Articles
Many mining operations have invested in online analysers and material intelligence systems to improve visibility, control variability, and support better decision making. Installing an analyser is often a major step forward. However, the long term value of that investment depends not just on the technology itself, but on how well it is supported, maintained, and integrated into day to day operations over time.
Material intelligence systems operate in demanding environments. Changes in material characteristics, operating conditions, throughput, and process configuration can all influence analyser performance. Without ongoing support, systems that initially delivered strong results can gradually drift from their optimal state. Data quality may degrade subtly, confidence in the measurement may decline, and operators may rely on the analyser less frequently in decision making.
In many cases, these issues are not immediately obvious. The analyser may still be producing data, but the data may no longer reflect true operating conditions with the accuracy or reliability required for effective control. When this happens, sites risk returning to reactive operating practices, despite having advanced measurement technology in place.
Ongoing support plays a critical role in preventing this erosion of value. Regular performance checks, calibration verification, and system health reviews help ensure that analysers continue to deliver reliable data as operating conditions evolve. Just as importantly, support teams provide continuity of knowledge, helping sites adapt their use of material intelligence as processes change.
Support also strengthens the connection between measurement and action. Even the most accurate analyser delivers limited value if its data is not trusted or understood by operators. Ongoing engagement helps ensure that data is interpreted correctly, alarms and thresholds remain relevant, and insights are translated into practical operational responses. This reinforces confidence in the system and embeds it into routine decision making.
As Jake Jones, Performance Assurance Manager at RTI, explains:
“An analyser doesn’t stand still, and neither does an operation. Our role is to make sure the data remains accurate, trusted, and useful as conditions change. When sites actively support their systems, they get far more value from the technology over the long term.”
This kind of performance focused support is particularly important for sites operating at scale or under variable conditions. Changes in ore source, blending strategy, or throughput can all influence analyser response. Without proactive oversight, these changes can introduce uncertainty into the data stream, reducing its usefulness just when operators need it most.
Ongoing support also provides an opportunity to identify improvement beyond the original installation scope. As operations mature in their use of real time material intelligence, new opportunities often emerge to refine control strategies, improve alignment between teams, or extract additional value from existing data. Performance assurance activities help surface these opportunities and ensure systems evolve alongside operational needs.
Importantly, supporting material intelligence systems is not about correcting failures. It is about protecting performance. Sites that actively maintain and support their analysers tend to sustain higher confidence in their data, respond more quickly to variability, and achieve more consistent outcomes over time. This transforms the analyser from a measurement device into a trusted operational asset.
For operations already using online analysers, long term success depends on treating material intelligence as an ongoing capability rather than a one off installation. With the right support framework in place, sites can maintain data integrity, adapt to change, and continue to realise value from their investment year after year.
To learn more about how RTI supports sites beyond installation, explore our approach to Performance Assurance and Product Support
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Nathan Kennedy
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