Hasil untuk "Risk in industry. Risk management"

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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.

Timo Dimitriadis, Yannick Hoga

Following several episodes of financial market turmoil in recent decades, changes in systemic risk have drawn growing attention. Therefore, we propose surveillance schemes for systemic risk, which allow to detect misspecified systemic risk forecasts in an "online" fashion. This enables daily monitoring of the forecasts while controlling for the accumulation of false test rejections. Such online schemes are vital in taking timely countermeasures to avoid financial distress. Our monitoring procedures allow multiple series at once to be monitored, thus increasing the likelihood and the speed at which early signs of trouble may be picked up. The tests hold size by construction, such that the null of correct systemic risk assessments is only rejected during the monitoring period with (at most) a pre-specified probability. Monte Carlo simulations illustrate the good finite-sample properties of our procedures. An empirical application to US banks during multiple crises demonstrates the usefulness of our surveillance schemes for both regulators and financial institutions.

Liu Suchan

This study breaks through the traditional industry boundary perspective and constructs a cross-industry collaborative integration model of the digital economy and the real economy. Based on an in-depth analysis of 12 industry cases in Asia, Europe and the United States, it is revealed for the first time that the blockchain data governance system can reduce the risk of enterprise data leakage by 37% (p<0.05), which is 12 percentage points higher than similar studies. The innovative discovery that the blocking effect of organizational inertia on transformation is significantly higher than that of technical factors confirms that a flat management structure can speed up the digitalization process of enterprises by 28%. Different from the existing technological determinism paradigm, the study proposes a three- dimensional collaborative framework of “policy-technology-organization” (PTO) and verifies its theoretical validity through a cross-cultural case comparison of Haier Intelligent Manufacturing and Siemens Digital Factory. The specially constructed digital transformation maturity assessment matrix (DMAM) solves the dilemma of policy lag, and regional pilots show that it improves policy adaptation efficiency by 31%. The study provides a new analytical tool for the reconstruction of the global value chain and has forward-looking guiding value for industrial transformation under the impact of generative AI.

Chakufwa K. Munthali, Victor Kasulo, Mavuto Tembo

Climate variability is expected to have a negative impact on agricultural production, particularly in sub-Saharan Africa, including Malawi, where the agricultural sector is a crucial part of the economy. This study focusses on increasing our understanding on the ability of smallholder farmers in Karonga, Malawi, to adapt to climate variability. To achieve this, an integrated framework was used to assess the factors that influence the adaptive capacity of smallholder farming systems in Karonga. The integrated indicator-based framework was used to assess financial, social, human assets, knowledge and information, institutions and entitlements, flexible and forward-looking decision-making, gender and power dynamics, natural assets, physical assets and the frequency of floods. Data were collected through face-to-face interviews, focus group discussions and observations. A total of 38 indicators from literature and observations in the study area were entered and analysed in SPSS and Excel using principal component analysis. The survey results indicate that the aggregate adaptive capacity of respondents is low. Component indicators, such as ownership of livestock, support from the community, livelihood diversification and gender of the decision maker, as well as access to a mobile phone and inorganic fertiliser, and share of more productive land, increased the adaptive capacity of smallholder farmers. On the other hand, component indicators such as less productive land, deforestation, capital exclusion from food for work, age of household head, financial assets and gross annual income reduced the adaptive capacity of smallholder farmers to climate change. Contribution: The article presents an integrated framework that considers both biophysical and socio-economic factors for assessing adaptive capacity. This framework offers a better understanding of the adaptive capacity of farming systems at the smallholder farmer level. The study’s findings provide insights into the dynamic nature of adaptive capacity and identify factors that either enable or constrain adaptive capacity at various levels.

Claudia Klüppelberg, Mario Krali

We provide a comprehensive review of causal dependence through a max-linear structural equation model. Such models express each node variable as a max-linear function of its parental node variables in a directed acyclic graph and some exogenous innovation. We reformulate results on structure learning and estimation, which we apply to a network of financial data. A new method, based on hard-thresholding and on the Hamming distance, estimates a sparse DAG for extreme risk~propagation.

Zhong-qiang Zhou, Ling Li, Ping Huang

The rapid expansion of internet finance and fintech has introduced increasingly sophisticated illegal financial activities that challenge traditional regulatory frameworks. Existing regulatory approaches often lack the depth and adaptability to effectively identify and manage these evolving risks. This study proposes a comprehensive, multi-dimensional risk monitoring model tailored to detect illegal financial activities, based on four core dimensions: investor behavior, intermediary platforms, investment products, and regional factors. The model integrates six primary and 26 secondary indicators, enabling quantitative assessment of risk levels and early warning detection. By applying this model to empirical data, our findings highlight specific risk drivers and propose policy recommendations aimed at strengthening regulatory measures. This framework provides regulatory bodies with a practical tool for timely intervention, thereby enhancing financial stability and protecting investor interests.

Kamil Fortuna, Janusz Szwabiński

The fragility of financial systems was starkly demonstrated in early 2023 through a cascade of major bank failures in the United States, including the second, third, and fourth largest collapses in the US history. The highly interdependent financial networks and the associated high systemic risk have been deemed the cause of the crashes. The goal of this paper is to enhance existing systemic risk analysis frameworks by incorporating essential debt valuation factors. Our results demonstrate that these additional elements substantially influence the outcomes of risk assessment. Notably, by modeling the dynamic relationship between interest rates and banks' credibility, our framework can detect potential cascading failures that standard approaches might miss. The proposed risk assessment methodology can help regulatory bodies prevent future failures, while also allowing companies to more accurately predict turmoil periods and strengthen their survivability during such events.

D. Rosado, Antonio Santos-Olmo Parra, L. E. Sánchez et al.

Cyber-physical systems (CPSs) are smart systems that include engineered interacting networks of physical and computational components. CPSs have an increasingly presence on critical infrastructures and an impact in almost every aspect of our daily life, including transportation, healthcare, electric power, and advanced manufacturing. However, CPSs face a growing and serious security issue due to the widespread connectivity between the cyber world and the physical world. Although risk assessment methods for traditional IT systems are now very mature, these are not adequate for risk assessment of CPSs due to the different characteristics of the later. As such, there is an urgent need to define approaches that will adequately support risk assessment for CPSs. To contribute to this important challenge, we propose a novel risk analysis technique for CPSs based on MARISMA, a security management methodology, and eMARISMA, a technological environment in the cloud. Our work contributes to the state of the art through the definition of the MARISMA-CPS pattern that incorporates a set of reusable and adaptable elements that allows risks in CPSs to be managed and controlled, which is aligned with the main CPSs frameworks, such as those defined by NIST and ENISA. A case study for a smart hospital is presented, showing how the reusability and adaptability of the proposal allows the proposed MARISMA-CPS pattern to be easily adapted to any CPS environment. Such adaptability is important to ensure wide application in the domain of CPSs. © 2022 Published by Elsevier B.V. CC_BY_NC_ND_4.0

Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo et al.

The COVID-19 disruption of supply chains has motivated manufacturing companies in Nigeria to build and maintain supply chain visibility, robustness, and resilience to remain third-party logistics providers. It is vital to have an adequate understanding of third-party logistics performance drivers. Most studies have concentrated on third-party logistics capability, while few others explored the impact of relational governance structures on performance. However, studies examining the synergy between third-party logistics capability and relationship management are scarce. The purpose of this study is to investigate the stimulators of third-party logistics performance in the Nigerian manufacturing industry. A descriptive survey, e-mail questionnaire, and PLS-SEM approach was used to collect and analyze the data from a sample of 364 manufacturing companies in Nigeria. The findings indicated that relationship management has a significant positive association with third-party logistics capability (β= 0.785, t = 3.457, p &lt; 0.001); relationship management has a significant negative association with supply chain risk (β= –0.209, t = 4.149, p &lt; 0.001); third-party logistics capability has a significant negative association with supply chain risk (β = –0.620, t = 3.199, p &lt; 0.001); supply chain risk has a significant negative association with logistics performance (β= –0.695 t = 5.396, p &lt; 0.001). Hence, relationship management, third-party logistics capability, and supply chain risk are drivers of third-party logistics performance. Therefore, supply chain partners should manage their relationships to strengthen third-party logistics capability and reduce all kinds of uncertainties and risks.

Mamoona Arshad

This study offers new insights into the moderators between abusive supervision and workplace deviance. Building on the conservation-of-resources theory, the study introduces coping resources as moderators between abusive supervision and the two dimensions of workplace deviance, that is, interpersonal and organizational deviance. The study identifies psychological safety, an intrapsychic state, as a moderator between abusive supervision and interpersonal deviance. Similarly, the research tests organizational identification as a moderator between abusive supervision and organizational deviance. The study tests the hypotheses by collecting two-source of data from various Pakistani organizations. The two source data from 122 supervisor-subordinate dyads provide support for the results. The study finds that low psychological safety strengthens the positive link between abusive supervision and interpersonal deviance. Besides, a low level of identification with an organization strengthens the positive association between abusive supervision and organizational deviance. Thus, the study extends the literature by highlighting the importance of several personal and coping resources for employees at work.

Andrea Teruzzi

The issue related to the quantification of the tail risk of cryptocurrencies is considered in this paper. The statistical methods used in the study are those concerning recent developments in Extreme Value Theory (EVT) for weakly dependent data. This research proposes an expectile-based approach for assessing the tail risk of dependent data. Expectile is a summary statistic that generalizes the concept of mean, as the quantile generalizes the concept of the median. We present the empirical findings for a dataset of cryptocurrencies. We propose a method for dynamically evaluating the level of the expectiles by estimating the level of the expectiles of the residuals of a heteroscedastic regression, such as a GARCH model. Finally, we introduce the Marginal Expected Shortfall (MES) as a tool for measuring the marginal impact of single assets on systemic shortfalls. In our case of interest, we are focused on the impact of a single cryptocurrency on the systemic risk of the whole cryptocurrency market. In particular, we present an expectile-based MES for dependent data.

O. N. Rimskaya, A. A. Parkhaev, N. A. Chomova

Scientific and technological progress amid the process of global digitalisation has prompted the demand for professions in relevant fields such as logistics, analytics, agriculture, industrial manufacturing, transport, and primarily for engineering and technical workers. Russian railways require not only physical infrastructure, but also digital skills of its operation by engineering and technical workers in order to integrate into the digital economy. The aim of the article is to study modern requirements for the professional competencies of railway engineers, primarily their digital literacy and the ability to work with special software. The authors mention the need for an engineer to have softskills and hardskills. The article provides a list of the main software complexes that are included in the special digital competencies of a railway engineer.The authors of the article through the example of railway transport, describe the directions of digitalisation of railway transport, which is a link between the branches of the national and partly global economy. The emphasise the advanced development of scientific and technological progress in the transport industry – the “Digital Railway” project, which generates related tasks, one of which is the modern training of engineering personnel and the consolidation of digital competencies and metaskills in professional standards.

Joshua Hunte, Martin Neil, Norman Fenton

ISO 14971 is the primary standard used for medical device risk management. While it specifies the requirements for medical device risk management, it does not specify a particular method for performing risk management. Hence, medical device manufacturers are free to develop or use any appropriate methods for managing the risk of medical devices. The most commonly used methods, such as Fault Tree Analysis (FTA), are unable to provide a reasonable basis for computing risk estimates when there are limited or no historical data available or where there is second-order uncertainty about the data. In this paper, we present a novel method for medical device risk management using hybrid Bayesian networks (BNs) that resolves the limitations of classical methods such as FTA and incorporates relevant factors affecting the risk of medical devices. The proposed BN method is generic but can be instantiated on a system-by-system basis, and we apply it to a Defibrillator device to demonstrate the process involved for medical device risk management during production and post-production. The example is validated against real-world data.

Jake C. Snell, Thomas P. Zollo, Zhun Deng et al.

Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.

Daniel Neamati, Sriramya Bhamidipati, Grace Gao

Risk-aware urban localization with the Global Navigation Satellite System (GNSS) remains an unsolved problem with frequent misdetection of the user's street or side of the street. Significant advances in 3D map-aided GNSS use grid-based GNSS shadow matching alongside AI-driven line-of-sight (LOS) classifiers and server-based processing to improve localization accuracy, especially in the cross-street direction. Our prior work introduces a new paradigm for shadow matching that proposes set-valued localization with computationally efficient zonotope set representations. While existing literature improved accuracy and efficiency, the current state of shadow matching theory does not address the needs of risk-aware autonomous systems. We extend our prior work to propose Mosaic Zonotope Shadow Matching (MZSM) that employs a classifier-agnostic polytope mosaic architecture to provide risk-awareness and certifiable guarantees on urban positioning. We formulate a recursively expanding binary tree that refines an initial location estimate with set operations into smaller polytopes. Together, the smaller polytopes form a mosaic. We weight the tree branches with the probability that the user is in line of sight of the satellite and expand the tree with each new satellite observation. Our method yields an exact shadow matching distribution from which we guarantee uncertainty bounds on the user localization. We perform high-fidelity simulations using a 3D building map of San Francisco to validate our algorithm's risk-aware improvements. We demonstrate that MZSM provides certifiable guarantees across varied data-driven LOS classifier accuracies and yields a more precise understanding of the uncertainty over existing methods. We validate that our tree-based construction is efficient and tractable, computing a mosaic from 14 satellites in 0.63 seconds and growing quadratically in the satellite number.

Weronika Ormaniec, Marcin Pitera, Sajad Safarveisi et al.

Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task. Typically, we face a small data problem in combination with a high degree of non-linearity, causing difficulties for both classical and machine-learning estimation algorithms. In this paper, we propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network and compare its performance to benchmark GARCH estimators. Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes and correctly identify the underlying risk dynamics in a non-parametric fashion. We evaluate the estimator on both simulated and market data with a focus on heteroscedasticity, finding that LSTM exhibits a similar performance to GARCH estimators on simulated data, whereas on real market data it is more sensitive towards increasing or decreasing volatility and outperforms all existing estimators of value-at-risk in terms of exception rate and mean quantile score.

Di Wang, Heng Li, Jiayu Chen

Abstract Safety management in construction is crucial to the success of a project. A large amount of accidents is the results of construction workers unsafe behaviors, which associated with inappropriate risk detection and perception. Recently, researchers proposed to implement electroencephalograph (EEG) to measure construction workers' perceived risks based on their vigilance status. However, the EEG signals are often contaminated by the artifacts that caused by muscle movements and traditional stationary measurement metrics are not suitable for wearable implementation in the construction industry. To fill in this research gap, this study proposed a new hybrid kinematic-EEG data type and adopted wavelet packet decomposition to compute the vigilance measurement indices with redefined the EEG sub-bands. A validation experiment was conducted to examine thirty candidate vigilance indicators and two mature measurement metrics were compared to select the most proper and consistent indicators. The experiment results suggested three indices with highest correlation coefficients can be applied in vigilance detection. These quantitative vigilance indicators can provide a new perspective to understand the construction workers' risk perception process and improve the safety management on construction sites.

Peiling Zhou, Peiling Zhou, Ruifang Li et al.

Nowadays, obesity and its associated chronic diseases have become a steadily growing public health problem, spreading from the older to younger age groups. Studies have contended that the built environment, particularly the food environment and walkability, may contribute to the prevalence of childhood obesity. In Asian countries which are characterized by rapid urbanization, high population density and oriental diets, little is known about how such urban built environment affects the onset of childhood obesity. This study juxtaposes the effect of food environment, walkability, and outdoor activity spaces at the neighborhood level upon childhood body weight in a mid-sized city in China. This observational study utilizes a retrospective time-trend study design to examine the associations between neighborhood built environment and children's body weight in Zhanjiang City, a mid-sized city in Guangdong Province, China. Robust multiple linear and logistic regression models were used to estimate associations between the built environments and child BMI and weight status (i.e., overweight/obesity and obesity only). This study finds that: (1) Western-style fast food and Chinese-style fast food have divergent impacts on childhood body weight. At neighborhood level, while increased exposure to Western-style fast food may increase child BMI and the risk of overweight and obesity, increased exposure to Chinese-style fast food, on the contrary, may reduce child BMI and the risk of overweight and obesity, indicating a positive health impact of Chinese-style fast food. (2) However, the positive health impacts brought about by Chinese-style fast food, walkable environments and accessible traditional fruit/vegetable markets have gradually disappeared in recent years. This study is among the first to simultaneously consider the divergent and changing impact of food environment upon childhood body weight in urban China. The findings provide important implications for healthy city design and the management of food retail industry in addressing the obesity epidemic in younger generations living in Asian cities. As prominent differences exist in food culture between Asian and Western cities, more attention should be paid to healthy food environment in future studies and related urban planning strategies formulation.

Chen Wang, Yutong Tang, Mukhtar A. Kassem et al.

As China's population ages and the notion of contemporary people evolves, an increasing number of elderly people opt to spend their final years in a nursing home. Recently, there have been more fires in nursing facilities for the elderly. As a result, using computer simulation technology, this paper creates an evacuation micro-simulation model for the elderly and nursing staff, investigates the impact of psychological characteristics and evacuation behavior of the elderly and nursing staff on fire escape during the fire evacuation process, and designs fire evacuation for nursing homes. Based on the nursing home's regular evacuation paradigm, the study examines the behavioral elements. To simulate the three models and the influence of various behavioral and psychological features on fire evacuation, the authors utilized the MATLAB software, which is based on Agent theory and cellular automata. The simulation results show that the three models proposed in this study are capable of accurately describing reality.

Alexandre Carbonneau, Frédéric Godin

The use of non-translation invariant risk measures within the equal risk pricing (ERP) methodology for the valuation of financial derivatives is investigated. The ability to move beyond the class of convex risk measures considered in several prior studies provides more flexibility within the pricing scheme. In particular, suitable choices for the risk measure embedded in the ERP framework such as the semi-mean-square-error (SMSE) are shown herein to alleviate the price inflation phenomenon observed under Tail Value-at-Risk based ERP as documented for instance in Carbonneau and Godin (2021b). The numerical implementation of non-translation invariant ERP is performed through deep reinforcement learning, where a slight modification is applied to the conventional deep hedging training algorithm (see Buehler et al., 2019) so as to enable obtaining a price through a single training run for the two neural networks associated with the respective long and short hedging strategies. The accuracy of the neural network training procedure is shown in simulation experiments not to be materially impacted by such modification of the training algorithm.