99% SLA adherence and 45% more parcels per route at network scale

This national CEP operator moves hundreds of thousands of parcels a week across multiple depots and a large pool of employee and contractor drivers. Growth had outpaced its tooling: parcels were allocated to depots and drivers manually, routes were built ad hoc, and there was no single view of network performance. During peak periods, low route density and slow reassignment pushed SLA adherence down and drove up cost per parcel.

Geofleet was deployed as the dispatch and visibility layer across the network. The AI Dispatch Agent assigns and sequences every parcel using real-time location, vehicle capacity, committed time windows, and live traffic — continuously, not once per wave. Dispatchers shifted from manual allocation to managing exceptions from a single control tower, with SLA-at-risk parcels flagged early so they can be re-prioritized before they breach.

SLA adherence reached 99% and held through peak season. Denser, smarter routes carried 45% more parcels per route without adding vehicles, and cost per parcel fell by 25%. The network gained one real-time view of every depot, driver, and parcel, and reassignments that once took phone calls now happen automatically in seconds.

For a high-volume CEP operator, those three numbers reinforce each other: denser routes lower cost per parcel, and continuous reassignment is what keeps those dense routes from slipping behind on SLA. The network captured efficiency and reliability at the same time, rather than trading one for the other.

At hundreds of thousands of parcels a week, small percentages are enormous absolute numbers. A 25% reduction in cost per parcel and 45% more parcels per route translate into substantial savings and capacity across the whole network — the kind of structural improvement that manual dispatch could never deliver at this scale.

SLA adherence is equally load-bearing in CEP: contracts and client relationships are built on it, and peak season is precisely when adherence usually slips and penalties bite. Holding 99% through peak protected both revenue and reputation.

Because the network was too large to risk a big-bang switch, Geofleet went live in stages. After connecting depot, driver, and parcel feeds and configuring zones, capacities, and SLAs, the AI first ran in advisory mode so dispatchers could approve, override, and effectively train it on network patterns.

Only once the team trusted the suggestions did continuous auto-dispatch go live across depots, with dispatchers managing exceptions. This staged approach let the operator scale automation without ever losing control of a network it could not afford to disrupt.