Mining is one of Australia's most data-intensive industries. A single site produces data from haul trucks, fixed plant, sensors, drill and blast systems, laboratories, maintenance and safety systems, and that's before finance and supply chain. Turning it into better decisions is a major source of productivity and safety improvement.
This article covers where analytics delivers the most value in mining, and the challenges to plan for.
Where analytics adds value in mining
Predictive maintenance
Combining sensor readings, operating hours and maintenance history helps predict equipment failures before they happen, so maintenance is planned rather than reactive and costly unplanned downtime is reduced.
It usually develops in stages. Start with condition monitoring: thresholds and alerts on key readings, such as temperature, vibration and pressure. Then add anomaly detection that learns each asset's normal behaviour. Finally, build failure prediction models once enough failure history has been captured. Platforms now include parts of this out of the box. Microsoft Fabric's Eventhouse, for example, has built-in anomaly detection that runs directly on live time-series data.
Productivity and operations
Analysing fleet, load and cycle-time data highlights bottlenecks in the load-and-haul process, and shows supervisors performance against plan during the shift, when there's still time to act.
Exploration and geology
Geospatial analysis of drilling, assay and survey data supports resource modelling and helps focus exploration where it's most likely to pay off.
Safety
Bringing together incident reports, inspections, fatigue indicators and operating data helps identify leading indicators of risk, so controls can be strengthened before incidents occur.
Energy, emissions and reporting
Energy and fuel are among the largest operating costs. Analytics shows where consumption can be reduced, and supports emissions reporting and decarbonisation planning.
Reporting is a strong reason to get this data right. Under the National Greenhouse and Energy Reporting (NGER) scheme, administered by the Clean Energy Regulator, a facility must report if it emits 25,000 tonnes or more of CO2-e (scope 1 and scope 2 combined) or produces or consumes 100 terajoules or more of energy in a reporting year. A corporate group must report at 50,000 tonnes or 200 terajoules. Reliable, automated collection of fuel, electricity and production data turns reporting from a yearly scramble into a by-product of everyday analytics.
Environmental performance
Monitoring water, dust, noise and rehabilitation data supports compliance and demonstrates performance to regulators and communities.
Market, pricing and supply chain
Analysing commodity prices, costs and logistics supports better planning, and supply chain analytics keeps critical spares and consumables available without excessive stock.
Challenges to plan for
- Data variety and quality: sensor, operational and manual data arrive in different formats and need cleaning and validation.
- Integration: fleet, plant, maintenance and ERP systems rarely share common identifiers.
- Remote locations: limited connectivity means designing for buffering and catch-up loads.
- Security: operational technology networks need careful protection when connected to analytics platforms. The Australian Signals Directorate's Principles of operational technology cyber security start from the principle that safety is paramount.
- Scale: high-frequency sensor data needs platforms that scale cost-effectively.
- Skills and change: insights only create value when supervisors and planners trust and use them.
Getting started
Start with one high-value question, such as the main causes of unplanned downtime, and build the data foundations to answer it well. Read our data engineering best practices for Australian mining, explore the mining dashboard in our live dashboard showcase, or talk to our analytics team.
