Human error is a frequently cited cause of problems in quality management but is rarely understood in depth. Too often, problem-solving teams focus only on mechanical faults or procedural gaps, overlooking the critical role of human behavior. Yet many failures trace back to human choices, decisions, or lapses.
To truly resolve root causes, quality professionals must understand not only what went wrong but also why individuals acted in certain ways. This perspective aligns with key expectations in standards like ISO 9001 and IATF 16949, which emphasize not just correction but prevention through systemic understanding. This article introduces a structured approach to human error analysis (HEA), explaining when to use it, how to apply it, and why it’s essential for sustainable problem-solving.
What is Human Error Analysis?
Human Error Analysis (HEA) is the systematic examination of mistakes made by individuals during task execution. Rather than assigning blame, it seeks to identify the systemic factors and latent conditions that made those mistakes possible.
Human errors generally fall into three main categories:
By identifying the nature of the error and its contributing conditions, HEA supports the design of systems that reduce the likelihood of recurrence.
What Causes Human Error?
Errors are rarely due to laziness or incompetence. In most cases, people do the best they can within flawed systems. Common contributors to human error include:
- Vague or overly complex work instructions.
- Excessive multitasking or environmental distractions.
- Fatigue, time pressure, or emotional stress.
- Inadequate training or supervision.
- Poorly defined roles and responsibilities.
Understanding these drivers enables organizations to address underlying weaknesses instead of targeting individuals.
NOTE: Employees work in the way we designed their workstation. This is one of the reasons why problem solving methods do not accept the indication of “human error” for root cause analysis. Human error is the effect of system solutions.
How to Apply Human Error Analysis in Problem Solving
Incorporating HEA into problem-solving frameworks like 8D, A3, or QRQC enhances the depth of root cause analysis. Here’s a structured method:
- Map the process: Document the sequence of activities leading to the error.
- Interview involved personnel: Approach with curiosity, not blame. Capture their understanding of events.
- Classify the error type: Use error taxonomy (skill-, rule-, or knowledge-based) to frame the issue.
- Investigate systemic factors: Explore conditions that influenced behavior (tools, environment, pressure).
- Validate through triangulation: Cross-check findings with data, direct observations, and third-party input.
- Implement system-level changes: Move beyond training to address root causes in the work environment and organisational structure. Effective system-level changes may include redesigning interfaces to prevent incorrect inputs, simplifying or standardising work instructions, automating repetitive tasks, adding real-time visual management tools, improving layout and ergonomics, and clarifying roles and communication channels. These interventions not only reduce the likelihood of human error but also enhance process stability and employee performance.
Example – Labelling Errors on a Production Line
Context: An Automotive Supplier assembled parts, placed them in kits (sequence components for each unique vehicle) and mislabelled them with incorrect part numbers.
The initial countermeasures focused on retraining staff, but errors continued. A deeper HEA revealed the following:
- The HMI (Human Machine Interface) interface allowed manual label part number selection without confirmation.
- Supervisors, under performance pressure, routinely skipped verification steps.
- SOPs were outdated and inconsistent.
What’s the solution? Redesigning the interface that required barcode verification, updated SOPs aligned with best practices, and leadership coaching.
Result: Zero label mix-ups and no further disruption to the customer.
Questions to consider:
Isn’t human error just a result of inattention?
Not exactly. Most errors stem from systemic issues that affect attention, memory, or decision-making. HEA helps you understand andaddress those issues.
Should we always consider human error in problem-solving…?
Yes, especially when humans play a role in the process. Ignoring human factors often means missing the true root cause.
And the title question of our article – Is it better to discipline staff or improve the system?
Systems should always come first. While accountability matters, resilient systems reduce the opportunity for error in the first place.
Best Practices for Human Error Analysis
- Promote a “Just Culture” that encourages openness while maintaining accountability.
- Educate engineers and managers on human error typologies.
- Include HEA in Failure Mode and Effects Analysis (FMEA) and Control Plan reviews.
- Use formal tools like the Human Factors Analysis and Classification System (HFACS) to guide assessments.
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Let’s sum up!
Human error is not the endpoint of an investigation – it’s the beginning of understanding deeper system vulnerabilities. By integrating human factors thinking into quality processes, organisations unlock powerful insights and more effective solutions like Error Proofing. Over time, this leads to measurable benefits such as reduced cost of poor quality (COPQ), improved compliance with regulatory and customer requirements, and a more resilient operational culture to ensure customer satisfaction.
Want to build stronger, error-resistant processes?
At Qualitywise, we offer training and consulting services that equip teams with the tools to analyze and mitigate human error. Whether you’re troubleshooting a production issue or developing robust systems, we help you get to the real root cause.
We invite you to the Human Error Analysis training where you will learn in detail the usefulness of Human error impact on problem solving.
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For people who want to know more:
IATF 16949: 2016 Requirements for quality management systems in serial production and the production of spare parts in the automotive industry, 1st edition, 2016
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