For decades, commodity operators and asset owners have focused heavily on one central goal: keeping material moving efficiently. Whether it’s iron ore, coal, wheat, or any other bulk commodity, the priority has been simple: avoid stoppages, prevent demurrage, and ensure the supply chain keeps flowing.
While these remain important priorities, the truth is that most automated systems and operators already have robust safeguards to prevent costly delays and operational penalties. Technologies for scheduling, movement control, and load planning are mature enough that major inefficiencies are increasingly rare.
So where else is the real value being unlocked today?
Not too surprisingly, the answer lies in quality management, an area where even small improvements translate into massive financial returns –
If a contract specifies a certain quality threshold, say, 61% FE for iron ore, 25% ash for coal, or 10% protein for wheat, traditional practice is to deliver above that requirement to stay safe. But doing so means you are literally giving away higher-value material.
A smarter approach is to deliver just enough above the specification to avoid penalties. For example:
The challenge? Quality varies across pits, stockpiles, silos, and batches. Guessing or using weighted averages is unreliable.
A modern Quality Management System (QMS) solves this by tracking, modelling, and predicting quality in real time so you know exactly what the final shipped quality will be.
The diagram makes the difference clear.
Traditional weighted-average planning produces a wide confidence range, shown by the broad brown curve. This wide range creates uncertainty: operators cannot be sure whether the final cargo quality will dip below the contract target or exceed it significantly.
Because of this uncertainty, operators set their planned target much higher than required to avoid penalties. This extra buffer is where value is lost.
A QMS, on the other hand, generates a narrow, high-confidence range, represented by the green curve. This is because QMS continuously models real-time data from stockpiles, rehandling movements, belts, and loading operations.
With this tighter range:
This is where millions of dollars are won or lost.
Weighted averages = low confidence, wide uncertainty
QMS modelling = high confidence, minimal uncertainty
Out-of-spec penalties can be brutal. If silica, for example, attracts a US$1 penalty per tonne, then going out of spec on half of a 500,000-tonne contract means:
250,000 tonnes × $1 penalty = $250,000 lost
A QMS flags these risks early and predicts where parameters are trending out of spec—long before the material reaches the point of no return. Operators can blend, divert, or reprocess accordingly.
In commodities like iron ore fines, moisture is more than just a quality parameter; it’s a safety risk. Too much moisture can make the material behave like a liquid, posing a real danger of cargo liquefaction, especially during storms.
Old methods like weighted averages hide moisture hotspots. A QMS, however:
This reduces safety risks and protects vessels, crew, and cargo.
Quality Management can and should be integrated into the stockpile management and task management landscapes to ensure predictions and operational decisions are contextualised into current capabilities:
Task Management systems (SAC) report quality sublot movements to QMS which can warn of out of specification trends and invoke on the fly changes to improve throughput and business outcomes
The industry is moving from guessing quality using weighted averages
To: Knowing quality through real-time predictive modelling
And in a world where margins swing by millions based on a single quality parameter, this shift is not just beneficial; it’s transformative!