Lifecycle risk assessment of a technological system using dynamic Bayesian networks

Investigations of technological systems accidents reveal that technical, human, organizational, as well as environmental factors influence the occurrence of accidents. Despite these facts, most traditional risk assessment techniques focus on technical aspects of systems and have some limitations of incorporating efficient links between risk models and human and organizational factors. This paper presents a method for risk analysis of technological systems. Application of the presented framework makes it possible to analyze the influence of technical, human, organizational, and environmental risk factors on system safety. It encompasses system lifecycle from design to operational phase to give a comprehensive picture of system risks. The developed framework comprises the following main steps: (1) development of a conceptual risk analysis framework, (2) identifying risk influencing factors in different levels of technical, human, organizational, and environmental factors providing the possibility of analyzing interactions in a multi‐level system, (3) modeling system risk using dynamic Bayesian network (DBN), (4) assignment of probabilities and risk quantification in node probability tables (NPTs) based on industry records and experts extracted knowledge, (5) implementation of the model for wind turbines risk analysis combining use of V‐model, risk factors, and DBN in order to analyze the risk, and (6) analyzing different scenarios and the interactions in different levels. Finally, the various steps of the framework, the research objective fulfillment, and case study results are presented and discussed.     

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.     

The influence of process-oriented organisational design on operational performance and innovation: a quantitative analysis in the financial services industry

Market competition requires that organisations excel at both operational performance and innovation. Though process orientation is intended to benefit operations in general, the impact of specific elements remains unclear. The purpose of this study is to identify the specific organisational design components of process orientation that simultaneously influence operational performance and innovation. We survey employees in the financial services sector (N = 1069) regarding the influence of six process-oriented organisation design components on operational performance and innovation using PLS-SEM. The results indicate that organisational structure and process improvement encourage both operational performance and innovation. Personal autonomy is partially beneficial, while the others have no joint influence on operational performance and innovation. This study is first in providing evidence regarding the influence of a broad variety of organisational design components of process orientation on operational performance and innovation. The study contributes to theory on how process orientation improves operational performance and innovation. The findings can serve as a guideline for managers and organisations as to which organisational design components should be implemented to improve operations.     

A semi-quantitative approach to risk analysis, as an alternative to QRAs

In quantitative risk analysis (QRA) risk is quantified using probabilities and expected values, for example expressed by PLL values, FAR values, IR values and F–N curves. The calculations are tedious and include a strong element of arbitrariness. The value added by the quantification can certainly be questioned. In this paper, we argue that such analyses often are better replaced by semi-quantitative analyses, highlighting assessments of hazards and barriers, risk influencing factors (RIFs) and safety improvement measures. The assessments will be based on supporting information produced by risk analysts, including hard data and analyses of failure causes and mechanisms, barrier performance, scenario development, etc. The approach acknowledges that risk cannot be adequately described and evaluated simply by reference to summarising probabilities and expected values. There is a need for seeing beyond the standard probabilistic risk results of a QRA. Key aspects to include are related to uncertainties in phenomena and processes, and manageability factors. Such aspects are often ignored in standard QRAs.     

The ‘PROCESO’ index: a new methodology for the evaluation of operational safety in the chemical industry

The acknowledgement of industrial installations as complex systems in the early 1980s outstands as a milestone in the path to operational safety. Process plants are social–technical complex systems of a dynamic nature, whose properties depend not only on their components, but also on the inter-relations among them. A comprehensive assessment of operational safety requires a systemic approach, i.e. an integrated framework that includes all the relevant factors influencing safety. Risk analysis methodologies and safety management systems head the list of methods that point in this direction, but they normally require important plant resources. As a consequence, their use is frequently restricted to especially dangerous processes often driven by compliance with legal requirements. In this work a new safety index for the chemical industry, termed the ‘Proceso’ Index (standing for the Spanish terms for PROCedure for the Evaluation of Operational Safety), has been developed. PROCESO is based on the principles of systems theory, has a tree-like structure and considers 25 areas to guide the review of plant safety. The method uses indicators whose respective weight values have been obtained via an expert judgement technique. This paper describes the steps followed to develop this new Operational Safety Index, explains its structure and illustrates its application to process plants.     

Incorporating management and organisational factors into probabilistic safety assessment

This paper is concerned with how management and organisational influences can be factored into risk assessments. A case study from the rail transportation sector illustrates how organisational factors can act as high level influences which are manifest as operational errors giving rise to major accidents. A model is proposed which describes the interrelationships between management influences, immediate causes and operational errors. This model can be used for organisational auditing, monitoring and system design. A strategy is described for collecting data from an existing organisation to develop a specific form of the generic model. The final issue addressed is the use of the model to quantify the effects of organisational influences on risk arising from human error. A numerical case study is provided to illustrate the approach.     

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

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

Examination of issues related to the development and implementation of real-time operational safety monitoring tools in the nuclear power industry

In recent years, risk and reliability techniques have been increasingly used to optimize deterministic requirements and to improve the operational safety of nuclear power stations. This paper discusses the historical development and current status of implementation of real-time operational safety monitoring tools in the nuclear power industry worldwide. A safety monitor is defined as a PC-based risk management tool, based on a plant specific PSA, which can be used to manage plant safety during the day-to-day operation of a nuclear power plant by planning maintenance activities and providing advisory information to plant operational staff in order to avoid high risk plant configurations. As this technique has only been applied in a few plants worldwide, the technology is still evolving and there are several technical and implementation-related issues which still need to be resolved. This paper attempts to summarize all such issues and describe how they have been addressed in several different applications of this technology around the world.     

On the use of the hybrid causal logic method in offshore risk analysis

In the Norwegian offshore oil and gas industry risk analyses have been used to provide decision support for more than 20 years. The focus has traditionally been on the planning phase, but during the last years a need for better risk analysis methods for the operational phase has been identified. Such methods should take human and organizational factors into consideration in a more explicit way than the traditional risk analysis methods do. Recently, a framework, called hybrid causal logic (HCL), has been developed based on traditional risk analysis tools combined with Bayesian belief networks (BBNs), using the aviation industry as a case. This paper reviews this framework and discusses its applicability for the offshore industry, and the relationship to existing research projects, such as the barrier and operational risk analysis project (BORA). The paper also addresses specific features of the framework and suggests a new approach for the probability assignment process. This approach simplifies the assignment process considerably without loosing the flexibility that is needed to properly reflect the phenomena being studied.     

Using transportation accident databases to investigate ignition and explosion probabilities of flammable spills

Risk assessment of hazardous material spill scenarios, and quantitative risk assessment in particular, make use of event trees to account for the possible outcomes of hazardous releases. Using event trees entails the definition of probabilities of occurrence for events such as spill ignition and blast formation. This study comprises an extensive analysis of ignition and explosion probability data proposed in previous work. Subsequently, the results of the survey of two vast US federal spill databases (HMIRS, by the Department of Transportation, and MINMOD, by the US Coast Guard) are reported and commented on. Some tens of thousands of records of hydrocarbon spills were analysed. The general pattern of statistical ignition and explosion probabilities as a function of the amount and the substance spilled is discussed. Equations are proposed based on statistical data that predict the ignition probability of hydrocarbon spills as a function of the amount and the substance spilled. Explosion probabilities are put forth as well. Two sets of probability data are proposed: it is suggested that figures deduced from HMIRS be used in land transportation risk assessment, and MINMOD results with maritime scenarios assessment. Results are discussed and compared with previous technical literature.     

Improvisation: theory,measures and known influencing factors

Interest in the potential of improvisation to enhance safety outcomes in safety critical situations has been increasing; however, improvisation also has the potential to make emergency situations worse rather than better. If organisations are to capitalise on improvisation's potential to produce safety benefits, a model of the factors that influence its effectiveness in safety critical situations is needed. This review draws together what is currently known about the factors influencing improvisation and the methods used to examine them. The review reveals that, unlike most contemporary ergonomics concepts, as yet no research has examined factors beyond organisational boundaries or examined the interactions of factors across different systems levels, both integral components of systems-based models. In closing, discussion is presented on the most appropriate research agenda for enhancing understanding of improvisation and its influencing factors.     

Risk indicators as a tool for risk control

This paper presents a general methodology for the establishment of risk indicators that can be used as a tool for risk control during operation of offshore petroleum installations. The risk indicators established are based on the platform specific quantitative risk analysis (QRA). The general methodology is evaluated against comparable approaches both in offshore and nuclear industry. There are two distinct features of this methodology. The first is that it is truly risk-based with the intention of covering the total risk picture. The second is that the identification of the risk factors contributing most to the total risk is based on realistic changes of each factor assessed by the platform personnel, not a theoretically assumed change. The set of risk indicators for one specific installation is presented along with test results.     

Comprehensive risk assessment for rail transportation of dangerous goods: a validated platform for decision support

Currently, the most advanced and well documented risk assessments for the transportation of dangerous goods by railway take into account:

(i) statistics-based loss of containment frequencies,

(ii) specification of potential consequences for a given release situations using event tree methodology as an organisational tool and

(iii) consequence calculation models to determine a risk figure known as CCDF (Complementary Cumulative Distribution Function).

Such procedures for the risk assessment (including for example decision-making on preventive measures) may offer only a limited insight into the causes and sequences leading to an accident and do not allow for any kind of predictive analysis. The present work introduces an enhanced solution, and a related software platform, which attempts to integrate loss of containment causes and consequences with system's infrastructure and its environment. The solution features:

(i) the use of a detailed Master Logical Diagram, including fault/event tree analysis to determine a loss of containment frequency based on different initiating events, scenarios and specific basic data,

(ii) the characterization of a resulting source term following a release situation, and

(iii) the calculation of various potential impacts on the neighbouring site.

Results are wrapped into a CCDF format for each selected traffic segment. The risk-related results are integrated on a software platform, structured as a decision support system using intelligent maps and a variety of GIS (Geographical Information System) data processing procedures. The introduction of the hot spot approach, allows us to focus on the most risk-relevant areas and to use information on various railway infrastructure elements (e.g. points, tunnels), are the basis of the new models employed. The software is applicable to any railway transportation system, comprising its technical infrastructure, rolling stock, human actions, regulation and management procedures. It provides the determination of the annual societal risk due to potential accident scenarios, while also revealing information on the potential causes of an accident taking into account spatial parameters. The approach and software have been validated by a case study done for a particular traffic segment of the Swiss Federal Railway company.     

Critical factors and paths influencing construction workers’ safety risk tolerances

While workers’ safety risk tolerances have been regarded as a main reason for their unsafe behaviors, little is known about why different people have different risk tolerances even when confronting the same situation. The aim of this research is to identify the critical factors and paths that influence workers’ safety risk tolerance and to explore how they contribute to accident causal model from a system thinking perceptive. A number of methods were carried out to analyze the data collected through interviews and questionnaire surveys. In the first and second steps of the research, factor identification, factor ranking and factor analysis were carried out, and the results show that workers’ safety risk tolerance can be influenced by four groups of factors, namely: (1) personal subjective perception; (2) work knowledge and experiences; (3) work characteristics; and (4) safety management. In the third step of the research, hypothetical influencing path model was developed and tested by using structural equation modeling (SEM). It is found that the effects of external factors (safety management and work characteristics) on risk tolerance are larger than that of internal factors (personal subjective perception and work knowledge & experiences). Specifically, safety management contributes the most to workers’ safety risk tolerance through its direct effect and indirect effect; while personal subjective perception comes the second and can act as an intermedia for work characteristics. This research provides an in-depth insight of workers’ unsafe behaviors by depicting the contributing factors as shown in the accident causal model developed in this research.     

Contextual and mediation analysis between TQM critical factors and organisational results in the EFQM Excellence Model framework

Following a total quality management (TQM) approach and the EFQM Model framework, this contribution aims to study the influence of TQM social factors on organisational results, by considering the TQM technical factors as a mediating variable in this relationship. Moreover, the effect of size and type of business on the implementation of TQM and results is analysed, as well as determining which TQM practices have a more important effect on the operational results. The research questions and hypotheses proposed in this research model are tested on a sample of 116 Spanish firms. The results support the reliability, validity and the high-predictive power of the EFQM Excellence Model as a framework for the implementation of TQM. Moreover, our findings indicate that TQM social factors provide a cornerstone for the success of the quality system. Similarly, TQM technical factors partially mediate the relationship between TQM social factors and results. It was also found that TQM social factors and processes management are the main predictors of operational performance. Finally, significant differences were found in TQM implementation, and in the key results of companies, based on their size; however, these differences are not significant based on the sector activity.     

A quantitative investigation of the technological innovation in large construction companies

The construction industry is considered as backwards and lack of innovation. The main driving power of technological innovation in the construction industry is large construction companies. However, the driving process of technological innovation in them has not been fully investigated in previous studies. This study constructs a quantitative model to explore the technological innovation driving process of large construction companies considering the key influencing factors for technological innovation. The sample data are the 57 large construction companies selected from the world's largest construction market – China. The results show significant positive correlation between innovation input and innovation output, and between innovation output and performance. The operational capacity of the companies is positively correlated with innovation input, whilst the capital structure is negatively correlated with innovation input. The technical employees have a positive impact on the performance. The underlying reasons are discussed and suggestions are given for the construction industry to improve the technological innovation capacity of construction companies. This research contributes to the literature of construction innovation and benefits practitioners by providing a quantitative approach to demonstrate the driving power of large construction companies for technological innovation.     

The contribution of human factors to accidents in the offshore oil industry

Accidents such as the Piper Alpha disaster illustrate that the performance of a highly complex socio-technical system, is dependent upon the interaction of technical, human, social, organisational, managerial and environmental factors and that these factors can be important co-contributors that could potentially lead to a catastrophic event. The purpose of this article is to give readers an overview of how human factors contribute to accidents in the offshore oil industry. An introduction to human errors and how they relate to human factors in general terms is given. From here the article discusses some of the human factors which were found to influence safety in other industries and describes the human factors codes used in accident reporting forms in the aviation, nuclear and marine industries. Analysis of 25 accident reporting forms from offshore oil companies in the UK sector of the North Sea was undertaken in relation to the human factors. Suggestions on how these accident reporting forms could be improved are given. Finally, this article describes the methods by which accidents can be reduced by focusing on the human factors, such as feedback from accident reporting in the oil industry, auditing of unsafe acts and auditing of latent failures.     

Investigating the multi-causal and complex nature of the accident causal influence of construction project features

Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement.     

Gray wolf optimizer approach to the reliability‐cost optimization of residual heat removal system of a nuclear power plant safety system

To ensure the safety of nuclear power plants (NPPs), nuclear regulatory agencies set technical specifications (TSs). TSs define the safety‐related operational measures and specify essential requirements and set specific limitations that is necessarily be followed by a nuclear industry to meet the requirements for the safety of an NPP. One of the important bases for the setting of TSs is the estimates of the availability and reliability of various systems and costs associated with an NPP. In this work, authors have presented a framework based upon a hodiernal nature‐inspired metaheuristic called multiobjective gray wolf optimizer (MOGWO) algorithm, which mimic the hierarchal and hunting behavior of gray wolves (Canis lupus), for technical specifications optimization of residual heat removal system (RHRS) of an NPP safety system. The efficiency of MOGWO in optimizing the TSs is demonstrated by comparing its results with a very popular swarm‐based optimization technique named multiobjective particle swarm optimization (MOPSO).