In the heart of Australia’s mining sector, a seismic shift is brewing, and consulting powerhouse McKinsey & Company is at the forefront of this transformation. The firm’s partners, Milan Korbel and Richard Sellschop, are urging miners to embrace a dual strategy of “digging dirt and data” to enhance productivity. It’s not just about unearthing minerals anymore; it’s about unearthing insights that can drive efficiency and profitability.
Korbel, who straddles the line between traditional mining practices and cutting-edge artificial intelligence through his work with QuantumBlack, acknowledges the skepticism that permeates the industry. Many mining companies have dabbled in AI, but results have often fallen short of expectations. “The main reason is because it’s difficult,” he points out, highlighting the challenges firms face in integrating AI into their operations. Companies have experimented with various partners and vendors, but the outcomes have not always been encouraging. Yet, Korbel insists that this reluctance is not entirely warranted. The potential for AI to streamline processing operations and optimize mobile fleet management is significant.
He likens AI to a “big brother” watching over refineries, providing operators and engineers with the insights they need to make informed decisions. With Australia’s labor productivity growth stagnating since 2014, McKinsey sees AI as a crucial lever to reinvigorate the mining sector. Sellschop, who leads McKinsey’s global mining services division, echoes this sentiment, stating that improving productivity is imperative for the industry’s growth.
The mining landscape is fraught with complexities. Unlike manufacturing, where conditions can be controlled, mining operations face unpredictable elements, fluctuating grades, and the relentless challenge of deeper excavations. Sellschop emphasizes that this environment makes it exceptionally tough to enhance productivity. “This is probably the most difficult industry in the world to raise productivity,” he asserts.
The concept of “digging dirt and data” encapsulates the need for miners to not only extract resources but also to harness the wealth of information generated in the process. Sellschop draws a parallel to navigation apps like Google Maps, which provide users with optimal routes based on real-time data. “You don’t want Google Maps to be telling you how it’s figuring out what the optimal route from A to B is,” he explains. “But it’s really quite useful when it does tell you ‘we recommend you save 10 minutes in your journey by taking a left here instead of carrying on straight.’”
This analogy underscores the importance of digesting and utilizing data effectively. The road to realizing AI’s full potential in mining is paved with challenges, but as Sellschop suggests, the value of this investment will become increasingly apparent.
As the mining sector grapples with these changes, the implications for the future are profound. Embracing AI and data analytics could not only enhance productivity but also redefine operational standards, setting the stage for a new era in mining. The question now is whether the industry can overcome its skepticism and seize this opportunity for transformation. The stakes are high, and the potential rewards even higher.
