Top 5 mining tech and data trends translated

Logo micromine 260Digitalisation, data analytics, AI and automation all featured consistently in this year’s mining trend forecasts. But what do the analysts’ really mean and how can their commentary be applied?

MICROMINE decodes five key data and tech trends that can boost your competitive edge in 2019 and beyond.

Data-driven strategy

Data already plays a pivotal role in mining but the ability to effectively manage and use growing pools of data to drive strategy is the next big challenge. The answer lays in “embedding” not “installing” systems and data into business practices. Embedding data management better connects your physical and digital assets and provides you with a complete picture of your supply chain.

A quality mining data solution provider like MICROMINE will examine your business needs and provide effective and efficient solutions that will feed information to both improve your processes and provide rich insights to underpin your business strategy.

The best software solutions to seek out are those that:
• are flexible, intuitive and easy to use;
• offer data validation features;
• deliver sophisticated and reliable analytics;
• provide flexible reporting;
• integrate with other systems; and
• offer rich data-driven insights that enhance your reporting to investors and your strategy development.

Quality providers like MICROMINE also work with companies to embed data management into company culture through training, education, and awareness. We recognise that the most effective business strategy is underpinned by data but that data comes from sources that need input and support too.

Automation and machine learning

Whether its autonomous vehicles, smart robots or drones, most miners are already onboard with automation. The question now is “what next?” Advances in machine learning and AI are the next things revolutionising our sector. They can increase production efficiencies, reduce costs and enhance safety but most people are confused by the terminology. In short:
• AI – involves a computer or computer-controlled robots that can perform tasks commonly associated with intelligent beings, like decision making and problem-solving.
• Machine learning – involves computers that can study algorithms and statistical information to perform tasks without instruction; relying on patterns and inference instead.
• Deep learning – is a machine learning method where systems ‘learn’ based on data representations, such as image or voice recognition, as opposed to algorithms.

These technologies allow miners to drill down (pun intended) to identify process improvements and efficiencies at a level that were impossible just a few years ago.

Using computer vision and deep machine learning, MICROMINE, for example, can install onboard cameras to loaders to track loading time, hauling time, dumping time and traveling empty time data. Through deep learning, this information is automatically mapped against complex data models to pinpoint potential production pipeline blockages or areas for improvement – and all in real time.

MICROMINE worked with the University of Western Australia to develop the solution, which is part of our underground fleet management system, Pitram, and conducted global testing before launching the product to the market in early 2019.

For those exploring these new technologies, this example highlights the importance of seeking information about the evolution, applications, and testing of a product before making your selection.

System integration

The ability for different systems to integrate and share data with one another is emerging as a critical success factor in company digital transformation strategies. When selecting and implementing systems company’s no longer need to be constrained by legacy systems. Siloed information systems will hamper efforts to effectively share data across your production pipeline and provide an incomplete picture of your operations. Instead, you should seek out systems with:
• scalability – solutions that offer modules or options that can be built on over time as the solution proves its worth; and
• integration – between product types, and geographical locations.
While we’d love everyone to only use MICROMINE, we know there will be preferred solutions for certain applications, so it is in everyone’s interests to enable the efficient transfer of data between packages. That’s why we’re an active participant in the METS sector broadly and participate in collaborative activities. We’ve taken a collaborative approach to data integration for years and that’s why we remain a market leader.

When selecting systems, you should also consider mobility alongside integration. Our Geobank Mobile product, for example, integrates with third-party data collection systems such as magnetic susceptibility devices, barcode, GPS, scale devices and onboard cameras while also offering to process using laptops and tablets for geologists in the field.


In its 2019 report, ‘Tracking the trends 2019: The top 10 issues transforming the future of mining’, Deloitte’s highlights the need for collaboration in some unexpected situations. “Although mining companies have taken significant steps to optimize their portfolios, they still struggle to respond to shifting macro-economic trends, which make long-cycle megaprojects particularly risky. Yet, rather than sharing the burden of this risk through collaboration, many mining companies continue to ‘go it alone’, tying up more capital over the long term and missing potential opportunities.” As an innovator in the field of mining software, MICROMINE recognises the value of collaboration and has worked with many in the METS sector to share knowledge and learnings to assist in advancing new technologies and innovations in the space in which we operate and can influence. This benefits our product and the industry.

MICROMINE worked with the sector and industry to develop the Australian CoalLog Standard, which provides standard data formats and codes for capturing geological and geotechnical data. Our Perth team participated in The Newcrest Crowd, a crowdsourcing and partnership platform that challenged innovators around the world to solve specific mining problems. And, late last year, we were selected by MinEx Cooperative Research Centre (CRC) as the sole software provider involved in the $218 million projects to develop new technologies to increase the discovery of new mineral deposits in Australia.

This collaborative ethos is valued by MICROMINE and has been highlighted by analysts as an issue for all to consider in all areas of the mining sector if we are keen to be leaders and disrupt, then catapult traditional mine planning and thinking to new heights.

Digital risk management

When configured properly, software system solutions provide a central and secure ‘single point of truth’. These systems and the data collected can feed risk profiling by identifying patterns and causational relationships. Should something go wrong, the same systems, and other technology like cameras and drones, can also provide an invaluable digital record to inform an investigation. Real-time data capture systems are growing in importance in risk and safety management and have been highlighted in many 2019 trend forecasts. Exploration and mine managers using our Pitram 4.10, for example, can make key decisions knowing they have access to accurate, near real-time data. They can adjust production processes and schedules to accommodate emerging issues or risks.

In the end, effective software solutions – even those not designed specifically to mitigate risk – can return their value many times over by providing answers to the ‘what’ and ‘why’ questions relating to risk. And the data sets captured can be used to predict the ‘when’ and ‘how’ of potential risks, so they can be addressed before they even happen.

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