Application of Dynamic Risk Approach to Mitigate Major Accidents

Major industrial accidents with catastrophic consequences routinely occur around the world and as the industry continue to grow, so will the system complexities and uncertainties. Hence, the need for a more dynamic approach to hazards identification and risk management, to proactively mitigate potential exposures in a real-time manner. Evidences suggests that, dynamic approach to risk management is capable to identifying and assessing developing and increasing industry risks and processes. The Piper Alpha investigation and derivation and adoption of safety case framework in the UK, was a proven approach to mitigate Major Accident Hazards on the front-end design of high-risk process facilities and through their lifespan. With increasing process systems complexities however, dynamic risk management an enhanced conventional method would be the next generation approach to ensure safer operations. This paper aims to stimulate discussions on the novel Dynamic Risk Management (DRM) approach, leveraging on advanced technologies such as Artificial Intelligence (AI) and the 4th Industrial Revolution (4IR) as a new risk management pathway to industrial accident prevention.

ACTIVE RISK MANAGEMENT: PROSPECTS FOR A DYNAMIC APPROACH

The article discusses the directions of development of the risk management system of organizations, based on the use of combined methods of assessment and decision-making in dynamic risk management. The relevance of the modernization of the complex for assessing and managing the risks of enterprises is caused by the processes of aggravation of competitive rivalry in the commodity markets, in the field of information technology and international politics, taking place in the world economy.

Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain

Foodborne pathogens are a major contributor to foodborne illness worldwide. The adaptation of a more quantitative risk-based approach, with metrics such as Food safety Objectives (FSO) and Performance Objectives (PO) necessitates quantitative inputs from all stages of the food value chain. The potential exists for utilization of big data, generated through digital transformational technologies, as inputs to a dynamic risk management concept for food safety microbiology. The industrial revolution in Internet of Things (IoT) will leverage data inputs from precision agriculture, connected factories/logistics, precision healthcare, and precision food safety, to improve the dynamism of microbial risk management. Furthermore, interconnectivity of public health databases, social media, and e-commerce tools as well as technologies such as blockchain will enhance traceability for retrospective and real-time management of foodborne cases. Despite the enormous potential of data volume and velocity, some challenges remain, including data ownership, interoperability, and accessibility. This paper gives insight to the prospective use of big data for dynamic risk management from a microbiological safety perspective in the context of the International Commission on Microbiological Specifications for Foods (ICMSF) conceptual equation, and describes examples of how a dynamic risk management system (DRMS) could be used in real-time to identify hazards and control Shiga toxin-producing Escherichia coli risks related to leafy greens.

Evolution of Approaches to Risk Management in Organizations

The article is aimed at studying the evolution of approaches to risk management in organizations. As result of the carried out study, three existing approaches were identified: the «Three Lines of Protection» model (2013), the «Three Lines» model (2020), and the «Dynamic Risk Management» model (2020). It is proposed to consider the «Three Lines of Protection» model as a transition from non-systemic risk management in organizations to a structured vision of this process with a clear definition of the responsibilities of key participants. The article explains the advantages and disadvantages of this approach. Its advantages include simplicity and clarity in use in practice. Among the shortcomings of the model are the following: an overly structured approach to risk management in organizations; too cautious attitude to possible risks; dubious versatility of the model; imperfection of its structure. Three directions for possible improvement of the model are defined. It is identified that in 2020 there was an updated approach to risk management in organizations – the «Three Lines» model. A comparison of the «Three Lines of Protection» model with the «Three Lines» model was carried out. The main advantages of the new approach are determined. The emergence of an alternative vision of the process of risk management and control in organizations against the background of new social challenges – the «Dynamic risk management» is researched. The advantages of this approach include its priority areas in risk management in organizations, i.e.: risk management taking into account their consequences, risk management based on actions, risk management with an orientation towards digital technologies. The differences between traditional models and the newest approach are specified. It is determined that each of the three principles of the latest approach improves the quality of risk management in organizations as participants in the survey conducted by Gartner company. Purpose of further research in this direction be the institutional registration of coordinated interaction between the two directions of the modern model of risk management and control of the risks.

A New Risk Management Methodology for Infrastructure Based on Real-Time Monitoring and Dynamic Interventions: An Example Application on an Air Handling Unit

For an effective risk management of complex buildings it is required to dynamically estimate the risk on the service and take proper responsive measures to contrast it. This implies being able to estimate the evolving probabilities of failures over time and the way their occurrence is trust in affecting the service. This is now possible thanks to the advent of new sensing technologies and data-driven models to estimate failure probabilities, as well as solid risk management methodologies to estimate their effect on the service. However, it needs to be considered that the implementation of a dynamic risk management in standard building operation has to consider the reconfiguration of some processes to include the use of enabling technologies. In this paper a new dynamic risk management methodology is proposed to consistently (i) model the service, estimate the risk, first (ii) statically, using fault tree analysis, and then (iii) dynamically, using sensing technologies for data gathering and data-driven models for dynamic probability estimate, and finally (iv) implement the required intervention measures to minimize the risk. Then an application of the methodology is presented, for the risk management of an air handling unit, using a convolutional neural network, and its outcomes discussed. Conclusions are also drawn on the implications of integrating such a methodology in the current whole building risk management process and several outlooks are proposed.