Abstract We theorize a notion of a digital supply chain (SC) twin – a computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design and implementation of the digital twins when managing disruption risks in SCs. The combination of model-based and data-driven approaches allows uncovering the interrelations of risk data, disruption modeling, and performance assessment. The SC shocks and adaptations amid the COVID-19 pandemic along with post-pandemic recoveries provide indisputable evidences for the urgent needs of digital twins for mapping supply networks and ensuring visibility. The results of this study contribute to the research and practice of SC risk management by enhancing predictive and reactive decisions to utilize the advantages of SC visualization, historical disruption data analysis, and real-time disruption data and ensure end-to-end visibility and business continuity in global companies.
This paper introduces a transformative framework for managing path-dependent financial risk by shifting from traditional distribution-centric models to a geometry-based approach. We propose the SigSwap as a new regulatory instrument that allows market participants to decompose complex risk into terminal price law and the underlying texture of the price path. By utilising the mathematical properties of the path-signature, we demonstrate how previously unmodellable risks, such as lead-lag dynamics and flash-crash spiralling, can be converted into transparent and linear risk factors. Central to this framework is the introduction of Signature Expected Shortfall, a risk metric designed to capture toxic path geometries that traditional methods often overlook. We also present a proactive monitoring system based on the Temporal Exposure Profile, which utilises anticipatory learning to detect potential liquidity traps and geometric decoupling before they manifest as realised volatility. The proposed methodology offers a rigorous alignment with global regulatory mandates, specifically the Fundamental Review of the Trading Book (FRTB), by providing a consistent bridge between physical stress-testing and risk-neutral hedging. Finally, we show that this algebraic approach significantly reduces computational complexity, enabling real-time, high-frequency risk reporting and capital optimisation for the modern financial ecosystem.
Casualties during emergency evacuations are often attributed to people’s panic-driven extreme behaviors rather than the accidents themselves. The propagation of panic is influenced by various factors. Based on the susceptible–infectious–recovered–susceptible (SIRS) model, a system dynamics (SD) model was developed using AnyLogic software to investigate the spread of panic emotions within a population. A case study focused on hospital emergency evacuations was conducted, wherein factors influencing panic propagation were divided into individual and group levels. The population was classified into three categories—staff, caregivers, and patients—and the effect of the ratio of these categories on evacuation efficiency was examined. Based on these classifications, an evacuation simulation experiment was conducted to examine the effects of panic emotions on evacuation efficiency. Results indicate that optimal hospital evacuation efficiency is achieved with a staff:caregiver:patient ratio of 2:2:1. The overall evacuation process is significantly impacted by panic, resulting in a 64 % increase in evacuation times when panic propagation is considered compared to scenarios where it is not. Furthermore, the initial 10 s following a disaster were identified as crucial for managing severe panic. Valuable insights for improving emergency evacuation management are provided by this study.
Zheng Gong, Obuks A. Ejohwomu, Fangyuan Shen
et al.
This research investigates how zero-energy building (ZEB) complexities influence energy performance risks (EPRs) and contribute to the energy performance gap (EPG). Despite the increasing adoption of ZEBs to meet net-zero carbon targets, the persistent discrepancy between predicted and actual energy use remains underexplored from a complexity perspective. Drawing on socio-technical systems (STS) theory and project complexity theory, this research develops an integrated Technology–Organisation–Environment (TOE) complexity framework to systematically link ZEB complexities with EPRs across the project lifecycle. A two-step mixed-methods approach was employed, combining a systematic literature review (SLR) and semi-structured interviews with 12 UK industry experts. The research identified 23 complexity factors, categorised into nine overarching themes, and demonstrated that technical, organisational, and environmental dimensions are equally significant and interrelated, influencing EPRs through structural, socio-political, and emergent mechanisms. Notably, emergent influences marked by uncertainty and dynamism were pervasive, and socio-political factors played a critical role in shaping organisational and environmental outcomes. The framework fills a gap in existing EPG frameworks by integrating project-specific complexities and extending complexity and risk management theory into the energy performance domain. It also provides decision-makers with a structured tool to better assess, manage, and mitigate complexity-driven risks.
This study examines the impact of monetary policy on financial stability in good years. It focuses on the impact of three monetary policy tools on financial stability. The study used the median quantile regression method to analyze 22 countries during the 2011 to 2018 period – a period which isolates the shock from the coronavirus disease 2019 (COVID-19) pandemic and the shock from the global financial crisis. The financial stability indicator is the country-level bank nonperforming loans ratio. The monetary policy indicators are broad money growth, broad money-to-GDP ratio and the central bank interest rate, while controlling for the inflation rate, total unemployment rate, efficiency ratio, institutional governance quality and economic growth rate. The findings reveal that high central bank interest rates impair financial stability by increasing the bank nonperforming loans ratio in African countries and developing countries. In contrast, high central bank interest rates improve financial stability in developed countries and emerging market countries. Furthermore, higher broad money growth improves financial stability in European banks, while broad money growth, broad money-to-GDP ratio and central bank interest rate do not have a significant effect on the NPL ratio of Asian banks.
Johanna L. Smith, Quenna Wong, Whitney Hornsby
et al.
Sharing diverse genomic and other biomedical datasets is critical to advance scientific discoveries and their equitable translation to improve human health. However, data sharing remains challenging in the context of legacy datasets, evolving policies, multi-institutional consortium science, and international stakeholders. The NIH-funded Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium was established to improve the performance of polygenic risk estimates for a broad range of health and disease outcomes with global impacts. Improving polygenic risk score performance across genetically diverse populations requires access to large, diverse cohorts. We report on the design and implementation of data sharing policies and procedures developed in PRIMED to aggregate and analyze data from multiple, heterogeneous sources while adhering to existing data sharing policies for each integrated dataset. We describe two primary data sharing mechanisms: coordinated dbGaP applications and a Consortium Data Sharing Agreement, as well as provide alternatives when individual-level data cannot be shared within the Consortium (e.g., federated analyses). We also describe technical implementation of Consortium data sharing in the NHGRI Analysis Visualization and Informatics Lab-space (AnVIL) cloud platform, to share derived individual-level data, genomic summary results, and methods workflows with appropriate permissions. As a Consortium making secondary use of pre-existing data sources, we also discuss challenges and propose solutions for release of individual- and summary-level data products to the broader scientific community. We make recommendations for ongoing and future policymaking with the goal of informing future consortia and other research activities.
Supply chain risk management (SCRM) is a critical aspect of contemporary business operations, necessitated by the complex and interconnected nature of global supply chains. This qualitative research delves into the intricacies of SCRM through in-depth interviews with industry experts, aiming to unravel the multifaceted nature of supply chain risks and the strategies employed for mitigation. Key themes emerged, including the diverse sources of risks encompassing operational, financial, environmental, and geopolitical factors, highlighting the need for a comprehensive approach to risk management. Challenges such as limited visibility, resource constraints, and organizational silos were identified, underscoring the importance of addressing these barriers to enhance SCRM effectiveness. Strategies for risk mitigation encompassed technological investments for enhanced visibility and predictive capabilities, collaboration and information sharing among supply chain partners, and the integration of sustainability principles into risk management practices. Leadership, organizational culture, and continuous learning emerged as critical factors in driving effective SCRM practices, emphasizing the need for proactive and adaptable approaches to navigate evolving risks. Overall, this study contributes to the existing body of knowledge on SCRM by providing valuable insights for practitioners and academics, and underscores the importance of holistic and proactive approaches to enhance supply chain resilience and agility in today's dynamic business environment.
Mohammad Mahdi Barati Jozan, Aynaz Lotfata, Howard J. Hamilton
et al.
Abstract Background The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. Methods In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. Results The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. Conclusions The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.
This essay expands the postcolonial agenda for future disaster studies that we suggested in conclusion of the book The Invention of Disaster. It provides some refined perspectives on how to capture the diversity and complexity of the world that we draw from the philosophy of Martinican poet and novelist Edouard Glissant. Glissant’s philosophy of creolisation and relation offers critical pathways towards pluralistic approaches to understanding what we call disaster in a world that is marked by hybridity and relationships rather than essentialism and nativism. A Tout-Monde, in Glissant’s terms, that is the combined additions of different and hybrid interpretations of disaster. Exploring the Tout-Monde of disaster studies will constitute a radical and forward-looking postcolonial agenda; radical in that it will challenge many of our scholarly assumptions, popular discourses as well as common-sense policies and practices.
In this paper, we propose the multivariate range Value-at-Risk (MRVaR) and the multivariate range covariance (MRCov) as two risk measures and explore their desirable properties in risk management. In particular, we explain that such range-based risk measures are appropriate for risk management of regulation and investment purposes. The multivariate range correlation matrix (MRCorr) is introduced accordingly. To facilitate analytical analyses, we derive explicit expressions of the MRVaR and the MRCov in the context of the multivariate (log-)elliptical distribution family. Frequently-used cases in industry, such as normal, student-$t$, logistic, Laplace, and Pearson type VII distributions, are presented with numerical examples. As an application, we propose a range-based mean-variance framework of optimal portfolio selection. We calculate the range-based efficient frontiers of the optimal portfolios based on real data of stocks' returns. Both the numerical examples and the efficient frontiers demonstrate consistences with the desirable properties of the range-based risk measures.
The food industry around the world faces many challenges regarding food safety due to the lack of understanding of HACCP and other food safety management systems. A HACCP is a system for ensuring food safety by identifying, assessing, and controlling risks to human health or reducing the occurrence of these risks to the extent that they do not cause any danger to the health of consumers. There are seven principles of HACCP that allow packaging companies to review their production through a structured approach to identifying risks. These principles are used as steps for defining a new HACCP program or making changes to an existing HACCP program.Conclusions were drawn that emphasized the importance of applying the HACCP system for food packaging to determine the potential risks that may be present, how to control them, and how to determine the extent to which the risk is eliminated or reduced to an acceptable level. And the necessity to have a list of raw materials suppliers of all raw materials for packaging and their suitability with different products and treatments. When packaging a specific product, the characteristics of both the product and the material must be studied well, with the necessary tests performed for them together to ensure the safety of food products and thus the safety of the consumer.Research problem: Spoilage of products due to lack of good planning to choose the appropriate packaging for the product. Lack of risk analysis in the packaging stages exposes the product to hazards due to ignorance of the critical control points.Confusion about understanding the difference between product spoilage and the severity of this and the product's lack of quality.Aims of the research: Awareness of conducting an analysis of the various risks and their potential sources that affect food safety at the packaging.Developing a set of steps and models to clarify steps to reduce risks or reduce them to the permissible limit.
Offshore oil and gas drilling operations are going to remote and harsh arctic environments with demands for heightened safety and resilience of operational facilities. The remote and harsh environment is characterized by extreme waves, wind, storms, currents, ice, and fog that hinder drilling operations and cause structural failures of critical offshore infrastructures. The risk, safety, reliability, and integrity challenges in harsh environment operations are critically high, and a comprehensive understanding of these factors will aid operations and protect the investment. The dynamics, environmental constraints, and the associated risk of the critical offshore infrastructures for safe design, installation, and operations are reviewed to identify the current state of knowledge. This paper introduces a systematic review of harsh environment characterization by exploring the metocean phenomena prevalent in harsh environments and their effects on the floating offshore structures performance and supporting systems. The dynamics of the floating systems are described by their six degrees of freedom and their associated risk scenarios. The systematic methodology further explores the qualitative, quantitative, and consequences modeling techniques for risk analysis of floating offshore systems in a harsh environment. While presenting the current state of knowledge, the study also emphasizes a way forward for sustainable offshore operations. The study shows that the current state of knowledge is inexhaustive and will require further research to develop a design that minimizes interruption during remote harsh offshore operations. Resilient innovation, IoT and digitalization provide opportunities to fill some of the challenges of remote Arctic offshore operations.
Henny Permatasari, Junaiti Sahar, Muchtarrudin Mansyur
et al.
The factors associated with changes in work patterns and working hours due to rotating shifts have an effect on the increased risk of health problems in workers. Manufacturing industry workers, specifically those on rotating shift schedules, are at a high risk of various health problems, such as cardiovascular diseases, circadian rhythm problems, social life problems, and stress. These health problems may be worsened by poor lifestyle habits, such as smoking, unhealthy diet, and infrequent physical activity. This research aimed to explore the experience of 12 manufacturing workers on rotating shift schedules in Greater Jakarta, Indonesia. Through a phenomenological approach, this qualitative study employed 12 participants selected from manufacturing industry shift workers. The participants were selected through purposive sampling whom met the inclusion criteria, namely working in three rotating shift patterns (morning, afternoon, and night shift), aged 20–50 years old, having at least three years of experience in shift work, and able to communicate well. Selection was done with the assistance of the supervisors of the participants working in the manufacturing industry. Thematic analysis yielded three themes: the reasons for working shifts, the effects of shift work, and efforts made to maintain health during working shifts. The findings of this study imply the need for occupational health nursing services as the main intervention at the primary and secondary prevention levels. Occupational health nurses provide occupational health nurs-ing services in the workplace in accordance with the nursing intervention model of fatigue management.
Abstrak
Suara Pekerja Manufaktur Indonesia dalam Sistem Shift Berputar. Dalam sistem kerja shift, faktor-faktor seperti perubahan pola kerja dan jam kerja dapat berdampak pada masalah kesehatan para pekerjanya. Pekerja shift manufaktur berisiko memiliki berbagai masalah kesehatan, seperti penyakit kardiovaskuler, gangguan irama sirkadian, gangguan pola kehidupan sosial, stres, didukung oleh perilaku pekerja seperti merokok, diet yang buruk serta aktivitas olah raga yang jarang dilakukan. Penelitian ini bertujuan untuk mengeksplorasi pengalaman para 12 pekerja di sector manufaktur di Indonesia, khususnya di daerah Jakarta dan sekitarnya. Peneliti menggunakan metode penelitian kualitatif dengan pendekatan fenomenologi. Partisipan berjumlah 12 didapatkan melalui teknik purposive sampling yang memenuhi kriteria inklusi: menggunakan sistem kerja rotasi tiga shift (pagi, sore, dan malam), berusia 20–50 tahun, memiliki pengalaman bekerja sistem rotasi shift minimal tiga tahun, dan mampu berkomunikasi dengan baik. Proses pemilihan partisipan dilakukan peneliti bersama key person, yaitu supervisor/leader dari beberapa pabrik manufaktur. Analisis tematik yang dilakukan menghasilkan tiga tema: alasan bekerja shift, dampak dari kerja shift, dan upaya para pekerja dalam menjaga status kesehatan. Temuan dari penelitian ini dapat menjadi implikasi bagi kesadaran terhadap perlunya tenaga kesehatan atau perawat di area kerja atau sektor industri sebagai upaya intervensi utama dan sekunder dalam pencegahan kecelakaan kerja untuk para pekerja dan keluarga pekerja melalui model keperawatan manajemen kelelahan kerja (MARI-KERJA).
Kata Kunci: kerja shift, pekerja manufaktur, perawat kesehatan kerja, sistem berputar
A Pure polyvinyl chloride (PVC) is a white, brittle material and it is the third-largest polymers produced after polyethylene and polypropylene as 40 million tons of PVC are produced yearly. The basic structure of PVC is (C2H3Cl)n and it is produced by polymerization of the vinyl chloride monomer (VCM) with a polymerization degree ranges from 300 to 1500. The chlorine content in PVC is about 57% by weight, which makes it less dependent on hydrocarbon content. In this paper, we are going to reveal the PVC additives and applications.
Engineering (General). Civil engineering (General), Risk in industry. Risk management
Buildings are exposed to risks from environmental hazards such as earthquakes, windstorms and floods. Substantial uncertainties from various sources are inevitably involved in the risk estimation and decision-making for activities such as design and disaster risk mitigation for buildings. Decision makers seek to achieve economic efficiency while ensure building safety by managing the extreme tail risk that is typically a concern when facing low-probability, high-consequence events. Thus, risk preferences and tolerances play an important role in the decision process, which often vary among different decision makers. The conventionally used minimum expected life-cycle cost criterion (MELC) fails to adequately cope with large uncertainty and risk preferences. To this end, this paper presents the application of a set of decision models beyond the MELC to support decision-making under uncertainty for buildings exposed to environmental hazards. The objective is to provide risk-informed decision support for decision-makers with a wide range of risk appetites while taking into account uncertainties involved in the life-cycle cost. The features, strengths and weaknesses of these decision models are discussed from a practical point of view. The application and selection of the decision models are demonstrated by two practical decision problems: (i) seismic design of a high-rise commercial building, and (ii) wind hazard mitigation for a low-rise residential building. These examples illustrate how the decisions for choosing seismic design levels and wind mitigation measures vary when different decision models and model settings are applied.
Gareth W. Peters, Matteo Malavasi, Georgy Sofronov
et al.
We focus on model risk and risk sensitivity when addressing the insurability of cyber risk. The standard statistical approaches to assessment of insurability and potential mispricing are enhanced in several aspects involving consideration of model risk. Model risk can arise from model uncertainty, and parameters uncertainty. We demonstrate how to quantify the effect of model risk in this analysis by incorporating various robust estimators for key model parameter estimates that apply in both marginal and joint cyber risk loss process modelling. We contrast these robust techniques with standard methods previously used in studying insurabilty of cyber risk. This allows us to accurately assess the critical impact that robust estimation can have on tail index estimation for heavy tailed loss models, as well as the effect of robust dependence analysis when quantifying joint loss models and insurance portfolio diversification. We argue that the choice of such methods is akin to a form of model risk and we study the risk sensitivity that arise from choices relating to the class of robust estimation adopted and the impact of the settings associated with such methods on key actuarial tasks such as premium calculation in cyber insurance. Through this analysis we are able to address the question that, to the best of our knowledge, no other study has investigated in the context of cyber risk: is model risk present in cyber risk data, and how does is it translate into premium mispricing? We believe our findings should complement existing studies seeking to explore insurability of cyber losses. In order to ensure our findings are based on realistic industry informed loss data, we have utilised one of the leading industry cyber loss datasets obtained from Advisen, which represents a comprehensive data set on cyber monetary losses, from which we form our analysis and conclusions.
Abstract In this article I examine how metals mining companies understand and act upon CSR as risk management and the consequences for community CSR projects. I begin by exploring the literature on CSR and development in the mining industry, motives for CSR engagement in the industry, and risk and risk management. I then draw on my research data to map how CSR programmes are seen as an important method of managing strategic challenges to firms — categorised here as reputational, operational or regulatory ‘risks’—and note how competition for capital and recent changes in the legal environment have furthered this process. A focus on CSR as risk management can illuminate the poor development outcomes of community CSR projects, despite recent rises in spending. ‘CSR as risk management’ introduces immanent limitations including treating CSR as PR, targeting those that pose the greatest threat rather than those with the greatest need, excessively simplifying complex processes and focussing on maintaining the status quo. In risk management thinking, CSR activities may be a high organisational priority, integrated into central decision-making processes and subject to a great deal of investment, but still see little progress towards inclusive development for those living closest to mining operations. I conclude by reflecting on what this means for future action and research.