Presented by Accenture

Mine(d) over matter – The future of autonomous operations

 ·6 Oct 2022

As self-driving industrial vehicles mature and the Internet of Things (IoT) as well as wireless connectivity become more widespread, the mining industry in South Africa has shown a growing interest in automating haul trucks and other mobile equipment.

Today, a number of companies have fleets of autonomous trucks, trains and loaders at mine sites, or are piloting the use of these vehicles. These efforts are a significant leap forward, but they are just scratching the surface of how the mining industry can use autonomous systems.

To reap those far-reaching benefits such as increased efficiency and productivity, miners should look beyond automated vehicles and bring autonomy to a range of mining activities, thinking of it as an embedded, fundamental capability. Doing so will make mining more safe, sustainable, and ultimately more competitive.

The building blocks of autonomy

According to our research, rather than just automating equipment, autonomous operations will encompass entire processes – in engineering, production and supporting business activities – that manage themselves and adjust to changing conditions with little or no human intervention.

But bringing more autonomy to mining will require further deployment of technologies ranging from the IoT to extensive connectivity.

The key to taking autonomy to the next level will be the increasing use of robotic process automation; descriptive, diagnostic, predictive and prescriptive analytics; scenario modelling and artificial intelligence (AI) technologies such as machine learning.

These technologies allow systems to understand the data flowing through operations, enable situational awareness, develop near-real-time insights into operations and determine what options to consider.

They can be used to support human operators and ultimately, guide automated decision-making and responses to changing conditions – a capability at the heart of genuinely autonomous operations.

Advanced analytics, machine learning and scenario modelling are already embedded in some mining operations and are being tried and tested in others.

For example, mining firms now use these technologies to enable autonomous mine design, using real-time data and AI-based insights to simulate and analyse various scenarios and optimise strategies for productivity, safety and environmental impact.

Factors to consider along the way to autonomy

  1. Focus on value: The autonomous path to value will differ for each business. Clear visibility of the key value drivers is critical to avoiding false starts and matching stakeholder expectations.
  2. Address the foundation: To take advantage of intelligent technologies, companies should ensure their IT architectures are ready to support communications between various systems and types of equipment. They should also assess the interoperability of systems, which is key to integrating operations across the mine and the value chain.
  3. Ensure data readiness: Using data from various sources is critical to AI- and analytics-based insights as well as decision making. Companies must give due consideration to their data-handling capabilities to capture and manage ever-growing volumes of data, while ensuring the data used to drive decision making is accurate and trusted. Data will need to be clean, consistent and refined into datasets that can be analysed readily.
  4. Manage the change: As a rule, technology changes faster than people, and the shift to autonomy could leave employees behind, if not carefully managed. Mining companies can help people feel more comfortable with new approaches by communicating clearly, building trust and preparing them to succeed in the new environment. Companies can also apply these concepts to local communities to manage fears about the potential impact of autonomous operations on employment and safety and to help maintain the license to operate granted by the community.

Evolving towards autonomy

In general, more autonomy in mining operations means jobs in the industry will become less blue-collar and more white-collar in nature. For example, mechanics who now respond to maintenance problems will instead work alongside AI to predict failures and perform preventative repairs.

And many operators will move from the hands-on running of a given machine to remotely overseeing multiple pieces of highly autonomous machinery. While fewer operators will be involved, people are likely to be needed in a range of new roles in maintenance, data processing, operational control and mine planning.

Mining companies can re-skill the workforce to keep up with these changes, and some are already doing so. In the future, such re-skilling initiatives will need to be ongoing and continuous because the speed of innovation – accelerated by AI – will mean that the skills needed in the industry will evolve constantly.

The move to greater autonomy will clearly require significant change. The change won’t need to be done all at once nor will all operations need to become autonomous. Instead, miners can move forward in gradual, targeted steps.

To start, companies can initially focus on areas where the opportunity for impact or success is exceptionally high, such as operations where there are higher levels of risk or bottlenecks that cause delays and drive-up costs.

Once the mine has introduced the desired level of autonomy to the targeted area, the company can move on to the next area until the roadmap and the overall effort is complete. This approach allows the company to keep its big-picture autonomy goals in mind while focusing on specific investments, providing opportunities to test and improve autonomous capabilities.

For miners, the widespread implementation of autonomous operations will represent a significant break from the past – and it is coming soon.

The technology is advancing quickly, and the demands of triple bottom line reporting – commitment to measuring social and environmental impact in addition to their financial performance – are only growing. Mining companies must move ahead rapidly and embrace new autonomy-based ways of working that will be fundamental to competing in the years ahead.

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