Machine Learning in Mining Operations

The mining industry is shifting toward digitization, technology like automation or robotics, and Industry 4.0 (or 5.0 depending on what you are reading). Artificial Intelligence (AI), Machine Learning (ML) , and specialized mining software are no longer futuristic concepts. These tools are driving driving intelligent, safe, efficient, sustainable and more predictable mining operations.

AI and ML are often talked about as high-tech concepts, but in mining, their value will come from solving real operational problems. Machine Learning is a type of AI that allows machines to continually learn from data and make predictions or decisions based on various inputs. At their core, these technologies help machines mimic human decision-making, identify patterns in complex datasets, and optimize processes.

AI and ML don’t replace human judgment and data analysis, they augment it, providing insight, predictions and foresight that make planning and execution more reliable.

The Benefits: Productivity, Safety, and Cost

The first wave of AI and autonomous technology in mining came about a decade ago with autonomous trucks. Since then, the potential benefits have expanded. Applied correctly, modern mining technologies allow teams to focus on the right work at the right time while reducing operational risk.

Driving Operational Excellence

Operational excellence is the foundation of the top tier mines. It’s about doing the right work, in the right way, every time. AI and ML have the power to strengthen this foundation by enhancing the core functions that matter most:

• Performance management
• Process optimization
• Strategy and planning
• Continuous improvement

Proven measures like Compliance to Plan, frameworks like Last Planner System, and software like CiteOps turn these principles into action. They allow teams to track execution against plans, identify where work is falling behind, and take corrective action before small issues become big problems. 

Challenges for Implementing Machine Learning

Implementing AI and ML isn’t without challenges. Economic, technological, workforce, and social barriers all exist. Success requires skilled teams, robust data, and a culture willing to embrace data-driven decision-making.

As mining companies continue to adopt digital tools, AI and ML will increasingly become part of the discussions and roadmap. Real-time monitoring, predictive insights, and automated planning will enable mines to achieve higher productivity, safer operations, and more predictable outcomes.

The biggest hurdle to applying AI or ML to mining operations lies in capturing, integrating and analyzing the required and relevant data to support these pertinent technologies.

Technology Investment Making Mines Smarter

AI, machine learning, and mining software are transforming how mines plan and operate. From improving compliance to plan, optimizing equipment, and enabling operational excellence, these tools are no longer optional, they’ve become essential. Top-tier mines are investing in the adoption of smart technologies thoughtfully, and will emerge safer, smarter, and more efficient operations.

AI and Machine Learning in Mining: Why Granular Data Matters

In recent years, the application of Artificial Intelligence (AI) and Machine Learning (ML) to revolutionise the industry has been a hot topic. Many discuss the importance of data in this transition, but few recognise the required granularity to support it.

Since 2010, miners have invested in ERP (finance and asset management), mine planning and FMS (with autonomous capabilities). With planning and real-time data capture covered, a critical gap remains in integrating operational planning and execution data.

The importance and impact of this data, still commonly managed using whiteboards, PowerPoint, Excel or paper, has become increasingly clear.

Mining Activity Conformance to Plan (CtoP) and Its Limitations

Conformance to Plan (CtoP) reporting of Mining Activities often excludes enabling or development activities essential for success. Traditional CtoP reporting highlights issues but lacks context. Operations apply insufficient rigour to managing enabling tasks as is required for effective collaborative planning. For AI/ML solutions to suggest improvements, they require deeper, more granular insight than what traditional short-term mine planning tools offer.

A mine plan optimistically lists key tasks to be completed, and once reviewed with all parties, establishes a baseline of critical path items. These items form the measurable data set for KPIs. However, KPIs and plan variances are diagnostic, not actionable. Supporting data that exposes contributing factors and enables tasks to be scheduled, assigned, executed, and evaluated is needed for action.

Expanding the Mine Plan to Capture All Mining Activities

An effective plan must include all work required to deliver critical path items. It should also recognise the impact of all processes on the schedule. Many sites do not complete this scheduling work, assuming other teams will complete tasks. Mining operations cannot understand true resourcing required without considering all tasks. Capturing this information on whiteboards, spreadsheets or paper hinders future analysis, especially by AI tools needing a data warehouse for ML analysis.

Data that represents progress to plan must be captured. In mining, many critical path items can be updated from third-party tools like FMS. Capturing enabling task progress is complex and requires an IT solution. Operations must compile these inputs from varying sources into a data lakehouse to provide a platform for rules-based and ML analysis. ML can then detect data trends useful for generative, predictive or agentic AI, and support real-time schedule optimisation.

Integrating Systems to Unlock Real-Time AI in Mining

Artificial Intelligence will continue to impact how we view plans and their execution. The key is ensuring all relevant data is captured and connected so ML can identify useful trends, highlight opportunities and reveal issues early.

To support effective reporting and analytics, teams must capture data, and establish relationships in real-time, for rapid decision making. Integrating ERP and FMS with operational execution data will provide richness to power advanced AI/ML.

Capturing Critical Execution Data with CiteOps

CiteOps is an integrated planning and execution software solution. This solution improves productivity by capturing short range plans and executional activities while fostering continuous feedback loops.

This enterprise-grade operational planning and execution platform specifically designed for mining and metals companies enables:

• Unified planning and execution
• Dynamic scheduling
• Centralised resource management
• Real-time decision making and
• Mobile execution

The Last Planner System (LPS), also called Collaborative Planning or Pull Planning is a project production workflow that delivers maximum value with minimal waste. This system empowers frontline leaders and crews to take an active role in planning and execution. Built on the principles of continuous improvement, it creates a more reliable, efficient workflow and improved outcomes. LPS aligns closely with familiar improvement principles like DMAIC (define, measure, analyse, improve, control), PDCA (plan, do, check, act) and SIC (short interval control).  

Who is a Last Planner in Mining Operations?

A Last Planner is the person closest to the work with the authority to make decisions and commitments about the schedule and tasks to be done. The system improves project flow through collaborative planning that focuses on what should be done, what can be done, what will be done, what was done and what can be learned. As the timeline approaches execution, plans should become more detailed and responsive. This ensures work is both achievable and valuable.  

In mining, the last planners would include supervisors, shift bosses, undermanagers, and crew leaders. These people are responsible for the work in greatest detail, closest to delivery with the greatest context to support decision-making. Planning should happen regularly – weekly, daily, and shiftly. It should involve short, focused discussions or meetings that build commitment to the schedule. Rather than a top-down approach, these meetings promote teamwork, accountability, ownership, and commitment to both shared and individual KPIs.  

This collaborative approach enhances efficiency by aligning teams. Tasks only begin when enabling tasks are complete and resources are ready. LPS identifies potential conflicts early by visualising and planning for environmental conditions, resources, dependencies, enablers and prerequisites.  

Although originally developed by Glen Ballard and Greg Howell for the construction industry, LPS has been successfully adapted for many fields including mining, software development, product design, education, and supply chain management.  

Last Planner System and Technology

Today, technology plays an increasingly important role in enabling its implementation and real-time coordination across complex mining operations. Commit Works has developed CiteOps to ensure that the right people do the right work at the right time. Technology can help link the action and focus of frontline teams with higher level plans. When tech tools support the successful management of people, resources, and processes, mines can drive operational excellence .  

Enabling (supporting) tasks and the Last Planner System

A core principle of the LPS is that work should only be committed when it is ready to be executed. When this happens, all prerequisites and constraints are confirmed to have been addressed. In mining operations, these prerequisites are commonly referred to as enabling tasks or activities. 

A collaborative planning system strengthens execution discipline by ensuring that work commitments are based on operational readiness rather than optimistic scheduling. Teams that surface constraints early, and assign responsibility for enabling tasks, can quickly verify that conditions are primed for production work to begin. This approach reduces reactive rescheduling, improves compliance to plan, and supports more reliable production outcomes.

Download the Commitment System Handbook for Underground Coal to learn more. 

Despite a decade of continuous improvement and technological advances, global mining operations are up to 28% less productive. If we are smarter and better equipped than ever, why is productivity decreasing?

McKinsey notes that mines employing innovative technologies to generate lasting value could see productivity skyrocket. One of the key elements of operational excellence is the integration of operating systems to support and deploy the right practices and the right behaviours. Top-tier miners are leading the way. Rio Tinto’s Safe Production System (RTSPS) and BHP’s continuous improvement-driven BOS reveal how leadership and systems thinking reshapes performance.

According to a 2023 study published in Sustainability, the most productive modern mines now include intelligent management and real-time decision-making. Scheduling management is crucial for optimising productivity. A well-implemented operating system leverages technology like CiteOps to ensure that every shift, every crew, and every leader knows the plan.

Implementing shift-level execution software is key to ensuring that clear, mine-wide targets are met. When feedback loops and data-driven decision-making are employed, potential production losses, inefficiencies and safety implications can be addressed as they arise.

The cause of management-system failures is often culture. Successful mining operations measure how business culture improvements feed excellence. Investments in software and technology can accelerate and deepen the cultural change needed to achieve operational excellence. Strong culture combined with a robust operating system delivers digital transformation at scale.

Adaptable or Collaborative Planning versus a Top-Down Approach

Top-down planning works well when it is clear what needs to be done and how. But where there is variability, judgment, creativity, non-routine work or exceptions, rigid planning can do more harm than good. As process variability increases, the cost of top-down planning goes up, while rigid adherence to inappropriate or misaligned plans or processes can prove counterproductive.

In businesses where there are many departments and teams across multiple shifts and roster patterns it can be extremely tough to coordinate work at the front line. This commonly results in tension and competition rather than collaboration between silos. This tension must be resolved to enable frontline leaders to coordinate safe and effective work where it happens. An effective visual commitment system is the key to changing this in complex variable organisations.

Collaborative or adaptable planning can overcome this failure of a top-down approach in the face of variability. The Last Planner System is a proven Lean Construction method that can improve outcomes in the mining industry. Dynamic scheduling is possible with the implementation of CiteOps’ scheduling tool which features Gantt charts, dependency management and resource allocation to practically address planning gaps. 

Download our Commitment System Handbook for frontline superintendents, planners and supervisors.