Introduction: The Challenge of Managing Fatigue
Fatigue is one of the most pervasive issues affecting workplaces across industries—yet it is also one of the hardest to measure. In operations with extended hours or shift work, such as healthcare, manufacturing, aviation, and transportation, the consequences of employee fatigue can be dire, contributing to accidents, injuries, and decreased productivity.
But do you know how fatigued your employees are right now? Can you quantify the current level of fatigue risk in your operation? Do you have data showing whether your company’s fatigue risk has improved or worsened over time?
If you don’t have a Biomathematical Fatigue Model in place, the answer to these questions is likely unclear. Simply put, unless you are using a scientifically validated method to measure and predict fatigue, it is virtually impossible to accurately gauge fatigue risk in your workforce.
What is a Biomathematical Fatigue Model?
A Biomathematical Fatigue Model is a sophisticated tool used to assess the risk of employee fatigue based on work and rest schedules. These models use algorithms based on established physiological principles—such as circadian rhythms and sleep patterns—to predict fatigue levels and help organizations mitigate fatigue-related risks.
For example, the Circadian Alertness Simulator (CAS) is a widely used biomathematical fatigue model that converts work and rest data into a fatigue risk score ranging from 0 to 100. The higher the score, the greater the risk of fatigue and related incidents. These models give employers an objective, data-driven way to assess and reduce fatigue risks, ensuring better workplace safety, higher productivity, and improved employee health.
Why Your Organization Needs a Biomathematical Fatigue Model: 4 Key Reasons
1 - Assess Real-Time Fatigue and Mitigate Risk
With a biomathematical fatigue model, you can measure fatigue risk on a real-time basis. This allows you to pinpoint which employees are at greater risk for fatigue-related incidents on any given day. Armed with this information, you can implement targeted countermeasures, such as modifying work schedules or adjusting workload distribution, to mitigate risk and prevent accidents before they occur.
Example: The CAS model can quickly identify which employees are most likely to experience fatigue based on their work patterns, enabling managers to take immediate action and reduce risks.
2 - Fatigue Risk-Informed Planning for Scheduling
One of the most powerful uses of a Biomathematical Fatigue Model is for fatigue risk-informed planning. You can analyze core work schedules, overtime assignments, shift rotations, and schedule deviations to understand how changes will impact fatigue levels.
Example: Implementing night shifts or extended overtime might seem operationally efficient, but a biomathematical fatigue model can show you the potential impact on worker alertness and risk. This allows you to plan schedules that minimize fatigue while optimizing operational efficiency.
3 - Benchmark Fatigue Risk and Track Improvements Over Time
A Biomathematical Fatigue Model doesn’t just give you a snapshot of fatigue risk at a given moment—it allows you to track fatigue trends over time. By documenting fatigue scores and comparing them across various work schedules, shifts, departments, and employee demographics, you can identify patterns that correlate with key performance indicators (KPIs) like accident rates, productivity levels, and health outcomes.
Example: By assessing fatigue risk across multiple months or even years, you can measure the effectiveness of fatigue mitigation strategies. If fatigue scores improve, it’s a good indication that your strategies are working.
4 - Safety Investigations & Workers' Compensation Analysis
Accidents or injuries due to fatigue are expensive—not just in terms of workers’ compensation claims, but also in lost productivity and damaged morale. A Biomathematical Fatigue Model allows you to analyze the fatigue score of an employee at the time of an incident, helping you understand how fatigue may have contributed to the event.
Tracking fatigue scores in conjunction with workers' compensation data allows you to quantify the financial impact of fatigue-related incidents and implement strategies to reduce future claims.
Example: By integrating fatigue risk data into your safety investigations, you can uncover patterns of fatigue that are linked to accidents, allowing for more targeted interventions to prevent future occurrences.
How a Biomathematical Fatigue Model Works
The power of a Biomathematical Fatigue Model lies in its ability to account for various factors that contribute to fatigue, including circadian rhythms, sleep quality, work shift patterns, and time-of-day fluctuations. For example, the CAS Fatigue Risk Model calculates a fatigue risk score based on on-duty and off-duty patterns. This data is then converted into a fatigue risk distribution, color-coded from green (low risk) to red (high risk).
Figure 1: Example of a CAS Fatigue Risk Distribution with a Risk Probability Curve
Example: The CAS Fatigue Risk Distribution
- Green: Low Fatigue Risk (Safe)
- Yellow: Moderate Fatigue Risk (Caution)
- Red: High Fatigue Risk (High Probability of Incident)
When you run multiple schedules through the system, this visual tool quickly communicates where the highest fatigue risks lie, allowing managers to focus on problematic work patterns and make improvements where they are needed most.
Case Study: Dupre Transport Uses Biomathematical Fatigue Models to Reduce Fatigue-Related Incidents
One of the most compelling examples of a Biomathematical Fatigue Model in action is the case of Dupre Transport, a trucking company based in Louisiana. Over a three-year period, Dupre used CAS Fatigue Risk Model to monitor the fatigue risk scores of their drivers.
By analyzing the fatigue scores, Dupre identified drivers who were at high risk of fatigue and implemented targeted dispatching strategies to reduce fatigue levels among these individuals. As a result, Dupre was able to:
- Significantly reduce accident rates (per million miles).
- Lower the cost per accident, improving the company’s bottom line.
This case illustrates how a Biomathematical Fatigue Model can help organizations identify their highest-risk employees and deploy resources more effectively to reduce fatigue-related incidents.
The Benefits of Using a Biomathematical Fatigue Model for Your Organization
- Objective Data: Provides an evidence-based, quantitative approach to fatigue risk management, removing guesswork from scheduling and risk assessment.
- Informed Decision-Making: Allows you to adjust work schedules and policies based on accurate fatigue data, reducing human error and improving safety.
- Improved Safety & Productivity: Helps reduce accidents, injuries, and downtime, leading to cost savings and improved worker performance.
- Enhanced Employee Well-being: By understanding and mitigating fatigue risk, you can promote better health and morale among your workforce.
Figure 2: Outcomes from a Fatigue Risk-Informed Organization
Conclusion: The Power of Data-Driven Fatigue Management
In today’s 24/7 work environments, it’s essential to move beyond guesswork and subjective assessments when managing fatigue risk. A Biomathematical Fatigue Model gives you the data and the tools you need to objectively measure fatigue, optimize schedules, and mitigate risks before they lead to incidents. Whether you’re a small operation or a global enterprise, using these models can help you create a fatigue risk-informed organization, improve safety, and boost productivity.
Remember: “You can’t improve what you don’t measure”—and with a Biomathematical Fatigue Model, you have the ability to measure and manage fatigue risk effectively.
For more information on how a Biomathematical Fatigue Model can help your organization, contact CIRCADIAN® to schedule a consultation and demonstration or get started with our fatigue risk management solutions today.
