Recent advances in predictive modelling and data-driven insights are transforming how the mining industry identifies, measures, and controls fatigue risk.
Over the last decade in the global mining industry, there has been a quite dramatic shift away from managing fatigue through a compliance-based approach towards a risk-based approach.
A risk-based approach is concerned with identifying, measuring, controlling (as far as reasonably practicable) and monitoring fatigue-related risks and hazards, rather than simply meeting guidelines or thresholds. This shift aligns with guidance changes across Safe Work Australia and other legislative bodies, as evident when comparing the new Model Code of Practice for Managing the Risk of Fatigue at Work with the previous Code of Practice published in 2013.
Methods used to control fatigue risks and hazards can be broadly categorised into three groups: predictive controls (work schedules and organisational factors); proactive controls (individual fatigue and fitness for work); and reactive controls (identifying the role of fatigue in incidents or events). In the mining industry, perhaps the most significant innovation in fatigue management is regarding predictive controls, mainly due to the increased (effective) use of biomathematical modelling.
Biomathematical models are complex mathematical models that use work scheduling information and/or sleep and wake behaviour as input data, and predict sleepiness, alertness, fatigue risk or other fatigue-relevant outcomes.
Many scientifically validated models exist; however, almost all of them are centred around a complex interplay between “homeostatic processes” (a cumulative drive for sleep that builds while humans are awake) and the impact of circadian rhythms (biological rhythms with a near-24-hour period) on human sleep and alertness.
Biomathematical modelling in of itself is not new; it has been used in aviation and military for over 20 years. The innovation, however, is in how these models are being used in the mining industry. The focus in mining had traditionally has been solely on estimating total fatigue risk across entire work designs or roster patterns; essentially, using these models to dichotomise “safe” and “unsafe” rosters in a go/no-go fashion.
But more recently we have observed increased interest in using these models not just as a go/no-go tool, but to gain an objective understanding of the magnitude and timing of fatigue risk within a given work structure.
This information can then be used for:
time-specific tasks (avoiding safety-critical functions during periods of high inherent fatigue risk)
assigning overtime and additional shifts in as safe a manner as possible
identifying times of “high-alert”, where awareness of fatigue and the controls need to be heightened.
This approach is not only richer in value but better aligned with the original intent of use for many of these model biomathematical models. We at Melius Consulting, alongside colleagues from the Appleton Institute at CQUniversity (with which I am also affiliated) and Rio Tinto Iron Ore, have recently had an article accepted into the Annals of Work Exposure and Health, which further discusses biomathematical model use in fly-in, fly-out (FIFO) mining contexts.
The use of biomathematical modelling requires a level of expertise in the workings of the models, the programs that house them, and the careful consideration of inputs, to obtain meaningful and accurate outputs. The last point is crucial; details of a work schedule – for example, start/finish times, commute durations, FIFO versus residential work – can have dramatic impacts on the inherent fatigue risk, which will be reflected in model outputs. As always, junk in equals junk out. Correctly interpreting outputs is essential and can be tricky for a naive user, and the visualisations that come from these models by default are often not easy to interpret. To counter this, we have a set of processes and tools that ensure, when biomathematical modelling is used, our findings are reliable, transparent and interpretable so actions can be swiftly and confidently taken to reduce fatigue risk.
Innovation is also evident in proactive controls, mainly through improvements and increased use of fatigue-detection technologies and individual health monitoring. The use of continuous monitoring technologies, particularly those that measure physiological factors (often ocular metrics) in real time to assess fatigue levels, is increasingly pervasive in transportation and vehicle operations.
Regarding health monitoring, sleep monitoring programs using wearable technology (which have both increased in acceptance and improved in accuracy, particularly for sleep measurement) can enable a better understanding and management of the organisational and individual factors that influence sleep and fatigue. In addition, as the impact of certain health factors and sleep disorders on fatigue risk is increasingly recognised, we are seeing success through programs that screen for these factors and provide pathways to treatment when concerns or diagnoses are observed.
Lastly, high-quality sleep health and fatigue education, while an administrative control, enables team members and leaders to make lifestyle changes and better fatigue-related decisions in the workplace, and thus its impact cannot be understated. As access to health information (both the good and the pseudoscientific) becomes increasingly available through social media, the importance of this education being rigorous, evidence-based, and delivered by field experts is at an all-time high.
Perhaps the lowest-hanging fruit for many organisations can be found by looking inwards at the health and safety data they already collect. Many mining organisations are collecting troves of data that can be used to better understand (and hence manage) fatigue-related trends in their workplaces, but this data is not tapped into.
Sources of this data include fatigue detection technologies, gate swipe-on/off systems, alcohol and other drug (AOD) testing, and human resources data. These data sources can be integrated and analysed to identify trends in fatigue prevalence and its potential impacts on workplace safety. Effective utilisation of existing data sources can lead to a better understanding, and ultimately better management, of fatigue risk in mining operations.
By Melius Consulting scientific consultant Dr Tim Smithies
This feature appeared in the March-April edition of Safe to Work.