Human Reliability Analysis in Complex Systems

Human Reliability Analysis (HRA) is the systematic study of the likelihood and consequences of human error within complex socio-technical systems. It integrates insights from cognitive psychology, ergonomics and reliability engineering to quantify how individual actions, organisational factors and environmental conditions interact to influence system performance and safety. Modern HRA frameworks have evolved from first-generation methods, which treated human error as a fixed probability, to second-generation approaches that explicitly model contextual influences, known as Performance Shaping Factors (PSFs). In parallel, advances in probabilistic risk assessment have enabled the incorporation of dynamic system models and real-time data, yielding a more nuanced view of how human actions evolve over time. The global significance of HRA is reflected in its adoption across domains as diverse as nuclear power, chemical processing, maritime operations and manufacturing. By providing structured techniques to identify human failure events, allocate error probabilities and evaluate mitigation measures, HRA helps decision-makers design more resilient systems, optimise training and refine procedural safeguards.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Research from all publishers

Recent work in manufacturing systems has extended traditional reliability assessment to include human-related failures alongside hardware and software faults. A comprehensive review of cyber-physical production environments highlighted challenges in data quality and offered opportunities for integrating machine learning into HRA to automate probability estimation and update models dynamically. In the textile industry, a structured approach using fuzzy DEMATEL has been applied to reveal interdependencies among PSFs and improve estimation of Human Error Probability (HEP), identifying experience and training as primary levers to enhance operator reliability. In the field of external hazard risk assessment, researchers have adapted cognitive-based methods to identify human failure events in flood response scenarios. Decomposition analyses using a Bayesian network variant of the Cognitive Reliability and Error Analysis Method (CREAM) demonstrated the feasibility of mapping emergency actions outside control rooms, while also pointing to the need for expanding crew failure mode libraries to capture diverse physical and communication tasks under stress.

Human Reliability Analysis in Complex Systems publication trend

The graph below shows the total number of articles in human reliability analysis in complex systems across all publications each year (not limited to Nature Index journals).

Technical terms

Human Reliability Analysis (HRA): A structured set of methods to assess the likelihood of human error and its impact on system safety and performance.

Human Error Probability (HEP): A quantitative estimate of the chance that a specific human action will fail under defined conditions.

Performance Shaping Factors (PSFs): Contextual, organisational and individual factors that influence human performance, such as workload, training, environmental conditions and team communication.

Probabilistic Risk Assessment (PRA): A methodological framework that models system behaviours and uncertainties to calculate the probability and consequences of adverse events, often integrating human and technical failure modes.

References

1. Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities. Journal of Manufacturing Systems (2024).
2. Human reliability analysis: Exploring the intellectual structure of a research field. Reliability Engineering & System Safety (2020).
3. Dynamic probabilistic risk assessment of decision-making in emergencies for complex systems, case study: Dynamic positioning drilling unit. Ocean Engineering (2021).
4. Identifying human failure events (HFEs) for external hazard probabilistic risk assessment. Reliability Engineering & System Safety (2023).
5. Dynamic Human Error Assessment in Emergency Using Fuzzy Bayesian CREAM. Journal of Research in Health Sciences (2020).