Building modern mining operations with data, analytics and IFS Cloud

Mining companies are navigating a difficult operating environment. Demand for critical minerals and precious metals remains strong, yet the path to profitable production is difficult. Operations must deliver more output while managing safety, rising costs, aging assets, workforce constraints, regulatory change, and expanding sustainability needs. At the same time, the industry is under pressure to modernize without disrupting production or adding unnecessary complexity. 

In a recent Baker Tilly webinar, professionals from Baker Tilly and IFS discussed how mining organizations can use IFS Cloud, data strategy and analytics to move from fragmented systems and reactive maintenance toward more integrated, predictive and data-driven operations. Below are the key takeaways from the webinar:  

Five trends shaping mining operations 

Mining companies are facing several converging trends that make modernization a strategic priority. 

• Higher demand, higher cost: Demand for critical materials remains strong, but producers are also managing diesel fuel fluctuation, declining ore grades and supply chain cost pressure.   
• Greater operating risk: Regulatory changes, export controls and supplier uncertainty are adding complexity to procurement, production and commercial planning.  
• Disconnected operational data: Mining companies often have large volumes of data, but it is decentralized across plant systems, data historians, fleet tools, enterprise resource planning (ERP) systems, enterprise asset management (EAM) platforms, spreadsheets and other platforms.  
• AI potential, AI skepticism: Artificial intelligence (AI) can support practical use cases, but adoption depends on clean, connected and trusted data.  
• Workforce pressure: The industry continues to face recruiting, retention and knowledge-transfer challenges, increasing the need for better tools that help workers make informed decisions. 

Mining operations span a complex value chain 

Miners do not all operate the same way. Some span the full lifecycle: exploration, extraction, processing, logistics, commercial operations and eventual closure or rehabilitation. Others focus on a specific part of that lifecycle, such as extraction, processing, land management, natural resource development or minerals-related manufacturing. 

That breadth matters because modernization needs to reflect how each organization actually operates. A mining company may depend on fleet systems, plant systems, operational technology, ERP, maintenance platforms, analytics tools, ESG systems and spreadsheets. In many environments, these systems were implemented to solve specific functional needs, not necessarily to work together as a single integrated operating model.  

The real barrier: Data, integration and visibility 

Many mining companies do not lack data. In fact, they often have more data than they can use effectively. The difficulty is turning that information into trusted, timely and actionable insight. When data is fragmented, leaders typically face several recurring problems: 

• Inconsistent reporting: Fragmented systems and disconnected data can make reporting inconsistent across the business. 
• Reactive maintenance: Without real-time visibility, teams may identify asset issues only after they affect operations. 
• Manual reconciliation: Spreadsheets and disconnected systems can force teams to spend time moving and reconciling data. 
• Root-cause challenges: When systems do not talk to one another, it becomes harder to understand what is driving performance issues. 
• Low trust in data: Decentralized data can create uncertainty about which information should be treated as the source of truth. 

This is why data maturity is becoming as important as operational maturity. Better systems are valuable, but only when they help leaders connect information across the business and use it to improve decisions.  

From reactive maintenance to predictive control 

Asset performance management is one of the most practical starting points for mining modernization. Mining is highly asset-intensive and asset performance influences throughput, safety, maintenance and cost. When high-value assets fail unexpectedly, the impact can be significant. 

Modern mining operations are moving toward real-time monitoring, predictive maintenance, AI-assisted operational insights, digital twins, condition monitoring and automated workflows. This progression does not happen all at once. It typically moves along a path: 

• Reactive: Teams respond after an issue occurs.  
• Preventive: Maintenance is planned to reduce the likelihood of failure.  
• Predictive: Data, trends and condition monitoring help identify issues before they disrupt operations.  
• Autonomous or AI-assisted: Systems help surface insights, recommend actions and support faster decision-making. 

Building the data foundation for trusted analytics 

A mature data strategy should include four core elements: 

• Governance: Common definitions, ownership and accountability for key metrics. 
• Data quality: Accurate, complete and timely data for assets, work orders, spare parts, operating conditions, safety and finance. 
• Integration: Connected systems across ERP, EAM, plant systems, fleet systems, historians, safety tools and external data sources. 
• Usability: Role-based insights for executives, mine managers, planners, reliability engineers, maintenance teams and frontline supervisors. 

The objective is to help leaders understand what is happening, why it is happening, what is likely to happen next and what action should be taken. 

Moving up the analytics maturity curve 

Mining organizations often want to move quickly toward advanced analytics and AI but it requires a practical foundation. Analytics maturity typically progresses through four stages: 

• Descriptive analytics: What happened? 
  This includes operational reports, dashboards, monthly production reports, equipment downtime, maintenance backlog and cost-per-ton reporting.  
• Diagnostic analytics: Why did it happen? 
  This helps identify root causes and relationships, such as whether downtime was driven by a specific asset class, parts availability issue, maintenance delay or operating condition.  
• Predictive analytics: What is likely to happen? 
  This includes forecasting equipment failures, production constraints, future maintenance needs and spare parts demand.  
• Prescriptive analytics: What should be done? 
  This supports recommended actions, such as prioritizing work orders, optimizing maintenance windows or modeling operational scenarios.  

The key for mining leaders is to view analytics as a progression. Jumping straight to AI without connected, governed and trusted data can create skepticism and poor outcomes. Starting with high-value operational questions allows organizations to build credibility, demonstrate value and expand over time. 

The KPIs mining leaders should connect 

Mining companies typically track many operational metrics. The challenge is that those metrics often remain trapped inside functional silos. To unlock strategic value, leaders should connect key performance indicators (KPIs) across operations, maintenance, finance, safety and sustainability. Key questions include: 

Operational: Are we producing efficiently? 

• Throughput 
• Equipment availability and uptime 
• Utilization rates 

Maintenance and reliability: Are we driving reliability or reacting to failure? 

• Mean time between failure 
• Mean time to repair 
• Planned versus unplanned maintenance 

Financial: Do we understand what drives cost? 

• Cost per ton 
• Maintenance cost per asset 
• Inventory turns for critical spares 

Safety and Sustainability: What does our risk landscape look like? 

• Incident rates 
• Near-miss trends 
• Environmental compliance metrics 

Where IFS Cloud fits in mining modernization 

IFS Cloud can help support mining modernization by connecting enterprise asset management, maintenance planning, operational intelligence, IoT-enabled data, workflows, analytics and AI-enabled insights. Its value is not only in managing individual functions, but in helping organizations connect asset records, work orders, maintenance plans, observations, performance data and decision workflows. 

Within IFS Cloud, asset performance management is built into enterprise asset management. That enables a closed-loop approach: 

• Capture data from machine sensors, historians, relational databases, application programming interface (APIs) and connected assets. 
• Detect issues using monitoring, rules, calculations, trends and operational intelligence. 
• Decide through asset performance details, observation flows, IFS.ai anomaly detection, failure modes, effects and criticality analysis (FMECA), risk indices and asset health scoring. 
• Act through work orders, notifications, cases, mobile work execution, maintenance plan updates and closed-loop feedback. 

The path forward for mining 

Mining modernization starts with a practical goal: connecting assets, operations and data so leaders can make faster, better decisions. For many mining companies, the issue is not a lack of technology, but fragmented systems, disconnected data and limited visibility across the business. 

Access the full recording for a deeper look at how IFS Cloud, data strategy and analytics can support more connected, reliable and data-driven mining operations here. 

Baker Tilly helps mining organizations address that challenge by combining mining industry experience, digital transformation capabilities, IFS Cloud implementation knowledge and data analytics expertise. Baker Tilly has experience serving 150+ mining industry clients, including companies across open-pit, strip and underground operations and across exploration, development and production. 

Ready to take the next step in your modernization journey?