Performance Shaping Factors Research Articles

Human Factor Interrelationships to Improve Worker Reliability: Implementation of MCDM in the Agri-Food Sector

Performance Shaping Factors (PSFs) are contextual, individual, and cognitive factors used in Human Reliability Analysis (HRA) to quantify the worker contribution to errors when performing a generic task. Although the empirical evidence demonstrates the existence of PSF interrelationships, the majority of HRA methods assume their independence. As a consequence, the resulting Human Error Probability (HEP) might be over- or underestimated. To deal with this issue, only a few qualitative guidelines or statistical-based approaches have been proposed so far. While the former are not well structured, the latter require a high computational effort and a proper number of input data. Therefore, the present paper provides an alternative approach to deal with the PSFs interaction issue to facilitate the identification of the most influential human factors on which to take corrective actions. To this purpose, Multi Criteria Decision Making (MCDM) methods may represent a structured, effortless, and easily replicable framework. Owing to their ability to deal with the interdependence of decision factors, DEMATEL and ANP are hence considered and afterwards compared, highlighting their strengths and weaknesses. Both methods are implemented in an agri-food company which produces pistachios in Southern Italy.

Mental Overload Assessment Method Considering the Effects of Performance Shaping Factors

The assessment of mental overload is essential as mental overload has been proved to be a critical cause of many accidents. Mental overload is affected by various performance shaping factors (PSFs), and harsh PSFs will either increase the task demand or decrease the overload threshold, which is rarely considered in the current mental overload assessment method. This research proposes a VACP-based mental overload assessment model which considers the effects of PSFs. In the VACP model, mental overload can be identified when the sum of the task demand values of every unit task is greater than the given threshold. In human reliability analysis, PSFs are mainly used as weighting factors to modify basic human error probability. In our research, by virtue of the quantitative relationship between human error and mental workload, the weighting factors of PSFs are converted to modify the task demand values and threshold for VACP activities. Furthermore, Bayesian Network (BN) is used to model the influence of PSFs and to calculate the probability of mental overload. The proposed method is applied to an accident involving a helicopter crash that occurred in Maryland, and the results show that, in comparison with the VACP method, the proposed method can more effectively identify the state of mental overload and provide a more rational explanation of the process of the accident.

A model to evaluate the Human Error Probability in inspection tasks of a production system

Investigating the dynamics of human-machine interaction and its impacts on production performance is a key issue in the context of Industry 4.0 because the concept of the “operator 4.0”, i.e., an operator integrated into a cyber-physical system, implies the need to manage complex human-machine systems. One of the most concerned fields on this topic is human reliability analysis, as the Human Error Probability (HEP) estimation by considering different work environment aspects. To this concern, the present work's purpose consists of assessing human error's impact on a manufacturing system by considering the different Performance Shaping Factors (PSFs) that affect the HEP. To this end, an analytical model has been developed to evaluate the human error in an inspection task to be accomplished in a full-real industrial case study. The HEP was estimated as a function of PSFs, including three different dimensions (i.e., task error proneness, operator's capabilities and characteristics of the work environment in the production system). It was found that the most impactful PSF affecting HEP depends on the working environment conditions in the production system. In this regard, the model shows that assuming an equal variation in the attributes related to all dimensions, the changes in working environment conditions from a physical and psychological point of view generate the most significant reduction in HEP. Consistently with these results, the average costs for reducing HEP by improving the working environment conditions are significantly higher than the average costs incurred to reduce the HEP considering the task error proneness and operator's capabilities.

Human reliability assessment of intelligent coal mine hoist system based on Bayesian network

The human reliability of intelligent coal mine hoist operation system is affected by many factors, in order to reduce the occurrence of human error in the hoist system and improve the reliability of the system. The characteristics of phased-mission task operation of hoists is combined, the phase dependence of human cognitive errors is considered and, a new human reliability evaluation method is proposed with the help of Bayesian network (BN) model in this paper. Firstly, the phase dependence of human cognitive errors was analyzed based on the cognitive behavior model. Then the human error analysis in the hoist system was carried out, and several main performance shaping factors are selected. Secondly, BN was used to build the human reliability model of the hoist system at each stage. Finally, it is found that the phase dependence of cognitive errors has a negative impact on the human reliability of the hoist system through the case analysis. At the same time, several main performance shaping factors (PSFs)were quantitatively analyzed by using the reverse reasoning ability of BN, which proves the effectiveness of the proposed method, and provides a scientific and reasonable theoretical basis for the development of effective human error prevention measures for the operation of intelligent coal mine hoists.

A taxonomy of factors influencing worker's performance in human–robot collaboration

AbstractThe occurrence of human errors significantly affects the performance and economic results of production systems. In this context, Human Reliability Analysis (HRA) methods play a key role in assessing the reliability of a man–machine system. Several HRA methods use Performance‐Shaping Factors (PSFs), that is, all the aspects of human behaviour and environment that can affect human performance, to evaluate the Human Error Probability (HEP). However, despite the greater emphasis given by researchers to define of PSFs in recent years, the changes caused by the new enabling technologies implemented in manufacturing systems and derived from the Industry 4.0 paradigm have not yet been fully explored. Focussing on Human–Robot Collaboration (HRC) in production systems, the authors aim to define a PSF taxonomy that is useful for HEP evaluations in collaborative environments. To the best of the authors' knowledge, HRA approaches have not been investigated yet for HRC applications. The proposed taxonomy, which results from the integration of the most significant factors impacting workers' performance in HRC into the PSFs provided by an HRA method, can represent an important contribution for researchers and practitioners towards improving HRA methods and their applications in the context of Industry 4.0.

An advanced approach to the system safety in sociotechnical systems

The safety performance of sociotechnical systems (STSs) and their main components (e.g., human, organization, and technology) vary due to numerous performance shaping factors (PSFs). However, previous research mainly focused on studying limited PSFs related to human functions, while organization and technology functions have often been ignored. This paper aims to propose a systematic approach to identify PSFs, quantify their importance level and influence on the performance of complex sociotechnical systems’ functions. To this end, we first developed a holistic PSFs Taxonomy based on sociotechnical systems design and then employed novel Interval-Valued Spherical Fuzzy Sets (IVSFS) and Best Worst Method to quantify the importance of performance. We tested the proposed model’s capability on maintenance operations in the chemical process plants and compared the model with the previous research considering fourteen criteria. The findings revealed the approach's effectiveness in dealing with epistemic uncertainty, vagueness, and fuzziness in the knowledge acquisition process. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors to maintenance operations. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance.

Future needs of human reliability analysis: The interaction between new technology, crew roles and performance

Human Reliability Analysis (HRA) is an important part of probabilistic risk/safety assessment (PRA/PSA) for nuclear power plants (NPPs). Data has played a central part in HRA, to underpin nominal task probabilities, providing time reliability estimates, and as a basis for multipliers for performance shaping factors (PSFs). New NPPs apply digital instrumentation & control (I&C) systems to support monitoring and control. Will this require new or updated HRA methods and more data as compared to today? We have seen that the combination of the new I&C technology and how it is used by the crew is decisive for the performance of the joint system, comprising the crew and the I&C technology. This paper focuses on the way in which new technology may affect human performance as well as the relation between crew roles, teamwork, and performance. For HRA, two questions are important: What is the impact of teamwork and assigned crew roles on recovery from failure and upset plant conditions? And how should dependency be treated in modern control rooms? This paper discusses these matters based on empirical studies in the OECD Halden Reactor Project and underlines the importance of empirical studies on these topics, since the interactions between new technology, prescribed work processes and actual practices, and crew behavior, are often not obvious. This paper is an extension of a paper presented at the ESREL 2020 – PSAM15 conference.

Importance measures for performance shaping factors of human reliability analysis

AbstractThe crew of a nuclear‐powered submarine under development in Brasil plays an important role in its safety. For this reason, crew members must be trained in procedures against undesired events. However, information that would help to accurately decrease human error probability (HEP) on board nuclear‐powered submarines is not available in the literature. Therefore, a methodology is required to obtain this information. This study introduces importance measures to evaluate the contribution of performance shaping factors (PSFs) and their levels to HEP. Importance measures Fussell‐Vesely, risk reduction worth, risk achievement worth, and Birnbaum importance, commonly used in probabilistic safety assessment, are introduced to identify PSFs and PSF levels that contribute the most to HEP. The proposed approach is focused on identifying HEP's main contributors, oriented toward decision‐making. The methodology uses the PSFs established in the Standardized Plant Analysis Risk‐Human Reliability Analysis method and a Bayesian network to quantify HEP. Moreover, the results of an application case demonstrate the method's effectiveness in integrating expert opinions into representative values of HEP and in identifying critical PSFs and PSF levels that should be the object of the decision‐making process to improve human performance.

A SEM-based research on influencing factors of team situation awareness in nuclear power plants

Team situation awareness (TSA) is one of the key factors that affect team decision-making and implementation. TSA is influenced by performance shaping factors (PSFs) in digital nuclear power plants (NPPs). In order to identify the influencing relationships and extent between TSA and PSFs, a model is needed to describe and correlate them. Firstly, based on TSA cognitive process, the main PSFs influencing the level of TSA are identified, including team knowledge and experience level, information display quality, attention and attitude, and team stress level. Then a conceptual model of TSA influencing factors is proposed to explore the path relationships and influencing mechanism between TSA and PSFs, and a structural equation model (SEM) that relates TSA to its PSFs is developed subsequently. Finally, by analyzing human factor events and small deviation reports, data from 178 samples were obtained and substituted into the structural equation model to analyze and identify the relationships between the PSFs. The results show that the preceding PSFs have significant effects on TSA. Based on path coefficients, positive effects were: team knowledge and experience level (0.504), information display quality (0.370), attention and attitude (0.249). Negative effect was: team stress level (−0.384). The results of this study can provide a theoretical basis for the prevention and control of TSA errors, and a qualitative analysis model for the quantitative evaluation of TSA reliability.

New success likelihood index model for large group human reliability analysis considering noncooperative behaviors and social network

Human reliability analysis (HRA) is a systematic method to qualitatively and quantitatively assess and analyze potential human errors for enhancing the safety and reliability of complex socio-technical systems. As a representative HRA approach, the success likelihood index method (SLIM) has been widely applied to quantify the effects of performance shaping factors on human performance and derive the human error probabilities of operational tasks. The influence quantification of performance shaping factors mainly relies on experts’ subjective judgments, where conflict opinions inevitably exist because of their different knowledge and experiences. Besides, the current SLIM studies often involved a small group of experts and assumed them to be independent, which is insufficient to tackle increasingly complicated HRA problems. In this article, a large group SLIM model is developed to calculate the human error probabilities of operational tasks considering experts’ noncooperative behaviors and social relations. Specifically, a large group of experts are included to provide task state assessments under complex uncertain linguistic environment, and clustered into subgroups based on a social network analysis. Then a consensus reaching process is performed to tackle the noncooperative behaviors of experts to obtain a consensus human error probability estimation. Finally, a case study of cargo loading process is provided to illustrate the practicality and effectiveness of the proposed large group SLIM model.

First insights into the human-related risks of tunnel inspection

Whereas human factors in the non-destructive testing (NDT) of metallic components are a poorly investigated topic (in comparison to other industrial fields such as aviation), human factors in the inspection of concrete components are even less known. Studies have shown that there is always some variability between individuals in their inspection results and that human factors affect the reliability of NDT inspections. And even though those human factors (the effects of and interaction between technology, organisation, environment, and individual characteristics) do not necessarily lead to negative inspection outcomes, their understanding is a vital step towards preventing possible structure-breaking failures and thereby ensuring the safety of industry, environment and infrastructure. To identify the possible human-related risks in the tunnel inspection, a systematic approach to risk management has been adopted. This involves identification of risks, their characterisation, and suggestions for risk treatment. In line with this approach, the inspections will be observed followed by interviews with inspectors to collect initial information about the inspections in the field and possible performance shaping factors. Furthermore, a human-oriented Failure Modes and Effects Analysis (FMEA) will be used to identify possible human-related risks throughout data collection and evaluation and to evaluate them with respect to their possible causes, consequences, the probability of their occurrence and with respect to existing and possible preventive measures. The results aim to increase our overall understanding of human factors related to NDT, provide first insights and understanding of human factors in tunnel inspection and suggest measures to prevent human error in that application.

A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors

A challenge to Human Reliability Analysis (HRA) for Nuclear Power Plants (NPPs) lies in the fact that dependencies among Performance Shaping Factors (PSFs) are difficult to deal with due to insufficient knowledge, information and data available. Existing treatment relies heavily on the subjective expert judgment and the dependencies are compromised with the quantities of PSFs, simultaneously, neglects their uncertain interactions. This study proposes a Bayesian Belief Network (BBN) framework for structuring the uncertain dependencies among PSFs and estimate the Human Error Probabilities (HEPs) giving due account to such dependencies. An Exploratory Factor Analysis (EFA) technique is used to analyze human error events and cluster the dependent PSFs into clusters, which serve as the nodes connecting the parent PSF nodes with the child HEP node. Monte Carlo (MC) sampling operationalizes the framework, accounting for the uncertainty that affects PSF clustering and the data filling of conditional probability tables is performed by a Fenton approach.The framework is illustrated by considering 89 human error reports of China NPPs. The results show that the human mental related PSFs complexity, stress/stressor and fitness for duty are highly and steadily dependent, however, the dependencies of the PSFs experience/training and work processes are determined by the specific system situations.

Human reliability analysis of conventional maritime pilotage operations supported by a prospective model

One of the most critical phases of a ship voyage is the port arrival and departure. In addition to the conventional bridge team, these activities also employ maritime pilots with advanced local knowledge to support the safe navigation. Recent works addressed the risk assessment of maritime pilotage operations based on expert opinions, AIS data, and accident reports. Despite these efforts, it is still hard to determine the extent of this contribution and the main factors driving the operation success. Looking forward to advance in the direction of better understanding the contribution of the human and organizational factors in maritime pilotage operations, this paper proposes a methodology for human reliability analysis (HRA) supported by the prospective Technique for Early Consideration of Human Reliability (TECHR) combined with Bayesian Networks (BNs). For a generic operation, the results suggest that employing one additional pilot onboard reduces the accident probabilities by about 2.38 to 3.49 times, while removing a pilot increases the accident probability by about 5.15 to 8.92 times (both compared to a base scenario with one pilot onboard). Additionally, the sensitivity analysis revealed the most important performance shaping factors (PSFs), which include domain knowledge, training & experience, and safety culture.

The impact of human error on medical procedures.

Human error has been studied for large decades with special application to critical infrastructures and processes where the impact of such errors can induce severe or catastrophic consequences. In this sense it is of utmost importance to extend this type of analysis to other fields as medicine. This study proposes a semi-quantitative human error risk assessment methodology, including the analysis of the so-called Performance Shaping Factors (PSFs), in order to contribute to health services improvement. A questionnaire including the considered PSFs is answered in order to determine the impact of each PSF and its influence on human error. It allows performing a Human Error Risk Assessment (HERA) for both the patient (HERAp) and the quality of the service (HERAq). The results show the PSFs with the highest impact factor. After applying corrective measures, it is possible to observe the impact on the reduction of the risk for patient and for the quality of the service. The application of the methodology with the inclusion of the impact of PSFs allows minimizing or mitigating failure modes with greater risk as well as increasing patient safety and promoting a better quality of medical procedures.

A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H.

HighlightsA BN version of the SPAR-H model is used to predict and warn of human errors to avoid maritime accidents and ensure the safety of seafarers.Performance-shaping factors (PSFs) are used as factors contributing to unsafe crew acts (UCAs).The conditional probabilities quantitatively describe the relationships among PSFs, UCAs, and human errors.The method offers a point for translating the research model into practical application.Unsafe crew acts (UCAs) related to human errors are the main contributors to maritime accidents. The prediction of unsafe crew acts will provide an early warning for maritime accidents, which is significant to shipping companies. However, there exist gaps between the prediction models developed by researchers and those adopted by practitioners in human risk analysis (HRA) of the maritime industry. In addition, most research regarding human factors of maritime safety has concentrated on hazard identification or accident analysis, but not on early warning of UCAs. This paper proposes a Bayesian network (BN) version of the Standardized Plant Analysis Risk–Human Reliability Analysis (SPAR-H) method to predict the probability of seafarers’ unsafe acts. After the identification of performance-shaping factors (PSFs) that influence seafarers’ unsafe acts during navigation, the developed prediction model, which integrates the practicability of SPAR-H and the forward and backward inference functions of BN, is adopted to evaluate the probabilistic risk of unsafe acts and PSFs. The model can also be used when the available information is insufficient. Case studies demonstrate the practicability of the model in quantitatively predicting unsafe crew acts. The method allows evaluating whether a seafarer is capable of fulfilling their responsibility and providing an early warning for decision-makers, thereby avoiding human errors and sequentially preventing maritime accidents. The method can also be considered as a starting point for applying the efforts of HRA researchers to the real world for practitioners.

Managing the mechanical integrity of pressure equipment using a layers of protection framework and incorporating integrity operating windows

A requirement of performance based regulatory regimes for managing process safety is to demonstrate the adequacy of control measures for reducing the risk of a major incident so far as reasonably practical (SFARP). Loss of mechanical integrity allowing loss of primary containment (LOPC) is recognized as a possible initiating step for a major incident. Often LOPC results from the action of damage mechanisms such as corrosion that are able to degrade or destroy the mechanical integrity of the containment envelope of pressure equipment. LOPC, allowing a major incident, generally arises from human error in the form of a lack of recognition of a damage mechanism that should have been identified in a HAZOP or risk assessment and captured in a standard operating practice supported by relevant training. In this paper, a layers of protection framework is proposed for assessing the performance standards and performance indicators required of control measures to allow demonstration that likelihood of LOPC has been reduced SFARP, where LOPC has resulted from a damage mechanism such as corrosion. The context is the requirement to demonstrate adequacy of the practices used to assure the integrity of the containment envelope of pressure equipment required to prevent a major incident. The assessment includes examining the adequacy of inspection as a control measure including the RBI practice of using industry failure frequency databases to underpin the justification of an extension of inspection intervals. An improvement is proposed that recognizes the role of integrity operating windows (following API RP 584) for managing the process environment including providing evidence of control of the service conditions and recording excursions from nominal service conditions. Underpinning the proposed improvement is consideration of the contribution of human error to LOPC events and the need to recognize performance shaping factors that minimize error.

A slim based approach for human error probability of steel scrap cargo operations as a critical process in the maritime sector

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.

Determining performance shaping factors to assess human error in the emergency response team in chemical process industries: a case study

Objectives. Human error (HE) plays a crucial role in the occurrence of accidents in chemical process industries (CPIs). Emergency response team (ERT) members are predisposed to HEs due to the nature of their work. The HE potential is influenced by the performance shaping factors (PSFs). Managing PSFs can diminish the human error probability (HEP) and consequently increase the emergency response success chance. This article aimed to determine the PSFs for ERT members in CPIs. Methods. First, an initial list of PSFs was searched and classified within human reliability analysis methods and studies. Then, an expert panel of the emergency management system was utilized to identify, classify and weight the initial PSFs. The fuzzy Delphi method and content analysis technique were applied to summarize and categorize the PSFs. Results. The results of the study showed that 11 PSFs had greater impacts on the ERT members’ error potential. Findings revealed that stress and physiological stressors, competency, and team and organization were the three most important PSFs. Conclusion. The most important and relevant PSFs can be effectively used in accurate HE assessment of ERT members in CPIs.

A Method for Identifying the Key Performance Shaping Factors to Prevent Human Errors during Oil Tanker Offloading Work

Oil tanker offloading is a human-related and high-risk task. A small operational error may trigger catastrophic accidents such as fire and explosion. It is recognised that more than 70% of industrial accidents are blamed for human errors, so preventing them is crucial. As human error is associated with a variety of Performance Shaping Factors (PSFs), it is meaningful to identify key PSFs for safe operations during oil tanker offloading process. However, some issues are obstacles to finding the crucial PSFs. The recording data of most PSFs are always incomplete and imperfect. Moreover, the standard for ranking PSFs should be rational. In addition, the performance of each PSF at the different stages is oil offloading is usually unstable and may change with time. As a result, this study aims to conduct a method that mainly relies on Grey Relational Analysis (GRA), the definition of “Risk” (combination of likelihood and impact), and Hierarchical Task Analysis (HTA) to find several significant PSFs to prevent human errors. GRA deals with the incomplete and imperfect data; the definition of “Risk” provides a rational basis for ranking PSFs; and HTA gives support for considering the PSFs’ changes at different stages of a task. The proposed approach is tested on a real engineering case of oil tanker offloading work at offshore terminal. The result indicates that the method can be applied to identify key PSFs, which in turn provides recommendations for human error prevention to ensure the safety both on board and at terminal.

Applying analytic hierarchy process and failure likelihood index method (AHP-FLIM) to assess human reliability in critical and sensitive jobs of a petrochemical industry

BackgroundGiven that human error is the most important cause of industrial accidents, it seems necessary to identify and analyze human error, assess human reliability, and reduce errors or prevent unfortunate consequences. This study aimed to evaluate human reliability in a petrochemical industry. MethodsCritical and sensitive jobs were identified by interviewing the staff and supervisors of the industry. Then, the most important human errors and Performance Shaping Factors (PSFs) in each job group were identified. Using the Analytic Hierarchy Process (AHP), PSFs and human errors were weighed and prioritized. Next, the Failure Likelihood Index (FLI) was calculated for each of the human errors identified in the selected tasks. Finally, by converting this index to human error, human reliability was calculated. ResultsFirefighters, site men, and mechanical repairmen had the highest risk and were selected to evaluate human reliability by the AHP-FLI method. Accordingly, six PSFs (training, experience, instruction, stress, task complexity, and environmental conditions) were identified as the most important factors in human error occurrence. The average human error probability was calculated as 0.019, 0.018, and 0.013 in the three job groups of firefighters, site men, and mechanical repairmen, respectively. ConclusionThis study showed that the AHP-FLI method could be a suitable method for assessing human reliability in the petrochemical industry since it could measure the impact of different PSFs on the probability of human error.