The destination bottleneck

A fleet's daily load count dropped from 108 to 25 over three weeks. The mine blamed the hauliers. The hauliers blamed road conditions. The logistics manager requested a formal investigation into driver behaviour.

TIHLO's monitoring data told a different story.

What the data showed

The trucks were moving. Transit times from pit to weighbridge were consistent with historical averages — if anything, slightly faster than the prior month. Departure rates from the mine gate were normal. The problem was not on the road. The problem was at the end of it.

Queue times at the destination increased from an average of 14 minutes to over three hours across the same period. A single weighbridge scale at the receiving facility had been taken offline for maintenance — unannounced, mid-month. With one fewer scale and no communication to the logistics chain, trucks were queueing. Queueing trucks were not returning to load. Load counts fell.

The mine was measuring the symptom — daily loads — not the cause.

Why this pattern is common

Mining logistics accountability defaults to measuring what is visible and attributable. The transport leg is visible: you can count trucks, measure speeds, track routes. The receiving facility is downstream — it is someone else's operation, often a different company, sometimes a different provincial jurisdiction.

When load counts fall, the default investigation looks at what is controllable: driver behaviour, vehicle availability, road quality. These investigations produce findings, even when those findings are not causal. Hauliers receive warnings. Relationships deteriorate. The actual constraint — a scale offline for three weeks — remains unaddressed.

Where we keep finding it

The destination bottleneck pattern emerges consistently in three conditions:

• Receiving facility infrastructure change — scale maintenance, power outages, staffing gaps — that is not communicated upstream to the logistics chain
• Seasonal capacity constraints at shared receiving facilities, where multiple operations compete for the same off-take infrastructure during peak production periods
• Administrative delays at the destination — SARS document checks, product quality disputes, permit validation — that extend dwell time without any visible transport failure

In each case, the data pattern is the same: transport metrics are normal; load counts are not. The gap lives in dwell time.

What useful monitoring surfaces

The diagnostic question is not "why are loads low?" It is "where in the cycle is time being lost?"

A monitoring layer that tracks the full load cycle — departure from mine gate, transit time, arrival at destination, dwell time at destination, return transit, return to gate — can isolate the constraint within two to three shifts. The mine in question above could have identified the destination bottleneck on day three. They identified it on day twenty-two, after a formal haulier investigation had been commissioned.

The investigation cost more than the scale repair.

The pattern will repeat. It repeats because the incentive structure in mining logistics is to attribute underperformance to the party that is most controllable — typically the transport contractor. When the actual constraint is downstream, the attribution is wrong, the intervention is wrong, and the loss continues.

Monitoring that covers the full cycle prevents this. Monitoring that covers only the transport leg amplifies it.