Human and organisational factors in maritime accidents: Analysis of collisions at sea using the HFACS

Over the last decade, the shipping industry has implemented a number of measures aimed at improving its safety level (such as new regulations or new forms of team training). Despite this evolution, shipping accidents, and particularly collisions, remain a major concern. This paper presents a modified version of the Human Factors Analysis and Classification System, which has been adapted to the maritime context and used to analyse human and organisational factors in collisions reported by the Marine Accident and Investigation Branch (UK) and the Transportation Safety Board (Canada).     

Operator error and system deficiencies: Analysis of 508 mining incidents and accidents from Queensland, Australia using HFACS

Historically, mining has been viewed as an inherently high-risk industry. Nevertheless, the introduction of new technology and a heightened concern for safety has yielded marked reductions in accident and injury rates over the last several decades. In an effort to further reduce these rates, the human factors associated with incidents/accidents needs to be addressed. A modified version of the Human Factors Analysis and Classification System was used to analyze incident and accident cases from across the state of Queensland to identify human factor trends and system deficiencies within mining. An analysis of the data revealed that skill-based errors were the most common unsafe act and showed no significant differences across mine types. However, decision errors did vary across mine types. Findings for unsafe acts were consistent across the time period examined. By illuminating human causal factors in a systematic fashion, this study has provided mine safety professionals the information necessary to reduce mine incidents/accidents further.     

Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents

Minor safety incidents on the railway cause disruption, and may be indicators of more serious safety risks. The following paper aimed to gain an understanding of the relationship between active and latent factors, and particular causal paths for these types of incidents by using the Human Factors Analysis and Classification System (HFACS) to examine rail industry incident reports investigating such events. 78 reports across 5 types of incident were reviewed by two authors and cross-referenced for interrater reliability using the index of concordance. The results indicate that the reports were strongly focused on active failures, particularly those associated with work-related distraction and environmental factors. Few latent factors were presented in the reports. Different causal pathways emerged for memory failures for events such a failure to call at stations, and attentional failures which were more often associated with signals passed at danger. The study highlights a need for the rail industry to look more closely at latent factors at the supervisory and organisational levels when investigating minor safety of the line incidents. The results also strongly suggest the importance of a new factor – operational environment – that captures unexpected and non-routine operating conditions which have a risk of distracting the driver. Finally, the study provides further demonstration of the utility of HFACS to the rail industry, and of the usefulness of the index of concordance measure of interrater reliability.     

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.     

Application of a human error framework to conduct train accident/incident investigations

Accident/incident investigations are an important qualitative approach to understanding and managing transportation safety. To better understand potential safety implications of recently introduced remote control locomotive (RCL) operations in railroad yard switching, researchers investigated six railroad accidents/incidents. To conduct the investigations, researchers first modified the human factors analysis and classification system (HFACS) to optimize its applicability to the railroad industry (HFACS-RR) and then developed accident/incident data collection and analysis tools based on HFACS-RR. A total of 36 probable contributing factors were identified among the six accidents/incidents investigated. Each accident/incident was associated with multiple contributing factors, and, for each accident/incident, active failures and latent conditions were identified. The application of HFACS-RR and a theoretically driven approach to investigating accidents/incidents involving human error ensured that all levels of the system were considered during data collection and analysis phases of the investigation and that investigations were systematic and thorough. Future work is underway to develop a handheld software tool that incorporates these data collection and analysis tools.     

Understanding the human factors contribution to railway accidents and incidents in Australia

Forty rail safety investigation reports were reviewed and a theoretical framework (the Human Factors Analysis and Classification System; HFACS) adopted as a means of identifying errors associated with rail accidents/incidents in Australia. Overall, HFACS proved useful in categorising errors from existing investigation reports and in capturing the full range of relevant rail human factors data. It was revealed that nearly half the incidents resulted from an equipment failure, most of these the product of inadequate maintenance or monitoring programs. In the remaining cases, slips of attention (i.e. skilled-based errors), associated with decreased alertness and physical fatigue, were the most common unsafe acts leading to accidents and incidents. Inadequate equipment design (e.g. driver safety systems) was frequently identified as an organisational influence and possibly contributed to the relatively large number of incidents/accidents resulting from attention failures. Nearly all incidents were associated with at least one organisational influence, suggesting that improvements to resource management, organisational climate and organisational processes are critical for Australian accident and incident reduction. Future work will aim to modify HFACS to generate a rail-specific framework for future error identification, accident analysis and accident investigation.     

A systems approach to accident causation in mining: An application of the HFACS method

This project aimed to provide a greater understanding of the systemic factors involved in mining accidents, and to examine those organisational and supervisory failures that are predictive of sub-standard performance at operator level. A sample of 263 significant mining incidents in Australia across 2007–2008 were analysed using the Human Factors Analysis and Classification System (HFACS). Two human factors specialists independently undertook the analysis. Incidents occurred more frequently in operations concerning the use of surface mobile equipment (38%) and working at heights (21%), however injury was more frequently associated with electrical operations and vehicles and machinery. Several HFACS categories appeared frequently: skill-based errors (64%) and violations (57%), issues with the physical environment (56%), and organisational processes (65%). Focussing on the overall system, several factors were found to predict the presence of failures in other parts of the system, including planned inappropriate operations and team resource management; inadequate supervision and team resource management; and organisational climate and inadequate supervision. It is recommended that these associations deserve greater attention in future attempts to develop accident countermeasures, although other significant associations should not be ignored. In accordance with findings from previous HFACS-based analyses of aviation and medical incidents, efforts to reduce the frequency of unsafe acts or operations should be directed to a few critical HFACS categories at the higher levels: organisational climate, planned inadequate operations, and inadequate supervision. While remedial strategies are proposed it is important that future efforts evaluate the utility of the measures proposed in studies of system safety.     

Definition of a short-cut methodology for assessing earthquake-related Na-Tech risk

Na-Tech (Natural and Technological) refers to industrial accidents triggered by natural events such as storms, earthquakes, flooding, and lightning. Herein, a qualitative methodology for the initial assessment of earthquake Na-Tech risk has been developed as a screening tool to identify which situations require a much more expensive Quantitative Risk Analysis (QRA). The proposed methodology, through suitable Key Hazard Indicators (KHIs), identifies the Na-Tech risk level associated with a given situation (i.e., a process plant located in a given territory), using the Analytical Hierarchy Process as a multi-criteria decision tool for the evaluation of such KHIs. The developed methodology was validated by comparing its computational results with QRA results that involved Na-Tech events previously presented in literature.     

Integrating human factors into process hazard analysis

A comprehensive process hazard analysis (PHA) needs to address human factors. This paper describes an approach that systematically identifies human error in process design and the human factors that influence its production and propagation. It is deductive in nature and therefore considers human error as a top event. The combinations of different factors that may lead to this top event are analysed. It is qualitative in nature and is used in combination with other PHA methods. The method has an advantage because it does not look at the operator error as the sole contributor to the human failure within a system but a combination of all underlying factors.     

Promotion Routes for Women Engineering Managers

ABSTRACT

Total quality management (TQM) has been increasingly accepted as a management philosophy. Criteria such as those contained in the Malcolm Baldrige National Quality Award (MBNQA) often guide TQM implementation. However, reports indicate that fully assessing TQM efforts is difficult and hiring experts to help with this task is expensive. Using survey instruments is an attractive alternative to full-scale assessment. Despite the availability of many papers covering organizational performance assessment, few instruments specifically address TQM evaluation. Furthermore, none of the TQM assessment instruments has been subjected to rigorous validation; thus, only modest confidence can be placed in results obtained from their use. This article proposes a quick and cost-effective instrument that is a simple questionnaire designed to assist organizations in conducting self-evaluations of their TQM programs. The proposed instrument is based on the seven categories of the MBNQA criteria and was developed in cooperation with the Excellence in Missouri Foundation, which administers the Missouri Quality Award (MQA). This article highlights how questionnaire results can be used to guide further TQM implementation efforts.     

Associations between task,training and social environmental factors and error types involved in rail incidents and accidents

Rail accidents can be understood in terms of the systemic and individual contributions to their causation. The current study was undertaken to determine whether errors and violations are more often associated with different local and organisational factors that contribute to rail accidents. The Contributing Factors Framework (CFF), a tool developed for the collection and codification of data regarding rail accidents and incidents, was applied to a sample of investigation reports. In addition, a more detailed categorisation of errors was undertaken. Ninety-six investigation reports into Australian accidents and incidents occurring between 1999 and 2008 were analysed. Each report was coded independently by two experienced coders. Task demand factors were significantly more often associated with skill-based errors, knowledge and training deficiencies significantly associated with mistakes, and violations significantly linked to social environmental factors.     

Determining the Critical Factors of Radical Innovation Using an Integrated Model of Fuzzy Analytic Hierarchy Process-Fuzzy Kano With a Case Study in Mobarakeh Steel Company

Understanding which radical innovation factor is of higher priority enables engineering managers to allocate the appropriate resources to innovation processes. The aim of this article is to develop a systematic approach for determining the most necessary factors affecting radical innovation. For this purpose, a model was proposed with the following three steps: (i) a comprehensive set of factors was extracted from the literature and customized for a case study company; (ii) quality attributes of the Kano model were applied and weighted as attributes for a fuzzy analytic hierarchy process; and (iii) the factors derived from the second step were weighted using a fuzzy Kano approach and then the critical or must-be factors were identified and weighted. All three steps have been examined at Mobarakeh Steel Company. Findings indicate that factors such as leadership and knowledge management are among the critical factors of radical innovation and must be improved to positively impact radical innovation. Prioritizing radical innovation factors enables engineering managers to allocate the appropriate resources to innovation processes.     

A Benefit/Cost/Deficit (BCD) model for learning from human errors

This paper proposes an original model for interpreting human errors, mainly violations, in terms of benefits, costs and potential deficits. This BCD model is then used as an input framework to learn from human errors, and two systems based on this model are developed: a case-based reasoning system and an artificial neural network system. These systems are used to predict a specific human car driving violation: not respecting the priority-to-the-right rule, which is a decision to remove a barrier. Both prediction systems learn from previous violation occurrences, using the BCD model and four criteria: safety, for identifying the deficit or the danger; and opportunity for action, driver comfort, and time spent; for identifying the benefits or the costs. The application of learning systems to predict car driving violations gives a rate over 80% of correct prediction after 10 iterations. These results are validated for the non-respect of priority-to-the-right rule.     

Review of advances in human reliability analysis of errors of commission, Part 1: EOC identification

In close connection with examples relevant to contemporary probabilistic safety assessment (PSA), a review of advances in human reliability analysis (HRA) of post-initiator errors of commission (EOCs), i.e. inappropriate actions under abnormal operating conditions, has been carried out. The review comprises both EOC identification (part 1) and quantification (part 2); part 1 is presented in this article. Emerging HRA methods addressing the problem of EOC identification are: A Technique for Human Event Analysis (ATHEANA), the EOC HRA method developed by Gesellschaft für Anlagen- und Reaktorsicherheit (GRS), the Misdiagnosis Tree Analysis (MDTA) method, and the Commission Errors Search and Assessment (CESA) method. Most of the EOCs referred to in predictive studies comprise the stop of running or the inhibition of anticipated functions; a few comprise the start of a function. The CESA search scheme—which proceeds from possible operator actions to the affected systems to scenarios and uses procedures and importance measures as key sources of input information—provides a formalized way for identifying relatively important scenarios with EOC opportunities. In the implementation however, attention should be paid regarding EOCs associated with familiar but non-procedural actions and EOCs leading to failures of manually initiated safety functions.     

Review of advances in human reliability analysis of errors of commission—Part 2: EOC quantification

In close connection with examples relevant to contemporary probabilistic safety assessment (PSA), a review of advances in human reliability analysis (HRA) of post-initiator errors of commission (EOCs), i.e. inappropriate actions under abnormal operating conditions, has been carried out. The review comprises both EOC identification (part 1) and quantification (part 2); part 2 is presented in this article. Emerging HRA methods in this field are: ATHEANA, MERMOS, the EOC HRA method developed by Gesellschaft für Anlagen- und Reaktorsicherheit (GRS), the MDTA method and CREAM. The essential advanced features are on the conceptual side, especially to envisage the modeling of multiple contexts for an EOC to be quantified (ATHEANA, MERMOS and MDTA), in order to explicitly address adverse conditions. There is promising progress in providing systematic guidance to better account for cognitive demands and tendencies (GRS, CREAM), and EOC recovery (MDTA). Problematic issues are associated with the implementation of multiple context modeling and the assessment of context-specific error probabilities. Approaches for task or error opportunity scaling (CREAM, GRS) and the concept of reference cases (ATHEANA outlook) provide promising orientations for achieving progress towards data-based quantification. Further development work is needed and should be carried out in close connection with large-scale applications of existing approaches.     

A new method for estimating human error probabilities: AHP–SLIM

Accidents in industrial settings are mainly a result of human errors (HEs); relevant fields that consider HEs have recently increased in number. For this reason, numerous methods to quantitatively estimate what is known as the HE probability (HEP) have been developed. However, it is difficult to obtain empirical data, which forces a heavy reliance on the judgment of experts in the field. During the process of judgments by experts, subjectivity plays an important role, causing difficulties in assuring consistency. To overcome this problem and to obtain a more accurate estimation, this study suggests a new and simple method. This method is referred to as AHP–SLIM, a type of HEP estimation using an analytic hierarchy process (AHP), which quantifies the subjective judgment and confirms the consistency of collected data. This new method also uses the process of expert judgment within the success likelihood index method (SLIM).