Development Of Risk Management Research Articles

HOW RISK MANAGEMENT CAN BE IMPLEMENTED IN NEW AGRO-PRODUCT DEVELOPMENT (NAPD)?: A SYSTEMATIC LITERATURE REVIEW

New product development is highly complex because it integrates technical challenges from both internal and external sources. The potential for uncertainty or failure exists at every step of the product development process. One of the important tools to minimize risks in product development is risk management in new product development. This paper aims to review some research literature on the concept, process, and category of risk management in new agro-product development. This research uses a literature review analysis to analyze the concept and process of risk management in new product development. The articles were selected from Emerald, Elsevier, Springer, MDPI, and Taylor & Francis databases. The findings of this study demonstrate the critical role of risk management in minimizing risks associated with new product development through the implementation of diverse strategies. Risk management involves identifying, analyzing, evaluating, and treating risks. A variety of internal and external factors influence risk in product development. Internal factors are design, production, financial, management (organizational), marketing, market, technology, and supplier risks. External factors include suppliers, customers, and retailers outside the company. Many businesses, including small and medium-sized enterprises (SMEs), have adopted risk management to prevent product failures and financial losses. This paper provides a research framework for applying risk management to new agroindustrial product development.

Pricing factors of cryptocurrencies

In recent years, cryptocurrencies have become a significant element of the modern economy, attracting the attention of investors, regulators, and researchers. Despite substantial progress in understanding the factors influencing cryptocurrency pricing, many aspects remain insufficiently studied. This article provides an overview of traditional factors such as fundamental, macroeconomic, financial, behavioral, and infrastructural ones, and introduces two new groups of factors: socio-economic and market manipulators. Socioeconomic factors represent a wide range of influences determined by the state of society and the economy, significantly impacting cryptocurrency pricing. Market manipulators, on the other hand, encompass methods such as pump-and-dump schemes, insider trading, and manipulations using stablecoins, which lead to substantial price fluctuations in cryptocurrencies. The review of existing traditional factors in combination with new ones allows for a more comprehensive assessment of the dynamics of cryptocurrency pricing. The introduction of these new groups of factors underscores the need for further research to gain a fuller understanding of the pricing mechanisms in the cryptocurrency market and to develop risk management strategies. This work provides a review of existing studies and highlights gaps that require researchers’ attention.

Quantitative risk assessment of road exposed to landslide: A novel framework combining numerical modeling and complex network theory

The quantitative analysis of the landslide risk posed to road networks is a challenging task owing to the uncertainty involved both in the potential landslide hazard and the road value. To address this challenge, this paper proposes a novel framework to assess the road risk in quantitative terms. The landslide hazard is assessed using the depth-integrated method with consideration of the landslide size probability and initial conditions. The potential direct losses associated with road disruption are determined by exposure analysis in a geographical information system. The indirect losses of the road caused by the landslide were analyzed through complex network theory with consideration to regional socioeconomic development and the time required for road restoration. The proposed framework was used to assess the road risk posed by the Chunchangba landslide, Xiaojin County, China. The results show that the volume size probability of landslides in the Xiaojin area could be assessed using the function PV=1/1+3.583×10−5V2×0.391. The depth-integrated method based hazards assessment results show the maximum impact area of the Chunchangba landslide reached 0.383 km2, and the landslide has a high probability of damaging the lower road and forming a barrier lake. The losses associated with road disruption caused by the landslide were estimated. The maximum direct losses reached 293,100 USD, while indirect losses reached 423,800 USD, which has the same importance as direct losses. The risk curve reveals that the maximum probability of the road risk associated with the Chunchangba landslide is 0.0175 %, 0.0189 %, 0.0190 %, and 0.0191 % for the time interval of 5, 10, 20, and 50 years, respectively, with losses of 0.177 million USD/year. The regional disaster mitigation strategy is analyzed based on quantitative risk analysis. The results show that a new 2.7 km road on the mountain opposite the Chunchangba landslide can reduce indirect losses by approximately 300 times. The findings of this study contribute to sustainable development and landslide risk management in mountainous areas.

Clinical Application and Risk Assessment of CAR-T Cell Technology in Cancer Immunotherapy

As a cutting-edge personalized treatment method, CAR-T cell technology transforms patients' own T cells through genetic engineering, making them have the ability to specifically identify and kill tumor cells. The aim of this paper is to thoroughly organize the foundational principles and clinical utility of CAR-T cell technology, highlighting its recent advancements in treating both hematological and solid tumors. Through a comprehensive assessment of CAR-T cell technology's efficacy and risks, this study aims to inform clinical decision-making. Initially, the paper outlines the basic mechanisms of CAR-T cell technology, followed by an analysis of its notable therapeutic outcomes in hematological tumors. Additionally, it addresses the challenges posed by solid tumor treatment, including antigen expression complexity and tumor microenvironment impact. Emphasis is placed on the crucial role of regulatory bodies in enhancing oversight and developing risk management strategies to safeguard patient safety.

A novel framework of ecological risk management for urban development in ecologically fragile regions: A case study of Turpan City, China

A novel framework of ecological risk management for urban development in ecologically fragile regions: A case study of Turpan City, China

Risk-oriented source apportionment and implications for mitigation strategies of VOCs in industrial parks: Insights from odor pollution and health risks

A typical industrial park in the upper reaches of the Yangtze River Economic Belt, which is 70 km from the Chongqing urban center, was used to investigate the occurrence and exposure of harmful volatile organic compounds (VOCs). An exposure risk method and a risk-oriented source apportionment approach were performed to assess the inhalation risks and apportion VOC sources, respectively. The quantitative relationships between risk factors and pollution sources were established, identifying key pathogenic and odorous VOCs. The quantitative emission reduction strategies were developed based on risk thresholds. Residents within the industrial parks face potential health risks due to pathogenic VOCs and nuisance odors, and workers in specific sectors experience two to six times higher risks than those in residential areas. Six risk sources were identified in the industrial park, ranked according to their contribution to VOC concentrations as follows: industrial sewage treatment (IST) (32.59 %), natural gas chemical industry (NGCI) (27.77 %), diesel vehicle exhaust (DVE) (12.04 %), pharmaceutical manufacturing industry (PMI) (11.14 %), chemical raw materials manufacturing (CRMM) (9.96 %), and iron and steel industry (ISI) (6.5 %). Among these, NGCI, IST, and CRMM were the top contributors to pathogenic risks, with contributions of 32.13 %, 29.71 %, and 21.71 % to non-carcinogenic risks, and 18.15 %, 19.87 %, and 27.99 % to carcinogenic risks, respectively. DVE produced significantly higher odor pollution compared to other sources, with intensities that were 3 to 10 times greater. The key pathogenic and odorous VOCs differ by source, resulting in varying control priorities for different VOC species. Reducing emissions from these six sources for 20 high-risk species (e.g., acrolein, 2-chlorotoluene, 1,2-dibromoethane, dichloromethane, and p-diethylbenzene) will simultaneously lower pathogenic and odor risks, with cumulative reduction rates ranging from 4.11 % to 93.75 %. This study provides quantitative control targets for VOCs from a health risk perspective, offering valuable guidance for developing risk management policies in industrial parks.

Идентификация опасностей при реализации объекта по строительству нефтеперерабатывающей установки подрядными организациями

The goal of the study is to develop risk management tools during the construction of an oil-refining plant. The study provides an investigation of hazards caused by contractors engaged in the construction of a large oil-refining plant; the dependences of hazard occurrence on conditions, number of employees, and types of jobs have been established; key risks of construction have been identified; and risk management tools with the identification of proactive barriers reducing risk levels and reactive barriers minimizing consequences of potential accidents during the construction have been proposed. The study is based on real initial statistical data relying on the practices of successful construction of a similar facility. The construction was implemented in accordance with a typical model used by large-scale oil-refining companies: via a special general construction contractor engaging subcontractors (more than 2000 employees). The construction of the plant was time- and area-restricted. The changes in number of employees were non-stationary. The construction of the plant included the stages of pre-commissioning and commissioning. The study results and the proposed risk management tools have been tested and are valuable for organizing an occupational safety management system during the construction of large facilities.

Enhancing investment attractiveness in wartime: a comprehensive analytical approach

The study focuses on analyzing the changes in investment attractiveness during wartime, when certain regions are occupied or destroyed, some enterprises are non-operational due to damaged production facilities, there is a significant outflow of labor force, and energy issues arise. The core issue of the study lies in the necessity of considering the complex political and economic situation’s impact on Ukraine’s investment climate. This requires developing more adaptive assessment methods that can account for both economic and socio-political risk factors. In such circumstances, reliable data is hard to obtain, which prompted the goal of substantiating the factors that characterize the country’s investment attractiveness. To provide a comprehensive assessment of investment attractiveness, an integrated indicator is proposed, which combines data reflecting investment attractiveness, including indices calculated by leading global organizations. The analysis includes such indices as the Innovation Development Index, the Corruption Perception Index, and the Judicial System Index. As a result, an average value of the changes was calculated, showing a comprehensive shift in the indicators related to investment attractiveness. The analysis using the integrated indicator demonstrated that investment attractiveness has decreased by 10 % during the war, but certain indicators, such as the Corruption Facilitation Index and the Judicial Index, have actually improved during the war. The results obtained can be used to develop risk management strategies for both international and local investors. They can also serve as a basis for creating recommendations for improving the investment climate, which in turn can attract capital and stimulate economic growth under challenging conditions

A 5-Year retrospective analysis of adverse events in dentistry at the Dental Hospital, Faculty of Dentistry, Chulalongkorn University

BackgroundPatient safety is a critical concern in dentistry. Adverse events (AEs) can harm patients, increase costs, and decrease satisfaction. Understanding AE types and frequencies is crucial for effective risk management and quality improvement. This study analyzes incident reports to identify preliminary incident patterns as a starting point for developing risk management strategies. However, under-reporting limits the ability to identify true incident patterns, highlighting the need for improved reporting systems and encouragement of incident reporting. Further research is underway to develop such a system and promote reporting to ensure sufficient data quality for effective risk management.MethodsA retrospective analysis of 1,618 incident reports from December 2018 to August 2023 was conducted. A validated classification system, developed from a 5-year retrospective analysis and approved by 14 experts, categorized patient safety incidents, aligning with Thailand’s Hospital Accreditation standards. Descriptive statistics summarized AE frequency and distribution.ResultsOf the reports, 752 were patient safety, 503 personnel safety, and 363 organizational safety incidents. Top patient safety incidents included medical record errors (176), accidental damage (66), post-operative complications (65), medical emergencies (64), and communication errors (53). Personnel safety incidents involved inappropriate working conditions (135) and work-related injuries with contact transmission risk (117). Organizational safety incidents mainly concerned policy and operational processes (131).ConclusionsThis study reveals the preliminary patterns of adverse events (AEs) in dental settings and underscores the limitations due to under-reporting, which affect the ability to fully understand true incident patterns. To effectively manage risks, there is a critical need for improving the existing incident reporting system and encouraging a culture of comprehensive reporting among dental professionals. Future efforts should focus on enhancing reporting systems to ensure high-quality data, enabling better identification of incident trends and supporting targeted risk management strategies to improve patient safety in dentistry.

Building event management skills through authentic assessment

ABSTRACT With a greater focus on employability upon graduation, universities are increasingly considering how the course experience links to knowledge and skill development. To assist with skill development, industry practice and authentic activities should be embedded into the curriculum. Designing and including authentic assessment helps to replicate real-life industry scenarios which support students’ development of professional competencies. This study examines an authentic assessment of planning and operating an event within an event management topic. Data was collected through a survey of students, and additionally the reflective practitioner approach was used to consider the development of the curriculum. This study found that students gained a greater understanding of the event process by, for example, using stakeholder theory in order to prioritise event objectives, produce a project plan including key milestones, and develop risk management strategies. Communication, time management and content management were identified as key areas of skill development by students.

AI-Driven Risk Management for Sustainable Enterprise Development: A Review of Key Risks

AI-Driven Risk Management for Sustainable Enterprise Development: A Review of Key Risks

Ex-ante evaluation of a cross-sectorial business model for risk management in new product development: the case of Haitian farming

Ex-ante evaluation of a cross-sectorial business model for risk management in new product development: the case of Haitian farming

Requirements Risk Management for Continuous Development: Organisational Needs

Information systems development has recently evolved from traditional to agile and continuous forms. Continuous development (CD) methods, such as development and operations (DevOps), integrate many well-regarded parts of agile development and add collaboration among an organisation’s development, operations and quality assessment departments. We argue that requirements risk management (RRM) poses additional challenges to projects where development work is carried out quickly and continuously. However, in the literature, most methods for prioritising requirements and managing risks are more suited to traditional development. This raises the need for new tools and methodologies to meet CD challenges. As these challenges constantly evolve, project management must be able to control CD, changes in the determination of requirements and the accompanying risks. Based on a systematic literature review, we define the key features of CD and develop a conceptual three-dimensional framework that can be used to understand the organisational needs of RRM for CD.

A Rank Graduation Box for SAFE AI

The growth of Artificial Intelligence applications requires to develop risk management models that can balance opportunities with risks.We contribute to the development of Artificial Intelligence risk models proposing a Rank Graduation Box (RGB), a set of integrated statistical metrics that can measure the “Sustainability”, “Accuracy”, “Fairness” and “Explainability” of any Artificial Intelligence application.Our metrics are consistent with each other, as they are all derived from a common underlying statistical methodology: the Lorenz curve.The validity of the metrics is assessed by means of their practical application to both simulated and real data. The results from the comparison of alternative machine learning models to simulated data are aligned with the generating models, in general indicating linear regression models as the most accurate, regression tree models as the most fair, Random Forest models as the most robust; and leading to model explanations similar to the true ones from the generating model.The outcomes from the application of Random Forest models to real data show that the proposed RGB metrics are more interpretable and more consistent with the expectations, with respect to standard metrics such as AUC, RMSE and Shapley values. The evidence also shows that the RGB metrics are very general and can be applied to any machine learning method, regardless of the underlying data and model.

China's Direct Investment in the Russian Soybean Market in the Context of Risk Management Strategy

This article is based on the multifaceted impact factors of China's direct foreign investment in the Russian soybean market, identifying potential risks encountered in the investment process, including political, economic, logistics and supply chain, socio-cultural, and environmental risks. On the basis of these risks, the paper proposes a comprehensive set of risk management measures, and validates the effectiveness and practicality of these risk mitigation strategies through the analysis of successful investment cases. Overall, the paper underscores the significance of comprehensive risk management for sustainable development, providing systematic and comprehensive risk management advice for Chinese enterprises engaged in direct investments in the Russian soybean market

Towards an open pipeline for the detection of critical infrastructure from satellite imagery—a case study on electrical substations in The Netherlands

Abstract Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI to natural hazards prevents their incorporation in large-scale risk assessments. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMap (OSM), processes the imagery and assets’ masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25 %, 50 % and 100 %) and hyperparameters settings (batch size and learning rate). The highest scoring experiment achieved an Average Precision at an Intersection over Union of 50 % of 30.93 and a tile F-score of 89.88 %. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable Artificial Intelligence.

Assessing landslide susceptibility in Chefchaouen, Morocco: An application of the landslide numerical risk factor method for sustainable urban development and disaster risk management

This research investigates landslide susceptibility in Chefchaouen, Morocco, using the Landslide Numerical Risk Factor (LNRF) method to inform sustainable urban development and disaster risk management. The study incorporates local factors such as geological characteristics, climatic conditions, land use patterns, and gravitational influences within the LNRF framework. The results demonstrate that slope, altitude, geological complexity, and precipitation are key determinants of landslide susceptibility, with complex geology and high rainfall significantly increasing risk. The research highlights the critical need for forest conservation, reforestation, and responsible land management to mitigate the heightened landslide vulnerability caused by human activities such as deforestation, overexploitation, and urban expansion. The study provides valuable insights for enhancing regional planning and natural resource management to reduce landslide risks effectively.

The Potential Risks of Mining and Investment in Digital Currencies based on Financial Technology Applications

This study aimed to explore the potential risks associated with mining and investing in digital currencies through financial technology (fintech) applications. The research approach employed a combination of quantitative and qualitative methods, utilizing an inductive-deductive framework. The researcher conducted online surveys, and interviews with professors and experts, and analyzed the collected data using Microsoft Excel and NVIVO software. The study also utilized illustrative graphs of digital currency markets, computing power charts, and energy consumption indexes related to digital currency mining. The findings revealed various potential risks of mining and investing in digital currencies, impacting individuals, businesses, and ecosystems. These risks encompass environmental concerns, excessive energy consumption, security vulnerabilities, and financial losses. Additionally, investing in digital currencies through fintech applications can lead to inadequate investor protections, market volatility, regulatory challenges, fraudulent activities, lack of transparency, and insufficient investor understanding. The value of this study lies in its ability to analyze the potential risks associated with mining and investing in digital currencies, offering valuable insights for individuals, businesses, and policymakers. It can aid stakeholders in making informed decisions, developing risk management strategies, and enhancing their overall understanding of the potential implications on individuals, businesses, and the ecosystem.

Risk-based asset management framework for highway retaining wall systems using wireless structural health monitoring data

Retaining walls are important structural systems used in the construction of highways. With asset management methods for retaining wall inventories lagging those developed for highway bridges, there is a need to develop risk management methods for these critical structural systems. A major challenge is the vast inventories of retaining walls that asset managers must manage and the inherent limitations of visual inspections. This study proposes an asset management framework for retaining walls based on risk assessments using structural monitoring data. First, a long-term wireless monitoring solution is proposed to measure wall tilt and strain over long periods of time. Second, an analytical framework is developed to separate wall thermal responses from lateral earth pressures responses with the latter used to extract estimated lateral earth pressure distributions. A statistical distribution of lateral earth pressures are used in a reliability assessment of the wall to provide a measure of failure probability that can be combined with failure consequences to estimate asset risk. To illustrate the proposed methodology, a reinforced concrete cantilever retaining wall panel is selected for long-term structural health monitoring. A wireless structural health monitoring system is installed to measure the tilt, strain, and temperature response of the wall continuously over 15 months. The study reveals the wall exhibits strong diurnal and seasonal variations offering insight into wall behavior under operational conditions. Hypothesized levels of corrosion in the steel reinforcement at the base of the wall are explored to estimate the wall reliability. Even under the assumption of 20% reinforcement section loss, the monitored wall was found to have a reliability index well above 3.0.

Tropical Cyclone Storm Surge‐Based Flood Risk Assessment Under Combined Scenarios of High Tides and Sea‐Level Rise: A Case Study of Hainan Island, China

AbstractIn the context of climate change, coastal flood risk is intensifying globally, particularly in China, where intricate coastlines and frequent tropical cyclones make storm surges a major concern. Despite local government's efforts to initiate coastal monitoring networks and qualitative risk guidelines, there remains a gap in detailed and efficient quantitative assessments for combinations of multiple sea‐level components. To address this, we develop the Tropical Cyclone Storm Surge‐based Flood Risk Assessment under Combined Scenarios (TCSoS‐FRACS). This framework integrates impacts of storm surges, high tides, and sea‐level rise using a hybrid of statistical and dynamic models to balance reliability and efficiency. By combining hazard, exposure, and vulnerability, it incorporates economic and demographic factors for a deeper understanding of risk composition. Applying TCSoS‐FRACS to Hainan Island reveals that the combined effects of storm surges, high tides, and sea‐level rise significantly amplify local coastal flood risk, increasing economic losses to 4.27–5.90 times and affected populations to 4.96–6.23 times. Additionally, transitioning from Fossil‐fueled Development (SSP5‐8.5) to Sustainability (SSP1‐1.9) can reduce the risk increase by approximately half. The equivalence in flood hazard between current high tides and future sea level under a sustainable scenario boosts confidence in climate change adaptation efforts. However, coastal cities with low hazard but high exposure need heightened vigilance in flood defense, as future risk could escalate sharply. Our study provides new insights into coastal flood risk on Hainan Island and other regions with similar profiles, offering a transferable and efficient tool for disaster risk management and aiding in regional sustainable development.