Fatigue Risk Management for Mobile Technical Teams

Overview

A client raised concerns after several road accidents involving mobile technical staff who were required to respond to after-hours callouts, often involving long-distance travel. Because the client retained overall health and safety accountability, there was a need for a practical, evidence-based way to show that fatigue-related risk was being addressed.

The challenge

The problem was not just long hours. The real risk was whether after-hours callouts were reducing technicians' opportunity for sleep and increasing the likelihood of fatigue-related driving impairment. The challenge was to move beyond anecdotal concern and build a measurable, repeatable way to identify workers whose sleep disruption created elevated operational risk.

My approach

I framed the issue through fatigue management, focusing specifically on insufficient sleep as the most measurable contributor to fatigue in this context. Using trip and vehicle sensor data, I identified after-hours activity and estimated how that activity reduced available sleep opportunity. I then developed a model to calculate sleep deficit against a standard eight-hour sleep requirement and track cumulative sleep debt across consecutive nights.

Tools and methodology

The analysis was built in QlikView, with Excel used as the reporting layer. The model was designed to identify probable sleep loss caused by after-hours work rather than diagnose fatigue medically. This made it possible to highlight repeated exposure patterns, estimate the duration of sleep deprivation, and identify technicians whose risk was increasing over time.

Why it mattered

The value of the work was in turning operational data into a practical risk-management tool. Instead of waiting for another incident, management could see which technicians were repeatedly exposed to insufficient sleep and where intervention might be needed. The scale of exposure made this especially important: in June 2022 alone, the workforce recorded 15,703 travel hours, equivalent to 654 days of travel time.

Outcome

The project created a defensible, data-led basis for fatigue risk oversight. It gave management a structured way to identify higher-risk patterns, support proactive decision-making, and demonstrate to the client that meaningful action was being taken. It also laid the groundwork for the next phase: automated daily notifications, agreed intervention thresholds, and a substitution plan for workers withdrawn from duty due to cumulative sleep loss.

This project transformed raw trip data into a practical fatigue risk model, helping shift the organization from reactive incident response to proactive safety management.