Combining task analysis and fault tree analysis for accident and incident analysis: A case study from Bulgaria

Application of Integrated Human Error Identification Techniques for Chemicals Cylinder Change Task

Operating Oil & Gas facilities come with inherent risks due to the hazardous nature of the processes and products involved. The potential for incidents is ever-present, and they can arise from various factors such as equipment failure, process upsets, human errors, deviations from established procedures, among others. In this paper, an analysis of data from incidents recorded in Saudi Aramco over the last 10 years, captured from Loss Prevention (LP) Safety Bulletins is discussed. Three categories of incidents are recognized within the analyzed LP data: Safety, Process Safety and Mechanical Integrity, were humans, processes and equipment failures, respectively, are identified as part of the main causal factors. A comparison with some global figures regarding incidents and its causes is also discussed, where it was observed some similarities in terms of the causal factors. The main objective of this review is to evaluate how and to what extent the ‘human factor’ is involved in the occurrence of incidents and accidents, based on the data collected and analyzed. This paper also includes information about the “12 Common Causes of Human Factors Errors”, known as the ‘Dirty Dozen’, a well-known concept in the aviation industry where it's claimed that 80% of maintenance mistakes involve Human Factors.

The development of a nuclear chemical plant human reliability management approach: HRMS and JHEDI

Book review

Quantification of performance shaping factors (PSFs)’ weightings for human reliability analysis (HRA) of low power and shutdown (LPSD) operations

Dear Editor-in-ChiefNurses spend much more time dealing with patients compared with other healthcare personnel. This group has a significant role in identification of threatening complexities of patients, removing medical errors and in keeping patients safe, on the other hand, a considerable amount of human errors is committed by nurses (1). Therefore, nurses have to face with many occupational risks compared with other jobs and may suits relating to occupational negligence (1). Human errors associated with nursing duties; in particular, medication error has been identified as a central concern for the nursing profession (1). As the result of such events, patient is hurt unwantedly and staff suffers a great deal of damage. These events often result from the change of systemic specifications in duties, group, work environment, and organization (3).The present study was a cross sectional research aimed at comparing two human error identification and assessment methods, SPAR-H and CREAM, in nursing practice at a hospital in Iran. We investigated 31 duties according to job description announced by Iran Nursing Organization and professors of nursing.In the first step, we analyzed occupational duties using Hierarchical Task Analysis (HTA) method (4). Next, different tasks are executed for each sub task. After completing CREAM and SPAR-H worksheets, we first grouped the worksheets for each duty and analyzed total error probability and error risk using SPSS-16 (Chicago, IL, USA). In order to compare the two techniques based on multivariable decision making, we used analytical hierarchy process (5).The findings of demonstrated that, the estimated human error probability by SPAR-H was higher than those estimated by CREAM, but the most critical task, taking initial measures for cardiopulmonary resuscitation until the arrival of resuscitation team, was the same in both techniques. Moreover, it was determined that both techniques have similar results in identifying and assessing critical tasks with high probability of human error (Table 1). Furthermore, there was more difference between these two techniques in ranking potential errors with low probabilities; for instance, preparing bed before arrival of patient have the rank of 27 when we used SPAR-H technique 27, while its rank using CREAM technique was determined at 10 (Table 1).Based on previous studies in this field and also eliciting domain expert knowledge, seven criteria were selected for comparing two techniques with each other (6). …

Achieving reliable instrument reprocessing requires finding the right balance among cost, productivity, and safety. However, there have been few attempts to comprehensively examine sterile processing department (SPD) work systems. We considered an SPD as an example of a socio-technical system - where people, tools, technologies, the work environment, and the organization mutually interact - and applied work systems analysis (WSA) to provide a framework for future intervention and improvement. The study was conducted at two SPD facilities at a 700-bed academic medical center servicing 56 onsite clinics, 31 operating rooms (ORs), and nine ambulatory centers. Process maps, task analyses, abstraction hierarchies, and variance matrices were developed through direct observations of reprocessing work and staff interviews and iteratively refined based on feedback from an expert group composed of eight staff from SPD, infection control, performance improvement, quality and safety, and perioperative services. Performance sampling conducted focused on specific challenges observed, interruptions during case cart preparation, and analysis of tray defect data from administrative databases. Across five main sterilization tasks (prepare load, perform double-checks, run sterilizers, place trays in cooling, and test the biological indicator), variance analysis identified 16 failures created by 21 performance shaping factors (PSFs), leading to nine different outcome variations. Case cart preparation involved three main tasks: storing trays, picking cases, and prioritizing trays. Variance analysis for case cart preparation identified 11 different failures, 16 different PSFs, and seven different outcomes. Approximately 1% of cases had a tray with a sterilization or case cart preparation defect and 13.5 interruptions per hour were noted during case cart preparation. While highly dependent upon the individual skills of the sterile processing technicians, making the sterilization process less complex and more visible, managing interruptions during case cart preparation, improving communication with the OR, and improving workspace and technology design could enhance performance in instrument reprocessing.

Task analysis identifies the specific tasks needed to accomplish human actions, and the information, control and task support required to complete those tasks. In the control room design, the functions allocated to personnel shall be performed analysis, and it is identified that the specific task can be accomplished by the personnel. Task analysis can provide one of the bases for the human system interface design decisions; match human performance requirements with human capabilities; provide input to procedure development and provide input to staffing, training, and communications requirements of the plant. The method about task analysis and the screening methodology used to select the tasks for analysis are very important. Usually, the scope of task analysis is divided into two complementary activities: function-based task analysis and traditional task analysis, or operational sequence analysis. Function-based task analysis is the functional decomposition (goal-means analysis) for normal power operations. The traditional task analysis, or operational sequence analysis is developed for a representative set of operational and maintenance tasks. The operational sequence analysis include OSA-1 and OSA-2.The task analysis considers alerts, such as alarms and warnings, decision-making, response, teamwork and communication, workload task support, workplace factors, situational and performance shaping factors and hazard identification. The paper also describes the selected basis of selecting the operational sequence to be analyzed in the operational sequence analysis. The method about task analysis and the screening methodology used to select the tasks for analysis are very important. The objective of task analysis review is to verify that the applicant undertook analyses identifying the specific tasks needed to accomplish personnel functions, and also the alarms, information, control- and task-support required to complete those duties. The paper mainly describes not only the above content, but also the reviewing work about task analysis and questions found in reviewing. In this article, there are some examples about the function-based task analysis. At last, the verification for task analysis is addressed simply.

The transportation of steel scrap cargoes in global trade has been increasing over the years. According to the researches, the demand of steel scrap materials will be more than double in the late 21st century whereby forecasting of steel scrap production and so steel scrap cargo operations will also evenly rise in ports. Increasing steel scrap cargo operations will bring along several undesirable accidents and injuries. Investigations show that the most paramount reason for these incidents or accidents related with steel scrap cargo operations in maritime sector is human error. In this sense, it is aimed to identify human error probabilities (HEPs) for steel scrap cargo operation that is performed frequently in maritime sector, especially in bulk carrier vessels. In this study, Success Likelihood Index Method (SLIM), which is one of the methods for Human Reliability Analysis (HRA), is used to determine HEPs in steel scrap cargo operations due to the limited data availability on this topic. Accordingly, the most common error modes that are determined via detailed literature review are ranked in accordance with HEP values. It is put forward which and how much error mode is affected from Performance Shaping Factor (PSF) such as education, supervision, environmental condition, equipment and tool condition, and experience mostly. According to the analysis results, “the falling piece of steel scrap on the deck during steel scrap loading or unloading operation” has the most probability occurrence. Consequently, it is understood that training and experience factors are critically important for preventing errors in steel scrap cargo operation in overall view. On the other hand, environmental condition, supervision, and equipment and tool condition factors include the prominent level of significance to bring down the probability of accruing of some specific errors. Accordingly, the proposed approach not only make a theory-based contribution to the maritime literature, but also to active contribution to the sector involving P&I Clubs, shipping companies, and classification societies toward focusing point for minimizing the accidents about steel scrap cargo operations.

Risk analyses require proper consideration and quantification of the interaction between humans, organization, and technology in high-hazard industries. Quantitative human reliability analysis approaches require the estimation of human error probabilities (HEPs), often obtained from human performance data on different tasks in specific contexts (also known as performance shaping factors (PSFs)). Data on human errors are often collected from simulated scenarios, near-misses report systems, and experts with operational knowledge. However, these techniques usually miss the realistic context where human errors occur. The present research proposes a realistic and innovative approach for estimating HEPs using data from major accident investigation reports. The approach is based on Bayesian Networks used to model the relationship between performance shaping factors and human errors. The proposed methodology allows minimizing the expert judgment of HEPs, by using a strategy that is able to accommodate the possibility of having no information to represent some conditional dependencies within some variables. Therefore, the approach increases the transparency about the uncertainties of the human error probability estimations. The approach also allows identifying the most influential performance shaping factors, supporting assessors to recommend improvements or extra controls in risk assessments. Formal verification and validation processes are also presented.

As manufacturers and technologies become more complicated, manufacturing errors such as machine failure and human error have also been considered more over the past. Since machines and humans are not error-proof, managing the machines and human errors is a significant challenge in manufacturing systems. There are numerous methods for investigating human errors, fatigue, and reliability that categorized under Human Reliability Analysis (HRA) methods. HRA methods use some qualitative factors named Performance Shaping Factors (PSFs) to estimate Human Error Probability (HEP). Since the PSFs can be considered as the acceleration factors in Accelerated Life Test (ALT). We developed a method for Accelerated Human Fatigue Test (AHFT) to calculate human fatigue, according to fatigue rate and other effective factors. The proposed method reduces the time and cost of human fatigue calculation. AHFT first extracts the important factors affecting human fatigue using Principal Component Analysis (PCA) and then uses the accelerated test to calculate the effect of PSFs on human fatigue. The proposed method has been applied to a real case, and the provided results show that human fatigue can be calculated more effectively using the proposed method.

Learning from the occurrence of accidents and incidents is essential to improving safety in organizations and their operational processes. It is a process of increasing the capacity for effective organizational action through the construction of knowledge and its embodiment in the practices of the organization. This paper examines how accidents and incidents are analyzed in companies handling high‐hazard activities in the province of Nordrhein‐Westfalia, Germany, and what changes are made to support effective organizational learning. The results suggest that there is limited communication flow between the stakeholders – design, operations, production and safety management – who are responsible for safe system operation. The consequence of which appears to be a reliance of these organizations on local changes to practices, rather than change in either equipment design or workplace layout.

Robotic systems (RSs) are often used for performing critical tasks with little or no human intervention. Such RSs must satisfy certain dependability requirements including reliability, availability, security and safety. In this paper, we focus on the safety aspect and propose a methodology and associated framework for safety assessment of RSs in the early phases of development. The methodology relies upon model-driven engineering approach and describes a preliminary safety assessment of safety-critical RSs using fault tree (FT) analysis (FTA). The framework supports a domain specific language for RSs called RobotML and includes facilities (i) to automatically generate or manually construct FTs and perform both qualitative and quantitative FTA, (ii) to make semantic connections with formal verification and FTA tools, (iii) to represent FTA results in the RobotML modeling environment. In the case study, we illustrate the proposed methodology and framework by considering a mobile robot developed in the scope of the Proteus project.

A hybrid method combining various Reliability (Fault Tree Analysis -FTA-, Reliability Block Diagrams -RBD), Multi Criteria Decision Making (Analytic Hierarchy Process -AHP), High Reliability Organisations (HROs) and Learning from Failures (LFF) framework tools, has been applied to a long-established pattern of small to medium chronic failures in a large Oil & Gas organisation. Through the Reliability techniques FTA/RBD, the root causes of the chronic failure pattern were identified, categorised and their interconnections established. The obtained root causes were then used as criteria and with the five principles of mindful organising as alternatives in constructing the AHP model. Finally, an attempt to extract useful generic lessons from the failure causes identified by applying the learning from failure (LFF) principles was made, in order to obtain guidelines for updating existent company routines. Despite a few shortcomings, some inherent to the tools themselves and some related to the method intent, reasonable results were obtained which appear to demonstrate the applicability of the approach. Abbreviations: AHP: analytical hierarchy process; CAPEX: capital expenditure; CBM: condition based maintenance; CTA: crisis tree analysis; CMMS: computerised maintenance management system; DMG: decision making grid; E&P: exploration and production; FTA: fault tree analysis; HRO: high reliability organisation; LFF: learning from failure; MCDM: multi criteria decision making; OPEX: operational expenditure; PSF: performance shaping factors; RACI: responsible accountable consulted informed; RBD: reliability block diagram; RBM: risk based maintenance.

In the civil aviation sector, human factors is the primary cause of many safety incidents. Aircraft flying, maintenance, and operations are the major tasks that are heavily dependent on professionals; thus, they are subject to human error probability. Human reliability analysis (HRA), which can evaluate human state and managing risk, has been developed over the years to identify, predict, and reduce human failures throughout aircraft operating procedures. Different generations of HRA tools have been developed to quantify the risks that are associated with safety accidents, including such as the Human Error Assessment and Reduction Technique, Technique for Human Error Prediction, Standardized Plant Analysis Risk Human Reliability Analysis, Cognitive Reliability and Error Analysis Methods, and Bayesian Network (BN). However, little is known about how these approaches are applied in aviation safety. This review aimed to systematically examine the current status of research on HRA in aviation accidents. A total of 13 studies were included and encompassed the studies of the first, second, and third generalizations of HRA alone or in combination with other methods (e.g., Improved Analytics Hierarchy Process, Functional Resonance Analysis Methods, Human Factor Analysis and Classification System, and Fault Tree Analysis). Results revealed that the third-generation HRA with BN was frequently used, showing great application potential for flight safety risk prevention and reduction. In the future, testing other third-generation HRA models driven by data in the field of airworthiness is necessary.