Using system simulation to model the impact of human error in a maritime system

Review of “Just culture – Balancing safety and accountability” and “The field guide to understanding human error”

Requirements volatility (RV) is one of the key risk sources in software development and maintenance projects because of the frequent changes made to the software. Human faults and errors are major factors contributing to requirement change in software development projects. As such, predicting requirements volatility is a challenge to risk management in the software area. Previous studies only focused on certain aspects of the human error in this area. This study specifically identifies and analyses the impact of human errors on requirements gathering and requirements volatility. It proposes a model based on responses to a survey questionnaire administered to 215 participants who have experience in software requirement gathering. Exploratory factor analysis (EFA) and structural equation modelling (SEM) were used to analyse the correlation of human errors and requirement volatility. The results of the analysis confirm the correlation between human errors and RV. The results show that human actions have a higher impact on RV compared to human perception. The study provides insights into software management to understand socio-technical aspects of requirements volatility in order to control risk management. Human actions and perceptions respectively are a root cause contributing to human errors that lead to RV.

Accidents provide important learning points to prevent future mishaps. In its approach to accident prevention one technique that Shell uses is the Tripod accident analysis tree.In the past there has been a tendency in all organisations to blame accidents on the people who suffer them and to see unsafe acts as arising from the stupidity, carelessness or recklessness of particular individuals. Accident investigations frequently cite 'human error' as the cause and many people still see such errors as the beginning and end of the accident sequence. The three more tangible components of such accident investigations are unsafe acts, failures of defences and the accidents themselves. Such unsafe acts or 'active failures' are often seen as the most important part of the accident story. In reality the story has its beginnings much earlier in areas such as Design, Procedures, Training or Maintenance Management, areas known as 'latent failures',To better understand the underlying causes of accidents, in 1988 Shell commissioned a behavioural research program called Tripod. Tripod does not address unsafe acts and hazards directly, but places them in context in a model of an accident which seeks to define the underlying organisational factors that provoke unsafe acts, and that allow errors to turn into accidents. The research has established that latent failures may be categorised into a small number of disruptive processes. These latent failures are incorporated into an accident diagram in the form of a 'tree', and improve our understanding of the causes of accidents, and direct our recommendations for improvement into areas that are likely to be the most effective.

As part of an overall effort to upgrade and streamline methodologies for safety analyses of nonreactor nuclear facilities at the Savannah River Site (SRS), a human error data base has been developed and is presented in this report. The data base fulfills several needs of risk analysts supporting safety analysis report (SAR) development. First, it provides a single source for probabilities or rates for a wide variety of human errors associated with the SRS nonreactor nuclear facilities. Second, it provides a documented basis for human error probabilities or rates. And finally, it provides actual SRS-specific human error data to support many of the error probabilities or rates. Use of a single, documented reference source for human errors, supported by SRS-specific human error data, will improve the consistency and accuracy of human error modeling by SRS risk analysts. It is envisioned that SRS risk analysts will use this report as both a guide to identifying the types of human errors that may need to be included in risk models such as fault and event trees, and as a source for human error probabilities or rates. For each human error in this report, ffime different mean probabilities or rates are presented to cover a wide range of conditions and influencing factors. The ask analysts must decide which mean value is most appropriate for each particular application. If other types of human errors are needed for the risk models, the analyst must use other sources. Finally, if human enors are dominant in the quantified risk models (based on the values obtained fmm this report), then it may be appropriate to perform detailed human reliability analyses (HRAS) for the dominant events. This document does not provide guidance for such refined HRAS; in such cases experienced human reliability analysts should be involved.

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.

Received: May 2013, Accepted: December 2013 Background: Human error is the most important cause of occupational and non-occupational accidents. Because, it seems necessary to identify, predict and analyze human errors, and also offer appropriate control strategies to reduce errors which cause adverse consequences, the present study was carried out with the aim of identifying human errors while operating meat grinder and offer suggestions in order to reduce human errors in this human-machine system. Materials and Methods: This is a descriptive study. In this ergonomic study the “Task Analysis for Error Identification (TAFEI)” technique was used in order to identify human errors while operating a meat grinder machine. According to this technique, firstly, tasks of human side of the interaction were described by Hierarchical Task Analysis (HTA) and then the State-Space Diagrams (SSDs) were drawn. Finally, after forming the TAFEI diagrams, the transition matrix table was prepared in order to identify human errors. Results: After completing all the steps of TAFEI technique, the transition matrix table was formed. Results showed 49 illegal transition states; therefore, 49 human errors were identified and described while operating the meat grinder machine. Conclusion: The results of this study showed how and under which conditions may meat grinder users do error in the human-machine interaction. In this regard, possible human errors resulting from non-ergonomic design of Iranian meat grinder machine were identified.

An extensive risk assessment of oil transportation in Prince William Sound, Alaska was finalized in 1996 that identified drifting icebergs, from Columbia Glacier, as one of the most significant oil spill risks remaining to be addressed. The Prince William Sound Regional Citizens’ Advisory Council (PWS RCAC) was a major participant in this risk analysis. As part of the groundwork for the ice detection project, PWS RCAC has also sponsered extensive studies of Columbia Glacier calving and drift patterns, iceberg size and distribution. A collaborative project, called the ice detection project, was developed by a multi stakeholder working group and provides an opportunity for an immediate and long-term solution using existing technology. One objective of the project is to verify the efficiency, effectiveness and reliability of existing radar technologies to provide mariners and the United States Coast Guard with real time information regarding ice conditions. A secondary objective is to promote the research and development through field testing of new and emerging technologies to determine the possible enhancement of conventional radar. In addition to PWS RCAC, stakeholders responsible for spearheading this project are: Alyeska Pipeline Service Company, Alaska Department of Environmental Conservation, Oil Spill Recovery Institute, United States Coast Guard, Prince William Sound Community College and National Oceanic and Atmospheric Administration. Each of the seven participants brings expertise and backing from the stakeholder they represent. The site chosen for the ice detection radar project is Reef Island (illustration 1), located adjacent to Bligh Reef, Prince William Sound. This location is ideal because of its proximity to Columbia Glacier, the source of the icebergs, as well as providing an unobstructed view of the shipping lanes. A fifty foot tower was installed at the site during the fall of 2001 and a conventional radar system is currently being configured for installation. The expectation is that the system will be up and running by July of 2002, giving real time information on ice in the tanker lanes to mariners in Prince William Sound. A second field test of an UHF radar prototype is planned for the summer of 2002. Field testing and ground truthing of the radar system is scheduled for the next five years.

2 - Human reliability assessment

The number of causalities, injuries, loss of life and properties accompanied with marine pollutions per year recorded from marine accident is quite alarming in Niger Delta. A very large proportion of these accidents have been attributed to human errors. This study elucidates the main human factors or errors that have contributed to the incidence of marine accident in the Niger Delta navigational waters. A descriptive and quantitative survey was carried out and data collected using well-structured questionnaires distributed to 400 respondents, out of which 287(71.8%) was retrieved. An appropriate statistical tool (SPSS Softwares-2010 Chicago version) such as multiple logistic regressions using The Adjusted Odd Ratio (AOR) was employed for predictors/independent variables at 95% confidence interval. The socio- demographic results showed that majority (78.4%) of the respondents are of sound mind with over 10 years of working experience. The P-value is 0.02 for the multiple logistic regressions; there exist a statistically significant relationship within the predictors (independent variables) and incidence of marine accident in the Niger Delta. The constituents of Human errors do not contribute equally towards marine accident incidence. Six(6) out of the ten(10) identified human related factors/error namely crew fatigue, unruly behaviour, unsafe vessel speed, commercial pressure from management, lack of maintenance culture, organisational structure/inadequate safety culture were frequent human causes of marine accident and are significantly related to safety performance. The highest ranking human error/factor is unsafe vessel speed of about 3.01 times higher odds of occurrence of marine accident while the least human factor is the influence of drugs and alcoholism with just 1.01 times higher odds. It is advisable for mariners to have positive and robust safety culture in place. Employers should recognise, encourage and reward Staff with impressive s

Risk and uncertainty: two different entities with different management activities or part of the same spectrum?

In this paper a new type of GIS system for maritime navigation safety is proposed. The system takes advantage of the properties of the kinetic Voronoi diagram, and uses the quad-edge data structure for the real-time maintenance of the spatial relationships of ships and other navigational objects. The locations of ships are updated using a standard onboard AIS transponder, and moving-points Voronoi algorithms. The spatial relationships are used for collision detection and avoidance. The system is aimed at tackling the main cause of marine accidents - human errors - by providing navigational aid and decision support to navigators. The brief introduction into static and kinematic Voronoi methods is given and is followed by the discussion of different types of GIS systems for marine purposes. The state-of-the-art in maritime safety and its major developments are also covered.

The contribution of human error related to occupational accident among traditional divers

The main cause of traffic accidents is drivers' human errors such as cognitive, judgment, and execution errors. To mitigate drivers' human errors, research on the measurement and quantification of driver workload as well as the development of smart vehicles is needed. Drivers' behavior while driving includes driving straight, turning left or right, U-turns, rapid acceleration, rapid deceleration, and changing lanes. To measure and quantify a driving workload, both the subjective workload and the behavior workload caused by varied driving behaviors should be taken into account on the basis of understanding the visual, auditory, cognitive, and psychomotor characteristics of the driving workload. In this paper, we analyze electroencephalogram (EEG) data collected through an urban road driving test. To overcome large deviations of EEG values among drivers, we used EEG variation rates instead of raw EEG values. We extracted five kinds of behavior sections from the data: left-turn section, right-turn section, rapid-acceleration section, rapid-deceleration section, and lane-change section. We then selected a reference section for each of these behavior sections and compared EEG values from the behavior sections with those from the reference sections to calculate the EEG variation rates, after which we made the statistical analysis. The analysis results of this study are being used to explain the cognitive characteristics of a driving workload caused by drivers' behavior in the vehicle information system, which will provide information for safe driving by taking into account the driving workload.

In this paper a new type of GIS system for maritime navigation safety is proposed. The system takes advantage of the properties of the kinetic Voronoi diagram (VD), and uses the quad-edge data structure for the maintenance of the spatial relationships of ships and other navigational objects. The locations of ships are updated in real-time using a standard onboard transponder, and moving-points VD algorithms. These spatial relationships are used for collision detection and avoidance. The system is aimed at tackling the main cause of marine accidents - human errors - by providing navigational aid and decision support to mariners. A brief introduction into static and kinematic Voronoi methods is given and is followed by a discussion of different types of GIS systems for marine purposes. The state-of-the-art in maritime safety and its major developments are also covered.

An empirical study on the basic human error probabilities for NPP advanced main control room operation using soft control