Hasil untuk "Risk in industry. Risk management"

H. Jo, Haejung Na

M. Beasley, Richard Clune, D. Hermanson

Bright Asante-Appiah, T. Lambert

Dhruv Bansal, Mayank Goud, Sourav Majumdar

In the Vasicek credit portfolio model, tail risk is driven primarily by the asset-correlation parameter, yet empirically is subject to correlation risk. We propose a stochastic correlation extension of the Vasicek framework in which the correlation state evolves as a diffusion on the circle. This representation accommodates both non-mean-reverting and mean-reverting dependence regimes via circular Brownian motion and von Mises process, while retaining tractable transition densities. Conditionally on a fixed correlation state, we derive closed or semi-closed form expressions for the joint distribution of two assets, the joint first-passage (default) time distribution, and the joint survival probability. A simulation study quantifies how correlation volatility and persistence reshape joint default-at-horizon, survival, and joint barrier-crossing probabilities beyond marginal volatility effects. An empirical illustration using U.S. bank charge-off rates demonstrates economically interpretable time-variation in a dependence index and shows how inferred stochastic dependence translates into materially different joint tail-event probabilities. Overall, circular diffusion models provide a parsimonious and operationally tractable route to incorporating correlation risk into Vasicek structural credit calculations.

Uday Kumar Kanike

This paper aims to explore the causes of supply chain management disruptions in the manufacturing sector. Supply chain disruptions have become a major concern for companies globally, posing significant risks to business operations, costs, earnings, and customer satisfaction. This review examines various factors contributing to these disruptions, including natural disasters, raw material unavailability, regulatory changes, technology breakdowns, labor shortages, transportation issues, and political instability. The review encompasses studies and research papers shedding light on the causes of disruptions in manufacturing supply chains. The findings highlight the importance of proactive measures in building resilient supply chains capable of effectively handling disruptions. These measures involve implementing robust risk management plans, strategic investments in technology, and developing collaborative relationships with customers and suppliers. Through these actions, manufacturing companies can enhance their supply chain's ability to withstand disruptions and ensure uninterrupted operations. In conclusion, this review emphasizes the critical importance of addressing supply chain disruptions in the manufacturing sector and advocates for a proactive and comprehensive approach to mitigating their impact. Effective risk management strategies, technology investments, and strong partnerships are key to enhancing the resilience of supply chains and minimizing the negative consequences of disruptions.

Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare et al.

This review paper explores the transformative role of advanced analytics in optimizing logistics and supply chain management, offering insights into industry applications, best practices, and future trends. As global supply chains become increasingly complex, integrating advanced analytics—encompassing data mining, predictive analytics, machine learning, and big data—has emerged as a critical driver of efficiency, cost reduction, and enhanced decision-making. The paper discusses how various industries, including manufacturing, retail, healthcare, and transportation, leverage advanced analytics to address specific challenges and improve overall supply chain performance. Additionally, it highlights the best practices for successful implementation, such as aligning analytics initiatives with business objectives, investing in the right technology infrastructure, and fostering a culture of data-driven decision-making. The paper also addresses the challenges and barriers to implementation, including technological, organizational, and regulatory hurdles. Finally, it examines emerging technologies like AI, IoT, and blockchain, poised to revolutionize supply chain management further. It identifies key opportunities for growth in areas such as sustainability, risk management, and customization. The paper concludes by emphasizing the importance of advanced analytics in shaping the future of logistics and supply chain strategies, offering recommendations for industry stakeholders to harness these technologies effectively.

Jing Shi, Ping Du, Huilong Luo et al.

Cadmium (Cd) pollution in mining areas is the most important challenge for soil environment management in China. Assessing the actual Cd pollution risk in various mining areas and identifying the core areas requiring supervision can provide a basis for government departments and industries to carry out detailed further investigations in key areas. In this study, we collated published data on metal mine circumjacent soil contaminated by Cd in China from 2002 to 2020 to conduct a comprehensive study on soil cadmium pollution and ecological and health risks in mining areas. The temporal and spatial variations of Cd concentrations and the pollution source were investigated. Results indicated that the Cd concentration in soil was strongly associated with the types of mining area. The Cd pollution in the circumjacent soil of lead-zinc and tungsten mines with high heavy metal pollution discharging coefficient was more serious than the soil around other mines. Identification of temporal and spatial variations for soil Cd in China indicated that the high Cd concentrations were found in the central, southern, and southwestern regions of China, and the distribution of mining activities in these regions are relatively concentrated. Meanwhile, a temporal turning point in the mean soil Cd concentration occurred in these regions in 2012, which indicated that the heavy metal control management policy implemented by the government was effective. The ecological risk of soil Cd pollution around mining areas was moderate to high. Health risk assessment showed that some regions adjacent mining areas had a high non-carcinogenic risk, notably, lead-zinc and tungsten mining areas were more serious. Supervision should focus on reducing ecological risks and protecting the safety of agricultural products rather than concentrating on health risks. The research results provide a reference for the priority management of contaminated soil in mining areas.

Umberto Della Monica, Kimberly-Annalena Munjal, Mark Paul Tamas et al.

To ensure security and stable quality, deeper cybersecurity evaluations are essential for the development of safety features and functionalities in vehicles. Among these, the AEB system is the most relevant. This research presents a comprehensive TARA of the AEB system, emphasizing the identification, validation, and mitigation of major cybersecurity threats and risks. We systematically examine potential attack vectors by utilizing the STRIDE threat model. This approach enables a detailed analysis of each security threat associated with AEB systems, providing insights into how malicious actors could use the attack paths. The assessment aligns with ISO/SAE 21434, which offers a robust framework for risk management in automotive cybersecurity and IT security, ensuring a thorough evaluation of a system’s architecture. By assessing the AEB system’s architecture against these standards, we identify key components and communication pathways that may be particularly prone to cyberattacks. The results of this analysis highlight critical flaws within the AEB framework and propose corrective measures to enhance cybersecurity resilience. This article provides a structured methodology for assessing and mitigating automotive cybersecurity risks in compliance with industry standards, aiming to facilitate the safe implementation of AEB technology and ultimately improve overall vehicle security and safety.

Lerato Matjila, Khathutshelo Nephawe, Yandisiwe Sanarana et al.

This study investigated cow longevity, culling reasons, and risk factors influencing culling in South African Holstein and Jersey dairy herds. Lactation records of 1,150,625 Jersey and 1,534,875 Holstein cows from 1864 herds, recorded through the National Milk Recording Scheme during the period 2000 to 2019, were analyzed. Longevity was calculated as length of productive life and number of completed lactations. Logistic binary regression was conducted to estimate the odds ratios (OR) for culling among different calving seasons, parities, and herd sizes. Holstein cows had mean productive life of 739.33 ± 434.31 days and 2.37 ± 1.08 lactations, while Jersey cows averaged 696.81 ± 415.44 days productive life and 2.47 ± 1.13 lactations. Leading reasons for culling were infertility (37.94 ± 0.48% Holstein; 30.46 ± 0.63% Jersey), mastitis (18.15 ± 0.38% Holstein and 18.16 ± 0.53% Jersey), and low milk yield (11.76 ± 0.32% Holstein and 19.76 ± 0.55% Jersey). Summer calving, third parity, and small herd size had the highest odds of culling. These findings suggest that herd management practices and selection objectives in South Africa should place high emphasis on cow fertility and udder health. Furthermore, cows calving in summer and those in third parity or small herds require particular attention to minimize culling. Such measures may help to reduce involuntary culling rates and thus increase herd profitability as well as dairy industry sustainability.

Dongdong Yang, Haijun Qiu, Quan Dong et al.

Neglecting terrain-induced channel aggradation can lead to severe consequences, including greater economic losses and higher casualties. On 11 August 2023, a catastrophic channelized debris flow triggered by short-duration intense rainfall in Jiwozi village near Xi’an City, Shaanxi Province, China, destroying three buildings and resulting in 24 fatalities, with three people missing. This study investigates the kinematic process and underlying mechanisms of the debris flow using field investigations, terrain analysis, and numerical simulations. The rain-flood method and Rapid Mass Movement Simulation (RAMMS) software were employed to simulate the propagation process of the debris flow across 20-year, 50-year and 100-year recurrence intervals. The impact of basal friction and erosion parameters on simulation accuracy were examined based on trial-and-error approaches. The optimal parameter combinations were confirmed by comparing simulated boundaries with observed data. At two potential channel blockage sites, simultaneous increases in flow height and rapid decreases in velocity were observed. A two-factor model combining maximum flow height and velocity was utilized to construct hazard zoning. This study underscores the critical influence of local terrain on the propagation and mobility of channelized debris flow during heavy rainfall.

Editorial Office

This Table of Contents reflects the print compilation of peer-reviewed articles published in the journal. Each article listed was originally published online under the journal’s open access model and remains individually accessible and citable. This compilation has been created solely for print distribution, reference, and archival purposes. No new research content is introduced. The publisher affirms that all articles included in this compilation have undergone the journal’s standard editorial and peer-review processes.

Michael Schmutz, Eckhard Platen, Thorsten Schmidt

In this paper we study the pricing and hedging of nonreplicable contingent claims, such as long-term insurance contracts like variable annuities. Our approach is based on the benchmark-neutral pricing framework of Platen (2024), which differs from the classical benchmark approach by using the stock growth optimal portfolio as the numéraire. In typical settings, this choice leads to an equivalent martingale measure, the benchmark-neutral measure. The resulting prices can be significantly lower than the respective risk-neutral ones, making this approach attractive for long-term risk-management. We derive the associated risk-minimizing hedging strategy under the assumption that the contingent claim possesses a martingale decomposition. For a set of nonreplicable contingent claims, these strategies allow monitoring the working capital required to generate their payoffs and enable an assessment of the resulting diversification effects. Furthermore, an algorithmic refinancing strategy is proposed that allows modeling the working capital. Finally, insurance-finance arbitrages of the first kind are introduced and it is demonstrated that benchmark-neutral pricing effectively avoids such arbitrages.

Yuxin Du, Dejian Tian, Hui Zhang

The paper investigates the robust distortion risk measure with linear penalty function under distribution uncertainty. The distribution uncertainties are characterized by predetermined moment conditions or constraints on the Wasserstein distance. The optimal quantile distribution and the optimal value function are explicitly characterized. Our results partially extend the results of Bernard, Pesenti and Vanduffel (2024) and Li (2018) to robust distortion risk measures with linear penalty. In addition, we also discuss the influence of the penalty parameter on the optimal solution.

Christian Laudagé, Felix-Benedikt Liebrich

Motivated by recent work on monotone additive statistics and questions regarding optimal risk sharing for return-based risk measures, we investigate the existence, structure, and applications of Meyer risk measures. Those are monetary risk measures consistent with fractional stochastic orders suggested by Meyer (1977a,b) as refinement of second-order stochastic dominance (SSD). These so-called $v$-SD orders are based on a threshold utility function $v$. The test utilities defining the associated order are those at least as risk averse in absolute terms as $v$. The generality of $v$ allows to subsume SSD and other examples from the literature. The structure of risk measures respecting the $v$-SD order is clarified by two types of representations. The existence of nontrivial examples is more subtle: for many choices of $v$ outside the exponential (CARA) class, they do not exist. Additional properties like convexity or positive homogeneity further restrict admissible examples, even within the CARA class. We present impossibility theorems that demonstrate a deeper link between the axiomatic structure of monetary risk measures and SSD than previously acknowledged. The study concludes with two applications: portfolio optimisation under a Meyer risk measure as objective, and risk assessment of financial time series data.

A. Hassoun, Janna Cropotova, H. Trollman et al.

Fish and other seafood products represent a valuable source of many nutrients and micronutrients for the human diet and contribute significantly to global food security. However, considerable amounts of seafood waste and by-products are generated along the seafood value and supply chain, from the sea to the consumer table, causing severe environmental damage and significant economic loss. Therefore, innovative solutions and alternative approaches are urgently needed to ensure a better management of seafood discards and mitigate their economic and environmental burdens. The use of emerging technologies, including the fourth industrial revolution (Industry 4.0) innovations (such as Artificial Intelligence, Big Data, smart sensors, and the Internet of Things, and other advanced technologies) to reduce and valorize seafood waste and by-products could be a promising strategy to enhance blue economy and food sustainability around the globe. This narrative review focuses on the issues and risks associated with the underutilization of waste and by-products resulting from fisheries and other seafood industries. Particularly, recent technological advances and digital tools being harnessed for the prevention and valorization of these natural invaluable resources are highlighted.

S. Paigude, Smita C. Pangarkar, Sheela Hundekari et al.

Artificial intelligence (AI) has the potential to transform the human resource (HR) industry by automating routine tasks, improving decision-making, and enhancing employee engagement and retention. In this paper, we explore the use of machine learning and deep learning techniques to boost employee retention in the HR industry. We review the current state of the art in AI for HR, including the use of predictive analytics, natural language processing, and chatbots for talent management and employee development. We also discuss the challenges and ethical considerations of using AI in HR, including issues of bias and the need for transparent and explainable algorithms. Finally, we present case studies of successful AI-powered HR initiatives that have demonstrated improvements in employee retention and engagement. Our findings suggest that AI has the potential to significantly enhance employee retention in the HR industry, but its implementation requires careful planning and consideration of potential risks and ethical issues.

Laura Macchion

Abstract Non-technical summary This study explores how corporate social responsibility and risk management intersect in the fashion industry, aiming to promote sustainability. It emphasizes the importance of integrating responsible practices into business strategies to mitigate risks and enhance long-term profitability. By focusing on a multinational fashion supply chain, the study examines real-world examples to highlight the challenges and opportunities in balancing brand image with ethical supply chain management. The findings provide insights into how companies can safeguard their reputation, manage complex supply chains, and contribute positively to sustainability goals in the fashion sector. Technical summary This paper investigates the relationship between corporate social responsibility (CSR) and risk management within the fashion industry. It conducts an in-depth case study of a prominent multinational fashion supply chain, analyzing 11 suppliers through interviews, observations, and internal documents. The study underscores that integrating CSR principles into risk management strategies helps mitigate supply chain risks and capitalize on business opportunities. It addresses gaps in existing literature by presenting empirical evidence of CSR-driven transformations in the sector, rather than merely documenting unsustainable practices. The study contributes by offering practical insights for fashion businesses aiming to achieve long-term success through sustainable practices. Key implications include the necessity for strategic integration of CSR into operational frameworks to protect corporate image, manage risks effectively, and foster sustainable growth in the competitive fashion marketplace. Social media summary From risk management to sustainable success: how corporate social responsibility shapes the future of fashion.

Yuri M. Macedo, Jhonathan L. de Souza, Adriano L. Troleis

This paper, adopting theoretical-methodological assumptions, aims to analyse the risk of municipal urban water shortage in the state of Rio Grande do Norte (RN), Brazil, through the results of the Water Shortage Risk Index (WSRI). The WSRI is the product of the analysis of 19 variables, in a systemic perspective that involves environmental, infrastructural, socioeconomic and state planning indicators. The survey was carried out in the 153 municipalities that make up the water supply system managed by the state concessionaire (representing 92% of the 167 municipalities in the State), in its seven water supply regions. The WSRI result identified 49.0% of the municipalities analysed in the ‘high’ and ‘very high’ risk classes; 40.5% as ‘medium’ risk and 10.0% as ‘low’ risk, with no occurrences of ‘very low’ risk. In absolute values, 1 municipality was classified as ‘very high’; 74 were classified as ‘high’; 62 as ‘average’; and 16 were considered to be at ‘low’ risk of water shortages. Contribution: To decrease and/or mitigate the results of the WSRI in the State, the transposition of basins, integration of supply systems, hydrogeological research, among others, were proposed.

Sourish Das, Bikramaditya Datta, Shiv Ratan Tiwari

This study examines how market risks impact the sustainability and performance of the New Pension System (NPS). NPS relies on defined contributions from both employees and employers to build a corpus during the employee's service period. Upon retirement, employees use the corpus fund to sustain their livelihood. A critical concern for individuals is whether the corpus will grow sufficiently to be sustainable or if it will deplete, leaving them financially vulnerable at an advanced age. We explore the impact of market risks on the performance of the corpus resulting from the NPS. To address this, we quantify market risks using Monte Carlo simulations with historical data to model their impact on NPS. We quantify the risk of pension corpus being insufficient and the cost to the Government to hedge the risk arising from guaranteeing the pension.

Yupeng Cao, Zhi Chen, Prashant Kumar et al.

The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering, and stock movement prediction (binary classification), the application of LLMs to financial risk prediction remains underexplored. Addressing this gap, in this paper, we introduce RiskLabs, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely integrates multimodal financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data to improve financial risk prediction. Empirical results demonstrate RiskLabs' effectiveness in forecasting both market volatility and variance. Through comparative experiments, we examine the contributions of different data sources to financial risk assessment and highlight the crucial role of LLMs in this process. We also discuss the challenges associated with using LLMs for financial risk prediction and explore the potential of combining them with multimodal data for this purpose.