Risk Identification Framework Research Articles

Vulnerability-oriented risk identification framework for IoT risk assessment

The proliferation of Internet of Things (IoT) systems across diverse applications has led to a notable increase in connected smart devices. Nevertheless, this surge in connectivity has induced a broad spectrum of vulnerabilities and threats, jeopardizing the security and safety of IoT applications. Security risk assessment methods are commonly employed to analyze risks. However, traditional IT and existing IoT-tailored security assessment methods often fail to fully address key IoT aspects: complex assets intercommunication, dynamic system changes, assets’ potential as attack platforms, safety impacts of security breaches, and assets resource constraints. Such oversights lead to significant risks being overlooked in the IoT ecosystem. In this paper, we propose a novel vulnerability-oriented risk identification framework comprising a four-step process as a core element of IoT security risk assessment, applicable to any IoT system. Our process enhances both traditional and IoT-specific security risk assessment methods by providing tailored approaches that address their crucial oversights for comprehensive IoT risk assessment. We validate our process with a case study of an IoT smart healthcare system using a proposed expert-driven approach. The results confirm that our process effectively identifies critical attack scenarios originating from the lack of proper security measures, mobility, and intercommunication processes of IoT devices in the healthcare system. Furthermore, our analysis reveals potential attacks that exploit the IoT devices as platforms to target the backend and user domains. We demonstrate the feasibility of our process for identifying realistic risks by conducting simulations of two derived attack scenarios using the Contiki Cooja network simulator.

An intelligent framework for rework risk identification in prefabricated construction processes based on compliance checking

An intelligent framework for rework risk identification in prefabricated construction processes based on compliance checking

Early warnings of systemic risk using one-minute high-frequency data

This study uses high-frequency principal component analysis (HF PCA) to extract information from stock prices to monitor and measure systemic risk in the financial system. The empirical analysis carried out in this study using one-minute returns of stocks included in the Russel 3000 index from 2003 to 2021 shows a clear relationship between the size of the realized eigenvalues and systemic increases in financial stress. We also found that realized eigenvectors can trace the role of firms/sectors as potential sources of financial stress in different periods. We measured the transmission of shocks from (to) the financial sector to (from) other sectors and the real economy. This provides a tool for analyzing the spread of this financial instability that could affect the functioning of the financial system to the extent that the real economy is seriously damaged. HF PCA is a risk identification framework that allows policymakers and central banks to detect risks in real-time and address potential threats to financial stability with the most appropriate policy tools.

Risk Management in Global Supply Chains: Addressing Vulnerabilities in Shipping and Logistics

This paper explores the critical aspects of risk management in global supply chains, with a specific focus on vulnerabilities within shipping and logistics. The interconnected nature of today's supply chains exposes them to various risks, ranging from natural disasters and geopolitical tensions to operational challenges and cybersecurity threats. The paper outlines a comprehensive framework for risk identification, assessment, and mitigation, emphasizing the importance of proactive strategies in addressing potential disruptions. Diversification of suppliers and transportation routes, technological investments, insurance coverage, and enhanced collaboration are highlighted as key mitigation measures. Through case studies, the paper examines successful risk management practices and draws insights from failures. Additionally, it delves into emerging trends, such as technological advancements and climate change adaptation, providing a forward-looking perspective on evolving risks. The conclusion underscores the dynamic nature of global supply chain risks and advocates for continuous improvement in risk management practices to ensure resilience and sustainability.
 Keywords: Risk Management, Global, Supply Chains, Vulnerabilities, Shipping and Logistics.

Data-Driven Approach to Risk Identification for Major Transportation Projects: A Common Risk Breakdown Structure

Identifying and evaluating risks is one of the most essential steps in risk management in construction projects. When technical and managerial complexity increases in major transportation projects, this becomes even more important. Currently, project teams are assumed to identify risks mostly based on their experience and expertise. It is a major issue that some state departments of transportation (DOT) project teams lack the risk management experience. This study proposes using a data-driven approach to unify and summarize existing risk documents to create a comprehensive risk breakdown structure (RBS). As a preliminary risk identification framework, a consolidated RBS were developed, using content analysis of public risk reports by various DOTs. Then, comparison was made between the developed RBS with 70 US transportation projects' risk registers. Natural language processing techniques, bidirectional encoder representations from transformers, were employed to calculate semantic text similarity to determine what percentage of risks are covered by generic RBS. The results showed that 70 generic risk templates cover almost 81% of the identified risks in the database of 70 major projects which is about 6000 individual risks. Project parties can use these results to discuss and identify context-specific risks as a starting point. The study also determined the interactions between risk items based on their co-occurrence using historical data. Research findings revealed the importance of considering interdependencies between risks in future studies.

IScene: An interpretable framework with hierarchical edge services for scene risk identification in 6G internet of vehicles

AbstractScene risk identification is essential for the traffic safety of Internet of Vehicles. However, the performance of existing risk identification approaches is heavily limited by the imbalanced historical data and the poor model interpretability. Meanwhile, the large processing delay and the potential privacy leakage threat also restrict their application. In this paper, a novel risk identification model is proposed that leverages the synthetic minority over‐sampling technique nearest neighbor (SMOTEENN) method to balance between high‐risk and low‐risk data. The risk identification model has fine interpretability by using recursive feature elimination cross validation (RFECV) with the Shapley additive explanation (SHAP) to analyze the importance of different features, and further elaborately design the Focal Loss function to tackle the disparity between the difficult and easy sample learning. The proposed interpretability scene risk identification framework, named iScene, is built on the infrastructure of 6G space‐air‐ground integrated networks (SAGINs) with blockchain assistance. The model updata efficiency and privacy preservation are effectively enhanced. An elastic computing offloading algorithm is applied to minimize the system overhead under the hierarchical edge service architecture. The experimental evaluation is carried out to verify the effectiveness of the proposed risk identification framework. The results indicate that the G‐Mean value is increased by 23.4%, while the task average response delay is reduced by 21.2%, compared to that in the traditional risk identification approaches with local computing services.

A Holistic Framework for Proactive Risk Identification in the Evolving Automotive Landscape

A Holistic Framework for Proactive Risk Identification in the Evolving Automotive Landscape

An integrated FMEA framework considering expert reliability for classification and its application in aircraft power supply system

The risk assessment of the integrated drive generator (IDG) is a significant problem in flight safety that directly affects the operation of the aircraft power system However, the current IDG risk assessment methods still have many limitations, leading to risk identification and classification that is not accurate and scientific. Therefore, this paper constructs a failure mode and effect analysis (FMEA) framework for IDG risk identification and classification considering expert reliability in unbalanced hesitant fuzzy linguistic term sets (UHFLTSs) environment. First, since the UHFLTSs can effectively describe uncertain and imprecise linguistic information of decision makers (DMs) and adapt it to practical applications, it is specially chosen to depict the evaluation of DMs. Second, the importance of experts is fully assessed by combining expert reliability and expert weight since the variation in expert reliabilities may have a substantial impact on the final sorting outcomes. In addition, the group best–worst method (GBWM) is combined with the entropy method to determine the risk factor weight. Third, to build up the accuracy and stability of classification, a new integrated TOPSIS-ELECTRE TRI decision framework is proposed to classify failure modes more rationally. The sensitivity analysis of parameters demonstrates the robustness of the proposed method. Meanwhile, the effectiveness and superiority of the method are verified both qualitatively and quantitatively. By comparing with several typical sorting methods, the proposed method has more realistic and stable classification results.

Flight Training Risk Identification and Assessment Based on the HHM-RFRM Model

Due to the unavoidable operational risks and insufficient risk management capabilities of beginner pilots in flight training, the challenge of risk control in aviation schools has become increasingly prominent. To ensure the safety of flight training in aviation schools and to reduce costs and increase revenue, the essential prerequisite for improving efficiency is risk management. Therefore, it is necessary to explore risk identification and assessment methods. This paper adopts the holographic modeling (HHM) method and risk filtering, rating and management (RFRM) theory. First, the HHM idea is used to construct a risk identification framework (HHM-PAVE) for flight training. Second, based on the dual criteria, multiple criteria and cloud model (CM) in the RFRM approach, an improved risk assessment matrix-cloud model (IPC-CM) is proposed and combined with the N-K model and Bayes’ theorem to propose a coupled risk scenario hazard measurement model (CR-HM) based on the HHM-RFRM approach in risk assessment. In the assessment process, the impact of risk factors on system stability as well as the uncertainty problem and coupling–risk quantification problem in expert assessment are considered to obtain scientific and objective quantitative assessment results. Finally, the risk identification and assessment experiments were conducted using HHM-RFRM on the flight training. The results show that the method can more accurately identify critical risk factors in a flight training system and provide a new perspective for risk prevention and control.

Distribution, chemical speciation and human health risk assessment of metals in soil particle size fractions from an industrial area

The problem associated with metal contamination in the soil is intensified in the vicinity of industrial areas as they are more susceptible to discharges from medium to small scale industries. However, limited studies are available that consider soil size fractions along with their speciation due to multi metal contamination. This information is essential to apprehend their individual and competitive mobility, bioavailability and possible de-contamination strategies. In the present study, soil samples (n = 10) were collected from Ranipet industrial area, India and analysed for total metal concentrations (Al, Cd, Cr, Cu, Fe, Mn, Pb, and Zn). The ecological risk assessment indicated substantial anthropogenic enrichment of chromium along with moderate enrichment of copper, manganese and zinc. Further, the samples were fractionized into 300–150, 150–50, 50–22 and < 22 µm; the finer fraction concentrations were used to assess human health risks. The Hazard Index (HI) values reached 2.37 for children and 0.30 in adults from non-carcinogenic risk; indicated children are at higher risk. The soil exposure risk assessment study in three different scenarios were considered; results indicated possible non-carcinogenic risk to the non-residents working in study area. The Cancer risk (CR) values followed the order CR (ingestion) > CR (dermal contact) > CR (inhalation). The speciation study performed in bulk and size fractions indicated the bioavailable forms dominated residual fractions irrespective of sizes but varied with metals. This study can be considered as a framework for risk identification and assessment from multi metal contamination in industrial area having small and shallow waterbodies.

Integrated system for the proactive analisys of risk infection in patient’s surgical route

Abstract Background The most common hospital safety incidents are Hospital-Acquired-Infections (HAI) and among these Surgical Site Infections (SSIs). Our study proposes the use of a proactive system to manage risk combining the new Risk Identification Framework by WHO (IPCAF), the Lean method and the hospital’s Procedure Analysis. Each of the methods has pros and cons, and there is no existing literature that researches the concurrent use of all three methods. We focused on analysing patients’ surgical route to demonstrate that using an integrated system for preventing SSIs delivers enhanced results and significantly contibutes to a reduction in occurence of SSIs. Methods We conducted a retrospective observational study from 18 March 2019 to 20 April 2019 at Azienda Ospedaliera Universitaria Federico II di Napoli, Italia (Europa). The study is structured in 3 phases: Phase 1- application of proactive risk management tools (18 March- 15 April 2019); Phase 2-integration of results with the elaboration of a single system for proactive risk management (15-20 April 2019); Phase 3- collection of epidemiologic data concerning SSI. We used the incidence of surgical site as efficiency indicator (1-10 March 2022). The endpoints identified were: Primary Endpoint: a reduction of infection occurrence in surgical sites Secondary Endpoint: identification of critical points and control points within the surgical process with relevant corrective measures Results The rate of incidence of SSIs was selected as the efficacy indicator for the system. Our study recorded a 2.40% incidence rate for SSIs in 2020, compared to an incidence rate of 3.80% in 2018 and of 3.5% in 2017. Conclusions Considering the economic impact of the infections, along with the increased incidence of mortality and morbidity, employing all available tools to try and reduce SSIs incidence becomes paramount. A small reduction can produce significant cost savings that can be invested in other prevention programs. Key messages • Integrated system in proactively and promptly identifying risks related to patients’ surgical routes is effectiveness. • The system can be adapted to different healthcare settings, to prevent adverse incidents by employing a risk management strategy, and to further enhance existing strategies.

Preliminary Study on Risk Identification and Assessment Framework for Fusion Radioactive Waste Management

Fusion reactors are expected to be safer, more environmentally friendly, and to have a lower nuclear proliferation risk, compared with other nuclear energy systems. However, it is widely recognized that a large amount of radioactive materials will be produced by a fusion reactor. Therefore, it is important to fully understand the overall radiation risk level of fusion radioactive wastes (radwaste) compared with existing nuclear energy systems. Studies on the treatment of the fusion radwaste have been currently focused on three ultimate options: clearance, recycling, and disposal by activation assessment of radioactive materials from the operation and decommissioning of fusion reactors. However, the radiation risk in the management of fusion radwaste, especially in the final disposal, was seldom studied. Based on the comparative analysis of fusion radioactive waste with ITER and fission reactors (e.g., pressurized water reactor, PWR), this paper tries to discuss how to determine the radiation risk in the process of fusion radwaste management on the premise of the current feasible industrial technology. On this basis, a risk assessment framework for repository disposal under normal degradation and external events is proposed.

Predictive risk modeling for major transportation projects using historical data

Most of the construction practices in the field of risk identification focus on the expertise, views, and judgments of subject matter experts. While the conventional expert-based approaches provide worth, several challenges exist due to time-consuming and expensive aspects. Moreover, limited experience in major projects makes public agencies susceptible to subjective judgment biases. To address these limitations, this study introduced a data-driven framework for risk identification using historical data and artificial intelligence techniques, particularly word embedding models. The model matches various risk items in past projects by considering the semantic meaning of words to find high frequency and consequence risks. Risk registers from more than 70 U.S. major transportation projects form the input dataset. The model is tested with more than 66% recall and 0.59 F1-score for risk detection for new projects. Acquired knowledge from previous projects assists project teams and public agencies to be well-equipped with a risk identification model instead of starting from scratch.

High-Temperature Disaster Risk Assessment for Urban Communities: A Case Study in Wuhan, China.

High-temperature risk disaster, a common meteorological disaster, seriously affects people’s productivity, life, and health. However, insufficient attention has been paid to this disaster in urban communities. To assess the risk of high-temperature disasters, this study, using remote sensing data and geographic information data, analyzes 973 communities in downtown Wuhan with the geography-weighted regression method. First, the study evaluates the distribution characteristics of high temperatures in communities and explores the spatial differences of risks. Second, a metrics and weight system is constructed, from which the main factors are determined. Third, a risk assessment model of high-temperature disasters is established from disaster-causing danger, disaster-generating sensitivity, and disaster-bearing vulnerability. The results show that: (a) the significance of the impact of the built environment on high-temperature disasters is obviously different from its coefficient space differentiation; (b) the risk in the old city is high, whereas that in the area around the river is low; and (c) different risk areas should design built environment optimization strategies aimed specifically at the area. The significance of this study is that it develops a high-temperature disaster assessment framework for risk identification, impact differentiation, and difference optimization, and provides theoretical support for urban high-temperature disaster prevention and mitigation.

Roadway design and construction in infrastructure limited contexts: a risk breakdown structure

The United Nation's sustainable development goals prioritize roadway development, as it is necessary for socio-economic prosperity. Despite rapid growth in the industry, progress has been crippled by budget and schedule overruns. There is often a lack of knowledge regarding risks that could impact project success. A content analysis of published research is performed to construct a preliminary risk identification and analysis framework—in the form of an initial risk breakdown structure (RBS) and codebook. They include 16 subcategories within the following six categories: ‘construction & planning’, ‘economic & financial’, ‘regulatory & legal’, ‘equipment, materials, & land’, ‘site disposition’, and ‘relations & recruitment’. This study provides researchers and project parties—including owners, contractors, consultants, and financial institutions—with a starting point to discuss and identify context-specific risks. It also suggests areas for future research.

Методичні підходи до оцінки фінансових ризиків аграрного сектору

The purpose of the article is to substantiate the methodological tools for identifying and assessing financial risks for the management of such on the example of the agricultural sector. Research methods. The methods used: the monographic method for reviewing scientific publications of researchers and analysis of regulatory framework for risk identification and assessment, systematic and comparative analysis for the formation of methodological approaches to financial risk assessment, empirical method and synthesis for analysis of food security indicators, indicators of state regulation of financial risks, graphical method to reflect the dynamics of individual studied indicators, abstract and logical for theoretical generalizations and conclusions. Research results. Methodical approaches to financial risk assessment are analyzed. The components of financial risk assessment methods, as a system of principles, practices and techniques of scientific research of risk identification and measurement, theoretical principles of using these tools in the study of security aspects of the national economy are specified. It is determined that financial risk applies to almost all areas of activity. It is a kind of assessment of decision-making in conditions of uncertainty in the field of financing, investment, asset management, and resource potential at different levels of government. For the macro level of financial risk assessment, the dynamics of the food security index as the main criterion of the consequences of financial security of the industry is analyzed; the state of the shadow economy as a determining factor in systemic financial risks; the level of budget support as a factor in the effectiveness of public finance policy and financial risk management. For the micro level, the classification of methods of financial risk assessment based on the ways of the financial condition of enterprises is clarified, and the relationship between them is argued. Express analysis of financial risks was performed based on financial reporting data according to analytical ratios on the example of PJSC Myronivsky Hliboproduct. Normative methods of determining financial risk following the coefficient analysis and cost-effectiveness analysis and the National Standard of Ukraine "Risk Management. Methods of general risk assessment " to justify the need for a unified system of methodological approaches to financial risk assessment. Scientific novelty. The necessity of forming a unified methodology for assessing the financial risks of the agricultural sector was proved, for which the financial resources of the industry for 2013-2020 were analyzed and it was found that they account for 9.4% of the resources of the economy. Methodological approaches to the assessment of financial risks by involving macro-levels of the agricultural sector in the field of food security have been expanded, as it serves as a guarantor of food security, and there is a need to constantly monitor . It is generalized that the content of the assessment of financial risks of agricultural enterprises is determined in accordance with the purpose and main tasks, which have a gradation at the hierarchy levels. The scheme of a technique of an estimation of financial risks of agrarian branch is constructed. Practical significance. The methodological support of financial risk assessment processes has been improved, which can serve as a basis for the formation of a single Methodology for the analysis of the financial condition and financial risks of agricultural enterprises. The stages of financial risk assessment for the agricultural sector according to the algorithm are proposed: detection - analysis - minimization, which can be useful for agricultural enterprises in carrying out a comprehensive financial analysis. Some conclusions of the study can be used in shaping the financial policy of the agricultural sector. Tabl.: 6. Figs.: 2. Refs.: 25.

A reinforcement learning-based framework for disruption risk identification in supply chains

Risk management is one of the critical activities which needs to be done well to ensure supply chain activities operate smoothly. The first step in risk management is risk identification, in which the risk manager identifies the risk events of interest for further analysis. The timely identification of risk events in the risk identification step is crucial for the risk manager to be proactive in managing the supply chain risks in its operations. Undertaking this step manually, however, is tedious and time-consuming. With the increased sophistication and capability of advanced computing algorithms, various eminent supply chain researchers have called for the use of artificial intelligence techniques to increase efficiency and efficacy when performing their tasks. In this paper, we demonstrate how reinforcement learning, which is one of the recent artificial intelligence techniques, can assist risk managers to proactively identify the risks to their operations. We explain the working of our proposed Reinforcement Learning-based approach for Proactive Risk Identification (RL-PRI) and its various steps. We then show the performance accuracy of RL-PRI in identifying the risk events of interest by comparing its output with the risk events which are manually identified by professional risk managers.

Integrated real-time flood risk identification, analysis, and diagnosis model framework for a multireservoir system considering temporally and spatially dependent forecast uncertainties

Uncertainties in flood forecasts that result from error propagation in meteorological–hydrological simulation processes present spatial and temporal dependences that are the principal sources of risk to real-time flood control in a multireservoir system. Risk analysis models that neglect such dependences potentially yield biased results regarding flood control operations. To enhance the accuracy of flood risk analysis and inform decision making regarding real-time flood control for a multireservoir system, this study proposed an integrated flood risk identification, analysis, and diagnosis model framework that incorporates the multidimensional dependences of forecast uncertainties. The framework includes an uncertainty identification model that uses the copula function to characterize the joint probability function of the mixed dependent uncertainties. It also incorporates a real-time flood risk analysis module that couples simulation and optimization to convert the complex multiobjective operation under uncertainty into a tractable model capable of flexibly evaluating individual and joint flood risks. The third component is a risk diagnosis module that calculates risk contributions using conditional probability, and identifies stable and vulnerable reservoirs within the system to alter operational decisions toward risk reduction. Methodologies were verified through application to a mixed four-reservoir system in the Pi River Basin, China. The principal findings were as follows. (1) Positive-dominated dependences of forecast uncertainties were identified because of error propagation through the meteorological and rainfall–runoff forecasts within the spatial and temporal continuity connection, and the copula function accurately described and quantified the multidimensional temporal and spatial dependences of the uncertainties. (2) In neglecting the spatial dependences of forecast uncertainties, the joint flood risk of the reservoir system was underestimated; in ignoring high-order temporal dependences, individual flood risk was underestimated owing to biased estimation in error variance. (3) Systematic reorganization of the operational strategies of diagnosed stable and vulnerable reservoirs could provide guidance for reduction and homogenization of flood risks. The proposed framework could be used to improve the accuracy of flood risk analysis and support reliable real-time flood control operation of a multireservoir system.

How Do Central Banks Identify Risks? A Survey of Indicators

For central banks, it is crucial to develop and maintain risk identification frameworks that allow them to detect in good time and address potential threats to financial stability with the most appropriate policy tools. This paper reviews the main indicators developed for this purpose by the Banco de Espana and by other central banks and prudential authorities. In this way, this stocktaking exercise contributes to improving the transparency and effective communication of the financial stability-related tasks carried out at the Banco de Espana. Some of the indicators are used in regular Banco de Espana surveillance activities, whereas others pertain to specific research activities. We classify our set of measures into two broad categories depending on the risk monitored: standard or systemic risks. Given the multidimensional nature of systemic risk, its identification goes beyond the sum of the standard risks explored in this paper (namely credit, macroeconomic, market, and liquidity and bank risks). This survey also classifies indicators by the type of institutional segment that triggers risks; namely, sovereigns, households, non-financial corporations, banks, non-bank financial sector, residential real estate and the financial markets. This work shows how the measures developed and regularly used at the Banco de Espana allow potential vulnerabilities to be comprehensively monitored. Nevertheless, maintaining an adequate risk-identification framework requires continuous adaptation to new theoretical developments and econometric tools, and, more importantly, to emerging challenges. In this respect, there is a current drive to develop new indicators to assess potential risks arising from climate change and those linked to the risk of system-wide cyber incidents. It is expected that the monitoring needs related to these risks will increase in the future.

Influencing Factors and Evaluation Model of Quality Risks in Intelligent Manufacturing Mobile Supply Chain

The intelligent manufacturing (IM) supply chain (SC) involves multiple distributed agents. The mobile supply chain (MSC) technology supports the real-time management of key information resources in the supply chain of IM products. This paper explores the influencing factors and evaluation model of quality risks in IM MSC, trying to make realistic evaluation of the actual quality risks of the enterprise. Firstly, the authors constructed a quality risk identification framework for IM MSC, and a hierarchical evaluation index system (EIS) based on the factors affecting quality risks. Besides, the features and attributes of four dimensions of quality risks were specified, and the corresponding intuitive triangular fuzzy numbers were given. Next, an evaluation model was established for the quality risks of IM MSC based on backpropagation neural network (BPNN). After the evaluation of quality risks, a contract model was designed for the quality risk control in IM MSC. The proposed EIS and models were proved effective through experiments.