Human Factors Classification Research Articles

Human error in maintenance: A case study from China

AbstractHuman error is an essential factor that affects the quality and safety of industrial production. To deeply understand the human factors that cause failures in an organization from the perspective of maintenance personnel, we propose an analytical approach combined with Fault Tree Analysis and qualitative analysis and apply this approach to maintenance task failure incidents. These proposed methods are based on human factor classification and the Human Factors Analysis and Classification System. We conduct a case study to prove the effectiveness of applying our approach to the Chinese manufacturing industry. Results enabled the disclosure of the “latent factors” of maintenance incidents, helped improve human error analysis in maintenance incidents and helped to understand the fundamental reasons that affect work reliability and cause maintenance failures. This case study focuses on the impact of critical human factors on organizational effectiveness and operational reliability during maintenance activities.

Investigating Cloud Computing Misconfiguration Errors using the Human Factors Analysis and Classification System

Abstract As organizations raced to leverage cloud computing to capitalize on its economic and flexible computing capacity, misconfiguration errors ascended to unprecedented levels. One security expert noted observing over 230 million misconfigurations daily. Cloud misconfiguration errors are problematic as malicious actors continually pursue missteps to gain unauthorized access. The complexity of cloud computing coupled with increased cloud usage and the lack of cloud expertise result in misconfiguration errors which are human errors. Leveraging the human factors analysis and classification system (HFACS) provides a framework for identifying and preventing causal pathways for misconfiguration errors. In cloud computing, practitioners can explore the linkages of the hierarchical levels and decision-making at each level that influences and enable the misconfigurations. Human errors in cybersecurity are rampant, and HFACS could provide comprehensive analyses to highlight how errors, mistakes, and violations propagate misconfiguration errors – a current blind spot in cloud computing.

A methodology to assess the causation relationship of seafarers’ unsafe acts for ship grounding accidents based on Bayesian SEM

Unsafe acts have been recognized as the substantial factors leading to maritime accidents. An obstacle to analyzing seafarers' unsafe acts involved in maritime accidents is the lack of data. Bayesian approach in structural equation modelling (SEM) estimation has attracted extensive attentions across other fields, due to its advantages that can handle complicated models and small sample size. Therefore, this study proposes an innovative methodology to address the multifactor risk coupling and causation involved in seafarers' unsafe act contributing to ship grounding accidents. Based on the principle of Bayesian SEM, the advantages of Grounded theory and Human Factors Analysis and Classification System (HFACS) are integrated into this methodology. Specifically, Grounded theory and HFACS model are applied to identify and structure seafarers' unsafe acts and their contributory factors, facilitating the development of the conceptual model. Then, by employing Bayesian SEM, the causation paths and effects of seafarers' unsafe acts are examined. The results reveal that organizational influences play a significant role in the causation mechanism of seafarers' unsafe acts, and preconditions for unsafe acts are the only variables that exert direct impact on unsafe acts. Moreover, unsafe supervision and preconditions for unsafe acts are served as intermediate factors affecting seafarers' unsafe acts. In addition, this results indicate the feasibility of Bayesian SEM in analyzing seafarers’ unsafe acts.

Cause analysis of unsafe acts of pilots in general aviation accidents in China with a focus on management and organizational factors

Objectives. General aviation (GA) safety has become a key issue worldwide and pilot errors have grown to be the primary cause of GA accidents. However, fewer empirical studies have examined the contribution of management and organizational factors for these unsafe acts. Flawed decisions at the organizational level have played key roles in the performance of pilots. This study provides an in-depth understanding of the management and organizational factors involved in GA accident reports. Methods. A total of 109 GA accidents in China between 1996 and 2021 were analysed. Among these reports, pilot-related accidents were analysed using the human factors analysis and classification system (HFACS) framework. Results. The significant effects of managerial and organizational factors and the failure pathways on GA accidents have been identified. Furthermore, unlike traditional HFACS-based analyses, the statistically significant relationships between failures at the organizational level and the sub-standard acts of the pilots in GA accidents were revealed. Conclusions. Such findings support that the GA accident prevention strategy that attempts to reduce the number of unsafe acts of pilots should be directed to the crucial causal categories at HFACS organizational levels: resource management, organizational process, failure to correct a known problem, inadequate supervision and supervisory violations.

Safety archetypes identification and behavior simulation for nuclear power plant operation human reliability improvement

Safe operation is the foundation and guarantee of the sustainable development of the nuclear power industry. With the continuous improvement of digital nuclear power plant (NPP) design and construction, fewer and fewer accidents are caused solely by mechanical failure. Human factors (HFs) have become the most important cause of NPP accidents. By combing insights from the framework of Human Factor Analysis and Classification System (HFACS) and introducing System Dynamics approach, this paper proposes a human reliability model which identifies four safety archetypes illustrating HF risk mechanisms in NPP operation, captures the PSF interrelationship and breaks through traditional independent PSF analysis. This model focuses on the path dependence characteristics of HF risk accumulation process in a form of S-shaped risk-time curve, and the derived scenario simulations provide suggestions for related stakeholders in nuclear power field to evaluate their decision-making on human reliability improvement better in a medium-and-long term vision.

Risk assessment of unsafe behavior in university laboratories using the HFACS-UL and a fuzzy Bayesian network

Introduction: Risk assessment for unsafe behavior is an important task in the management of university laboratories. Yet related research activities are still in the early stages. This paper attempts to deepen the insight and provide a basis for further research. Method: As traditional methods are inadequate in terms of quantitative assessment and uncertainty handling, this paper proposes a method to assess the risk of unsafe behavior in university laboratories using the human factor analysis and classification system for university laboratories (HFACS-UL)-fuzzy Bayesian network (BN) approach. A BN structure was established using the HFACS-UL model for the identification of factors influencing unsafe behavior. Using a fuzzy BN approach, parameters are learned based on prior knowledge and expert experience. The model is then applied for inference analysis to identify the main risk factors. The key agents were also analyzed along with meta-networks to determine further preventive and control measures. Results: Taking chemistry laboratories of a university as an example, the results show that the probability of unacceptable unsafe behavior in chemical laboratories is 86%, indicating that commitment and cooperation from different agents are required. Of the 24 risk factors, poor organizational climate, with a sensitivity value of 24.1%, has the greatest impact on unsafe behavior. The most fundamental factor contributing to the occurrence of unsafe behavior is inadequate legislation, which in turn results in unacceptable external factors and inadequate supervision, thus forming the most likely causal chain. The functional department, lab center director, and secondary faculty leadership team are the most critical agents. Conclusions: Results from the chemistry laboratories demonstrate the credibility of the model. Practical applications: This study may help provide technical support for risk management in university laboratories.

An adaptive model for human factors assessment in maritime operations

Complexity in marine operations requires a robust and dynamic framework for human error assessment to aid safety-based decision making under uncertainty.The current study presents a dynamic Human Factors Analysis and Classification System for Maritime Accident. The model is used for human factor assessment in critical maritime operations, considering the influential factors of dynamic cognitive human behavior and complex interactions among core risk factors. The HFACS-MA structure consists of five levels of human factors based on the International Maritime Organization (IMO) guiding principles and the Human Factor Analysis and Classification System (HFACS) concept. Based on three accident case studies, the study explores five levels of human role to develop a robust model structure for critical maritime operations. The developed structure is translated into a novel Bayesian network (BN) structure, capturing the dependencies among the risk influencing factors for the three accident scenarios. The developed model framework for the accident scenarios emphasizes the changing characteristics of human performance influential factors and the dynamic operating environment. The demonstrated case studies further confirm the model adaptiveness in human factor assessment, considering the dynamic decision-making influential factors and operational uncertainty.

Human factor analysis and classification system for the oil, gas, and process industry

AbstractHuman error can cause severe problems in industrial oil, gas, and chemical processes. Although human factors analysis and classification systems (HFACSs) for the oil and gas industry (HFACS‐OGI) and the small‐scale chemical industry (HFACS‐CSMEs) have been independently developed, there are some limitations in providing detailed information on the contributing factors. Therefore, a new HFACS must be developed that can be commonly applied to the oil, gas, and chemical fields. This paper develops an integrated HFACS that can be applied to the oil, gas, chemical, and power plants, called HFACS‐OGAPI, by analyzing 45 accidents that occurred in domestic and overseas oil, gas, chemical, and power plants, based on the HFACS‐OGI system. An integrated HFACS‐OGAPI that can be applied to the oil, gas, chemical, and power plants is developed. The framework effectively identifies the contributing factors by providing specific information on Tiers 1, 2, 3, and 4 for Levels 1, 2, 3, 4, and 5. The contributing factors are effectively identified by analyzing the BP Texas Refinery explosion (2005), the Kleen Energy natural gas explosion (2010), and the Aghorn Operating Waterflood Station accidents (2019) with the developed framework. A checklist suitable for the HFACS‐OGAPI is developed by considering the contents developed by the National Aeronautics and Space Administration and the Department of Defense to determine the contributing factors. The developed checklist is expected to effectively identify and improve on the causes of human error through periodic inspection.

On the causation of seafarers’ unsafe acts using grounded theory and association rule

Unsafe acts of seafarers have been widely recognized as the direct causes of maritime accidents. Previous studies have demonstrated the significant role of unsafe acts, and provided numerous factors that affect seafarers’ unsafe acts. However, the complex relationship among factors has not been explored. Therefore, in the present study, grounded theory is employed with the objective of determining the seafarers’ unsafe acts and their contributory factors in a systematic way, and within the framework of human factor analysis and classification system (HFACS), the causation of seafarers’ unsafe acts is hierarchized. Then, rough set theory is integrated with association rule mining to explore the potential causation of seafarers’ unsafe acts. The results highlight the development of theoretical analysis framework for seafarers’ unsafe acts, and the causation of unsafe acts is presented from three perspectives: “factors→unsafe acts”, “factors self-association”, and “unsafe acts self-association”. Finally, the safety countermeasures are provided. Results from this study would help relevant stakeholders to tailor prevention measures for seafarers’ unsafe acts, and enhance the safety management level in seafarers.

Research on simulation of risk control strategy for air traffic controllers’ unsafe acts

In order to effectively control the risk of unsafe acts of air traffic controllers, a risk control system model of the unsafe acts of controllers was constructed by using system dynamics, and the effectiveness of the control strategies was analyzed by simulation. Based on Human Factors Analysis and Classification System (HFACS) model, the causal relationship between environmental factors, organizational influences, safety supervision, adverse states of controllers and the risk of unsafe acts of controllers was analyzed, and the flow diagram was drawn. The validity of the model was verified by the validity test. The risk level of controller errors and violations was simulated and analyzed from organization influence, safety supervision and combination. The results show that the model can simulate the effectiveness of the control strategies. In the risk control of the controllers’ errors and violations, the control effect of combined strategy is the best, in which improving the rules and standards and strengthening safety education and training are the best strategies. Using system dynamics to build the risk control system model of unsafe acts of controllers can simulate and analyze the effectiveness of control strategies, and provide theoretical basis for controlling the risk of unsafe acts of controllers.

Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS

The risk factors affecting workers' unsafe acts were comprehensively identified by Human Factors Analysis and Classification System (HFACS) and grounded theory based on interview data and accident reports from deep coal mines. Firstly, we collected accident case and field interview data from deep coal mines issued by authoritative institutions. Then, the data were coded according to grounded theory to obtain relevant concepts and types. The HFACS model was used to classify the concepts and categories. Finally, the relationship between core and secondary categories was sorted out by applying a story plot. The results show that risk factors of unsafe acts of deep coal mine workers include environmental factors, organizational influence, unsafe supervision and unsafe state of miners, and the main manifestations of unsafe acts are errors and violations. Among them, the unsafe state of miners is the intermediate variable, and other factors indirectly affect risky actions of coal miners through unsafe sates. Resource management, organizational processes and failure to correct problems are the top three risk factors that occur more frequently in unsafe acts. The three most common types of unsafe act are unreasonable labor organization, failure to enforce rules, and inadequate technical specifications. By combining grounded theory and the HFACS framework to analyze data, risk factors for deep coal miners can be quickly identified, and more precise and comprehensive conceptual models of risk factors in unsafe acts of deep coal miners can be obtained.

A hybrid model integrating FMEA and HFACS to assess the risk of inter-city bus accidents

The incidence of inter-city bus accidents receives a lot of attention from the public because they often cause heavy casualties. The Human Factors Analysis and Classification System (HFACS) is the prevailing tool used for traffic accident risk assessment. However, it has several shortcomings, for example: (1) it can only identify the potential failure modes, but lacks the capability for quantitative risk assessment; (2) it neglects the severity, occurrence and detection of different failure modes; (3) it is unable to identify the degree of risk and priorities of the failure modes. This study proposes a novel hybrid model to overcome these problems. First, the HFACS is applied to enumerate the failure modes of inter-city bus operation. Second, the Z-number-based best–worst method is used to determine the weights of the risk factors based on the failure mode and effects analysis results. Then, a Z-number-based weighted aggregated sum product Assessment is utilized to calculate the degree of risk of the failure modes and the priorities for improvement. The results of this study determine the top three ranking failure modes, which are personal readiness from pre-conditions for unsafe behavior, human resources from organizational influence, and driver decision-making error from unsafe behavior. Finally, data for inter-city buses in Taiwan in a case study to illustrate the usefulness and effectiveness of the proposed model. In addition, some management implications are provided.

Study of Occupational Safety Risks in Prefabricated Building Hoisting Construction Based on HFACS-PH and SEM.

As the concern for environmental pollution and occupational safety caused by the construction industry is gradually increasing worldwide, the prefabricated building model has become a type of construction promoted by sustainable societies. In China, the management codes of prefabricated buildings are not mature enough and safety accidents occur frequently during the construction process. Therefore, how to analyze and determine the main factors that affect the safety of the construction of prefabricated buildings has become a problem to protect the lives and health of construction workers. In this study, we focused our research on the accident-prone component-hoisting construction phase. First, through the questionnaire and accident data, the traditional human factors analysis and classification system (HFACS) was improved into the HFACS–prefabricated building hoisting (PH) risk model. This study also established a comprehensive safety prevention and control system for the component-hoisting process of prefabricated buildings by combining the factor analysis of using structural equation modeling (SEM). The prevention and control measures to avoid the occurrence of prefabricated building component-hoisting accidents were also proposed from four aspects: external environment, organizational factors, prerequisites for triggering accidents, and unsafe leadership behaviors. The results showed the following: (1) For the external environment, occupational safety and health system standards should be established and safety supervision responsibilities should be implemented. (2) For organizational factors, safety management systems should be improved with more capital investment. (3) For unsafe leadership behaviors, safety education and training should be strengthened to ensure workers’ optimal physical and psychological states. (4) For the prerequisite of accidents, it is necessary to create a good hoisting work environment.

Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis.

This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space’s characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms.

The Influence of Human-Organizational Factors on Falling Accidents From Historical Text Data

This paper firstly proposes a modified human factor classification analysis system (HFACS) framework based on literature analysis and the characteristics of falling accidents in construction. Second, a Bayesian network (BN) topology is constructed based on the dependence between human factors and organizational factors, and the probability distribution of the human-organizational factors in a BN risk assessment model is calculated based on falling accident reports and fuzzy set theory. Finally, the sensitivity of the causal factors is determined. The results show that 1) the most important reason for falling accidents is unsafe on-site supervision. 2) There are significant factors that influence falling accidents at different levels in the proposed model, including operation violations in the unsafe acts layer, factors related to an adverse technological environment for the unsafe acts layer, loopholes in site management in the unsafe on-site supervision layer, lack of safety culture in the adverse organizational influence layer, and lax government regulation in the adverse external environment layer. 3) According to the results of the BN risk assessment model, the most likely causes are loopholes in site management work, lack of safety culture, insufficient safety inspections and acceptance, vulnerable process management and operation violations.

A Human Factors Approach to Vaginal Retained Foreign Objects

This study aimed to apply a structured human factors analysis to understand conditions contributing to vaginal retained foreign objects (RFOs). All potential vaginal RFO events from January 1, 2000, to May 21, 2019, were analyzed by trained human factors researchers. Each narrative was reviewed to identify contributing factors, classified using the Human Factors Analysis and Classification System for Healthcare. An 890-bed, academic medical center in Southern California. Patients who underwent a vaginal procedure in which a vaginal RFO-related event occurred were included in this study. However, no patient information was included, and only the relevant details from their procedures were included. No interventions were developed or implemented. Over the 19-year period, 45 events were reported. The most common items were vaginal packing and vaginal sponges (53.33%). Less frequently retained items involved broken instruments (20.20%). Most cases were laparoscopic hysterectomies or vaginal deliveries. Based on the Human Factors Analysis and Classification System, 75 contributing factors were identified, consisting primarily of preconditions for unsafe acts (communication challenges, coordination breakdowns and issues with the design of tools/technology) and unsafe acts (errors). Although rare, vaginal RFOs do occur. The top 2 contributing factors were skill-based errors and communication breakdowns. Both types of errors can be addressed and improved with human factors interventions, including simulation, teamwork training, and streamlining workflow to reduce the opportunity for errors.

Elektronik seyir cihazlarının deniz kazalarına etkileri

In recent years, maritime-related organizations and companies have moved to a risk-based approach. To determine the risks, it is necessary to understand comprehensively why accidents occur and how it develops. The most effective measures need to be identified to implement the accident prevention measures successfully. According to the results of scientific studies conducted in the past, 80% of human factors risks were effective in marine accidents. Nowadays, maritime technologies are the most effective method for reducing the risks of human factors. However, the use of electronic navigation devices has not eliminated accidents. In this study, the accident reports for collision and grounding due to the electronic navigation devices' risk was evaluated using Human Factors Analysis and Classification System (HFACS) method. As a result of the study, more than half of the visible (active) causes of accidents have been identified as operating failure factors in electronic navigation equipment. Recommendations to prevent the occurrence of accident factors have been made.

Applicability and Usefulness of the HFACS-GMI

To present information such as causes of accidents and their consequences on the Ghanaian mining industry in the safety literature, classification schemes for incident analysis within the safety literature were studied. Human Factor Analysis and Classification Scheme (HFACS) emerged suitable for incident analysis. Base on its suitability for incident analysis within the Ghanaian Mining Industry (GMI), a derivative of the HFACS, namely HFACS-GMI, was proposed. This research seeks to study the usefulness and the applicability of the HFACS-GMI. Collectively, 56 incident investigation reports were obtained from an open cast gold mine in Ghana and analysed using the HFACS-GMI. Two cases, an equipment damage incident and an injury incident, were used to demonstrate the coding processing in identifying the causal factors. The analysis shows that most mishaps are associated with adverse workplace/operator conditions (151 references), with the physical environment (72.2%) being cited as the major causal code under the tier. Management decision showed a major contribution (74.1%) to mishap under the causal codes. Most cases were attributed to mistake error (57.4%) followed by the contravention (51.1%) of set rules and procedures with the operator's act tiers. Inadequate work standards (27.8%) and failure to ensure competency (24.1%) under the operational process and leadership flaw causal codes, respectively, were identified as the most cited nanocode. Management decision is critical in a mishap and should be given much attention in developing accident prevention strategies. The study has demonstrated that HFACS-GMI is very useful and applicable for incident analysis within the mining industry and is recommended to study causal factors across the mines.

A data centered human factor analysis approach for hazardous cargo accidents in a port environment

Recently, hazardous cargo accidents such as those at Beirut and Tianjin ports have raised serious concerns over how high-risk cargoes should be handled. While the role of human error in such accidents is widely known, knowledge of how the related factors interact is less developed. To that end, the present study analyzed 352 hazardous cargo accidents occurring at ports between 1960 and 2018. A novel research framework, the Human Factors Analysis and Classification System for Port Environment Hazardous Cargo Accidents (HFACS-PEHCA) was developed for the analysis. The findings revealed that violations, limited intellect, inappropriate supervision, and an inadequate safety culture were the most prominent factors involved in hazardous cargo accidents. Correlations between these factors were established and the probabilities of five accident causation paths were calculated. The most likely way for a hazardous cargo accident to occur was via the path “Deficient safety culture → Inappropriate supervision → Limited Intellect → Violations.” The study draws on these insights to propose safety procedures to mitigate the risk of accidents at ports dealing with hazardous cargo.

Semantic Network Analysis Using Construction Accident Cases to Understand Workers' Unsafe Acts.

Unsafe acts by workers are a direct cause of accidents in the labor-intensive construction industry. Previous studies have reviewed past accidents and analyzed their causes to understand the nature of the human error involved. However, these studies focused their investigations on only a small number of construction accidents, even though a large number of them have been collected from various countries. Consequently, this study developed a semantic network analysis (SNA) model that uses approximately 60,000 construction accident cases to understand the nature of the human error that affects safety in the construction industry. A modified human factor analysis and classification system (HFACS) framework was used to classify major human error factors—that is, the causes of the accidents in each of the accident summaries in the accident case data—and an SNA analysis was conducted on all of the classified data to analyze correlations between the major factors that lead to unsafe acts. The results show that an overwhelming number of accidents occurred due to unintended acts such as perceptual errors (PERs) and skill-based errors (SBEs). Moreover, this study visualized the relationships between factors that affected unsafe acts based on actual construction accident case data, allowing for an intuitive understanding of the major keywords for each of the factors that lead to accidents.