Digital transformation is reshaping the mining industry, but navigating this complex process requires a clear framework for progress. In this article, we’ll cover how our Mipac digital maturity model provides mining operations with a structured roadmap to assess their current capabilities, identify gaps, and achieve sustainable improvements through phased, strategic investments.
Why Digital Maturity Matters in Mining
The mining industry is at a pivotal moment as it grapples with increasing pressure to improve productivity, reduce costs, and minimise environmental impact—all while navigating the challenges of remote operations and fluctuating commodity markets.
Digital technologies have emerged as powerful tools for addressing these demands. From real-time monitoring and advanced automation to data-driven decision-making and predictive maintenance, these technologies promise transformative gains. However, the sheer scale and complexity of mining operations mean that digital transformation is not a one-size-fits-all solution.
This is where a digital maturity model becomes invaluable. Providing a structured framework allows organisations to assess their current capabilities, identify gaps, and map out a clear, phased path toward advanced digital capabilities.
A digital maturity model ensures that foundational elements like robust infrastructure and data integration are in place before introducing more sophisticated technologies like machine learning or autonomous systems. Without such a roadmap, companies risk investing in solutions that fail to deliver sustainable value due to missing critical building blocks.
Moreover, following a maturity model aligns stakeholders across functions, prioritises investments where they matter most, and helps overcome cultural resistance by demonstrating incremental successes. In the mining sector, where digital adoption often lags behind other industries, a maturity model offers a practical guide to achieving meaningful and lasting transformation.
The Mipac Digital Maturity Model
To address these challenges, Mipac has worked with MMM (Mining, Minerals, and Metals) operations worldwide to develop our Mipac Digital Maturity Model, tailored to the unique needs of the mining sector. This model mirrors other maturity frameworks, like safety maturity models, but it is explicitly designed for the digital landscape.
Drawing on decades of experience, we’ve built this model to guide clients at every stage of their transformation, from manual operations to predictive, autonomous systems.
Why a Digital Maturity Model?
A structured maturity model provides several critical advantages:
- Assessment of Current State: It helps mining companies identify where they are on the journey, uncovering gaps in infrastructure, processes, and technology.
- Phased Progression: It maps out an achievable path forward, ensuring that foundational elements—such as connectivity, control systems, and data integration—are in place before layering on advanced capabilities.
- Alignment and Focus: It aligns cross-functional teams on priorities, ensures smarter resource allocation, and prevents wasted investments.
- Tailored Solutions for Legacy Systems: The model addresses the complexity of effectively integrating old and new systems by working with operations ranging from modern facilities to those running on decades-old infrastructure.
Our global experience informs Mipac’s approach. We’ve collaborated with operations worldwide, helping organisations modernise and optimise their facilities while recognising the constraints of legacy systems and workforce readiness. Mining operations that have existed for decades often rely on manual workflows, limited instrumentation, and outdated automation. Our digital maturity model helps these operations transition gradually, avoiding the pitfalls of trying to leapfrog directly to advanced solutions without laying the groundwork.
Let’s take a look at the model
Key Stages of the Digital Maturity Model
The digital maturity model consists of five major stages:
Stage 1: Regressive
The regressive state reflects a baseline where operations lack the foundational tools to leverage digital technology effectively. Organisations in this stage often experience high operational variability and inefficiency, leading to inconsistent performance and limited potential for scalability.
Key characteristics include:
- Limited or no standardisation
- Minimal instrumentation and connectivity
- Predominantly manual plant operation
Stage 2: Reactive
In the reactive state, operations often respond to problems as they arise, relying heavily on manual interventions to maintain stability. This stage is marked by focusing on short-term fixes rather than long-term planning, which can lead to operational bottlenecks and higher costs.
Key characteristics include:
- Control systems may exist but are not up-to-date
- No process historian
- Equipment monitoring and operation are still manual
- Instrumentation is not routinely maintained or calibrated
Stage 3: Planned
The planned state represents a transition toward greater control and predictability as companies begin to standardise processes and integrate automation into their workflows. This stage allows organisations to set clearer performance targets, but there is still untapped potential for optimisation and innovation.
Key characteristics include:
- A reasonable amount of automation
- Online measurement and monitoring
- Operational data informs daily plans and priorities
- Examples of monitored systems:
- Pump condition monitoring
- SAG mill, flotation circuit, and smelting performance
- Maintenance is planned based on runtime hours
- Base level instrumentation is correctly installed and regularly maintained and calibrated
Stage 4: Proactive
In the proactive state, organisations shift from reacting to issues to anticipating them, using data insights to prioritise efforts and prevent problems.
This stage of decision-making becomes more dynamic and financially driven, laying the groundwork for advanced, system-wide integration.
Key characteristics include:
- Automated alerts and notifications to developing issues
- Clear identification of system bottlenecks
- Real-time performance monitoring in financial terms
- Planned maintenance based on performance indicators such as vibration and throughput
Stage 5: Predictive
The predictive state is the pinnacle of digital maturity and is defined by a high degree of automation and self-regulation. In this stage, systems adjust in real-time to optimise outcomes without human intervention.
This stage enhances operational resilience and drives sustainability through smarter resource use and reduced environmental impact. Breakdowns are predicted before they happen, and repairs are planned and scheduled with sufficient time to prepare all necessary resources.
Key characteristics include:
- Always on technology, enabling people to be more effective in their roles
- Automated, real-time performance adjustments with minimal human intervention
- Feedforward control based on upstream or downstream inputs
- Maintenance shutdowns predicted by algorithms analysing online instrumentation (e.g., temperature, pressure, vibration)
Where is the MMM sector?
Today, most operations in the (MMM) sector, including Tier 1 sites, are still in the reactive or planned stages of digital maturity.
Digital transformation across industry sectors is well documented by the world’s leading consulting groups.
According to the 2021 Boston Consulting Group report on the Digital Acceleration Index, the metals and mining industry lags 30% to 40% behind sectors like automotive and chemicals in digital advancement.
This research was also supported by EY’s Paul Mitchell back in 2020 who stated:
“A digital disconnect in the mining and metals sector, however, has created a gap between the potential from digital transformation and the poor track record of successful implementations. Addressing this disconnect will be critical for mining companies to succeed in the rapidly changing digital world.
The digital disconnect exists, not because of a lack of engagement from the sector, but because of a range of practical issues that continue to challenge the industry.”
Back at our Maturity Model, many mining operations aspire to rapidly leap to the predictive stage without addressing foundational elements. This ambition highlights the sector’s challenges and opportunities in digital transformation.
The reality is that transitioning to advanced digital stages requires a structured approach. MMM operations frequently need help with barriers such as prioritising where to start, workforce unfamiliarity with digital solutions, cultural resistance to change, and operational constraints due to remote or rugged environments.
Addressing the Gap Between Strategy and Execution
One of the primary challenges in the MMM sector is bridging the gap between ambitious digital strategies and their execution. While digital technologies promise increased throughput, lower costs, and simplified processes, effective implementation requires customised solutions, agile methodologies, and a focus on long-term sustainability.
Leaders in the sector often succeed by aligning their technology investments with operator needs and the specific characteristics of their facilities, such as focusing on the bottleneck.
Leveraging Phased Implementation for Success
A phased approach to digital maturity—addressing bottlenecks one step at a time—remains the most practical and impactful way forward. For instance, companies that invest first in understanding their limitations (e.g., control systems and instrumentation) and then target improvements on appropriate unit operations often see quicker returns. Scaling up proven solutions across the operation in waves allows for managing complexity while delivering consistent results.
The Role of Data as an Asset
Another critical insight is the undervaluation of data within the sector. Despite the potential of real-time data to transform operations, many companies treat data collection as an added cost rather than a strategic asset. On the other end of the spectrum, some operations are swimming in too much data causing analysis-paralysis. Emphasising data integration and analysis of meaningful data, coupled with tools such as predictive analytics, can unlock significant operational efficiencies.
Overcoming Workforce and Cultural Challenges
Workforce readiness and cultural acceptance are central to sustaining digital transformation. Clearly articulating an inspiring why, upskilling employees, fostering a digital culture, and involving operators in the design and implementation of solutions help ensure long-term success. Establishing digital centres of excellence and leveraging tools like e-learning and simulators can accelerate the adoption.
By addressing these challenges through incremental investments and strategic planning, MMM organisations can achieve sustainable digital maturity while maximising value and operational resilience.
The Mipac Approach to Digital Maturity
With pressure on the bottom line and constant operational demands, we find our clients are spread across the different stages of the digital maturity journey. One thing they have in common is a desire to transition towards predictive. A common pitfall is wanting to transition all the way through to predictive in one leap without changing or implementing the fundamentals.
There are many METS and technology suppliers out there who promote the machine learning silver bullet. However, our clients are realising that they can’t do that. You can’t run before you can walk or, in some cases, crawl. You have to put in the hard work, put the foundations in place, and transition through those steps.
The good news is that you don’t have to do it all at once. You don’t have to do this for your entire minerals processing plant. You can do it in phases, in small slithers of investment.
A good place to start is understanding your limitations across your operational technology systems: your control system, your network architecture, your historian, etc. You might need to upgrade your control system to the latest version. You may find that your operation will benefit from an automated start sequence, for example.
Then, you shift to your unit operations to identify the bottleneck. Perhaps that’s your flotation circuit, and you will start to look at what small slithers of investment are required there.
- Do you have instrumentation installed?
- Do you have the right instrumentation installed?
- Do you have it installed in the correct locations?
- Is your instrumentation maintained, calibrated, and scaled correctly?
Then, what are the control philosophies around the flotation circuit? Do you have appropriate feed-forward mass pull control in place? So, you’re taking a thin slice at your unit operation. You’re investing a bit of capital to generate a return. That return is either distributed to your stakeholders, reinvested in that unit operation, or you move to your next bottleneck and invest it there.
Once you’ve invested and optimised, say, three or four unit operations, you’re starting to see interactions between those unit operations. Now, you need to start improving and optimising the system that makes up your unit operations. Now, you are at a stage where an advanced process control (APC) solution, with or without machine learning, could be implemented. Importantly, applying an APC solution too soon will waste time, energy and money for no return.
Check out the work we have done with Ok Mining Limited in Papua New Guinea to improve their operations and automate their systems to achieve optimal operational efficiency through digital maturity.
The Digital Transformation Flywheel
When you get the approach and engagement right you will see the digital transformation fly-wheel turn towards a culture of continuous improvement, innovation and operational excellence:
- Enabling infrastructure allows you to measure and analyse
- Engaging staff and leveraging their expertise allows you to implement solutions and create value
- Creating value breeds success and buy-in to technology as an enabler to solve additional problems via continuous improvement.
- A continuous improvement mindset inspires a pursuit of operational excellence and further innovation
- .Now, the flywheel can’t be stopped and gains momentum, creating increasing levels of value that can either be redeployed to stakeholders or reinvested for further value creation.
Supporting Mining’s Digital Transformation
At Mipac, we pride ourselves on meeting our clients wherever they are on the maturity spectrum. By tailoring solutions to each operation’s specific needs, we ensure that progress is sustainable and delivers real value. Our maturity model is not just a tool for assessing readiness—it’s a proven roadmap for transformation, built on years of hands-on experience with mining clients who face the complex challenges of modernising aging infrastructure while staying competitive.
With this model, mining companies gain the clarity and direction needed to embrace digital transformation confidently, improving operational efficiency, safety, and environmental performance while ensuring long-term viability in a rapidly evolving industry.
Get in touch if you want to learn more about our Digital Maturity Model, diagnostic and roadmap co-development approach to help you deliver your ambitions. We’ll set you up with a Mining 4.0 expert who can share some of our clients’ success stories.
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