits with the vehicle. A container-side problem goes to the maintenance line: seal, lid, plate rails. A vehicle-side problem calls for looking at placement and parking, and at the body if need be. Mix the two up and both sides get repaired while neither improves.
The table only fills if containers rotate between vehicles. Tying containers to vehicles makes the operation easier but rules the comparison out; swapping them on a few runs a month produces the data without disturbing the work. The same container giving the same result in two different vehicles is the clearest information about it, and ten measurements in one vehicle do not produce that.
The runs compared should be alike in regime, length and stop count. If they are not, the table is still built, but the result is read as a signal rather than a verdict and the differences are noted. Share the table with the field by container and vehicle ID; a table sorted by driver name changes the subject and brings personal data rules into play.
