The scenario below is illustrative. It is modelled on typical mid-sized open-pit deployments to show where the gains come from. It does not describe a single named customer, and your results will depend on your operation.
The Starting Point
Picture a mid-sized open-pit operation running 25 articulated dump trucks (ADTs) and 6 excavators, with no on-vehicle weight measurement. Operators rely on visual estimates at the loading face, leading to frequent overloading (tire damage and safety concerns) and underloading (wasted hauling capacity).
End-of-shift tonnage reports are based on truck counts multiplied by an assumed average payload, so management has no way to identify which loading sites or shifts are underperforming. The numbers are known to be inaccurate; nobody can say by how much, or where.
The Deployment
Kimax weight sensors go on all 25 ADTs and Perigon cycle analytics is configured across the operation. A deployment of this size typically looks like:
- Day 1-3: Weight sensor installation during routine maintenance windows, with no production downtime
- Day 4-5: Geo-zone configuration for the loading faces, dump sites, and major haul road segments
- Day 6-7: Perigon calibration and operator training
- Day 8: System live with full real-time data streaming
From the first week, the operations team has real-time visibility into exact load weight per truck per cycle, cycle times from pit to dump and back, tonnage per loading site per shift, and overload/underload incidents as they happen.
What the Data Typically Reveals
The first weeks of real data almost always surface patterns that were invisible before. In an operation like this one, expect findings such as:
- Systematic underloading: average payloads several percent below target, often concentrated on specific operators or machines, and invisible without per-cycle weight data
- Loading face imbalance: some faces producing far more tonnage per shift than others despite similar equipment assignments
- Queue time waste: minutes lost per cycle queuing at the primary dump, which across a fleet is the equivalent of losing whole cycles per truck per shift
The Modelled Results
With real-time data driving decisions such as eliminating underloading, rebalancing truck assignments, and staggering dispatch to kill queues, an operation of this profile can realistically target:
- Double-digit gains in daily tonnage throughput from payload optimization and rebalanced loading faces
- A sharp drop in overloading incidents as real-time alerts catch overloads before trucks leave the loading face
- Lower fuel consumption per ton, since better load optimization means fewer trips for the same output
- Reduced queue time at dumps once the bottleneck is visible and dispatching is staggered
- Shift-by-shift performance visibility as shifts and loading sites become comparable for the first time
Key Takeaway
The pattern repeats across operations: the tonnage being left on the table cannot be recovered until it can be measured. Real-time weight data on every truck turns end-of-shift guesswork into per-cycle decisions, and that is where the throughput comes from.
Want to model this for your own fleet and targets? Book a demo and we will walk through it on your numbers.