Risk Mitigation Framework Research Articles

Proactive Obsolescence Management Methods for C5ISR Systems: Insights from Practitioners

Obsolescence is a significant challenge for the Command, Control, Communications, Computers, Cyber, Intelligence, Surveillance and Reconnaissance (C5ISR) community. Obsolescence can negatively affect a C5ISR system’s cost, schedule, performance, and readiness. This article examines the challenge of obsolescence for C5ISR systems by focusing on the U.S. Army at Aberdeen Proving Ground, Maryland, and their industry partners. Data were gathered by conducting interviews with 20 individuals who had experience with C5ISR systems: 10 government Army civilians and 10 industry partner employees. The objective of this study is to synthesize insights from the experiences of government and industry practitioners that mitigate diminishing manufacturing sources and material shortages (DMSMS) challenges. The obsolescence mitigation areas described in this article include proactive and reactive obsolescence mitigation, obsolescence mitigation methods, opportunities for alternative components and planned improvements, the importance of DMSMS contracting language, and obsolescence management practices to avoid. This article also offers approaches grounded in practitioner experiences to mitigate obsolescence through a preliminary proactive obsolescence management model, risk mitigation framework, and metrics. The combination of the obsolescence mitigation approaches discussed in this article has the potential to achieve greater system readiness, more availability, better maintainability, and lower costs for C5ISR systems.

Navigating the Storm: Cutting-Edge Risk Mitigation and Analysis for Volatile Markets

In volatile markets, risk mitigation and analysis play a crucial role in ensuring financial stability and profitability. This paper presents a new framework for risk mitigation and analysis tailored specifically for volatile markets. The framework combines data analysis, statistical modeling, and domain expertise to provide a inclusive and proactive approach to managing risks. The key theories and beliefs underlying the framework are discussed, with a focus on the use of logistic regression as the core risk predictor. The framework's development process, including data collection and preprocessing, feature engineering, and model selection, is outlined. Moreover, the incorporation of the Weight of Evidence (WoE) technique to enhance the interpretability and effectiveness of the logistic regression model is explained. The proposed framework aims to encourage market participants with valuable insights into risk levels and facilitate informed decision-making and effective risk mitigation strategies in volatile market environments.

Does the Internal Control Environment Drive the Performance of Cooperatives? A PLS-SEM Perspective

This study examined the relationship between the internal control environment and financial performance of deposits taking savings and credit cooperative societies (DT-SACCOs) in Kenya. Using the Agency theory as a framework, the study focused on integrity and ethical values, commitment to competence, organization’s structure, management philosophy, and operating styles. The research utilized a quantitative approach and PLS-SEM as the main design, with data collected through a structured questionnaire from 110 DT-SACCO managers. The study revealed that the internal control environment positively impacts financial performance. Commitment to competence (β = 0.441, p <0.05), the organization’s structure (β= 0.466, p <0.05), and management philosophy and operating styles (β= 0.267, p <0.05), while integrity and ethical value had a negative impact (β=-0.317, p >0.05). The study provides valuable insights for policymakers, regulators, and DT-SACCOs, emphasizing the importance of a strong internal control framework for effective risk mitigation and enhanced governance practices. By focusing on building a robust internal control environment, DT-SACCOs can optimize their financial performance. The study extends the literature on the link between internal control environment and performance in Kenya and provides a basis for future research.

A Risk Mitigation Framework for Community-Based Financing in Geothermal

A Risk Mitigation Framework for Community-Based Financing in Geothermal

Machine Learning–Based Decision Support Framework for Construction Injury Severity Prediction and Risk Mitigation

Construction is a key pillar in the global economy, but it is also an industry that has one of the highest fatality rates. The goal of the current study is to employ machine learning in order to develop a framework based on which better-informed and interpretable injury-risk mitigation decisions can be made for construction sites. Central to the framework, generalizable glass-box and black-box models are developed and validated to predict injury severity levels based on the interdependent effects of identified key injury factors. To demonstrate the framework utility, a data set pertaining to construction site injury cases is utilized. By employing the developed models, safety managers can evaluate different construction site safety risk levels, and the potential high-risk zones can be flagged for devising targeted (i.e., site-specific) proactive risk mitigation strategies. Managers can also use the framework to explore complex relationships between interdependent factors and corresponding cause-and-effect of injury severity, which can further enhance their understanding of the underlying mechanisms that shape construction safety risks. Overall, the current study offers transparent, interpretable and generalizable decision-making insights for safety managers and workplace risk practitioners to better identify, understand, predict, and control the factors influencing construction site injuries and ultimately improve the safety level of their working environments by mitigating the risks of associated project disruptions.

Strategic framework for natural disaster risk mitigation using deep learning and cost-benefit analysis

Abstract. Given trends in more frequent and severe natural disaster events, developing effective risk mitigation strategies is crucial to reduce negative economic impacts, due to the limited budget for rehabilitation. To address this need, this study aims to develop a strategic framework for natural disaster risk mitigation, highlighting two different strategic implementation processes (SIPs). SIP-1 is intended to improve the predictability of natural disaster-triggered financial losses using deep learning. To demonstrate SIP-1, SIP-1 explores deep neural networks (DNNs) that learn storm and flood insurance loss ratios associated with selected major indicators and then develops an optimal DNN model. SIP-2 underlines the risk mitigation strategy at the project level, by adopting a cost–benefit analysis method that quantifies the cost effectiveness of disaster prevention projects. In SIP-2, a case study of disaster risk reservoir projects in South Korea was adopted. The validated result of SIP-1 confirmed that the predictability of the developed DNN is more accurate and reliable than a traditional parametric model, while SIP-2 revealed that maintenance projects are economically more beneficial in the long term as the loss amount becomes smaller after 8 years, coupled with the investment in the projects. The proposed framework is unique as it provides a combinational approach to mitigating economic damages caused by natural disasters at both financial loss and project levels. This study is its first kind and will help practitioners quantify the loss from natural disasters, while allowing them to evaluate the cost effectiveness of risk reduction projects through a holistic approach.

Blockchain architecture and its applications in a bank risk mitigation framework

This study proposes a simple two-period model to consider consumers’ borrowing behaviour in a decentralised consensus and information distribution platform. Based on this model, we develop a bank risk mitigation framework and find that decentralised digital identity and encryption technology are the most important factors for attaining market equilibrium between decentralised consensus and information distribution. Specifically, the greater the scope of digital identity construction and the more blockchain consensus records there are, the less likely the borrower will default. Our study provides meaningful practical implications for bankers and policy regulators to help them better understand consumers’ borrowing behaviour and decisions to default.

Assessment of existing reinforced‐concrete bridges under road‐traffic loads according to the new Italian guidelines

AbstractThe Italian “Guidelines for risk classification, safety assessment and structural health monitoring of existing bridges” were recently approved to define the technical principles for the management of the national network of existing bridges. This is a powerful tool to establish a common framework for risk mitigation and safety assessment of reinforced concrete (R.C.) bridges that represent most of the existing stock. The guidelines are code‐based and allow practitioners to deal with the concepts behind the modern design in terms of target reliability and partial safety factors as well as properly developed traffic‐load models. This paper illustrates a novel methodology for safety assessment and performance class definition of existing R.C. bridges according to the guidelines. A prestressed R.C. bridge is finally presented as a case study to show the completeness of the procedure.

Implementing Industrial Symbiosis Incentives: an Applied Assessment Framework for Risk Mitigation

Industrial symbiosis (IS) is a business model that proposes symbiotic exchanges, allowing the flow of resources, wastes, and utilities between companies. In recent years, IS initiatives have been exponentially growing around the world. This can be attributed to the increasing awareness on the possibility of obtaining economic, environmental, and social benefits through the implementation of this model. Despite the exponential growth of IS initiatives, the companies are still facing problems in the achievement of reliable and permanent synergies. Over the years the literature has identified several factors in the IS emerging process. Incentives are among these factors, being defined as unlocking tools or mechanisms related to diverse dimensions such as economic, political, social, intermediaries, process, and technology. Authors believe that the large-scale implementation of IS incentives has not been properly addressed. In order to promote facilitated IS implementation and achieve a replicator effect, incentives should be fully addressed. In many case studies, it has been observed that the incentives for IS can be threatened by risks, compromising the implementation, and hindering the emerging process. This study developed a dedicated framework that is composed of incentive identification from best practices of IS and expert consultation; a risk assessment model based on risk factors identification and clustering; and finally, the mitigation actions based on the assessment outputs. The main result of this study is one set of mitigations actions that correlate the implementation levels (clusters) and the potential stakeholders involved.

Occurrence, behavior, and human exposure and health risks of potentially toxic elements in edible mushrooms with focus on Africa.

Understanding the occurrence, behavior, and fate of potentially toxic elements (PTEs) in the substrate-mushroom-human nexus is critical for assessing and mitigating their human health risks. In this review, we (1) summarized the nature, sources, and biogeochemical behavior of PTEs in the substrate-mushroom systems; (2) discussed the occurrence, exposure, and human health risks of PTEs in mushrooms with emphasis on African geological hotspots such as metalliferous and highly mineralized substrates; (3) developed a 10-step conceptual framework for identifying, assessing, and mitigating the human health risks of PTEs in mushrooms, and highlight future directions. High human exposure risks potentially exist in Africa due to the following: (1) widespread consumption of mushrooms from various metalliferrous and highly mineralized substrates such as serpentines and mine waste dumps, (2) inadequate and poorly enforced environmental health and food safety regulations and policies, (3) limited environmental and human health monitoring data, and (4) potential synergistic interactions among PTEs in mushrooms and human health stressors such as a high burden of human diseases and infections. Although the human health effects of individual PTEs are well known, scientific evidence linking human health risk to PTEs in mushrooms remains weak. A framework for risk assessment and mitigation, and future research directions are recommended.

An Integrated Flood Risk Assessment and Mitigation Framework: A Case Study for Middle Cedar River Basin, Iowa, US

Property buyout is one of the most frequently preferred flood mitigation applications by decision-makers for long-term risk reduction. Due to its high-level funding requirements as a mitigation solution, it requires extensive benefits and costs analysis for the selected region. Many communities in the State of Iowa experienced extreme flood events (i.e., 1993, 2008, 2014, 2019), which resulted in a heavy economic impact over the last couple of decades. Nearly 3000 property acquisitions have been made between 2007 and 2017 using federal programs. This study presents a web-based Flood Risk Assessment and Mitigation Environment (FRAME), which provides visual data analytics capabilities to analyze property and community level benefit-cost analysis for property acquisitions. The FRAME allows users to explore and visualize historical mitigation projects and buyouts, and evaluate avoided damages for their communities. As a case study, a detailed benefit-cost analysis of historical property buyouts and direct losses of existing properties in the Middle Cedar watershed in Iowa is studied using stream gauge data from the United States Geological Survey (USGS). Projected stream gauge datasets, which are outputs of two climate scenarios (A1FI-fossil intensive and A2-low emission), are also utilized to assess future avoided losses for acquisitions and possible direct economic losses for existing properties. Case study results indicate that the average benefit-cost ratio (BCR) for buyouts in the studied region is around 0.86. Nearly half of the buyouts reached 4.72 BCR in low emission and 6.3 BCR in fossil intensive climate projections if future floods are considered.

Towards an Online Risk Mitigation Framework for Political Brands Subject to Computational Propaganda

The influence of computational propaganda on democratic processes globally has necessitated the exploration of mitigation strategies for political brands. The risk associated with computational propaganda includes the spread of misleading information about a political brand over social networking sites through bots (i.e. automated software applications that are programmed to do certain tasks) and newsfeed algorithms at rapid speeds. Apart from bots being used to facilitate the spread of disinformation (i.e. misleading information spread with the intent to manipulate or deceive), human curators also play a role. Research into addressing computational propaganda in the field of political communication is limited, compelling the need to look to consumer brand experts for insight. The current study used semi-structured interviews of senior consumer brand social media practitioners and applied content analysis to develop a framework for mitigating online brand risk associated with computational propaganda for political brands. This computational propaganda brand risk mitigation framework proposes two approaches to curb the effects of computational propaganda, namely, a preventative approach and a recovery approach. Based on established theoretical and practical concepts, this mitigation framework could guide the easing of online brand risk and associated crises for political brands.

Safeguards for Using Viral Vector Systems in Human Gene Therapy: A Resource for Biosafety Professionals Mitigating Risks in Health Care Settings.

Health care workers who work daily with human body fluids and hazardous drugs are among those at the highest risk of occupational exposure to these agents. The Occupational Safety and Health Administration's (OSHA) Bloodborne Pathogens Standard (29 CFR 1910.1030) prescribes safeguards to protect workers against health hazards related to bloodborne pathogens. Similarly, the United States Pharmacopeia General Chapter 800 (USP <800>), a standard first published in February 2016 and implementation required by December 2019, addresses the occupational exposure risks of health care workers at organizations working with hazardous drugs. With emerging technologies in the field of gene therapy, these occupational exposure risks to health care workers now extend beyond those associated with bloodborne pathogens and hazardous drugs and now include recombinant DNA. The fifth edition of the Biosafety in Microbiological and Biomedical Laboratories (BMBL) and the National Institutes of Health Guidelines for Research Involving Recombinant or Synthetic Nucleic Acid Molecules (NIH Guidelines) mostly govern work with biohazardous agents and recombinant DNA in a laboratory research setting. When gene therapy products are utilized in a hospital environment, health care workers have very few resources to identify and reduce the risks associated with product use during and after the administration of treatments. At the Abigail Wexner Research Institute at Nationwide Children's Hospital, a comprehensive gap analysis was executed between the research and health care environment to develop a program for risk mitigation. The BMBL, NIH Guidelines, World Health Organization Biosafety Manual, OSHA Bloodborne Pathogens Standard, and USP <800> were used to develop a framework for the gap analysis process. The standards and guidelines for working with viral vector systems in a research laboratory environment were adapted to develop a program that will mitigate the risks to health care workers involved in the preparation, transportation, and administration of gene therapies as well as subsequent patient care activities. The gap analysis identified significant differences in technical language used in daily operations, work environment, training and education, disinfection practices, and policy development between research and health care settings. These differences informed decisions and helped the organization develop a collaborative framework for risk mitigation when a gene therapy product enters the health care setting. With continuing advances in the field of gene therapy, the oversight structure needs to evolve for the health care setting. To deliver the best outcomes to the patients of these therapies, researchers, Institutional Biosafety Committees, and health care workers need to collaborate on training programs to safeguard the public trust in the use of this technology both in clinical trials and as FDA-approved therapeutics.

Implementing a framework for digital preservation risk assessment and mitigation at the US National Archives

The United States National Archives and Records Administration (NARA) required a methodology to analyse its holdings in order to better understand its digital preservation risks, and elected to undertake a file format profile and risk analysis of its born-digital electronic records to inform the creation of preservation plans. The development of this methodology allows NARA to document its risk assumptions, understand the state of its digital preservation risks, and provide transparency into its current risk mitigation decisions and capabilities. This article reviews the process for the creation of an electronic record holdings file format profile, development of a format risk analysis and preservation prioritisation instrument, and the creation and public release of draft digital preservation actions plans.

Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation

The probabilistic analysis on condition monitoring data has been widely established through the energy supply process to specify the optimum risk remediation program. In such studies, the fluctuations and uncertainties of the operational data including the variability between source of data and the correlation of observations, have to be incorporated if the efficiency is of importance. This study presents a novel probabilistic methodology based on observation data for signifying the impact of risk factors on safety indicators when consideration is given to uncertainty quantification. It provides designers, risk managers and operators a framework for risk mitigation planning within the energy supply processes, whilst also assessing the online reliability. These calculations address the involved and, most of the time, unconsidered risk to make a prediction of safety conditions of the operation in future. To this end, the generalized linear model (GLM) is applied to offer the explanatory model as a regression function for risk factors and safety indicators. Hierarchical Bayesian approach (HBA) is then inferred for the calculations of regression function including interpretation of the intercept and coefficient factors. With Markov Chain Monte Carlo simulation from likelihood function and prior distribution, the HBA is capable of capturing the aforementioned fluctuations and uncertainties in the process of obtaining the posterior values of the intercept and coefficient factors. To illustrate the capabilities of the developed framework, an autonomous operation of Natural Gas Regulating and Metering Station in Italy has been considered as case study.

Network-of-Networks Framework for Multimodal Hazmat Transportation Risk Mitigation: Application to Used Nuclear Fuel in Canada

Abstract The transportation of hazardous materials (hazmat) is a complex process that continues to receive considerable attention from practitioners and researchers alike. A multimodal system that ...

Parameterized fragility models for multi-bridge classes subjected to hurricane loads

Past hurricanes in the United States have highlighted the structural vulnerability of bridges exposed to storm surge and wave loads, triggering the development of fragility models. These models are essential for estimating the likelihood of structural failures in bridges during extreme events to support risk mitigation and resilience enhancement. However, the literature still lacks fragility models that can accommodate the effects of spatial variability of surge and wave loads and bridge characteristics like superstructure type and span slope. In addition, the presence of different types of superstructure within the same bridge necessitates the development of fragility models which can handle such variations. In this light, first, this study develops fragility models for individual spans of different bridge classes (concrete girder, steel girder, slab, box girder) considering spatial variability of wave loads and variations in structural characteristics. Herein, nonlinear dynamic analyses are employed for calculating the structural response and stepwise logistic regression is used for deriving the predictive fragility models. Then, a bridge system fragility assessment framework is proposed for system reliability assessment of the entire bridge, which accounts for different types of superstructures within the bridge and partially correlated span failures. In order to demonstrate the application of the fragility models and the system fragility assessment framework for regional level risk mitigation, the performance of bridges in the Houston-Galveston region is evaluated for two storm scenarios. The results show that the system failure probability obtained using no correlated span failure assumption is close to the actual failure probability obtained using the proposed approach highlighting the effects of correlations (or the lack of) on the system fragility.

Risk in port logistics: risk classification and mitigation framework

Ports are important nodes in maritime transportation networks. However, port managers face different types of risks in port operations which may disrupt cargo flows in the logistics chain. These risk factors are expected to increase in view of such global issues as climate change and geopolitical changes. An overview of risk classifications and mitigation strategies in port logistics can help academics and practitioners prepare for managing port logistics risks. Consequently, this paper presents a conceptual discussion of port logistics risks and illustrates an exemplary define-measure-analyse-improve-control (DMAIC) implementation framework to highlight potential risk management and mitigation strategies in port operations. Our discussion advances knowledge on the different risk factors with a practical step-by-step approach for addressing the risk in port logistics.

Risk in port logistics: risk classification and mitigation framework

Ports are important nodes in maritime transportation networks. However, port managers face different types of risks in port operations which may disrupt cargo flows in the logistics chain. These risk factors are expected to increase in view of such global issues as climate change and geopolitical changes. An overview of risk classifications and mitigation strategies in port logistics can help academics and practitioners prepare for managing port logistics risks. Consequently, this paper presents a conceptual discussion of port logistics risks and illustrates an exemplary define-measure-analyse-improve-control (DMAIC) implementation framework to highlight potential risk management and mitigation strategies in port operations. Our discussion advances knowledge on the different risk factors with a practical step-by-step approach for addressing the risk in port logistics.

Economic benefit of ensuring uninterrupted water supply during prolonged electricity disruptions – City of Tshwane case study

Mitigating the impact of electricity disruptions on water supply was investigated as a case study on the City of Tshwane. This study found that current institutional arrangements between electricity suppliers (Eskom), water service providers and water service authorities are insufficient. Therefore, a Risk Analysis and Mitigation Framework of Integrated Water and Electricity Systems, or RAMFIWES, was developed. Risks associated with water supply interruptions due to electricity disruption events were analysed. Risk categories that were addressed are short-term disruptions of less than one day, medium-term disruptions of up to a week and long-term electricity disruptions up to a month or even longer. The direct economic benefits of ensuring uninterrupted water supply in the event of electricity disruption events were analysed through cost versus benefit analyses. It was found that the direct benefit/cost ratio of supplying water during electricity disruption events is 5.6 for wet-industries and an exceptional 117 for other economic sectors in Tshwane. The overall benefit/cost ratio is 15.5. This benefit is possible at a low increase in the normal municipal bills of only 0.5%.