Earthmoving fleet productivity is calculated by breaking the dump-to-dump cycle into measurable time components and comparing truck capacity against the loading unit's output — a method known in dispatching systems as shovel-truck matching. The cycle starts the moment a truck leaves the dump empty and ends when it completes its next dump, not when it leaves the shovel. This dump-to-dump framing matters because it captures the full round trip, including every delay that accumulates along the way, rather than isolating only the loading phase.

Fleet management systems (FMS) used in open-pit mining and large-scale earthmoving operations typically split this cycle into eight components: Travel Empty, Queue at Load, Spot at Load, Loading, Travel Full, Queue at Dump, Spot at Dump, and Dumping. The terminology differs between vendors — what one system calls "Queue at Dump" another may label "Wait at Sink" — but the underlying structure is consistent across platforms.

Why Break the Cycle Into Components?

Tracking each phase separately lets operators spot where time is leaking. A rise in travel-empty time on a specific haul route over several days points to deteriorating road conditions. A recurring spike in queue time at one shovel every afternoon signals an allocation imbalance, often tied to shift patterns such as post-lunch truck availability. Isolating the affected component tells a fleet manager where to investigate before the issue compounds across a shift.

What Is the Difference Between Over-Trucking and Under-Trucking?

Truck allocation — deciding how many trucks serve each shovel or excavator at any given moment — is distinct from fleet sizing, which is the longer-term, strategic decision on what size trucks to buy and how many to own. Allocation is the real-time operational question of distributing available trucks to meet production targets.

  • Over-trucked: too many trucks assigned to a shovel, so they queue. This wastes fuel on idling trucks and removes capacity that could be serving a starved shovel elsewhere in the pit.
  • Under-trucked: too few trucks, so the shovel hangs idle between loads. Because a single shovel typically costs more per operating hour than a single truck, hang time is expensive — though the overall economics depend on the specific fleet, since several idling trucks in a queue can collectively cost more than the shovel they are waiting for.

No mine or quarry operation stays perfectly balanced for an entire shift. Equipment breakdowns, workshop visits, crib breaks, and changing face conditions mean an operation that starts over-trucked at one shovel can become under-trucked at another within the hour. Continuous reallocation is one of the primary functions an FMS performs.

What Is the Target for Queue Time?

The theoretical target is zero queue time at the shovel, but that is rarely achievable in practice. Most operations instead aim for queue times under one minute. A good practical outcome is a truck arriving at the shovel while the currently loading truck is roughly halfway through its load — the arriving truck waits only briefly, and the shovel transitions to the next load with minimal hang time.

What Reduces Effective Fleet Capacity?

Three distinct factors erode the productive capacity of a truck fleet, and each requires a different management response:

  • Delays — short-duration interruptions such as waiting at intersections, right-of-way pauses, or stops for light vehicles. These inflate individual cycle times, but the truck remains part of the active fleet. They are addressed through cycle time analysis and operational fixes.
  • Standbys — planned non-productive time including refuelling, personal breaks, crib breaks, and shift changeover. Standbys reduce the effective operating hours available in a shift without taking the truck out of service. They are managed through shift scheduling.
  • Downs — breakdowns that remove the truck from the fleet entirely. A truck in the workshop no longer exists for allocation purposes.

Beyond these formally tracked categories, micro-influences such as traffic congestion on single-lane haul roads, poor road conditions after rain, or congestion at fuel bays also show up in cycle data — typically as creeping travel times or unexplained variance — even when they are never logged as formal timecodes.

For fleet managers and dispatchers, the practical value of this framework lies in diagnosis rather than a single output number: by watching which cycle component drifts and whether a shovel is trending toward over- or under-trucked, operations can intervene before lost operating hours turn into a measurable productivity gap.