Figure 1: Speed Limit Engineered for Humans – Source: FIFOAustralia Instagram.
“I increasingly approach problems to find psychological solutions, rather than logical ones. Because if there were a logical solution, it probably would have already been solved, thereby eliminating the problem. All that’s left are the psychological problems.”
— Rory Sutherland
Fleet Management Systems (FMS) in mining operations collect a staggering amount of data: truck speeds, idle times, fuel consumption, maintenance logs, operator shift patterns, event triggers, and much more. This data is often used to solve operational problems with logical solutions—reducing cycle times, improving fuel efficiency, and tightening up shift performance.
But as Rory Sutherland suggests, many persistent problems are no longer logical. If the right procedure or best practice already exists, yet performance still varies, then the issue is probably psychological. That is: human.
The Behaviour Behind the Numbers
Every data point in an FMS system represents a decision or a behaviour. Dig deep enough, and you’ll find patterns—not just in machines, but in people.
For instance, you might see that one operator consistently idles 20% longer than others under the same conditions. Or that load times spike during certain times of day. These aren’t just mechanical or environmental anomalies—they’re behavioural signals.
Understanding these nuances requires more than a spreadsheet—it demands the ability to process, model, and correlate massive datasets over time. This is where high-performance computing (HPC) infrastructure becomes essential.
At 4D-SPEC, we’ve seen how mining companies are beginning to treat their fleet data like scientific experiments—running simulations, comparing behavioural cohorts, and testing the effects of micro-interventions on operator performance. These aren’t simple analytics problems; they’re big data problems.
Psychology as a Productivity Tool
Small, psychologically informed tweaks—enabled by deep data analysis—can drive big improvements:
- Dashboards that compare operator performance (fuel use, idling, safety events) can encourage positive behaviour through peer benchmarks.
- Subtle in-cab feedback loops (“You’re in the top 25% for haul efficiency today—great job”) can shape operator habits better than generic training.
- Behavioural pattern recognition can pre-emptively flag fatigue or stress—long before it becomes a safety incident.
Figure 2: The authors current performance on the fatigue management leaderboard (FML).
These strategies only emerge when you’re able to crunch large datasets quickly and repeatedly—something that’s often beyond the reach of standard office IT setups. That’s why a growing number of mining operations are turning to on-demand HPC solutions like those offered by 4D-SPEC, where mining engineers and data teams can run complex analyses, simulations, and ML models at scale.
From Data to Insight to Impact
We tend to treat fleet systems as mechanical tools, but they’re really human systems. And the key to unlocking safety, efficiency, and productivity gains lies not just in equipment maintenance, but in behaviour management. High-quality data is the first step—but high-performance computing is what makes it actionable.
The insight is simple: your fleet doesn’t just tell you what’s happening—it tells you why it’s happening. And understanding the “why” means combining psychology, behavioural economics, and the raw horsepower to see patterns others can’t.
At the intersection of human behaviour and machine data, the next wave of mining innovation is forming. And it’s powered not just by better trucks—but by better thinking, better questions, and better tools to answer them.
