How Data is Transforming Decision Making in Mining

The mining industry is facing increasingly problematic challenges in meeting the worlds demand for critical minerals. Factors including slower economic growth, more complex ore bodies, ore dilution, sustainability and talent shortages all making the goal of operational excellence far more difficult to achieve. Operators are being tasked with gaining further visibility and transparency across the value chain, adapting to challenges, optimising their processes and making time critical decisions to meet the rising global demand for critical minerals – all in real time.

Financial data insights form a substantial bedrock for much of the macro decision making processes. Monthly production reports, corporate publications, and external articles can be complex to understand and whilst they can be an indicator of the direction a company is heading, their role in day-to-day operations can be minimal.

Companies rely on data for effective decision making, and deciding the exact type of data that mining operators require is important. This has transformed how data is obtained and analysed, with the onset of new, more advanced digital data systems.

Mining companies are now able to incorporate digital data systems that can assist with overcoming these challenges. These digital data systems are not a simple replacement for conventional methods of decision making, that is using data obtained through material research, surveying, and laboratory sampling among other things, but a compliment and enhancement.

During the initial stages of mining, modern sensors are adding value to geographic information systems (GIS) which are proving to be effective means of identifying mineral deposits and monitoring the impact of mining activities on the environment. For green field sites, those with minimal to no previous exploration, being able to accurately identify the location of mineral deposits and use this data in decision-making, is critical for avoiding costly mistakes.

Data generation and collection technologies for example, can capture comprehensive mineral information, making it an excellent addition to mining exploration, and developments to existing practices which will only make emerging technologies more accessible.

Incorporating real time monitoring systems that seamlessly integrate into the existing mining architecture and systems have become crucial for enhancing operational efficiency and maximising the potential of material harvest.  Due to the decline in ore grade that is being experienced globally, operators need to be able to maximise their yield and effective control their ore grade and sorting. Laboratory sampling is a common way of identifying material composition but comes with a noticeable lag which can range from hours to days. This is where these real time monitoring systems can quickly provide accurate data on material composition and the value of the material.

Data based insights and digital technologies provide companies with the means to balance the demands for material, versus the interests of governments and the environment, which can often be in conflict.

Mining companies frequently need access to water for reducing the impact of dust on the environment and for maintaining ore grade. This creates an uneasy balance between monitoring their water consumption as to not risk fines for overuse, and ensuring they reduce dusk particles arising from the material travelling on belt.

Technological advances and emerging technologies will make data more readily available for mining companies, highlighting a need for these companies to make the most of the equipment early and leverage the competitive advantages it provides.

Real Time Instruments (RTI) is a leading provider of elemental analysis technology, combining physics and innovating thinking to support and solve the world’s demand for critical materials.

By empowering mining operations to maximise resource efficiency, enhance environmental safety, and enable sustainable practices, RTI provides value through real analysis, real data, in real time.

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