Manufacturing process risk analysis model considering human and organizational factors

Human and organizational factors (HOFs) play an important role in electric misoperation accidents (EMAs), but research into the reliability of human factors is still in its infancy in the field of EMAs, and further investment in research is urgently required. To analyze the HOFs in EMAs, a hybrid method including the Human Factors Analysis and Classification System (HFACS) and fuzzy fault tree analysis (FFTA) was applied to EMAs for the first time in the paper. HFACS is used to identify and classify the HOFs with 135 accidents, reorganized as basic events (BEs), intermediate events (IEs), and top event (TE), and develop the architecture of fault tree (FT). Fuzzy aggregation is employed to address experts’ expressions and obtain the failure probabilities of the BEs and the minimal cut sets (MCSs) of the FT. The approach generates BEs failure probabilities without reliance on quantitative historical failure statistics of EMAs via qualitative records processing. The FFTA–HFACS model is applied for quantitative analysis of the probability of failure of electrical mishaps and the interaction between accident risk factors. It can assist professionals in deciding whether and where to take preventive or corrective actions and assist in knowledgeable decision-making around the electric operation and maintenance process. Finally, applying this hybrid method to EMAs, the results show that the probability of an EMAs is 1.0410 × 10−2, which is a risk level that is likely to occur and must be controlled. Two of the most important risk factors are habitual violations and supervisory violation; a combination of risk factors of inadequate work preparation and paralysis, and irresponsibility on the part of employees are also frequent errors.

Human and organizational factors within the public sectors for the prevention and control of epidemic

Investigation into the role of human and organizational factors in security work against terrorism at large-scale events

Fire probability prediction of offshore platform based on Dynamic Bayesian Network

Aviation has for many years been one of the leading industries in addressing human and organisational factors (HOF) within its different sectors – flight operations, air traffic control, ground operations, maintenance, etc. In particular aviation has led the way in mandating a range of measures that address HOF issues – reporting systems, shift-handover procedures, etc (e.g. UK CAA, 2003). A key element of the regulation has been the mandating of initial and continuation training in HOF for virtually all personnel working in aviation maintenance. By contrast the development of practice and regulation of risk and safety management in aviation has lagged behind process, power and nuclear industries (Gambetti et al., 2012; Leva et al., 2012). ICAO published its requirements for Safety Management in 2009 (ICAO, 2009) and these are still being translated into regulations by local aviation authorities. For example the EASA regulations require implementation of Safety Management Systems (SMS) in airlines by 2013. As a consequence of this historical sequence – the development of HOF regulations prior to safety management regulations – organisations are facing the challenge of integrating two programmes with related objectives developed to meet the requirements of different regulations. HOF training in aviation maintenance, in the European context, normally comprises of a two day initial training classroom based workshop supplemented by a one-day continuation training workshop every 2 years. The continuation training typically comprises a refresher of key HOF concepts and information about company specific challenges. E-learning and blended learning are sometimes used for continuation training, but their acceptance by the local aviation authorities is variable. This paper reports an initiative to integrate HOF continuation training within a risk management context in an aviation maintenance company.

Despite the modernization and automation in marine technology and the implementation of safety-related regulations, marine accidents are still a main concern for global maritime transportation. It is well known that human error is the key contributor, but human error data is hard to be collected and classified and the factors which influence human behavior should be identified. For this reason, the goal of this paper constructed a case-based HFACS (Human Factor Analysis and Classification System) method for marine human factor data collection and classification, and the data collected produced statics for analysis and safety management.

Analysis of 2004 German general aviation aircraft accidents according to the HFACS model

The healthcare system (HCS) is one of the most crucial and essential systems for humanity. Currently, supplying the patients' safety and preventing the medical adverse events (MAEs) in HCS is a global issue. Human and organizational factors (HOFs) are the primary causes of MAEs. However, there are limited analytical methods to investigate the role of these factors in medical errors (MEs). The aim of present study was to introduce a new and applicable framework for the causation of MAEs based on the original HFACS. In this descriptive-analytical study, HOFs related to MEs were initially extracted through a comprehensive literature review. Subsequently, a Delphi study was employed to develop a new human factors analysis and classification system for medical errors (HFACS-MEs) framework. To validate this framework in the causation and analysis of MEs, 180 MAEs were analyzed by using HFACS-MEs. The results showed that the new HFACS-MEs model comprised 5 causal levels and 25 causal categories. The most significant changes in HFACS-MEs compared to the original HFACS included adding a fifth causal level, named "extra-organizational issues", adding the causal categories "management of change" (MOC) and "patient safety culture" (PSC) to fourth causal level", adding "patient-related factors (PRF)" and "task elements" to second causal level and finally, appending "situational violations" to first causal level. Causality analyses among categories in the HFACS-MEs framework showed that the new added causal level (extra-organizational issues) have statistically significant relationships with causal factors of lower levels (Φc≤0.41, p-value≤0.038). Other new causal category including MOC, PSC, PRF and situational violations significantly influenced by the causal categories of higher levels and had an statistically significant effect on the lower-level causal categories (Φc>0.2, p-value<0.05). The framework developed in this study serves as a valuable tool in identifying the causes and causal pathways of MAEs, facilitating a comprehensive analysis of the human factors that significantly impact patient safety within HCS.

Application of the HFACS-PV approach for identification of human and organizational factors (HOFs) influencing marine accidents

The increasing complexity of modern transportation systems reinforces the critical role of human and organizational factors (HOF) in shaping safety outcomes. While extensive research has examined human performance, safety culture, and organizational systems separately, the literature offers limited cross-sector evidence on how these dimensions interact to influence operational resilience. This gap restricts the development of integrated safety strategies applicable across diverse transport domains. This study addresses this gap by investigating the integration of HOF across four sectors—aviation, rail, road, and water transport. A qualitative research design was adopted, involving twenty semi-structured interviews with industry professionals representing operational, supervisory, and safety-management roles. Interview data were analyzed using a hybrid inductive–deductive coding approach, and factor salience was quantified using a five-point importance scale to enable cross-sector comparison. The findings show substantial variation in HOF integration across sectors. Aviation demonstrated the highest level of integration, supported by strong leadership engagement, mature reporting systems, and structured training practices. Rail and water transport exhibited moderate but uneven integration, with communication and situational awareness emerging as core strengths. In contrast, road transport revealed high human-factor salience—particularly fatigue management and decision-making—yet weaker organizational support mechanisms. Across all sectors, the most influential factors included situational awareness, teamwork and communication, leadership commitment, and non-punitive reporting systems. The study contributes a cross-sector perspective on how integrated human–organizational approaches enhance safety resilience. The findings provide actionable implications for regulators and industry leaders seeking to design targeted, sector-appropriate interventions that strengthen both human performance and organizational safety systems.

Occurrence Human Factors Analysis Model is developed for China civil aviation based on the Human Factors Analysis and Classification System (HFACS) and the actual operation conditions and characteristics of China civil aviation to enhance the classification, analysis and utilization of safety information. In addition, to generate a model which can provides quantitative analysis supportive in aviation incident analysis, a human factor analysis model based on OHFAM and Bayesian network is established. This model constructed by combing hill-climbing search method with CH score function is a Bayesian network which uses three layers nodes to represent causality between human factors and incidents. The specific impact degree of human factors on aviation incidents is represented by conditional probability parameters of the model. It is useful in aviation incident analyses and deductions.

ABSTRACTIn-depth investigations of major offshore accidents show that technical, human, operational and organisational risk influencing factors (RIFs) all have crucial effects on the accident sequences. Nonetheless, the current generation of quantitative risk analysis (QRA) in the offshore petroleum industry has focused on technical safety systems while applications and findings in the non-technical fields are to a large extent missing. There have also been parallel efforts to develop methods for the formal inclusion of human and organisational factors (HOFs) into QRA. Examples from the offshore petroleum industry include ORIM, BORA, Risk_OMT, etc. This paper presents a review of QRA models that have been developed for the offshore petroleum industry, allowing HOFs integrated in a systematical way. The main intention of this study is to summarise and evaluate how these QRA models effectively seek answers to the key questions in this line of research: (i) What are the RIFs that affect the risk? (ii) How do these factors influence the risk? (iii) How much do these factors contribute to the risk? Further, the weakness and challenges of the reviewed models are pinpointed based on a substantial data set of actual leaks that have occurred in the Norwegian sector. Following the close scrutiny of these models, their progress, limitations, validity and suitability are addressed and discussed in detail. Based on these insights, future work is suggested to enhance and improve the QRA framework for including the installation specific conditions of technical and non-technical RIFs in a more comprehensive and defensible way.

Reliability studies for coding contributing factors of incident reports in high hazard industries are rarely conducted and reported. Although the Human Factors Analysis and Classification System (HFACS) appears to have a larger number of such studies completed than most other systems doubt exists as the accuracy and comparability of results between studies due to aspects of methodology and reporting. This paper reports on a trial conducted on HFACS to determine its reliability in the context of military air traffic control (ATC). Two groups participated in the trial: one group comprised of specialists in the field of human factors, and the other group comprised air traffic controllers. All participants were given standardized training via a self‐paced workbook and then read 14 incident reports and coded the associated findings. The results show similarly low consensus for both groups of participants. Several reasons for the results are proposed associated with the HFACS model, the context within which incident reporting occurs in real organizations and the conduct of the studies.

It is well known that human error is the key contributor in most marine accidents. Nevertheless, human error is not the cause of accidents or mishaps in the new view on human errors; it is the deeper trouble of the MMEM (Man-Machine-Environment-Management) system. The key point for human error analysis is to find out the connection between human error and the features of system elements, that is the contexts of human error (Operating environment, Organization influence, etc). For this reason, this paper shows an approach for mining the human factors data which influence the occurrence of human error. This approach bases on the data collected on HFACS (Human Factor Analysis and Classification System) Model. It can be used for analysis of human factors and risk management of marine industry.

Hot air balloons play a significant role in tourism in Türkiye. Despite offering a unique experience, they are also associated with a relatively high risk of accidents. According to the Turkish Ministry of Transport and Infrastructure (2024), hot air balloon accidents account for 36% of aviation incidents, highlighting the need for closer examination of these occurrences. Since human factors constitute the dominant contributory element in aviation accidents, it is crucial to analyze hot air balloon incidents from this perspective. This study examines two serious hot air balloon accidents in Türkiye that occurred between 2021 and 2024, focusing on the human factors involved. Using a mixed methodology, including the 5 Whys Root Cause Analysis (RCA) and the Human Factors Analysis and Classification System (HFACS), the research investigates systemic issues. Data were collected from official accident reports published by the Transport Safety Investigation Center under the Ministry of Transport and Infrastructure of the Republic of Türkiye. The analysis reveals significant patterns in decision-making errors, insufficient meteorological assessments, inadequate training, and organizational shortcomings. The findings show that both accidents were driven by systemic failures rather than pilot error alone. These included issues with organizational processes, supervisory oversight, and safety culture. The study emphasizes the need for improved meteorological systems, better training for adverse weather conditions, and enhanced safety management practices that stress the importance of a proactive safety culture and stronger operational planning to address human factors and reduce risks in hot air balloon operations.