The mining industry is under pressure to increase critical minerals production to support the global energy transition, while trying to improve productivity and reduce costs. The sector also faces growing environmental and regulatory constraints, as well as a generalised talent shortage.
Amid this landscape, companies are turning to real-time operations data, advanced analytics, production optimisation, predictive maintenance, energy management and automation to boost productivity, reduce risk and attract the next generation of engineering and operations workforce, says Glenn Kerkhoff, global industry principal for mining, metals and minerals at AVEVA.
How is real-time data changing the way mining teams monitor performance and respond to issues on the ground?
Companies that have access to live data with advanced analytics and operational insights will be able to monitor and optimise mobile fleet performance, plant throughput, equipment health and energy consumption to make faster and more informed decisions.
Instead of waiting for end-of-month production reports, management teams and supervisors can see problems as they emerge – such as a shovel underperforming or a mine operating below target – and respond immediately. This shift helps reduce downtime, increase utilisation and productivity, maintain product quality and improve collaboration, while helping operators to meet production targets.
What role do predictive analytics and AI play in modern maintenance strategies across the mining value chain?
Mining operations depend on large, mobile equipment and fixed plants, where minimising unplanned downtime and optimising spare parts inventory is essential for maximising availability, maintaining productivity and ensuring continuous uninterrupted operation.
However, many companies struggle to implement consistent maintenance practices across their global operations. Adopting a centralised, digitally integrated approach enables remote oversight and a shift from reactive to predictive maintenance. By monitoring trends in sensor data, teams can detect early warning signs of failure. These analytical models allow operations to plan interventions, extend equipment life and improve safety.
How can firms move from spreadsheet-based reporting to proactive, automated insights?
Organisations can begin this shift by collecting and managing operational data in centralised systems like AVEVA PI System and building automated visualisations with platforms like Microsoft Power BI. This setup enables real-time performance tracking, continuous analytics, standardised key performance indicators (KPIs), and faster problem detection and root cause analysis.
In the mining industry, where decisions are often delayed by data silos and manual reporting, automation transforms reporting from a retrospective task into a continuous improvement tool – informing both site-level and corporate decision makers.
What are the key steps involved in implementing digital dashboards?
For mining operators, dashboards should be designed with simplicity and usability in mind. Visual tools – such as red-yellow-green indicators – help frontline teams quickly interpret asset status or production targets without needing advanced analytics skills.
Mobility is also a key issue. Many supervisors work out in the field and need a mobile platform to keep in touch with equipment health and performance, as well as to track production against target.
Why is workforce buy-in essential for digital transformation, and how can it be cultivated?
Technology will not benefit businesses if it’s not being used. This means mining companies should involve management, operators and engineers early in the process through gathering input and requirements, making tailored adjustments and offering clear training.
Beginning with small, focused use cases helps demonstrate value, builds trust and reduces resistance. Aligning digital tools and solutions with real operational problems is important for long-term adoption. Digital access empowers the entire workforce by leveraging data insights to transform operations.
How are mining companies adapting their talent strategies for a more data-driven future?
Mining companies are embracing a hybrid approach; they are upskilling experienced workers who know the processes and equipment, while also hiring new talent skilled in data science and AI. Existing employees can offer important operational context and experience, while fresh talent can bring in technical know-how and innovative thinking.
What role do Microsoft platforms play in digital transformation across mining operations?
Microsoft’s ecosystem is helping mining companies bridge the historic divide between IT and OT. Azure provides the cloud backbone to aggregate vast volumes of operational data, while Power BI turns that data into actionable insights for both the control room and boardroom. Integrated with AVEVA’s industrial platforms, these tools enable continuous monitoring of asset health, energy usage and plant performance across multiple sites.
Take Agnico Eagle, the world’s third-largest gold producer. Faced with fragmented systems and limited access to critical datasets, the company turned to Azure, Power BI and Databricks to centralise and contextualise its operations data. The result? Streamlined analytics, faster insights and a scalable foundation for AI-enabled decision-making across its global mines network.
Agnico Eagle’s integrated operations centres now serve as digital control hubs, supporting proactive responses and vast gains in efficiency, safety, and sustainability. Early pilot results show three per cent efficiency gain in truck performance and cost reductions from improved operator practices and asset health.
Similarly, American copper producer Asarco has completed a major digital transformation, supported by systems integration from Casne Engineering and advanced digital tools from Microsoft and AVEVA.
Historically, all of Asarco’s processing plants operated in silos. There was little interdepartmental communication, leading to repeated failures and missed production targets. To address these issues, the company implemented AVEVA PI Vision, AVEVA PI System, and Power BI to gain visibility into real-time plant performance.
Casne, known for its expertise in IT and operational technology integration, infrastructure monitoring and advanced analytics, helped Asarco implement standardised key performance indicator reporting and centralised data pipelines – creating a more responsive and predictive operational model across its mining sites.
The improvements have been immense. Supervisors can act early. Predictive analytics catches major issues before they happen – like identifying a lube pump malfunction through a minor temperature rise.
Microsoft’s and AVEVA’s combined solutions work to support more connected, data-driven teams. These are essential attributes in geographically dispersed and operationally intense industries like mining.
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