Human And Organizational Factors Research Articles

An approach of identifying the common human and organisational factors (HOFs) among a group of marine accidents using GRA and HFACS-MA

In considering effective countermeasures for preventing the same (or similar) type of accidents from happening again, an investigation and analysis to identify the common human and organisational factors (HOFs) among a group of accidents would be beneficial for decision making. The present study aims at proposing an approach that is capable of identifying the common HOFs between or among accidents, in line with the concept of reason’s swiss cheese model, without losing the context details of individual accidents. This approach applies why-because analysis, Human Factors Analysis and Classification System (HFACS) for Maritime Accidents (HFACS-MA, a derivative of HFACS) and grey relational analysis (GRA) to constitute a systematic analysis procedure that is divided into three stages. The first two are to identify the causal HOFs involved in every accident and to figure out the causation among them, and then the categories of the identified HOFs are classified according to the HFACS-MA in turn. Having these analysis results, the GRA, and two associative analysis processes, constituting the third stage of the procedure, are applied to identify the common HOFs from those accidents concerned. An experimental case study, with five marine accidents, is utilised to demonstrate that the analysis results of the proposed approach can not only illustrate the common HOFs among these accidents, but also reveal a comprehensive insight into each analyzed accident. Some considerations, including the future work, associated with the proposed method are also discussed and concluded in this article.

Integrated reliability and maintainability analysis of Computerized Numerical Control Turning Center considering the effects of human and organizational factors

Purpose Reliability, maintainability and availability of modern complex engineered systems are significantly affected by four basic systems or elements: hardware, software, organizational and human. Computerized Numerical Control Turning Center (CNCTC) is one of the complex machine tools used in manufacturing industries. Several research studies have shown that the reliability and maintainability is greatly influenced by human and organizational factors (HOFs). The purpose of this paper is to identify critical HOFs and their effects on the reliability and maintainability of the CNCTC. Design/methodology/approach In this paper, 12 human performance influencing factors (PIFs) and 10 organizational factors (OFs) which affect the reliability and maintainability of the CNCTC are identified and prioritized according to their criticality. The opinions of experts in the fields are used for prioritizing, whereas the field failure and repair data are used for reliability and maintainability modeling. Findings Experience, training, and behavior are the three most critical human PIFs, and safety culture, problem solving resources, corrective action program and training program are the four most critical OFs which significantly affect the reliability and maintainability of the CNCTC. The reliability and maintainability analysis reveals that the Weibull is the best-fit distribution for time-between-failure data, whereas log-normal is the best-fit distribution for Time-To-Repair data. The failure rate of the CNCTC is nearly constant. Nearly 66 percent of the total failures and repairs are typically due to the hardware system. The percentage of failures and repairs influenced by HOFs is nearly only 16 percent; however, the failure and repair impact of HOFs is significant. The HOFs can increase the mean-time-to-repair and mean-time-between-failure of the CNCTC by nearly 65 and 33 percent, respectively. Originality/value The paper uses the field failure data and expert opinions for the analysis. The critical sub-systems of the CNCTC are identified using the judgment of the experts, and the trend of the results is verified with published results.

Benefits of systems thinking for a human and organizational factors approach to safety management

This paper highlights the value of systems theory and its application to human and organizational factors (HOF). HOF specialists consider multiple systems characteristics in their analyses but are often unaware of the relevant theory applied in their analysis. We argue that a structured effort to take key systems characteristics into account in HOF practice would increase the depth and breadth of safety management analyses and help HOF specialists to act more effectively on industrial socio-technical systems. First, the paper identifies the following seven system theory characteristics: constitution, multi-axis representation, limit, emergence, variety, coherence, and causal interaction, which are then illustrated with examples from the HOF field. Finally, we discuss the two main benefits of integrating system thinking in a HOF approach to safety management: (1) an improved understanding of the inner workings of an industrial socio-technical system; and (2) a compendium or a reference to guide for decision-making and the implementation of actions within the industrial socio-technical systems.

Offshore drilling blowout risk model – an integration of basic causes, safety barriers, risk influencing factors and operational performance indicators

Offshore drilling is an activity inherent to the oil and gas industry as it is essential for confirming the economic feasibility of hydrocarbon reservoirs. However, operational uncertainties and risks inherent to typical major accident hazards are associated with the performance of this activity, where blowouts are assumed to be one of the major contributors to risk in offshore drilling. This paper presents an accident precursor risk model for offshore drilling blowouts that integrates the blowout basic causes, safety critical barriers and elements, risk influencing factors (RIFs) including human and organisational factors (HOFs), and operational performance indicators (OPIs). The method, which was adopted to design the model and allow its customisation for reflecting the characteristics of any drilling rig, well and operation is also described and demonstrated using a theoretical example. The model is hybrid, as it combines bow-tie diagram (fault tree and event tree) with directed acyclic graphs (DAGs), to account for the effect of the RIF on the performance of safety barriers through the direct observation of pre-defined OPI.AbbreviationsBHAbottom hole assemblyBOPblowout preventerBORAbarrier and operational risk analysisDAGdirected acyclic graphDPdynamically positionedECDequivalent circulating densityG&Ggeological and geophysicalHChydrocarbonHOFshuman and organisational factorsHP/HThigh pressure/high temperatureHSEhealth, safety and environmentIADCInternational Association of Drilling ContractorsIMOInternational Maritime OrganisationIRPAindividual risk per annumISMInternational Safety Management CodeISOInternational Standard OrganisationLMRPlower marine riser packageLWDlogging while drillingMODUsmobile offshore drilling unitsOPIsoperational performance indicatorsRIFsrisk influencing factorsROPrate of penetrationROVremote operated vehicleRPMrotation per minuteSINTEFStiftelsen for industriell og teknisk forskning or ‘The Foundation for Scientific and Industrial Research’TFstechnical factorsWOBweight on bitWOCweight on cement

Predicting the unpredictable: Consideration of human and organisational factors in maintenance prognostics

Human performance is a major contributor to system performance and maintenance errors can have a significant influence on system reliability. However, existing reliability modelling approaches lack any methodologies to take account of maintenance actions in predicting system failure probability. The primary objective of this work is the development of a methodological framework to enable the integration of human and organisational factors (HOFs) as quantitative metrics within prognostics maintenance models. Inclusion of significant HOF metrics derived from performance shaping factors (PSFs) should reduce the predictive uncertainty of models developed for system failure probability estimation. This research investigates human error during maintenance activities as one variable contributing to system failure events. The hypothesis is that including HOF metrics derived from the performance shaping factors (PSFs) influencing maintenance tasks can reduce the predictive uncertainty of models developed for system failure probability estimation, provided that the PSFs are found to be significant predictors of the failure event. This research uses a case study from the biopharmaceutical industry to demonstrate the industrial application of the developed methodology. Regarding the case study, current field data is unable to isolate a process variable that can reliably predict sudden component failure. Technician error during installation and system maintenance activities is therefore investigated as a potential significant variable. This applied research explores how human errors can be discovered and accounted for within the reliability modelling process. The use of PSFs in this way forms one part of the development of data driven soft-sensors using a knowledge fusion approach. This soft sensor approach utilises a combination of quantitative and qualitative information in the form of laboratory tests, historical industrial process data, and metrics derived from human factors analysis, the combination of which is unique in the literature.

Human Error Probability Estimation in Atex-hmi Area Classification: from Therp to Fuzzy Cream

ATEX (explosive atmosphere) risk assessment is required when any equipment or system can potentially cause explosive atmospheres. Although many operations are performed by operators, influences from human and organizational factors (HOFs) are mostly neglected. In order to address HOFs influence, the ATEX-HMI (Human-Machine Interaction) methodology is proposed: it aims at integrating HOFs into each step of the standard ATEX risk assessment. The first step faced is Area classification: the ATEX-HMI methodology introduces the Human Error Probability (HEP) into the zone calculation procedure. Human Reliability Analysis (HRA) is the main method for the HEP assessment, thus two HRA methods were applied for the ATEX-HMI methodology. The first one, THERP, demonstrated an efficient quantitative precision, but it was not easily applicable to real industries. Then, the Cognitive Reliability and Error Analysis Method (CREAM) was adopted, together with a complementary FUZZY application for quantitative analysis. The present paper introduces the FUZZY CREAM application, also showing a comparative analysis of THERP and FUZZY CREAM, on the basis of a food plant case study.

On the need for online decision support in FPSO–shuttle tanker collision risk reduction

More than 100 Floating Production Storage and Off-loading (FPSO) concepts are in operation at the moment worldwide, and due to increasing demand many are currently under construction or being converted from commercial tankers. Still, operational risk is an issue that is not considered thoroughly in the initial feed stage, especially when compared to fixed installations. Floating installations are more dependent on manual control of some of the marine systems during normal operations, as well as during critical situations. Accidents can be initiated by errors induced by human and organizational factors (HOF), technical (design) failures, environmental conditions, or a combination of all three. Therefore, effective means are extremely important to prevent or mitigate the effects of potential operational accidents that can result in serious accidents. Updated statistics in 2013 revealed that the frequency of operational accidents during the last ten years was significantly above the target value that has been aimed for, in fact it was concluded that the frequency was around one order of magnitude higher. Hence, the objective of this article is to assess the hazards, the existing barriers, the risk level, and the risk reduction potentials more thoroughly.

Human and organizational factor risk assessment in process industry and a risk assessment methodology (MEDIA) to incorporate human and organizational factors

Human and organizational factors (HOF) contribute to large number of accidents in process industries, therefore it is of prime importance to include HOF into risk assessment. In this paper, a newly developed methodology Method for Error Deduction and Incident Analysis (MEDIA) is presented. MEDIA is a taxonomy based HOF assessment methodology which can be used to quantify the HOF risk based on an accidental database (EMARS). Primarily, MEDIA analyzes different organizational characteristics and their effect on human action's outcome. This methodology also accounts for available risk reduction factors and critically of human action failure

Integration of HOFs into ATEX Risk Assessment Methodology

The current ATEX (Explosive Atmosphere) risk assessment methodology relies on a semi-quantitative approach based on the following indexes: probability of an explosive atmosphere formation, probability of the presence of an effective ignition source and consequences. The whole risk assessment procedure can be separated into four steps ---- area classification, ignition sources identification, consequence analysis, and risk evaluation. However, when reviewing the whole ATEX risk assessment procedures and their results, despite many operations on plant and equipment containing dangerous substances are performed by operators, the human and organizational influences are neglected. The study proposed in this paper is to develop an ATEX risk assessment methodology with the integration of human and organizational factors (HOFs), in order to provide an advanced methodology able to analyse the HOFs influences on ATEX hazards. The developed ATEX risk assessment methodology introduces in the procedure the Technique for Human Error Rate Prediction (THERP) to identify human and organizational weaknesses, and also provides a tool for calculating the human error probability (HEP) in the first two risk steps ---- area classification and ignition sources identification. During the risk evaluation procedure, human risk level, based on the results of HEP, has been introduced into the original ATEX risk assessment methodology.

A Human and Organisational Factors (HOFs) analysis method for marine casualties using HFACS-Maritime Accidents (HFACS-MA)

A dedicated Human and Organisational Factors (HOFs) framework for maritime accidents investigation and analysis is developed in this paper. A prototype of the framework is proposed and named as Human Factors Analysis and Classification System for Maritime Accidents (HFACS-MA). There are five levels in the framework which is in line with the core concepts of HFACS, Reason’s Swiss Cheese Model and Hawkins’ SHEL model. The framework also complies with the International Maritime Organization (IMO) guidelines. In addition to the framework, the proposed method integrates the HFACS-MA with a Why-Because Graph for accidents analysis providing a complement measure using HFACS. A case study regarding the Herald of Free Enterprise disaster demonstrates the proposed method and shows how a comprehensive insight into the accident can be gained via the integration of the analysis results as a complement to the HFACS analytical results. Several advantages that the framework provides for accident analysis are elaborated. Finally some considerations, including the further work, associated with the HFACS-MA are discussed and concluded in this paper.

A compound methodology to assess the impact of human and organizational factors impact on the risk level of hazardous industrial plants

This paper presents a compound methodology devised to relate Human and Organizational Factors (HOFs) to operators’ response time in critical operations within hazardous industrial plants. The methodology has been based on a sensitivity analysis of the nine “families” of the Common Performance Conditions (CPCs), as defined in the CREAM technique, in order to verify and rank their influence on the operators’ response time. To prove the methodology, a series of pilot experiments have been designed and performed so that human response is evaluated in a Virtual Environment (VE) reproducing the control room and a specific plant section. This environment enables the analyst controlling the simulation to perform the sensitivity analysis acting through a supervisory station and manipulating the control functions in order to vary each CPC rate around its nominal value. Experiments were run with the variation of one CPC at a time aiming at the detection and containment operation of a gas leakage in a pressure-reduction NG terminal. The whole case study has been run within the framework of the VIRTHUALIS EU project.

Monitoring human and organizational factors influencing common-cause failures of safety-instrumented system during the operational phase

Safety-instrumented systems (SISs) are important safety barriers in many technical systems in the process industry. Reliability requirements for SISs are specified as a safety integrity level (SIL) with reference to the standard IEC 61508. The SIS reliability is often threatened by common-cause failures (CCFs), and the beta-factor model is the most commonly used model for incorporating the effects of CCFs. In the design phase, the beta-factor, β, is determined by answering a set of questions that is given in part 6 of IEC 61508. During the operational phase, there are several factors that influence β, such that the actual β differs from what was predicted in the design phase, and therefore the required reliability may not be maintained. Among the factors influencing β in the operational phase are human and organizational factors (HOFs). A number of studies within industries that require highly reliable products have shown that HOFs have significant influence on CCFs and therefore on β in the operational phase, but this has been neglected in the process industry. HOFs are difficult to predict, and susceptible to be changed during the operational phase. Without proper management, changing HOFs may cause the SIS reliability to drift out of its required value. The aim of this article is to highlight the importance of HOFs in estimation of β for SISs, and also to propose a framework to follow the HOFs effects and to manage them such that the reliability requirement can be maintained.

Conditions for the successful integration of Human and Organizational Factors (HOF) in the nuclear safety analysis

This communication presents some elements which come from the experience feedback at CEA about the conditions for the successful integration of HOF in the nuclear safety analysis. To point out some of these conditions, one of the concepts proposed by Edgar Morin to describe the functioning of "complex" systems: the dialogical principle has been used. The idea is to look for some dialogical pairs. The elements of this kind of pair are both complementary and antagonist to one another. Three dialogical pairs are presented in this communication. The first two pairs are related to the organization of the HOF network and the last one is related to the methods which are used to analyse the working situations. The three pairs are: specialist - non-specialist actors of the network, centralized - distributed human resources in the network and microscopic - macroscopic levels of HOF methods to analyse the working situations. To continuously improve these three dialogical pairs, it is important to keep the differences which exist between the two elements of a pair and to find and maintain a balance between the two elements of the pairs.

Quantitative risk analysis of oil and gas drilling, using Deepwater Horizon as case study

According to the National Commission on the BP Deepwater Horizon Oil Spill and Offshore Drilling, the Macondo blowout requires a reassessment of the risks associated with offshore drilling. The Commission recommends a proactive, risk-based performance approach specific to individual facilities, operations and environments, similar to the safety case/Quantitative Risk Analysis (QRA) approach in the North Sea. A review of a 15 QRAs from the North Sea reveals that the analyses to a large extent only to calculate the frequency of blowout based on the number of drilling operations. None of the reviewed analyses were initiated based on Risk Influence Factors (RIFs) uncovered in the conceptual phase of well planning. The QRAs do not include Human and Organisational Factors (HOFs). As seen in the Macondo blowout, most of the findings were related to HOFs, e.g. working practice, competence, communication, procedures and management. The narrow drilling window related to deepwater drilling has to be controlled by safety barriers that are dependent on HOFs. There is some research relating to the incorporation of HOFs in QRAs. Further improvements in methodology and datasets are necessary to ensure that the QRAs are valid for the individual facilities, operations and environments.

A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors

IntroductionFocusing on people and organizations, this paper aims to contribute to offshore safety assessment by proposing a methodology to model causal relationships. MethodThe methodology is proposed in a general sense that it will be capable of accommodating modeling of multiple risk factors considered in offshore operations and will have the ability to deal with different types of data that may come from different resources. Reason's “Swiss cheese” model is used to form a generic offshore safety assessment framework, and Bayesian Network (BN) is tailored to fit into the framework to construct a causal relationship model. The proposed framework uses a five-level-structure model to address latent failures within the causal sequence of events. The five levels include Root causes level, Trigger events level, Incidents level, Accidents level, and Consequences level. To analyze and model a specified offshore installation safety, a BN model was established following the guideline of the proposed five-level framework. A range of events was specified, and the related prior and conditional probabilities regarding the BN model were assigned based on the inherent characteristics of each event. ResultsThis paper shows that Reason's “Swiss cheese” model and BN can be jointly used in offshore safety assessment. On the one hand, the five-level conceptual model is enhanced by BNs that are capable of providing graphical demonstration of inter-relationships as well as calculating numerical values of occurrence likelihood for each failure event. Bayesian inference mechanism also makes it possible to monitor how a safety situation changes when information flow travel forwards and backwards within the networks. On the other hand, BN modeling relies heavily on experts' personal experiences and is therefore highly domain specific. Impact on Industry“Swiss cheese” model is such a theoretic framework that it is based on solid behavioral theory and therefore can be used to provide industry with a roadmap for BN modeling and implications. A case study of the collision risk between a Floating Production, Storage and Offloading (FPSO) unit and authorized vessels caused by human and organizational factors (HOFs) during operations is used to illustrate an industrial application of the proposed methodology.

A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation

The paper presents an innovative approach to integrate Human and Organisational Factors (HOF) into risk analysis. The approach has been developed and applied to a case study in the maritime industry, but it can also be utilised in other sectors. A Bayesian Belief Network (BBN) has been developed to model the Maritime Transport System (MTS), by taking into account its different actors (i.e., ship-owner, shipyard, port and regulator) and their mutual influences. The latter have been modelled by means of a set of dependent variables whose combinations express the relevant functions performed by each actor. The BBN model of the MTS has been used in a case study for the quantification of HOF in the risk analysis carried out at the preliminary design stage of High Speed Craft (HSC). The study has focused on a collision in open sea hazard carried out by means of an original method of integration of a Fault Tree Analysis (FTA) of technical elements with a BBN model of the influences of organisational functions and regulations, as suggested by the International Maritime Organisation's (IMO) Guidelines for Formal Safety Assessment (FSA). The approach has allowed the identification of probabilistic correlations between the basic events of a collision accident and the BBN model of the operational and organisational conditions. The linkage can be exploited in different ways, especially to support identification and evaluation of risk control options also at the organisational level. Conditional probabilities for the BBN have been estimated by means of experts’ judgments, collected from an international panel of different European countries. Finally, a sensitivity analysis has been carried out over the model to identify configurations of the MTS leading to a significant reduction of accident probability during the operation of the HSC.