There is nothing more important than the frontline having absolute clarity and belief in the controls that are identified as being the most critical. These are the controls they rely on in practice, every day, every task. Achieving this starts with the frontline’s involvement in the identification and definition of the critical controls, setting up the subsequent steps to ensure the critical controls are integrated into site procedures and processes and ultimately implemented and verified in the field. Ensuring the critical controls are clearly represented in each of these steps is symbolic of a “golden thread’ that allows stakeholders to trace the control from its origin to final implementation.

A common finding from reviewing Critical Control Management (CCM) approaches is that this thread was never fully established or it has been so stretched and twisted it breaks. This results in a disconnected approach that can lead to misalignment, confusion and misguided efforts, seriously threatening operational buy-in and the effectiveness of the approach. Conversely, a clear and traceable golden thread drives alignment, clarity and confidence in the approach.

This presentation explores the threats and opportunities to establishing and maintaining this golden thread through the CCM process. It highlights how establishing this connection can provide the foundation from which an efficient and effective approach to control identification, implementation and verification can be built and sustained.

Safety inspections are essential in open-cut coal mining to prevent accidents, ensure regulatory compliance, and maintain smooth operations. However, traditional inspections often rely on manual checks, paper-based reporting, and human judgment, which can be time-consuming, inconsistent, and prone to errors. The integration of artificial intelligence (AI) and mobile technology is transforming the way inspections are conducted, providing real-time insights, improving accuracy, and helping mining companies manage risks more effectively.
This presentation will explore how AI-powered inspection solutions are revolutionizing safety audits in open-cut coal mining. By leveraging AI-driven image analysis, automated workflows, and mobile data collection, operators can enhance critical inspection processes, including:
• Highwall and Slope Stability Monitoring – AI-assisted image recognition can detect cracks, erosion, and structural weaknesses in highwalls and slopes. By identifying potential failures early, mining operators can take preventative measures, reducing the risk of landslides and collapse.
• Pre-Start and Maintenance Inspections for Heavy Equipment – AI-powered image analysis can automatically identify wear, corrosion, fluid leaks, or incorrect assembly in machinery. This helps prevent unexpected failures, extends equipment lifespan, and ensures compliance with safety standards.
• Hazard Detection and Risk Prevention – AI-driven technology can scan for missing safety barriers, inadequate signage, and unsafe work conditions. By automating hazard identification, mining companies can improve worker safety and reduce costly incidents.
• Fire and Electrical Safety Checks – AI-enhanced thermal imaging can detect overheating motors, exposed wiring, and fire risks before they become dangerous. Early identification allows maintenance teams to take quick action, preventing electrical failures and potential fires.
This session will provide insights into how AI-powered inspections improve safety outcomes, support compliance with regulations, and streamline reporting processes. Attendees will learn practical strategies to implement AI-driven inspections and how this technology can create a safer, more efficient, and proactive approach to mine safety management.

We rely on critical controls to keep our people safe. But how do we know if those controls are effective in practice?

Typically, in our organisations, we focus on counting activities such as audits, safety interactions and incident investigations. We assume that if we perform these activities, then our controls will be effective. And the more of these activities we perform, the more confident we become.

However, this assumption is not correct. Simply counting these activities provides little information on whether our controls that keep people safe are actually working effectively.
To understand if our controls are effective, we need to take a different approach.

Rather than simply measuring whether these activities have been undertaken, we need to focus on what information they are actually providing.

To do this, we need to analyse and bring together the learnings in real time from a wide range of data sources, such as safety interactions, audits and incidents. When combined, these learnings help build a more complete picture of how the control is performing and assist leaders to make informed decisions. This presentation will explain the key elements of this approach and show how we can implement it in practice, including through the use of Artificial Intelligence.

Problem
Fatigue presents both a safety and health risk to a workplace, its workers and others. Fatigue can be caused by a number of inter-related workplace and/or personal risk factors. High risk workplaces will often have factors that include, but are not limited to, shift work, extended work hours, limited breaks, job demands or adverse environmental conditions that often need to be considered when managing workforce fatigue.
Under legislation, workplaces are required to eliminate or, where elimination is not possible, minimise the risks identified in their work activities. However, controlling workplace risk is not the only solution because the workplace cannot control what workers do when they are not at work. Thus, workers are often best placed to know whether they are fatigued and play a critical role in managing workplace fatigue.

Solution
The FatigueTech platform is a digital solution that integrates with existing work-hours systems to automate the implementation and monitoring of an organisations Fatigue Risk Management System. FatigueTech is used by individuals who work in a high fatigue risk work environment and provides best practice information, assessment and communication tools. It alerts organisational leaders who can subsequently support individuals to implement mitigation strategies, as well as collect real time data to help design, adapt and enhance fatigue management practices and solutions in the organisation.

The FatigueTech platform (www.fatiguetech.com.au) has already been implemented on a full-features basis in complex, multi-site organisations including Glencore Australia, BHP, Ausgrid, Transport for NSW, Laing O'Rourke and Holicare.

Other organisations have adopted a staged approach to FatigueTech implementation, starting with selected high-needs features and adding modules over time, including New Hope (Bengalla Mine), Westrac CAT, and Idemitsu (Boggabri Coal).

While air quality, noise, and vibration controls are often framed as engineering requirements, they are increasingly recognised as critical components of worker wellbeing. This abstract examines how intelligent operator cabins—incorporating real-time monitoring, filtration, ergonomic design, and adaptive systems—are transforming how mines support mental and physical health.

Modern cabin designs now integrate environmental sensors for carbon dioxide levels, temperature, humidity, and vibration, providing continuous feedback and automated alerts when thresholds are exceeded. These indicators correlate not only with physical fatigue but also with reduced cognitive function and reaction time—risk factors for both near misses and critical incidents.

Additionally, filtration and pressurisation systems aligned with AS/NZS ISO 23875 are now being deployed alongside alert systems that notify operators of system failures or declines in air quality. This combination provides a consistent baseline of air quality and contributes to psychological safety by reassuring workers that conditions are being monitored and maintained in real time.

Ergonomic layout, seat design, acoustic insulation, and display placement further support operator comfort and reduce fatigue over long shifts. Emerging systems even adjust HVAC output or filter flow based on environmental changes and occupancy.

Mining operations in Queensland that have adopted these intelligent cabin designs report improvements in operator alertness, reduced complaints about fatigue symptoms, and improved compliance with exposure targets.

As cabins evolve from compliance-driven enclosures into health-focused workspaces, their role in supporting psychological and physiological wellbeing will become a central element of intelligent site design.