Beyond traditional data collection, a new era of proactive decision-making is transforming the mining and construction sectors, utilising AI and ML to process vast amounts of data to provide real-time, actionable recommendations.
The Internet of Things (IoT) has become well established in the mining and construction sector, adding value through “connected assets”, which all carry sensors, transmitting streams of vital data to owners and original equipment manufacturers (OEM) and helping improve efficiencies through data insights.
Be they excavators, generators, graders or skid steer loaders, modern equipment is being fitted with communication technologies that deliver critical data that can inform vital decisions. However, the next stage of this digital evolution is already upon us: deploying artificial intelligence (AI) and machine learning (ML) to interpret this data and guide decision-making for owners and managers.
While traditional telemetry empowers machine owners by providing data, it does present challenges in terms of managing the data they are presented with. AI meets this need, processing data at high speed and allowing customers to optimise efficiencies and gain a competitive advantage.
This has exciting implications for Africa, with its skills shortages in data analytics. Where OEM-supported AI can fill the gap, the continent has the opportunity to build great data-driven efficiencies in mining and construction.
AI can be deployed in the sector by sending the information extracted from connected assets to a centralised OEM location for processing. ML and AI algorithms then analyse the data and present it to customers through easy-to-use applications.
AI extracts data insights in real time, complete with recommended responses, meaning that owners are making agile decisions that fundamentally affect their businesses and those of their customers.
Globally, the mining sector has embraced AI. In one case, mining house BHP has partnered with Microsoft, using AI to boost copper production at its operation in Chile. In Pakistan, Barrick Gold uses AI to identify likely resource deposits from geological data. In construction, Caterpillar deploys AI to gain insights and to enhance customer efficiency.
In the latter case, there are three fundamental ways that AI technology is making work easier and more effective for the construction industry.
“It all starts with connectivity,” says Ogi Redzic, Caterpillar’s chief digital officer and senior vice president. CAT has more than 1.4 million connected assets in the field, all generating oceans of data every day.
Processing that vast amount of data is a critical part of the process – and only AI and ML have the speed and interpretive capability to derive the full benefit from this information.
“We process the data and create machine learning and AI algorithms that help us extract value,” Redzic says. “Then we present that to our customers through different applications.”
Among these applications is the Caterpillar VisionLink app, which allows customers to track their equipment and gain insights around how much fuel it is using, how parts are performing, whether and when it needs servicing and more.
Condition monitoring allows the AI application to detect possible issues and alert owners to take action before there is any risk of failure.
The idea is to ensure that equipment is running as reliably as possible; to get ahead of failures and incorporate them into scheduled maintenance programmes.
In the CAT case, data from more than 20 different sources goes into its condition monitoring technology — everything from equipment sensors to inspections to fluid analysis. This data gives dealers an accurate picture of performance and health, and allows them to recommend exactly what parts and services are required, and when, to ensure optimised performance.
To close the loop, data and smart technology also drive the purchase of the parts needed to keep machines running at top efficiency. QR codes on vehicles allow owners to scan, browse and order parts within seconds from their mobile phone, in an e-commerce ecosystem that is customised to their needs.
“Caterpillar offers 1.5 million parts, but the client might only need a couple of parts,” Redzic says.
AI is used to interpret data from connected assets all over the world, then cross-referenced with parts-purchase data to offer predictive parts suggestions, based on individual machine usage. Customers see these tailored, serial-number-specific recommendations the moment they log on.
Despite the compelling use cases across businesses and around the world, Africa has its own unique context, and AI deployment must be executed according to the continent’s specific needs.
Conscious of this, the African Union has devised a thorough Continental Artificial Intelligence Strategy, focused on guiding the rollout of the technology.
The report notes that AI has positive and transformative potential for African development, but that avoiding potential risks will require building the right capabilities. These range from sustained investment in infrastructure to huge sets of quality data, skills and an inclusive AI start-up enterprise environment.
These are significant requirements; however, the continent has established itself as a place of innovation, unburdened by the traditions of legacy technology.
In this context, and with a private sector geared to rolling out the technology as quickly and effectively as possible, Africa is well positioned for a step change in its mining and infrastructure development, thanks to the power and exponential potential of artificial intelligence.
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