MaxMine have announced the successful deployment of a production-grade machine learning (ML) system for load and dump classification across its Australian mining customers, including Glencore, NRW Holdings, and Macmahon.
Now fully operational for six months, the system has delivered significant operational improvements, including:
- Reduced workload for site teams by minimising missed or incorrect loads.
- Higher accuracy production tracking, particularly in complex and edge-case scenarios.
- High internal development efficiency through structured data and modern ML pipelines, enabling the system to handle diverse edge cases without requiring bespoke development.
Shaun Mitchell, CEO at MaxMine, said, "Our successful implementation of this new machine learning system reinforces what has been observed across the industry: organisations succeeding in AI are those that have the highest-quality datasets. As AI adoption accelerates across mining and other critical industrial sectors, having high-fidelity, ground-truthed data becomes essential for delivering accurate results, improving operational visibility and enabling faster, more informed decision-making.”
This milestone comes as MaxMine executive chair Tom Cawley is appointed mining sector lead for the newly established AI Accelerator Cooperative Research Centre (AI CRC). The AI CRC aims to build Australia’s sovereign AI creation capacity, enabling sectors such as mining to develop their own AI tools rather than relying on offshore capabilities.
Tom Cawley, Executive Chair, MaxMine, said, "Despite increasing investments in AI, industries such as mining still struggle to move beyond pilot initiatives to achieve large-scale operational outcomes.
"Gartner estimates that 60% of AI projects fail due to a lack of AI-ready data, with 42% of organisations abandoning AI initiatives before they reach production. At MaxMine, we’ve demonstrated that Australia has the capability to develop advanced AI tools that work effectively at scale in mining. I hope my role at the AI Accelerator CRC will encourage further innovation across the sector and help to strengthen Australia’s competitive edge in the critical minerals market."
The new ML system, deployed within a private, secure environment, draws on over 14 million hours of high-quality, labelled operational data, alongside high-resolution datasets across load and haul operations. This foundation has enabled MaxMine’s latest ML model to deliver stable, high-confidence results in live production environments with industry-leading accuracy.
Professor Anton Van Den Hengel, Chief Scientist at the Australian Institute of Machine Learning and Interim CEO at the AI Accelerator CRC, added, “The ability for a model to be deployed with such accuracy across such a range of asset and site types is rare. MaxMine's ability to do this points to their uniquely rich, accurate, and human error-free data sets, paired with long-term, multi-site, multi-machine training data sets."