Human Factors Analysis And Classification System Framework Research Articles

SEM-based study on the impact of safety culture on unsafe behaviors in Chinese nuclear power plants

This paper uses 135 Licensed Operator Event Reports (LOER) from Chinese nuclear plants to analyze how safety culture affects unsafe behaviors in nuclear power plants. On the basis of a modified human factors analysis and classification system (HFACS) framework, structural equation model (SEM) is used to explore the relationship between latent variables at various levels. Correlation tests such as chi-square test are used to analyze the path from safety culture to unsafe behaviors. The role of latent error is clarified. The results show that the ratio of latent errors to active errors is 3.4:1. The key path linking safety culture weaknesses to unsafe behaviors is Organizational Processes → Inadequate Supervision → Physical/Technical Environment → Skill-based Errors. The most influential factors on the latent variables at each level in the HFACS framework are Organizational Processes, Inadequate Supervision, Physical Environment, and Skill-based Errors.

Human Factors Analysis by Classifying Chemical Accidents into Operations

In the chemical industry, organizational and operational human factors significantly contribute to accidents. Chemical accidents occur in various operations of the industry due to a range of factors. Understanding the relationship between these factors and the accidents that happen is crucial in preventing similar accidents from happening repeatedly and promoting sustainability. Therefore, this study was divided into five operations: maintenance repair, process, loading unloading, storage, and shutdown startup of the chemical industry, to provide a more concrete, intuitive explanation of the interplay between causes and illustrate the routes to failure. The data were collected from 251 accident reports from various online data. The study was analyzed using the Human Factors Analysis and Classification System (HFACS) method as a conceptual framework. Each level’s frequency variables were obtained to define nominal and ordinal data. The chi-square test and Fisher’s exact test were used in the difference analysis of data in the model. The results show that the high-frequency accidents caused under the HFACS framework were organizational processes in the process (63.73%), in the storage (70.58%), and in the shutdown startup (91.66%), and skill-based errors in the maintenance repair (81.81%) and in the loading unloading (66.03%). Furthermore, resource management, technological environment, and personal readiness were significantly correlated with the operations. Human factors have differences in different operations in the chemical industry.

Development of effective human factors interventions for aviation safety management.

In the aviation industry, safety management has moved away from capturing frontline failures toward the management of systemic conditions through organizational safety management systems (SMS). However, subjective differences can influence the classification of active failures and their associated systemic precursors. With levels of professional experience known to influence safety attitudes, the present research examines whether experience levels among airline pilots had an impact on the classification of causal factors using the Human Factors Analysis and Classification System (HFACS). Differences in the paths of association between categories were evaluated in an open-system context. Pilots working in a large, international airline were categorized into high (≥10,000 total flight hours) and low (<10,000 h) experience groups and asked to classify aircraft accident causal factors using the HFACS framework. One-way ANOVA tests were carried out to determine experience effects on the utilization of the HFACS categories, and chi-squared analyses were used to assess the strength of association between different categories within the framework. Results from 144 valid responses revealed differences in the attribution of human factors conditions. The high experience group was more inclined to attribute deficiencies to high-level precursors and found fewer paths of associations between different categories. In contrast, the low experience group presented a greater number of associations and was comparatively more affected by stress and uncertainty conditions. The results confirm that the classification of safety factors can be influenced by professional experience, with hierarchical power distance impacting the attribution of failures to higher-level organizational faults. Different paths of association between the two groups also suggest that safety interventions can be targeted through different entry points. Where multiple latent conditions are associated, the selection of safety interventions should be made with consideration of the concerns, influences, and actions across the entire system. Higher-level anthropological interventions can change the interactive interfaces affecting concerns, influences, and actions across all levels, whereas frontline-level functional interventions are more efficient for failures linked to many precursor categories.

A data-driven method for modeling human factors in maritime accidents by integrating DEMATEL and FCM based on HFACS: A case of ship collisions

A data-driven method is proposed to measure the static and dynamic interrelations between human factors contributing to maritime accidents. The proposed method integrates the superiorities of the HFACS (Human Factors Analysis and Classification System), DEMATEL (Decision-Making Trial and Evaluation Laboratory), and FCM (Fuzzy Cognitive Map). First, a maritime accident scenario is defined based on 240 ship collision accident reports, then the human factors at different levels are identified and structured within the HFACS framework under the guidance of grounded theory. Second, the DEMATEL method is adopted to determine the causal relationship between human factors and the importance ranking of each factor based on historical accident statistics. The FCM model is developed in the end to realize dynamic prediction and diagnostic inference of human factors involved in maritime accidents. More objective and realistic results are presented by applying the proposed method without the necessity of expert judgment. The results show that “Lookout negligence”, “Poor safety management practice of the shipping company”, and “Failure to take effective collision avoidance actions promptly” are the top three important human factors, additionally, “Not familiar with COLREG” has the highest influencing degree and “Lookout negligence” has the highest influenced degree in the human factors network.

Human Factors Analysis and Classification System (HFACS): As Applied to Asiana Airlines Flight 214

The Human Factors Analysis and Classification System (HFACS) is a safety tool that aids in the identification and analysis of organizational factors that contribute to aircraft accidents. By using the HFACS model, safety investigators can better understand the existing conditions that contribute to accidents, which then allows for the development and implementation of safety programs to prevent these conditions. In this study, the HFACS framework was utilized to identify the human factors that contributed to the Asiana Airlines flight 214 accident that occurred on July 6, 2013. The results of this study indicate that inadequate pilot training, lack of upper-level supervision, and recurring deviation from standard operating procedures (SOPs) largely contributed to this accident. These findings emphasize the various organizational levels that serve a role in aviation accidents, highlighting the importance of practicing a proactive approach to safety and mitigating hazards within the upper levels of an organization before they lead to disaster on the front line.

Analysis of Human Factors Relationship in Hazardous Chemical Storage Accidents.

Human factors are important causes of hazardous chemical storage accidents, and clarifying the relationship between human factors can help to identify the logical chain between unsafe behaviors and influential factors in accidents. Therefore, the human factor relationship of hazardous chemical storage accidents was studied in this paper. First, the human factors analysis and classification system (HFACS), which originated from accident analysis in the aviation field, was introduced. Since some items were designed for aviation accident analysis, such as the item “Crew Resource Management”, it is not fully applicable to the analysis of hazardous chemical storage accidents. Therefore, this article introduced some modifications and changes to make the HFACS model suitable for the analysis of hazardous chemical storage accidents. Based on the improved HFACS model, 42 hazardous chemicals storage accidents were analyzed, and the causes were classified. After analysis, we found that under the HFACS framework, the most frequent cause of accidents is resource management, followed by violations and inadequate supervision, and finally the organizational process and technological environment. Finally, according to the statistical results for the various causes of accidents obtained from the improved HFACS analysis, the chi-square test and odds ratio analysis were used to further explore the relevance of human factors in hazardous chemical storage accidents. The 16 groups of significant causal relationships among the four levels of factors include resource management and inadequate supervision, planned inappropriate operations and technological environment, inadequate supervision and physical/mental limitations, and technological environment and skill-based errors, among others.

Dynamics Simulation of the Risk Coupling Effect between Maritime Pilotage Human Factors under the HFACS Framework

Maritime pilotage is an important guarantee for the safety of water traffic in port. The pilot is affected by the complex port environment, the differences of crew and equipment of different ships, the physical and psychological pressure of the pilot himself, as well as the management factors from the pilot station and maritime safety administration. In order to avoid pilotage accidents (PAs), it is necessary to study the coupling effect of human-organizational factors (HOFs) on PAs. In this paper, from the perspective of HOF risk coupling in pilotage, the problem of HOF risk coupling in maritime pilotage is studied by using the hierarchical classification idea of the human factors analysis and classification system (HFACS) and the method of system dynamics (SD). First of all, HFACS is used to analyse the HOF risk causal elements (RCEs) in pilotage, and 70 RCEs are summed up in four layers; secondly, the SD coupling model of RCEs is constructed; finally, based on a dataset of PAs collected by the Shanghai Harbour Pilot Association, the coupling simulation of RCEs in pilotage is carried out, and the volatility is evaluated. In general, the safety situation of maritime pilotage has been improving in the Shanghai port. However, four RCEs (negligence, habit, pilotage experience, and violations) in unsafe acts and two RCEs (teamwork and personal safety awareness) in precondition for unsafe acts contribute the most to maritime PAs and need to be paid attention to.

Improved HFACS on Human Factors of Construction Accidents: A China Perspective

Human errors are one of the major contributors of accidents. In order to improve the safety performance, human errors have to be addressed. Human Factors Analysis and Classification System (HFACS) has been developed as an analytical framework for the investigation of the role of human errors in aviation accidents. However, the HFACS framework did not reveal the relationships describing the effect among diverse factors at different levels. Similarly, its interior structure was not exposed. As a result, it is difficult to identify critical paths and key factors. Therefore, an improved Human Factors Analysis and Classification System in the construction industry (I-HFACS) was developed in this study. An analytical I-HFACS mechanism was designed to interpret how activities and decisions made by upper management lead to operator errors and subsequent accidents. Critical paths were highlighted. Similarly, key human factors were identified, that is, “regulatory factors,” “organizational process,” “supervisory violations,” “adverse spiritual state,” “skill underutilization,” “skill-based errors,” and “violations.” Findings provided useful references for the construction industry to improve the safety performance.

Proactive Safety Management in Trauma Care: Applying the Human Factors Analysis and Classification System.

This article examines the reliability of the Human Factors Analysis and Classification System (HFACS) for classifying observational human factors data collected prospectively in a trauma resuscitation center. Three trained human factors analysts individually categorized 1,137 workflow disruptions identified in a previously collected data set involving 65 observed trauma care cases using the HFACS framework. Results revealed that the framework was substantially reliable overall (κ = 0.680); agreement increased when only the preconditions for unsafe acts were investigated (κ = 0.757). Findings of the analysis also revealed that the preconditions for unsafe acts category was most highly populated (91.95%), consisting mainly of failures involving communication, coordination, and planning. This study helps validate the use of HFACS as a tool for classifying observational data in a variety of medical domains. By identifying preconditions for unsafe acts, health care professionals may be able to construct a more robust safety management system that may provide a better understanding of the types of threats that can impact patient safety.

Incorporating Hofstede’ National Culture in Human Factor Analysis and Classification System (HFACS): Cases of Indonesian Aviation Safety

National culture plays an important role in the application of ergonomics and safety. This research examined role of national culture in accident analysis of Indonesian aviation using framework of Human Factors Analysis and Classification System (HFACS). 53 Indonesian aviation accidents during year of 2001-2012 were analyzed using the HFACS framework by authors and were validated to 14 air-transport experts in Indonesia. National culture is viewed with Hofstede’ lens of national culture. Result shows that high collectivistic, low uncertainty avoidance, high power distance, and masculinity dimension which are characteristics of Indonesian culture, play an important role in Indonesian aviation accident and should be incorporated within HFACS. Result is discussed in relation with HFACS and Indonesian aviation accident analysis.

Paths between latent and active errors: Analysis of 407 railway accidents/incidents’ causes in China

The effectiveness and the safety of railway operations depend on many factors and latent errors pose the greatest risk to system safety. The Human Factors Analysis and Classification System (HFACS) has been widely used as an analytical framework for the investigation of the role of human errors in railway accidents/incidents; however, relatively few published studies with empirical evidence formally describe the independences between human factors by HFACS. Meanwhile, the occurrence frequencies of human factors and their main norms should be estimated with a large number of accidents/incidents data according to various professions and accidents. We collect and filter 407 railway accident/incident reports in China between the year of 2003 and 2014 using the HFACS framework. The results show that the four errors with the largest percentage of occurrence in the railway system are ‘organization process’, ‘inadequate supervision’, ‘personal readiness’ and ‘skill-based errors’. Several interesting relationships and the pattern of the ‘paths between categories’ at the four levels in HFACS are explored by this analysis for preventing or reducing the number of human errors. The methodology is useful in presenting how a railway accident/incident or near miss occurs and revealing significant interdependences between human factors. This study highlights a way for the rail industry to look more closely at latent factors at the supervisory and organizational levels when investigating active errors of the accidents.

An investigation on unintended reactor trip events in terms of human error hazards of Korean nuclear power plants

A new approach for finding the hazards of human errors, and not just their causes, in the nuclear industry is currently required. This is because finding causes of human errors is really impossible owing to the multiplicity of causes in each case. Thus, this study aims at identifying the relationships among human error hazards and determining the strategies for preventing human error events by means of a reanalysis of the reactor trip events in Korea NPPs. We investigated human errors to find latent factors such as decisions and conditions in all of the unintended reactor trip events during the last dozen years. In this study, we applied the HFACS (Human Factors Analysis and Classification System), which is a commonly utilized tool for investigating human contributions to aviation accidents under a widespread evaluation scheme. Using the HFACS framework, we tried to find out not the causations but all of the hazards and their relationships in terms of organizational factors. Through the trial, we proposed not only meaningful frequencies of each hazards also correlations of them. Also, considering the correlations of each hazards, we suggested useful strategies to prevent human error event. A method to investigate unintended nuclear reactor trips by human errors and the results will be discussed in more detail.

Routes to failure: Analysis of 41 civil aviation accidents from the Republic of China using the human factors analysis and classification system

The human factors analysis and classification system (HFACS) is based upon Reason's organizational model of human error. HFACS was developed as an analytical framework for the investigation of the role of human error in aviation accidents, however, there is little empirical work formally describing the relationship between the components in the model. This research analyses 41 civil aviation accidents occurring to aircraft registered in the Republic of China (ROC) between 1999 and 2006 using the HFACS framework. The results show statistically significant relationships between errors at the operational level and organizational inadequacies at both the immediately adjacent level (preconditions for unsafe acts) and higher levels in the organization (unsafe supervision and organizational influences). The pattern of the ‘routes to failure’ observed in the data from this analysis of civil aircraft accidents show great similarities to that observed in the analysis of military accidents. This research lends further support to Reason's model that suggests that active failures are promoted by latent conditions in the organization. Statistical relationships linking fallible decisions in upper management levels were found to directly affect supervisory practices, thereby creating the psychological preconditions for unsafe acts and hence indirectly impairing the performance of pilots, ultimately leading to accidents.