Prelude

While reviewing past presentations, I came across a human factors course I taught for BLR in a webinar a few years ago. It was an exciting opportunity, as human factors is an area I consider essential for creating safer workplaces, particularly in complex manufacturing operations. This work also coincided with my achievement of becoming an instrument-rated private pilot—a role where managing human error is a constant imperative. Exploring these concepts in depth inspired the development of an engaging presentation, which serves as the foundation for this article.

“We cannot change the human condition, but we can change the conditions under which humans work.”
—James Reason

And now, with the rise of AI, we have powerful new tools to change those conditions faster and smarter than ever before.

Introduction

Workplace accidents rarely stem from a single point of failure. More often, they are the result of a chain of errors, oversights, and latent conditions that align in just the wrong way. Human factors analysis provides a powerful framework for understanding how and why these errors occur—and more importantly, how to prevent them.

This article explores human error reduction, human factors psychology, and the Human Factors Analysis and Classification System (HFACS). It also outlines strategies organizations can apply to identify, control, and prevent workplace accidents, with real-world examples from aviation and chemical manufacturing.

Human Factors Overview

Human factors is the study of how humans interact with their environment, tools, systems, and organizations. It draws from psychology, engineering, ergonomics, and organizational science to design safer, more effective workplaces.

Key definitions include:

• Human Factors (Murrell, 1965): The scientific study of the relationship between humans and their working environment.
• Human Factors Psychology (Meister, 1989; Sanders & McCormick, 1993): The study of how humans accomplish work-related tasks in the context of human-machine systems, applying knowledge about human abilities and limitations to design tools, jobs, and environments.
• Human Error (Reason, 1990): A failure in a planned sequence of mental or physical activities that does not achieve the intended outcome, without interference from outside chance.
• Human Performance Improvement (DOE, 2009): The application of systems and models to reduce human error, manage controls, and improve outcomes by addressing the environment and conditions that shape behavior.

In short, human factors is about designing work to fit people, rather than expecting people to fit poorly designed systems.

Human Fallibility and Performance Modes

Human beings are inherently fallible. Even highly trained, competent professionals make mistakes—particularly under stress, distraction, or in poorly designed systems.

Research identifies three performance modes that influence error likelihood:

1. Skill-Based Mode: Actions are automatic, such as driving a familiar route. Errors here are often slips or lapses in attention. Typical error rate: 1 in 1,000 to 1 in 10,000 actions.
2. Rule-Based Mode: Workers follow learned rules to adapt to changing conditions. Errors often involve misinterpretation or applying the wrong rule to a situation. Typical error rate: about 1 in 100 to 1 in 1,000 decisions.
3. Knowledge-Based Mode: Responses are required in unfamiliar or novel situations. Errors often stem from incomplete mental models or poor situational awareness. Typical error rate: as high as 1 in 2 to 1 in 10 decisions.

Understanding these modes matters because they allow leaders to predict when errors are likely and design interventions accordingly. For example, automation can reduce reliance on memory in skill-based tasks, training can reinforce rule-based responses, and simulations can prepare workers for rare knowledge-based scenarios.

When I was training to become an instrument-rated pilot, I quickly realized how easy it is to lose situational awareness—the overall perception a pilot has of their current position, tasks, and the requirements needed to safely operate the aircraft. At that time, I was flying with the older instrument panels commonly referred to as “steam gauges.” These round dials, with needles pointing to number scales altitude, airspeed, and rate of descent or climb, provided essential information—but under pressure, interpreting them accurately and quickly could be difficult.

Over the years, aviation has shifted from these analog systems to digital “glass cockpits” that provide data-rich, graphic displays. These modern systems often include moving maps, integrated performance indicators, and more intuitive visuals, making it easier for pilots to interpret critical information in real time. Secondary systems—like iPads equipped with advanced navigation apps—add another layer of redundancy by displaying additional maps, alerts, and even voice cues. Together, these innovations significantly enhance situational awareness and allow pilots to recover it more quickly if lost.

Aviation accidents such as Eastern Air Lines Flight 401 (1972), where crew fixation on a landing gear indicator light led to unnoticed altitude loss and a crash, illustrate how human fallibility interacts with performance modes. Similarly, in chemical manufacturing, the 2005 BP Texas City refinery explosion was linked to rule-based and knowledge-based performance breakdowns under abnormal startup conditions.

The U.S. Department of Energy (DOE) has applied these principles extensively through its Human Performance Improvement (HPI) Handbook. The handbook translates concepts like error-likely situations, performance modes, and latent organizational weaknesses into practical tools for industrial operations. DOE facilities use HPI to anticipate where human limitations intersect with complex systems—such as nuclear operations, maintenance, and high-hazard chemical processes. By embedding practices like pre-job briefs, peer checks, and error precursors into daily work, HPI enables organizations to systematically reduce the frequency and severity of errors. This framework has proven so effective in the energy sector that many manufacturing and chemical companies have since adopted its methods as a model for operational reliability and safety.

The Human Factors Analysis and Classification System (HFACS)

HFACS (Human Factors Analysis and Classification System) was developed by Douglas Wiegmann and Scott Shappell for the U.S. Navy and Marine Corps, building on James Reason’s influential “Swiss Cheese Model” of accident causation. HFACS provides a comprehensive framework for understanding how human error contributes to accidents by identifying failures at multiple organizational and operational levels. Its structure allows investigators and safety professionals to look beyond immediate mistakes and uncover deeper systemic issues.

The framework categorizes failures into four primary levels:

1. Organizational Influences – These are the overarching factors that shape how work is performed, including resource allocation, safety culture, management priorities, and organizational policies. Deficiencies at this level can create conditions that make errors more likely, such as insufficient staffing, inadequate training programs, or conflicting safety and production pressures.
2. Unsafe Supervision – This level focuses on how supervisors and managers guide and control operations. It includes failures in planning, inadequate oversight, failure to correct known problems, and poor enforcement of procedures. For example, a supervisor who allows shortcuts or fails to provide timely feedback can inadvertently set the stage for unsafe acts.
3. Preconditions for Unsafe Acts – This level addresses the situational, environmental, and personal factors that increase the likelihood of errors or violations. Examples include fatigue, stress, poor communication, ergonomic challenges, or high-pressure operational conditions. These preconditions often interact with organizational and supervisory factors to create a heightened risk environment.
4. Unsafe Acts – These are the errors or violations committed by individuals, which are often the most visible contributors to accidents. HFACS differentiates between errors (slips, lapses, or mistakes due to knowledge or skill gaps) and violations (deliberate departures from rules or procedures). Understanding these distinctions helps organizations tailor interventions to prevent recurrence.

By examining incidents through the HFACS lens, organizations can systematically identify the root and systemic causes of accidents, rather than focusing solely on frontline human error. Its structured approach facilitates targeted corrective actions, training, and policy changes to reduce risk. While initially applied in aviation and nuclear power, HFACS has increasingly been adopted in complex industrial settings, including chemical manufacturing, where understanding human error is critical to operational safety.

In chemical manufacturing operations, HFACS provides a practical framework to analyze incidents ranging from process upsets to near-misses. By mapping errors to organizational influences, supervisory practices, preconditions, and unsafe acts, safety teams can identify patterns that contribute to risk, such as inadequate procedure enforcement, high workload periods, or recurring training gaps. Applying HFACS in these environments supports proactive interventions—modifying processes, improving supervision, enhancing training, and reinforcing safety culture—to prevent accidents before they occur. This approach aligns human factors analysis directly with operational excellence, helping to create safer, more resilient manufacturing systems.

Applications Beyond Accident Investigation

Human factors analysis is valuable in many contexts:

• Accident Investigations: HFACS provides structure for identifying systemic and individual contributors to accidents.
• Product & Equipment Design: Norman’s Human Design Principles emphasize simplicity, visibility, natural mapping, and design for error.
• Litigation: Human factors analysis can clarify whether accidents stemmed from negligence, systemic flaws, or unforeseeable conditions.
• Job & Procedure Design: Well-designed procedures reduce cognitive load and make safe actions the path of least resistance.

Strategies for Reducing Human Error

Strategies for Reducing Human Error
Preventing accidents requires more than training—it requires systems thoughtfully designed to anticipate, detect, and tolerate human fallibility. By layering multiple strategies, organizations can build robust defenses that reduce both the likelihood and impact of errors. Below are five complementary strategies, illustrated with examples from aviation and chemical manufacturing, along with practical guidance for application.

1. Error Elimination
The most effective approach is to remove hazards entirely, so that no mistake can activate them. This strategy focuses on designing systems where risk simply cannot exist.

• Aviation: Modern fly-by-wire systems replace mechanical linkages with computerized controls, eliminating entire categories of potential pilot and maintenance errors. By removing direct mechanical dependencies, these systems prevent errors before they can arise.
• Chemical Manufacturing: Replacing highly toxic solvents with safer alternatives removes both the exposure risk for operators and the potential for catastrophic chemical releases. By designing out the hazard, the system inherently becomes safer.

How to Apply:

• Conduct a hazard audit to identify elements that can be removed or replaced.
• Substitute high-risk materials, processes, or equipment with inherently safer alternatives.
• Simplify system designs to remove unnecessary complexity that could introduce errors.

2. Error Occurrence Reduction
This strategy aims to make errors less likely through system design, standardization, and procedural controls. By reducing opportunities for mistakes, human performance becomes more reliable.

• Aviation: Standardizing cockpit layouts across aircraft models helps pilots operate controls instinctively, reducing the chance of confusing throttle, flap, or landing gear levers.
• Chemical Manufacturing: Hose connections that are keyed or color-coded prevent operators from connecting incompatible lines, thereby avoiding hazardous chemical mixing and process errors.

How to Apply:

• Use standard operating procedures (SOPs) consistently across teams.
• Design interfaces, tools, and controls to reduce complexity and the potential for confusion.
• Apply ergonomics principles to ensure workspaces align with natural human behavior.

3. Error Detection
Even the best-designed systems cannot prevent all errors. Detection strategies focus on identifying mistakes quickly, allowing timely intervention before harm occurs.

• Aviation: Takeoff configuration warnings alert pilots if flaps, trim, or other critical controls are incorrectly set, providing immediate feedback to prevent accidents.
• Chemical Manufacturing: Distributed control systems continuously monitor process conditions, triggering alarms as parameters drift toward unsafe limits. Rapid detection enables operators to intervene before a process deviation escalates into a serious incident.

How to Apply:

• Implement real-time monitoring systems for critical parameters.
• Use alarms, indicators, or dashboards that provide clear, immediate feedback.
• Regularly audit systems to ensure detection mechanisms are functioning correctly.

4. Error Recovery
When errors occur, systems should allow safe correction. Recovery strategies give operators the ability to intervene or normalize conditions without catastrophic consequences.

• Aviation: Pilots are trained to execute a “go-around” if a landing approach becomes unstable, making recovery a normal, supported action rather than forcing continuation under unsafe conditions.
• Chemical Manufacturing: Pressure relief valves and emergency shutdown protocols allow systems to stabilize safely if process limits are exceeded, preventing explosions or uncontrolled releases.

How to Apply:

• Establish clear recovery procedures and train personnel to execute them under stress.
• Design fail-safe and fail-soft mechanisms that allow safe system operation after an error.
• Simulate error scenarios regularly to ensure recovery measures are effective and well understood.

5. Error Consequence Reduction
Despite the best prevention and detection systems, some errors will occur. This strategy minimizes the severity of outcomes to protect people, equipment, and the environment.

• Aviation: Redundant hydraulic, electrical, and navigation systems allow aircraft to continue safe operation even if individual components fail, reducing the risk of disaster.
• Chemical Manufacturing: Secondary containment, such as spill basins or dikes, limits the spread of leaks, safeguarding workers and the surrounding environment from exposure or contamination.

How to Apply:

• Incorporate redundancy in critical systems to maintain operation despite failures.
• Install physical barriers, spill containment, or other engineering controls to limit consequences.
• Conduct risk assessments to identify potential worst-case scenarios and design mitigation strategies accordingly.

Integrated Approach:
Together, these strategies create a layered “defense-in-depth” system. By anticipating human fallibility and designing operations to prevent, detect, recover from, and mitigate errors, organizations strengthen resilience and ensure safer operations in both aviation and chemical manufacturing.

Peer Checking: Lessons from Aviation

A useful example of human factors error reduction strategies used in aviation that I have personal experience with is the practice of readback between pilots and air traffic controllers. When a controller issues an instruction, the pilot is expected to repeat back the critical elements of that instruction. If the pilot’s readback is accurate, the controller responds with “readback correct, proceed.” This process ensures that instructions are both received and understood before being carried out, reducing the chance of miscommunication in high-stakes environments.

Although this is a very specific aviation example, the principle of peer checking has broad application in industrial settings. Having a second set of eyes involved in critical steps introduces additional perspectives on the situation, constraints, and potential risks. This shared verification not only strengthens accuracy but also brings in diverse risk awareness, making operations more resilient to error.

Human Error Assessment and Reduction Technique (HEART)

While developing training for a client focused on human error reduction, I discovered the HEART tool. It serves as an excellent complement to the other human factors concepts covered in this article, enhancing our ability to assess and mitigate potential errors effectively.

The Human Error Assessment and Reduction Technique (HEART) is a well-established method for evaluating human reliability in operational systems. Developed by British ergonomist Jeremy Williams, HEART provides a structured framework to identify potential error points and quantify the likelihood of human error in a given task.

HEART relies on 38 recognized “error-producing conditions”, which cover a broad range of factors that can increase the probability of mistakes, including time pressure, complexity, inadequate training, or environmental stressors. By systematically assessing these conditions, organizations can better understand where human performance may be vulnerable and take proactive steps to mitigate risk.

This technique is highly adaptable and can be applied to key operations across industries, from chemical manufacturing to aviation. By mapping tasks against HEART’s error-producing situations, safety professionals can prioritize interventions, redesign procedures, improve training, and implement controls that enhance overall system reliability.

Ultimately, HEART serves as a powerful tool for turning human factors insights into practical safety improvements, helping organizations reduce errors and create safer, more resilient operational environments.

How AI Enhances Human Performance Across the Error Spectrum

AI strengthens human reliability not in just one area, but across the entire journey of work—from anticipating risks before they occur, to recognizing mistakes as they unfold, to helping workers recover quickly and limiting consequences.

Anticipating and Preventing Errors
AI excels at analyzing vast streams of operational data to spot patterns that humans might overlook. By flagging early warning signs—such as subtle process deviations, fatigue risks, or environmental triggers—AI shifts organizations from reactive problem-solving to proactive error prevention. In doing so, it creates space for humans to focus on higher-level decision-making rather than monitoring every detail.

Recognizing Errors in Real Time
Once work is underway, AI systems act like an extra set of eyes and ears. Real-time monitoring tools can detect anomalies as they develop, from equipment vibration signals to unusual process parameters, alerting workers before a small misstep escalates. This immediate feedback loop reduces the likelihood of latent errors compounding into serious incidents.

Supporting Recovery and Corrective Action
Even with strong systems in place, errors still occur. AI can help workers recover more effectively by offering context-specific guidance, such as step-by-step corrective procedures or decision support during unexpected events. Much like an experienced mentor, AI doesn’t just point out that something is wrong—it helps chart the safest path back to stability.

Mitigating Consequences When Things Go Wrong
Finally, when errors do slip through, AI contributes to reducing their impact. Automated shutdown systems, predictive containment measures, or rapid communication tools can limit harm to people, equipment, and the environment. By acting faster than human reflexes allow, AI provides an additional safeguard when every second counts.

In Summary:
AI doesn’t replace human judgment—it augments it. By predicting, detecting, correcting, and mitigating errors, AI strengthens system resilience, reduces risk, and supports safer, more reliable operations across complex industries like aviation, chemical manufacturing, and energy.

Conclusion

Human error is not a moral failing—it is a predictable outcome of human limitations interacting with complex systems. By studying these interactions through human factors analysis, organizations can build safer, more reliable, and more resilient operations.

Aviation’s adoption of HFACS and human performance tools shows what is possible when human fallibility is acknowledged and managed. Chemical manufacturing and other high-risk industries can—and must—apply the same lessons.

When leaders design systems that anticipate mistakes, build in detection and recovery, and minimize consequences, they protect workers, safeguard communities, and ensure sustainable performance.

Final Thought

We can’t eliminate human fallibility—but we can design systems that anticipate it, tolerate it, and prevent it from turning into tragedy.

That’s the real value of human factors analysis: creating workplaces where people and systems succeed together.

References and Resources

• Reason, J. (1990). Human Error. Cambridge University Press.
• Wiegmann, D., & Shappell, S. (2003). A Human Error Approach to Aviation Accident Analysis: The Human Factors Analysis and Classification System. Ashgate.
• U.S. Department of Energy. Human Performance Improvement Handbook.
• Sanders, M., & McCormick, E. (1993). Human Factors in Engineering and Design.
• Norman, D. (2013). The Design of Everyday Things.
• Williams, J.C. (1985) HEART – A proposed method for achieving high reliability in process operation by means of human factors engineering technology. in Proceedings of a Symposium on the Achievement of Reliability in Operating Plant, Safety and Reliability Society (SaRS). NEC, Birmingham
• Brandon, C. (2024, November 24). Harnessing AI to revolutionize safety and EHS management: A vision for the future. LeadingEHS.com. https://leadingehs.com/2024/11/24/harnessing-ai-to-revolutionize-safety-and-ehs-management-a-vision-for-the-future/
• Zhao, Y., Zhang, J., & Li, X. (2024). Artificial intelligence for safety and reliability: A descriptive review. Journal of Cleaner Production, 396, 136365. https://doi.org/10.1016/j.jclepro.2023.136365
• Khurram, M.; Zhang, C.; Muhammad, S.; Kishnani, H.; An, K.; Abeywardena, K.; Chadha, U.; Behdinan, K. Artificial Intelligence in Manufacturing Industry Worker Safety: A New Paradigm for Hazard Prevention and Mitigation. Processes 2025, 13, 1312. https://doi.org/10.3390/pr13051312