Hasil untuk "Risk in industry. Risk management"

D. Ivanov, A. Dolgui, B. Sokolov

The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two isolated areas, i.e. the impact of digitalisation on SC management (SCM) and the impact of SCM on the ripple effect control. To the best of our knowledge, this is the first study that connects business, information, engineering and analytics perspectives on digitalisation and SC risks. This paper does not pretend to be encyclopedic, but rather analyses recent literature and case-studies seeking to bring the discussion further with the help of a conceptual framework for researching the relationships between digitalisation and SC disruptions risks. In addition, it emerges with an SC risk analytics framework. It analyses perspectives and future transformations that can be expected in transition towards cyber-physical SCs. With these two frameworks, this study contributes to the literature by answering the questions of (1) what relations exist between big data analytics, Industry 4.0, additive manufacturing, advanced trace & tracking systems and SC disruption risks; (2) how digitalisation can contribute to enhancing ripple effect control; and (3) what digital technology-based extensions can trigger the developments towards SC risk analytics.

Robert E. Hoyt, Dudley L. Moore, Andre P. Liebenberg

Y. Zou, A. Kiviniemi, Stephen Jones

Pham Quang Tin, Ta Nguyet Minh, Nguyen Ho Thanh Dat et al.

Amidst the 4.0 Revolution, the integration of innovative information technology solutions has become indispensable for firms seeking to enhance operational performance and gain competitive advantages. The restaurant industry is no exception, as Restaurant Management Systems (RMS) have increasingly attracted interest from both service providers and users. However, the underlying mechanisms driving the RMS adoption behavior (BH) of restaurant frontline personnel remain theoretically underexplored. This study addresses the identified gap by employing a unified theoretical framework that integrates the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and the Innovation Diffusion Theory (IDT) to examine the influence of individual psychological and perceptual factors, along with innovation-specific characteristics, on the BH of RMS, with a particular focus on the restaurant industry in Vietnam. Using partial least squares structural equation modeling on a sample of 316 respondents, this study found the direct impact of Perceived Usefulness, along with perceptions of Compatibility, Observability, Risk on potential users Attitude (ATT) toward RMS, while Perceived ease of use and Trialability exerted no significant influence. The findings revealed that ATT, Subjective Norms, and Perceived Behavioral Control play crucial roles in shaping future adopters BI, which, in turn, could directly impact their BH. It was also confirmed that the relationship between BI and BH can be moderated significantly by gender and job position in the context of restaurants. These insights offer valuable implications for RMS providers to refine their promotion solutions, and for policymakers to design supportive measures that foster RMS adoption within the restaurant industry.

Qiao-Yun Hong, Yan Huang, Jie Yang et al.

With the global prevalence of obesity and diabetes continuing to rise, metabolic diseases caused by excessive sugar intake have become a significant public health issue. In this context, various sweeteners as sugar substitutes have been widely used in the food industry. Sweeteners are highly favored for their good safety profile, cost-effectiveness, low-calorie properties, and potential blood sugar regulation effects, and their applications have extended to fields such as pharmaceuticals and daily chemicals. However, recent studies indicate that the impact mechanisms of sweeteners on human health are more complex than previously understood, and the long-term safety of their use has sparked widespread concern in both academia and the public. This review systematically examines relevant literature from the past three decades, employing evidence-based medicine methods for screening and meta-analysis, aiming to comprehensively assess the potential effects of sweeteners on human metabolic indicators (including blood glucose homeostasis and body fat composition) and cancer risk. The discussion will unfold in the following four sections: (1) Definition and classification of sweeteners; (2) Application areas of various sweeteners; (3) Beneficial effects of sweetener use on human health; (4) Adverse effects of sweetener use on health issues in different population groups. Current evidence suggests that the rational use of specific types of sweeteners within recommended dosage ranges can effectively improve blood glucose control, promote weight management, and play a positive role in maintaining oral health. However, excessive or long-term use of certain sweeteners may disrupt gut microbiota balance, affect glucose and lipid metabolism homeostasis, increase cardiovascular disease risk, and potentially be associated with the occurrence of certain malignant tumors. Notably, sweetener exposure during pregnancy may affect the fetus through mechanisms such as epigenetic modifications, necessitating special caution in sweetener selection for pregnant women. This review aims to provide clinicians, nutritionists, and food science professionals with the latest evidence-based medical evidence, guiding consumers to make informed sweetener choices by weighing health benefits against potential risks. It also offers scientific basis for formula optimization and product development in the food industry, thereby promoting public health.

Hossein Sayyadi Tooranloo, Reihaneh Hafizi Atabak

Failure Modes and Effects Analysis (FMEA) is an effective risk management technique widely used for performance improvement and risk mitigation in various fields of industry and science. However, FMEA has been justifiably criticized for its many shortcomings and limitations. The current study proposed a novel model for FMEA based on single-valued neutrosophic (SVN) approach to overcome the shortcomings of classical FMEA in the area of risk prioritization. This approach offers advantages over previous models and provides a means to evaluate Failure modes (FMS), while dealing with vague concepts and insufficient data In this approach, SVNSs are used to deal with the uncertainty and vagueness in information. Analytic Hierarchy Process (AHP) that is integrated with SVNSs is used to determine the weight of risk factors for FMEA. In the end, risk items are ranked by SVN version of VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) based on the obtained factor weights. To demonstrate its applicability, the method is applied to a case study of Iran Central Iron Ore Co. (ICIOC) (Bafgh, Iran). The results indicate that the FMS of Sanctions, Price and cost fluctuations and Inflation and exchange rate changesand are the most important in ICIOC. As a result, this method helps to improve the performance of this company by identifying and prioritizing failure cases in conditions of ambiguity and uncertainty, which can be used in other industries as well.

Arafat Rahman

This study aims to investigate the relationship between carbon pricing mechanisms and carbon intensity among India's top 100 publicly listed companies, as reported by the Carbon Disclosure Project (CDP). It specifically investigates how internal carbon pricing, science-based targets (SBTs), corporate social responsibility (CSR), and research and development (R&D) investment influence carbon disclosure practices and environmental transparency. A total of 253 firm-year observations from 2015 to 2021 were collected from CDP reports, OSIRIS financial database, annual reports, and India's National CSR Portal. The study applies multiple linear regression analysis to assess the influence of the identified variables on carbon intensity, measured as total CO₂ emissions (Scope 1, 2, and 3) per unit of sales. Content analysis was employed to validate disclosure attributes aligned with stakeholder and legitimacy theories. The regression results show a significant negative relationship between R&D and carbon intensity, highlighting the potential of innovation in reducing emissions. Science-based targets and CSR investments, however, show a significant positive association with carbon intensity, suggesting that high-emission firms are more likely to adopt visible sustainability initiatives. Internal carbon pricing was found to have no statistically significant influence on emission intensity. The findings provide actionable insights for Indian regulators and global policymakers. Emphasis should be placed on incentivizing science-based targets and R&D-driven decarbonization strategies while making internal carbon pricing mechanisms more effective. Investors can play a crucial role by demanding transparency, while firms must enhance their sustainability reporting frameworks to overcome barriers to disclosure and strengthen stakeholder trust. This study contributes to the accounting and environmental disclosure literature by being the first of its kind to empirically analyze the effect of internal carbon pricing and SBTs on carbon intensity in the Indian context. It offers timely, evidence-based insights relevant to achieving Sustainable Development Goal 13 (Climate Action) and supports global efforts in transitioning toward low-carbon economies.

Amin Haeri, Jonathan Vitrano, Mahdi Ghelichi

Risk management in finance involves recognizing, evaluating, and addressing financial risks to maintain stability and ensure regulatory compliance. Extracting relevant insights from extensive regulatory documents is a complex challenge requiring advanced retrieval and language models. This paper introduces RiskData, a dataset specifically curated for finetuning embedding models in risk management, and RiskEmbed, a finetuned embedding model designed to improve retrieval accuracy in financial question-answering systems. The dataset is derived from 94 regulatory guidelines published by the Office of the Superintendent of Financial Institutions (OSFI) from 1991 to 2024. We finetune a state-of-the-art sentence BERT embedding model to enhance domain-specific retrieval performance typically for Retrieval-Augmented Generation (RAG) systems. Experimental results demonstrate that RiskEmbed significantly outperforms general-purpose and financial embedding models, achieving substantial improvements in ranking metrics. By open-sourcing both the dataset and the model, we provide a valuable resource for financial institutions and researchers aiming to develop more accurate and efficient risk management AI solutions.

Abiodun Finbarrs Oketunji

This research presents a framework for quantitative risk management in volatile markets, specifically focusing on expectile-based methodologies applied to the FTSE 100 index. Traditional risk measures such as Value-at-Risk (VaR) have demonstrated significant limitations during periods of market stress, as evidenced during the 2008 financial crisis and subsequent volatile periods. This study develops an advanced expectile-based framework that addresses the shortcomings of conventional quantile-based approaches by providing greater sensitivity to tail losses and improved stability in extreme market conditions. The research employs a dataset spanning two decades of FTSE 100 returns, incorporating periods of high volatility, market crashes, and recovery phases. Our methodology introduces novel mathematical formulations for expectile regression models, enhanced threshold determination techniques using time series analysis, and robust backtesting procedures. The empirical results demonstrate that expectile-based Value-at-Risk (EVaR) consistently outperforms traditional VaR measures across various confidence levels and market conditions. The framework exhibits superior performance during volatile periods, with reduced model risk and enhanced predictive accuracy. Furthermore, the study establishes practical implementation guidelines for financial institutions and provides evidence-based recommendations for regulatory compliance and portfolio management. The findings contribute significantly to the literature on financial risk management and offer practical tools for practitioners dealing with volatile market environments.

Sharif Al Mamun, Rakib Hossain, Md. Jobayer Rahman et al.

A Bayesian analytics framework that precisely quantifies uncertainty offers a significant advance for financial risk management. We develop an integrated approach that consistently enhances the handling of risk in market volatility forecasting, fraud detection, and compliance monitoring. Our probabilistic, interpretable models deliver reliable results: We evaluate the performance of one-day-ahead 95% Value-at-Risk (VaR) forecasts on daily S&P 500 returns, with a training period from 2000 to 2019 and an out-of-sample test period spanning 2020 to 2024. Formal tests of unconditional (Kupiec) and conditional (Christoffersen) coverage reveal that an LSTM baseline achieves near-nominal calibration. In contrast, a GARCH(1,1) model with Student-t innovations underestimates tail risk. Our proposed discount-factor DLM model produces a slightly liberal VaR estimate, with evidence of clustered violations. Bayesian logistic regression improves recall and AUC-ROC for fraud detection, and a hierarchical Beta state-space model provides transparent and adaptive compliance risk assessment. The pipeline is distinguished by precise uncertainty quantification, interpretability, and GPU-accelerated analysis, delivering up to 50x speedup. Remaining challenges include sparse fraud data and proxy compliance labels, but the framework enables actionable risk insights. Future expansion will extend feature sets, explore regime-switching priors, and enhance scalable inference.

Detcharat Sumrit, Jirawat Keeratibhubordee

In this study, a novel risk assessment framework designed for evaluating the challenges of plastic packaging waste management in the context of reverse logistics is introduced. The framework leverages Failure Mode Effect Analysis (FMEA) to address decision-making in a fuzzy environment. To augment the traditional FMEA risk criteria, encompassing severity (S), occurrence (O), and detection (D), three additional essential risk criteria are introduced: cost of failure (C), complexity of failure resolution (H), and impact on business (I). These newly incorporated criteria significantly enhance the capacity to convey the multifaceted risks inherent in reverse logistics for the plastic recycling sector. Furthermore, a comprehensive literature review and expert validation are conducted to identify ten distinct failure modes. To subjectively and objectively determine the risk criteria weightings, a combination of Analytic Hierarchy Process (AHP) and LOgarithmic Percentage Change-driven Objective Weighting (LOPCOW) is employed. Finally, the Additive Ratio Assessment (ARAS) approach is applied to prioritize such failure modes. Recognizing the inherent imprecision and uncertainty associated with human decision-making, the trapezoidal fuzzy set (TrFS) is adopted throughout all decision-making processes. To showcase the proposed framework effectiveness, the framework is applied as a case study to a waste plastic recycling manufacturer in Thailand.

Rizky Yuli Ikhwanuddin, Zulkifli Djunaidi

Introduction: The 2022 National Occupational Health and Safety (OHS) Profile in Indonesia shows that the human factor in safety is a factor that influences the risk of workplace accidents. The mining accidents statistical data in Indonesia, in which there was a 100% increase of workplace accidents in 2022, have given this industry an urgency to get a special attention to study risk-taking behavior at the workplace. At the organizational level, PT. XYZ (a mining contractor company in Indonesia) has internally measured its safety maturity level and is currently in calculative level, which indicates that the OHS management system has been implemented but the number of unsafe behaviors and unsafe conditions on site is still high. This paper explores quantitative results from research which aims to obtain an overview of employees’ internal factors leading to rule-breaking acts at the workplace. Methods: This paper uses cross-sectional design research with quantitative approach. Using stratified random sampling, a sample of 283 employees of PT. XYZ Site A participated in this study, ranging from managers, supervisors, and workers. Data were collected through a questionnaire with open-ended questions referring to a study from Safe Work Australia and analyzed quantitatively using statistical Chi-Square statistical test. Results: From the results of the Chi-Square test, the independent variables that have a value of Asymp. Sig. (2-sided) below 0.05 (95% CI) and lead to rule-breaking act at workplace are risk-taking behavior acceptance (0.018), normalizing minor accidents (0.002), and decision to take risk (0.000). Conclusion: Employees’ internal factors of risk-taking behavior acceptance, normalizing minor accidents, and decision to take risk have positive and significant effect on rule-breaking acts at the workplace. It is recommended that organizations implement a proper risk management with ALARP principle, safety empowering leadership, and safe behavior trainings to minimize rule-breaking acts at the workplace

Imen Khanchel, Naima Lassoued, Cyrine Khiari

This study explores the implications and effectiveness of pollution control bonds (PCBs) in reducing carbon risk, focusing on a sample of 242 U S. electric utility firms from 2012 to 2022. The research investigates the association of PCB issuances with (i) absolute (unscaled) carbon emissions levels and their three scopes; and (ii) carbon emissions intensity and its three scopes. Using quantile regressions covering the 5th, 25th, 50th, 75th, and 90th quantiles, along with the Propensity Score Matching (PSM) methodology (where the treatment group includes firms using PCBs and the control group comprises firms that do not opt for PCBs), the findings show a significant reduction in carbon emissions due to PCB issuance. Substantial differences were observed between the treatment group and the control group across various quantiles of carbon emissions, particularly for companies with medium to high carbon footprints, both in terms of overall CO2 emissions and scope 2 CO2 emissions. Moreover, disparities between the two groups were notable across all quantiles of scope 1 CO2 emissions. Additionally, among the companies using PCBs, those with lower risk profiles exhibited a smaller carbon footprint, measured by scope 3 CO2 emissions, in comparison to their counterparts. Furthermore, the study highlights a more pronounced impact of PCBs issuance during the second phase of the Kyoto Protocol and the commitment period of the Paris Agreement. The results remain robust when alternative measures of carbon risk are applied. These findings carry significant implications for municipal and financing authorities, as well as investors within the U.S. electric utility industry. This research contributes novel insights to the field of electric utility management.

Stéphane Crépey, Botao Li, Hoang Nguyen et al.

We present a unified framework for computing CVA sensitivities, hedging the CVA, and assessing CVA risk, using probabilistic machine learning meant as refined regression tools on simulated data, validatable by low-cost companion Monte Carlo procedures. Various notions of sensitivities are introduced and benchmarked numerically. We identify the sensitivities representing the best practical trade-offs in downstream tasks including CVA hedging and risk assessment.

Marcel Bluhm, Adrian Cachinero Vasiljević, Sébastien Derivaux et al.

Stablecoins have turned out to be the "killer" use case of the growing digital asset space. However, risk management frameworks, including regulatory ones, have been largely absent. In this paper, we address the critical question of measuring and managing risk in stablecoin protocols, which operate on public blockchain infrastructure. The on-chain environment makes it possible to monitor risk and automate its management via transparent smart-contracts in real-time. We propose two risk metrics covering capitalization and liquidity of stablecoin protocols. We then explore in a case-study type analysis how our risk management framework can be applied to DAI, the biggest decentralized stablecoin by market capitalisation to-date, governed by MakerDAO. Based on our findings, we recommend that the protocol explores implementing automatic capital buffer adjustments and dynamic maturity gap matching. Our analysis demonstrates the practical benefits for scalable (prudential) risk management stemming from real-time availability of high-quality, granular, tamper-resistant on-chain data in the digital asset space. We name this approach Crypto Asset-Liability Management (CALM).

Minqiang Xu, Xiaoxue Ma, Yulan Zhao et al.

Maritime transportation plays a critical role in global trade, and studies on maritime transportation safety management are of great significance to the sustainable development of the maritime industry. Consequently, there has been an increasing trend recently in studies on maritime transportation safety management, especially in terms of safety risk analysis and emergency management. Therefore, the general idea of this article is to provide a detailed literature review of maritime transportation safety management based on 186 articles in the Web of Science (WOS) database published from 2011 to 2022. The purposes of this article are as follows: (1) to provide a statistics-based description and conduct a network-based bibliometric analysis on the basis of the collected articles; (2) to summarize the methodologies/technologies employed in maritime transportation safety management spatiotemporally; and (3) to propose four potential research perspectives in terms of maritime transportation safety management. Based on the findings and insights obtained from the bibliometric and systematic review, the development of a resilient maritime transportation system could be facilitated by means of data- or intelligence-driven technologies, such as scenario representation, digital twinning, and data simulation. In addition, the issues facing intelligent maritime shipping greatly challenge the current maritime safety management system due to the co-existence of intelligent and non-intelligent maritime operation.

M. M. Balashov

Over the past decade, the global energy sector has undergone major fundamental and structural changes as part of the global energy transition. The energy industry of the Russian Federation, as a key player in the global energy market and the world economy as a whole, is undergoing similar changes. In this case, in terms of ensuring high competitiveness and long-term energy security of the state, it is crucial to set priorities and build models of sustainable development for each of the industries related to the energy sector. Indeed, the process of replacing carbon-intensive energy sources with a systematic increase in the share of new, renewable energy sources (RES) should be gradual and consistent to avoid imbalances in energy systems and maintain equity for all stakeholders. In this context, the search for advanced, low-carbon energy sources is a priority for the vast majority of countries around the world. In addition, the development of renewable energy is one of the goals of Russia՚s energy strategy until 2035. At the same time, despite the obvious advantages of the Russian power industry such as the absence of dependence on budget funds, the overwhelming majority of private investment in the industry, the availability of effective mechanisms for attracting investment and the basic principle of balancing the interests of all market participants, there are also negative consequences of this approach. The nationwide task of developing the energy system and increasing the availability of electricity on the territory of the Russian Federation in terms of financing is becoming an exclusive burden on electricity consumers themselves; even insignificant risks in their operation can turn into a threat not only to sustainable development, but also to their very existence. In this context, the analysis of key directions and proposals to minimise the economic impact of the global energy transition on large energy-intensive industrial consumers of electricity and capacity is of particular relevance.

Irasema Alcántara-Ayala, Ian Burton, Allan Lavell et al.

In the 2020s, understanding disaster risk requires a strong and clear recognition of values and goals that influence the use of political and economic power and social authority to guide growth and development. This configuration of values, goals, power and authority may also lead to concrete drivers of risk at any one time. Building on previous disaster risk frameworks and experiences from practice, since 2010, the ‘Forensic Investigations of Disasters (FORIN)’ approach has been developed to support transdisciplinary research on the transformational pathways societies may follow to recognise and address root causes and drivers of disaster risk. This article explores and assesses the achievements and failures of the FORIN approach. It also focuses on shedding light upon key requirements for new approaches and understandings of disaster risk research. The new requirements stem not only from the uncompleted ambitions of FORIN and the forensic approach but also from dramatic and ongoing transformational changes characterised by climate change, the coronavirus disease 2019 (COVID-19) pandemic and the threat of global international confrontation, among other potential crises, both those that can be identified and those not yet identified or unknown. Contribution: Disasters associated with extreme natural events cannot be treated in isolation. A comprehensive “all risks” or “all disasters” approach is essential for a global transformation, which could lead to a better world order. To achieve this, an Intergovernmental Panel for Disaster Risk is suggested to assess risk science periodically and work towards sustainability, human rights, and accountability, within a development and human security frame and on a systemic basis and integrated perspective.

Yang Yu, Bing Guo, Chenglong Wang et al.

AbstractPrevious studies on carbon storage simulation had ignored the difference of carbon intensity among various vegetation types inner the same land use. In this paper, The PLUS model was used to predict the land use change under multi-scenarios from 2030 to 2060, and the vegetation type data were supplemented by CA model to obtain the land cover-vegetation datasets from 2030-2060. Combined with the carbon density table of vegetation type, the future land use carbon storage during 2030-2060 under multi-scenarios in Beijing-Tianjin-Hebei region were analyzed. The main conclusions were as follows: (1) The spatial distribution of carbon storage in Beijing-Tianjin-Hebei region showed a pattern of ‘high in northeast-southwest and low in southeast-northwest’; (2) The carbon storage in Beijing-Tianjin-Hebei region during 1990-2020 showed a decreasing trend; (3) During 2030-2060, the carbon storage in Beijing-Tianjin-Hebei region showed a continuous decreasing trend in the absence of policy intervention, while that under the ecological protection and farmland protection scenarios showed an increasing trend; (4) Under different development scenarios, there were obvious significances of carbon storage in spatial distribution.

Armin Asaadi, Armita Atrian, Hesam Nik Hoseini et al.

Project managers play a crucial role in the success of projects. The selection of an appropriate project manager is a primary concern for senior managers in firms. Typically, this process involves candidate interviews and assessments of their abilities. There are various criteria for selecting a project manager, and the importance of each criterion depends on the project type, its conditions, and the risks associated with their absence in the chosen candidate. Often, senior managers in engineering companies lack awareness of the significance of these criteria and the potential risks linked to their absence. This research aims to identify these risks in selecting project managers for civil engineering projects, utilizing a combined ANP-FMEA approach. Through a comprehensive literature review, five risk categories have been identified: individual skills, power-related issues, knowledge and expertise, experience, and personality traits. Subsequently, these risks, along with their respective sub-criteria and internal relationships, were analysed using the combined ANP-FMEA technique. The results highlighted that the lack of political influence, absence of construction experience, and deficiency in project management expertise represent the most substantial risks in selecting a project manager. Moreover, upon comparison with the traditional FMEA approach, this study demonstrates the superior ability of the ANP-FMEA model in differentiating risks and pinpointing factors with elevated risk levels.