Extracting Insights: Leveraging Mining Business Lessons for Successful AI Deployment

AI is revolutionizing the way mining conglomerate BHP utilizes operational data to enhance daily decision-making processes. In a recent blog post shared by the company, BHP emphasizes the significance of leveraging AI for analyzing data from sensors and monitoring systems. The main goal is to identify patterns and detect issues in plant machinery, providing decision-makers with valuable insights to improve efficiency, safety, and environmental sustainability.

Instead of asking “Where can we use AI?” BHP’s approach focused on identifying repetitive decisions and determining the information needed to enhance them effectively.

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BHP highlights the comprehensive impact of AI on its operations, spanning from mineral extraction to customer delivery. The company shifted from conducting pilot rollouts to embedding AI as a core operational capability. By targeting specific problems affecting performance, measurable changes were implemented to drive results.

By implementing AI, BHP successfully minimized unplanned machinery downtime and optimized energy and water consumption. Each use case was assigned an owner and a key performance indicator (KPI), with results regularly reviewed to ensure continuous improvement.

Where BHP uses AI daily

Besides focusing on predictive maintenance and energy optimization, BHP explored AI applications in advanced operations such as autonomous vehicles and real-time staff health monitoring. These innovations are transferable to asset-heavy industries like logistics, manufacturing, and heavy industry.

Predictive maintenance

AI-driven predictive maintenance involves planning repairs during scheduled downtime to prevent unexpected failures and costly stoppages. By analyzing equipment data from sensors, AI models predict maintenance requirements, reducing breakdowns and enhancing safety. BHP extensively applies predictive analytics to its load-and-haul fleets and materials handling systems, enabling real-time monitoring of machine health.

Energy and water optimization

Through predictive maintenance, BHP achieved significant savings in water and energy consumption at its facilities in Escondida, Chile. Real-time analytics empower operators to identify anomalies and automate corrective actions, enhancing operational efficiencies.

Autonomy and remote operations

BHP integrates AI in autonomous vehicles and machinery to reduce operational risks and human errors. By centralizing operational data flow through regional centers, AI enables optimized decision-making in remote facilities.

AI-integrated wearables play a vital role in monitoring staff health and safety, particularly in challenging work environments. Wearables like ‘smart’ hard-hat sensors analyze vital signs and provide real-time alerts to supervisors, enhancing worker safety.

A plan leaders can run

Based on BHP’s successful AI deployment, decision-makers can adopt a strategic approach to leverage AI in operational challenges. The following plan outlines key steps:

1. Select a reliability and resource-efficiency problem tracked by operations teams and establish a KPI.
2. Map the workflow to determine who will act on the output and their corresponding actions.
3. Implement basic governance for data quality and model monitoring, aligning performance with operational KPIs.
4. Initiate decision support in high-risk processes, validating controls before automation.

(Image source: “Shovel View at a Strip Mining Coal” by rbglasson is licensed under CC BY-NC-SA 2.0.)

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