Supply chain optimisation through intelligent automation.
More than most operators use, and less than most vendors imply. Trip and shift-level questions — utilisation, idle time, dwell at sites, fuel per job — are well supported by standard reporting intervals. Sub-second questions such as fine-grained component wear need additional hardware. The first question about any fleet AI use case is the resolution the data was recorded at, not which model to use.
Utilisation analysis, almost always. Position and ignition data over a few months tells you which assets are working, which are idle, and which are kept for a seasonal peak that no longer exists. It is largely arithmetic rather than machine learning, needs no new hardware, and typically funds the rest of the programme.
At your own sites and on the last mile, your historical data helps. For everything in between, a commercial traffic feed will usually outperform a model trained on your fleet alone. We would combine the two rather than rebuild what already works.
Yes, and we would also recommend landing the raw telemetry in your own store alongside the provider's dashboard. Once you keep the raw stream, decisions about what you can analyse later stop being irreversible — which is the constraint that ends most fleet analytics projects.