Mining conglomerate BHP describes AI as the way it’s turning operational data into better day-to-day decisions. A blog post from the company highlights the analysis of data from sensors and monitoring systems to spot patterns and flag issues for plant machinery, giving choices to decision-makers that can improve efficiency and safety – plus reduce environmental impact.
For business leaders at BHP, the useful question was not “Where can we use AI?” but “Which decisions do we make repeatedly, and what information would improve them?”
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BHP describes the end-to-end effects of AI on operations, or as it puts it, “from mineral extraction to customer delivery.” Leaders had decided to move beyond pilot rollouts, treating AI as an operational capability. It started with a small set of problems that affected the company’s performance; places where change could be measured in results.
The company found it could avoid unplanned downtime of machinery, plus it tightened its energy and water use. Each use case addressing a small but impactful problem was given an owner and an accompanying KPI. Results were reviewed with the same regularity used for other operational performance monitoring elsewhere in the company.
Where BHP uses AI daily
In addition to BHP focusing specifically on areas such as predictive maintenance and energy optimisation, it gave consideration to using AI in more adventurous yet important operations such as autonomous vehicles and real-time staff health monitoring. Such categories can translate well to other asset-heavy environments, across logistics, manufacturing, and heavy industry.
Predictive maintenance
Predictive maintenance is the process of planning repairs in scheduled downtime to reduce unexpected failures and costly, unplanned stoppages. Here, AI models analyse equipment data from on-board sensors and can anticipate maintenance needs. This cuts breakdown numbers and reduces equipment-related safety incidents. BHP runs predictive analytics across most of its load-and-haul fleets and its materials handling systems. A central maintenance centre provides real-time and longer-range indications of machine health and potential failure or degradation.
Prediction has become an integral part of its machinery-heavy operations, where previously, such information was presented as ‘just another’ report, one that could get lost in the bureaucracy of the company. It models and defines thresholds which trigger actions directly to teams planning maintenance.
Energy and water optimisation
Deploying predictive maintenance in this manner at its facilities in Escondida in Chile, the company reports savings of more than three giga-litres of water and 118 gigawatt hours of energy in two years, attributing the gains directly to AI. The technology gives operators real-time options and analytics that identify anomalies and automate corrective actions at multiple facilities, including concentrators and desalination…
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