Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions

Environmental and safety risk assessment for sustainable circular production: Case study in plastic processing for fashion products

Even if sustainability and circularity are the most challenging goals today, industrial waste minimization is rarely discussed and practical methods to reduce risks related to hazardous waste in manufacturing processes are not frequently applied yet. The case of Italian company specialized in plastic processing for fashion products, has the chance to design and test a new integrated methodology to reduce the risks for ecosystem and workers associated to hazardous waste. Focusing the attention to standard operations, extraordinary conditions, and emergency situations, all activities included in waste collection, storage and transport are identified and the risks associated to the environmental impacts and the occupational health&safety are analysed. Research results demonstrate the opportunity to adopt one method to analyse both environmental and health&safety risks associated to activities and conditions involved in industrial waste management. The case study confirms the relevance of integrated approaches and the necessity of simplified tools to support companies in adopting integrated risk management.

Contribution of Bayesian networks as a robust tool in risk assessment under sustainability considerations, a case study of Bandarabbas refinery

Background and purpose

Refineries are among the industrial centers that supply the energy and raw materials to downstream industries. To achieve sustainable development goals, creating appropriate balance between economical and environmental goals has always been the focus of managers and policy makers in the societies. Bayesian Network model has become a robust tool in the field of risk assessment and uncertainty management in refineries. The focus of this research is to prioritizing different units from the point of view of social and ecological aspects for facilitating the decision making process in the context of waste material treatment in Bandarabbas refinery in line with the sustainable development goals.

Materials and methods

The methodology of this research is based on risk assessment with the aid of Bayesian Networks. To this end, first material flow analysis of the processes procured risk identification, subsequently influence diagram and Bayesian Network structure were designed. After completing conditional probability tables, finally risk factors were prioritized. What is more, sensitivity analysis of the model performed by applying three approaches namely predictive, diagnostic, and considering only one risk.

Conclusion

According to the risk assessment results, Amine treatment and Fuel units were classified as the most significant risk factors, whereas Pipelines and Plant air & instrument air system were identified as the most environmental friendly units. In addition, sensitivity analysis of the model provided appropriate framework to shed some light on the circumstances of determining dominant risk factors whether only one or concurrently all of the endpoints are evaluated.

Risk assessment of marine oil spills using dynamic Bayesian network analyses

Oil spills are serious threats to the marine ecosystem. Especially when an oil spill is faced with extreme weather, the consequences might be more severe. Until now, no such researches focus on the risk of these extreme scenarios. This paper proposes a novel dynamic assessment method to quantify the risk of oil spills in extreme winds based on dynamic Bayesian networks (DBNs). The physical models of advection, spreading, evaporation, and dispersion are transformed into DBNs, and the vulnerability model is established according to coastline types and socio-economic resources. By integrating all the sub-models, the overall DBN to quantify the dynamic risk of oil spills occurring in extreme winds is obtained. The proposed method is demonstrated by the Laizhou Bay. The developed model is validated by a three-axiom-based approach. Temporal and spatial dynamics of risk caused by oil spills in potential locations could be calculated. Based on the developed DBN, the risk of the Laizhou Bay coast caused by oil spills in annual extreme wind speeds corresponding to different mean recurrence intervals is studied. In addition, the effects of the occurrence time of annual extreme winds are also researched.

Bayesian inference modeling to rank response technologies in arctic marine oil spills

Marine oil spills have a detrimental effect on aquatic systems. Yet, it is challenging to select appropriate technologies in the Arctic because of limited logistics support, inclement weather conditions, and remoteness, and limited research has been conducted in this direction. This article suggests a method to rank the oil response technologies, including mechanical recovery, chemical dispersant, and in-situ burning, for use in Arctic oil spill risk assessment and preparedness planning. The proposed Preference Learning based Bayesian Inference Modeling offers data-driven ranking of systems by learning a label function and considers factors such as ice covered sea areas, cold weather, and spill volume. A data generation system is developed to produce numerous oil spill scenarios, using a state-of-the-art engineering tool. Results demonstrate that the model, while simple, can efficiently and accurately select the best available technique, making it suitable primarily for marine pollution preparedness and response planning in strategic risk assessments.

A Risk Treatment Strategy Model for Oil Pipeline Accidents Based on a Bayesian Decision Network Model

Risk treatment is an effective way to reduce the risk of oil pipeline accidents. Many risk analysis and treatment strategies and models have been established based on the event tree method, bow-tie method, Bayesian network method, and other methods. Considering the characteristics of the current models, a risk treatment strategy model for oil pipeline accidents based on Bayesian decision network (BDNs) is proposed in this paper. First, the quantitative analysis method used in the Event-Evolution-Bayesian model (EEB model) is used for risk analysis. Second, the consequence weights and initial event likelihoods are added to the risk analysis model, and the integrated risk is obtained. Third, the risk treatment strategy model is established to achieve acceptable risk with optimal resources. The risk treatment options are added to the Bayesian network (BN) risk analysis model as the decision nodes and utility nodes. In this approach, the BN risk analysis model can be transformed into a risk treatment model based on BDNs. Compared to other models, this model can not only identify the risk factors comprehensively and illustrate the incident evolution process clearly, but also can support diverse risk treatment strategies for specific cases, such as to reduce the integrated risk to meet acceptable criterion or to balance the benefit and cost of an initiative. Furthermore, the risk treatment strategy can be updated as the risk context changes.

Fire system safety risk cognition model and evaluation of major public safety risks

Along with the expansion of city scale, and the increase in the density of population and buildings, the occurrence of a major public fire safety incident in cities will lead to a serious threat to the overall public safety and the sustainable economic and social development of the relevant region. A simple and practical safety risk assessment method of fire prevention in gas stations is of great value for disaster prevention and application in key industries. The constituent elements of a gas station fire prevention safety system are complex, and include equipment, materials, environment, operation, and other factors. This element of information has randomness and temporal dynamics. To promote the transformation of the safety supervision mechanism of gas stations, realize the dual objectives of risk classification and risk dynamic management, and control the gas stations' safety systems the gas stations safety systems are the objectives of our paper. By taking the "fire" risk point of fire prevention in gas stations' system as the research object, this paper puts forward the cognitive path of fire prevention in gas stations' safety system for risk disasters, and explains the coordination between characteristics of inherent, initial, and real risks and the structure of the risk system's attributes. A realistic risk assessment model of fire risk with inherent and dynamic risks is established. An example was introduced to apply the real risk model, and the results were consistent with the actual prediction results, thereby showing the effectiveness and practicability of this method. This risk assessment method can provide a scientific basis for the prevention of fires and control of the fire prevention safety system, showing the changes in risk levels in different stages, and providing risk warning for project managers in taking prompt corresponding risk control countermeasures and improving the efficiency of risk management.

The Importance of Risk Management: What is Missing in ISO Standards?

The overall aim of this article is to contribute to the further development of the area of risk analysis and risk management in the International Organization for for Standardization (ISO) standards by strengthening its scientific basis. Industrial standards, especially ISO standards, are the tools organizations use to manage their risk, through following their guidance and complying with their requirements. Organizations confirm their compliance with these standards through certification, which means that they heavily depend upon the quality of the ISO standards to enable them to effectively manage their risk. The purpose of this study is to investigate what guidance is given on key elements of risk management and how well ISO standards are aligned with state-of-the-art risk management literature. Eighteen ISO standards, all addressing risk management, were reviewed in this study with regard to risk terminology and guidance. The results of the study confirm the increasing importance of risk management for business. However, the study also shows a lack of guidance on doing risk analysis in the industrial standards examined. The ISO management system standards and guidelines are not aligned with the scientific literature on risk and are not appropriate for the management of risk arising from complex interactions and emergent behavior that is inherent in present-day sociotechnical systems.

Developing a Risk Analysis Strategy Framework for Impact Assessment in Information Security Management Systems: A Case Study in IT Consulting Industry

Organizations must be committed to ensuring the confidentiality, availability, and integrity of the information in their possession to manage legal and regulatory obligations and to maintain trusted business relationships. Information security management systems (ISMSs) support companies to better deal with information security risks and cyber-attacks. Although there are many different approaches to successfully implementing an ISMS in a company, the most important and time-consuming part of establishing an ISMS is a risk assessment. The purpose of this paper was to develop a risk assessment framework that a company followed in the information technology sector to conduct the risk assessment process to comply with International Organization for Standardization (ISO) 27001. The findings analyze the conditions that force organizations to invest in protecting information and the benefits they can derive from this process. In particular, the paper delves into a multinational IT consulting services company that undertakes and implements large business support installation and customization projects. It explains the risk assessment process and the management of the necessary configurations so that its functions are acceptable and in line with information security standards. Finally, it presents the difficulties and challenges encountered.

Prioritizing risk mitigation measures for binary heavy metal contamination emergencies at the watershed scale using bayesian decision networks

Water pollution accidents have the characteristics of high uncertainty, rapid evolution and are difficult to control, thus posing great threats to human health, ecological security, and social stability. During the last 10 years, China has faced the occurrence of six extraordinarily serious heavy metal contamination pollution events at the watershed scale. This has alerted governments and enterprises of the significance of emergency decision-making. To quantitatively prioritize risk mitigation strategies for heavy metal emergencies, a Bayesian Decision Network-based probabilistic model is proposed under the Drivers-Pressures-States-Impacts-Responses (DPSIR) framework. A Copula-based exposure risk model is embedded to simulate the fate of heavy metal ions for each risk reduction option, whose joint probability distributions can then be used as input parameters in the Bayesian Decision Network. This method was applied to the emergency response prioritization for acute Cr(VI)-Hg(II) contamination accidents in the Danshui River watershed. The results indicated that comprehensive measure (M5) was the best option for decreasing ecological and human health risks. As for a single risk mitigation strategy, risk source prevention (M1) was the best alternative compared to exposure pathway interruption (M2) and human/ecological receptor protection (M3-M4). This probabilistic method can not only address the uncertainties between certain risk sources and receptors in the BDN structure, but also realize the risk system optimization in a satisfactory/preferred mode under the DPSIR framework. Overall, it provides the probabilistic risk estimates for watershed-scale risk management and policy making for local risk managers and stakeholders.

Achieving Organisational Alignment, Safety and Sustainable Performance in Organisations

When looking at socio-technical systems from a systems thinking and systemic perspective, it becomes clear that mental models govern the behaviours and determine the achievements of socio-technical systems. This is also the case for individuals, being systems themselves and, as such, being elements of those socio-technical systems. Individual behaviours result from individual perceptions (mental models). These individual behaviours ideally generate the desired outcomes of a system (team/organisation/society) and create value. However, at the same time, mental models and the associated individual behaviour also bring about unwanted consequences, destroying or diminishing value. Therefore, to achieve safety and to attain sustainable safe performance, understanding and managing mental models in organisations is of paramount importance. Consequently, in organisations and society, one needs to generate the required mental models that create successes and, at the same time, to avoid or eliminate damaging perceptions and ideas in order to protect the created value. Generating and managing mental models involves leadership; leadership skills; and the ability to develop a shared vision, mission and ambition, as this helps determine what is valuable and allows for aligning individual mental models with those that preferably govern the system. In doing so, it is possible to create well-aligned corporate cultures that create and protect value and that generate sustainable safe performance. To achieve this aim, a systemic organisational culture alignment model is proposed. The model is based on the model of logical levels of awareness according to Dilts (1990), Argyris’s ladder of inference (1982) and the organisational alignment model proposed by Tosti (1996). Furthermore, ISO 31000 (2009, 2018) and its guidance are proposed as a practical tool to accomplish this alignment and sustainable safe performance in organisations. Altogether, these elements define Total Respect Management as a concept, mental model and methodology.