May 6, 2025

The human-automation balance in mining

Why people still matter in a digital plant

Automation has limits — and that’s where people come in

Walk into a modern control room — whether at a copper concentrator in Queensland, an iron ore operation in the Pilbara, or a smelter in North America — and see something remarkable. Banks of screens flicker with live data, alarms ping softly in the background, operators sit calmly at their stations, and sophisticated algorithms hum away behind the scenes.

It’s a snapshot of what the mining and processing industries have been working toward for decades: faster, safer, and more efficient plants thanks to automation.

But here’s the truth most outsiders don’t see — and that every operations manager or general manager instinctively knows: the more we automate, the more important the human becomes.

It’s this human-automation balance that’s now shaping the next chapter of operational performance. And it’s not just a technical problem — it’s an organisational and cultural one, too.

The rise of automation: promise meets reality

Over the past decade, automation has swept through control rooms, transforming mineral processing plants’ operations. Advanced process control (APC), machine learning algorithms, and digital twins are slowly becoming mainstream tools. According to the Industrial Automation Association, 72% of heavy industry sites globally have increased their automation footprint in the past five years — a figure even higher in mining.

Automation has unlocked real gains:

Improved equipment uptime
Greater consistency in product quality
Faster response to process disturbances
Reduced manual handling and exposure to hazards

But the promise of automation often comes wrapped in complexity. Automated systems can detect and act on deviations at scale, but also introduce new challenges in human performance, oversight, and trust.

The paradox? Even on the most automated sites, 85% of incidents still rely on human intervention.

Why operators remain indispensable

Automation is unmatched at processing repeatable tasks, crunching data, and executing pre-programmed logic. But the reality of plant operations is anything but predictable.

“While automation excels at managing routine and predictable operations, it is the human operator who must interpret, adapt, and respond when unexpected situations arise.”

Bainbridge, 1983

Feed grades fluctuate, weather systems roll in, ore bodies change, and equipment degrades. Unexpected events, once-in-a-decade failures, or novel disturbances still fall squarely into the operator’s hands.

What makes operators irreplaceable isn’t just their technical knowledge — it’s their tacit knowledge. This includes the ability to:

Notice subtle shifts (“that cyclone pump doesn’t sound right”)
Recognise weak signals that automation misses
Balance competing priorities under pressure
Apply lateral thinking when established procedures fall short

Humans bring context, creativity, and adaptability. They see shades of grey where automation sees black and white. They apply judgment where algorithms apply rules. And when things go wrong, their experience often prevents a bad day from becoming catastrophic.

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The Automation Paradox: more automation, more human oversight

Many organisations assume that they can shrink or deskill the human workforce as they automate. But research paints a different picture.

“Automation changes the nature of operator work — it reduces manual tasks but increases monitoring demands and cognitive load”

Bainbridge (1983), Ironies of Automation

This is the heart of the automation paradox: the better your automation, the more critical your human oversight becomes, especially in rare, high-stakes events when the automation hands back control.

This shift creates several risks:

Skill atrophy: Operators lose practice and confidence when systems run smoothly for long periods.
Complacency: Over-trusting automation leads to slower detection of problems.
Cognitive overload: During a handover, operators must quickly build situational awareness, often under stress.

Smart sites are investing not just in automation but also in strengthening the human-automation partnership, treating both as evolving systems that need calibration and care.

Designing for the right human-automation balance

What does “getting it right” look like on the ground?

1. Human-centred interface design

It’s not enough for systems to *work* — they must work *for people*. That means designing dashboards and alarm systems that provide clear, actionable information, not data dumps.

“Effective control room design depends on involving operators early — technology succeeds when people shape it, not just use it.”

Based on principles from HSE, 2020; ISA, Human Factors Guide for Control Room Evaluation

This requires:

Prioritising critical information over nice-to-have data
Reducing alarm noise to avoid overload
Using visualisation tools that align with operator mental models
Regularly updating interfaces based on user feedback

2. Dynamic automation

Rather than locking into rigid manual or automatic modes, the trend is towards adaptive automation — systems that flex depending on the task and operator load. For example:

Automating routine tasks during stable operations
Offering semi-automatic modes during complex troubleshooting
Providing decision support tools during high-stakes events

This gives operators the right level of control at the right time, supporting both performance and resilience.

3. Continuous learning and skills development

Automation isn’t set-and-forget, and neither is human capability. Ongoing investment in operator training, simulator exercises, and feedback loops is crucial.

Leading sites are:

Running simulator-based scenario training
Conducting post-incident reviews that include human factors
Investing in cross-disciplinary training between automation engineers and operators

It’s about building a workforce as agile and adaptive as the technology it works with.

The organisational challenge: aligning people, process, and technology

From Mipac’s work across hundreds of sites, three challenges consistently surface:

Over-reliance on automation: Operators start trusting the system blindly, reducing their own vigilance and eroding manual skills.
Integration friction: Retrofitting automation into legacy plants often creates unintended complexity, not just technically but also in workflows and communication.
Siloed priorities: Automation engineers, operations teams, and management sometimes talk past each other. Transparent governance, shared KPIs, and cross-team collaboration are essential.

Mipac’s Perspective: putting humans at the centre of digital transformation

At Mipac, we see digital transformation not as a technology project, but as a human project.

We help sites optimise their human-automation balance by:

Designing control systems that enhance operator decision-making
Supporting change management during automation rollouts
Providing training and simulation tools that keep human skills sharp
Embedding continuous improvement loops to evolve systems over time

Our goal? To help create plants that are not just more automated, but more human-centric and resilient.

What’s next for the industry?

Looking ahead, several trends are reshaping the future of control rooms:

Artificial intelligence (AI) provides predictive insights but still requires human oversight.
Remote operations centres that centralise expertise but risk disconnecting from site realities.
Workforce renewal, as experienced operators retire and digital-native talent comes in.

The challenge—and the opportunity—is to combine the best of both worlds: harnessing automation’s precision while amplifying human strengths.

Digging deeper into the human-automation balance

If this article has sparked your interest, these three research papers are well worth your time. They go beyond surface-level discussions and explore the real-world complexity of balancing human expertise with automation — something every operations or general manager in processing will face sooner or later.

Automation in the Mining Industry: Review of Technology, Systems, Human Factors, and Political Risk Rogers, Kahraman, Drews and Powell, Mining, Metallurgy & Exploration (2019)

This paper gives a clear-eyed overview of how automation is reshaping mining. What makes it stand out is its emphasis on human factors and political risk — reminding us that even the best technology can stumble without buy-in from the people on the ground.

Cognitive Demands and Mental Workload: A Field Study of Mining Control Room Operators Mohammadian et al., Heliyon (2022)

This field study gets into the nuts and bolts of what operators actually experience in automated control rooms. It highlights how automation can reduce workload in some areas but also introduces new mental demands — a valuable insight for anyone overseeing operational teams.

Human Factors in Control Room Design & Effective Operator Participation Pikkar, et al., SPE Intelligent Energy Conference (2016)

This paper argues that good control room design is as much about people as it is about hardware or software. It makes a strong case for involving operators early in system development to improve safety, performance, and acceptance — lessons that apply across industries.

Ironies of Automation Bainbridge (1983)

This paper discusses the ways in which automation of industrial processes may expand rather than eliminate problems with the human operator. Some comments will be made on methods of alleviating these problems within the ‘classic’ approach of leaving the operator with responsibility for abnormal conditions, and on the potential for continued use of the human operator for on-line decision-making within human-computer collaboration.