Hasil untuk "Risk in industry. Risk management"

M. Gerhard-Herman, H. Gornik, Coletta Barrett et al.

Ali H. Diabat, K. Govindan

Ibrar Yaqoob, K. Salah, R. Jayaraman et al.

S. Mangla, Pradeep Kumar, M. Barua

Lili Qu, Nan Hong, Jieru Tan

Accurate prediction of shipping freight indices, represented by the Baltic Dry Index (BDI), is crucial for operational decision-making and risk management in the shipping industry. Existing models mainly rely on historical time-series data and often overlook the influence of unstructured information such as market sentiment. To address this limitation, this study proposes a dynamic freight rate prediction framework integrating a shipping text sentiment index. First, a shipping news sentiment index is constructed using a RoBERTa-based pre-trained model to quantify the impact of market sentiment on freight rate fluctuations. Second, the BDI series is decomposed and reconstructed through Variational Mode Decomposition (VMD) and Fuzzy C-Means (FCM) clustering to extract multiscale features. Finally, a deep learning based multi-step prediction model is developed by incorporating the sentiment index into the forecasting process. Empirical results show that the proposed model significantly outperforms benchmark models without sentiment information in terms of MAE, RMSE, and R², and demonstrates greater robustness under extreme market conditions. These findings provide a novel methodological framework for improving freight rate forecasting accuracy and offer practical decision support for shipping enterprises.

Agung Kwartama

The growing complexity of the maritime industry underscores the importance of economic management as a critical competency for professionals navigating challenges in shipping, port operations, and sustainability. This study explores the integration of financial literacy and sustainability into maritime management education to align with industry demands. By focusing on practical tools such as case studies and simulations, the research provides a comprehensive analysis of how these methodologies enhance economic decision-making skills and operational efficiency. Through a qualitative approach, semi-structured interviews and open-ended questionnaires were conducted with 40 participants, including maritime professionals, educators, and graduates. Thematic analysis revealed that financial literacy equips students with a deeper understanding of economic principles, enabling informed decisions in resource allocation, risk management, and sustainability planning. Case studies and simulations emerged as transformative tools, fostering critical thinking and bridging the gap between theoretical learning and industry application. Despite these successes, the study also identifies areas for growth, particularly in embedding sustainability within financial decision-making frameworks. The findings emphasize the need for collaborative efforts between academia and industry to address pedagogical challenges, optimize resource utilization, and enhance curriculum relevance. This research contributes to advancing economic management education by highlighting actionable strategies to develop a workforce that is both financially literate and sustainability-focused, ensuring resilience in an evolving maritime sector.

Fadhil Adita Ramadhan, Agus Mansur, Nashrullah Setiawan et al.

This study integrates the House of Risk (HOR) approach with the Fuzzy Inference System (FIS) to manage supply chain risks in steel fabrication by addressing market uncertainties and operational challenges to enhance stability and productivity. The study begins with risk identification using HOR and the calculation of fuzzy aggregate risk priority (FARP) based on severity and frequency. A Mamdani based FIS is then applied to prioritize risks and develop mitigation strategies, leveraging data from expert interviews and literature reviews. The findings highlight supplier order failures as the top risk with the highest FARP score, leading to the proposal of 50 mitigation actions, including managed inventory systems and supplier diversification, to strengthen supply chain resilience and reduce vulnerabilities. However, this study is limited to the steel fabrication industry and relies on expert opinions and secondary data, which may affect generalizability. Future research can apply this approach to other industries and incorporate realtime data for validation. The proposed mitigation strategies offer actionable insights for supply chain managers, helping companies improve operational stability and adapt effectively to market uncertainties. By introducing an integrated HOR and FIS approach, this study provides a dynamic and systematic framework for comprehensive supply chain risk management, offering original insights to the field.

Marina Brogi, Valentina Lagasio, Danilo Mercuri et al.

Recently, there has been a growing focus on sustainability issues within the financial sector. However, quantitative analysis of social and governance aspects has been challenging due to difficulties in data modelling. With the recent regulatory updates for standardised disclosure, the next step could be the implementation of stress tests, given their widespread use in the supervisory environment. The article analyses the possibility of borrowing certain aspects of rating agency methodologies for a future exercise. It identifies common elements that need to be analyzed and highlights areas where institutional intervention will be necessary.

Nan Zheng, Danhuai Guo

Rapid and accurate acquisition and analysis of information is crucial for emergency management, but traditional methods have limitations such as incomplete information acquisition and slow processing speed. The natural language oriented spatial scene reconstruction method provides a new solution for emergency management, but existing generative models have limited understanding of spatial relationships and lack high-quality training samples. To address these issues, this paper proposes a novel spatial scene reconstruction framework. Specifically, the BERT based spatial information knowledge graph extraction method is used to encode the input text, label and classify the encoded text, identify spatial objects and relationships in the text, and accurately extract spatial information. Additionally, a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition, and based on the obtained biases, a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph. Finally, use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints. In addition, a high-quality training sample set of “text-scene-knowledge graph” was constructed.

P. Saługa, Katarzyna Szczepańska-Woszczyna, R. Miśkiewicz et al.

Our knowledge of discount rates plays an important role both in the discounted cash flow decision-making process and in the later phases of a project’s lifetime. It is useful than both for management and cash-flow monitoring purposes at operating stages. Investors putting money into power generation projects expect an appropriate rate of return to compensate them for a minimum acceptable real return available in the market (risk-free rate of interest) and the project’s specific risk. Due to its essential nature in the financial and economic evaluation of projects (it is the only parameter that reflects the risk), it is reasonable to assume that investors would also be interested in constituent components of that indicator. The discount rate is one parameter in the discounted cash flow analysis that takes into account the risk of a venture. Further, the previous research in this area has focused mainly on the dimension of this variable, and the structure of this parameter has not been dealt with any other studies. The proposed idea of this study met the expectations of the industry—it aimed to present a typical project implemented in the energy industry, a relatively simple methodology that allowed estimating the components within the cost of equity capital of the enterprise. In the power generation sector, one can find various types of discount rates—assessed for multiple technologies, at different development stages, and expressed differently. Owing to the know-how and decades-long experience, coal-fired power projects’ remarks may be a good benchmark for alternative low carbon technologies. That is why, in this work, a discount rate for valuing investment in new coal-fired power projects was evaluated. This assessment was made on the “bare-bones” assumption, meaning evaluations at 100% equity, after-tax, in constant (real) currency units. The analysis of the discount rate structure was performed by applying the procedure of the classical sensitivity analysis having the accuracy of key input parameters. Finally, the risk factors within the risk-adjusted discount rate were calculated. The obtained results showed the importance of individual risk factors within the risk-adjusted discount rate used in coal energy projects, which would enable a more pragmatic approach to controlling this parameter by decision-makers and understanding the risk.

Mengmeng Wang, Zhaoqian Liu

With environmental issues increasingly becoming prominent in today’s business world, firms may need to pay extra attention to developing their environmental strategies and capabilities in response to environmental concerns and achieving sustainable growth. While a broad consensus exists on the value of green innovation, current empirical research on how different types of green innovation strategies may account for the international performance of a firm remains scant. Addressing this gap is important because determining how to better manage a firm’s green innovation strategies nowadays has become increasingly important for firms hoping to achieve and maintain their sustainable performance advantages. This study aims to bridge this gap by systematically examining how various types of green innovation strategies (i.e., green product, green process, and green service innovations) can be beneficial to firms in an emerging market economy. This study also examined the important role that potential risks of supply chain play in shaping the relationships between various types of green innovation strategies and firm performance. This study proposes that the effective management of supply chain risks may be important to the successful implementation of green innovation strategies because green innovation has increasingly become a collaborative effort. This study empirically tested the hypotheses by gathering survey data from a sample of 337 firms in China’s manufacturing industries. Results demonstrate that the green innovation strategies of firms are positively related to their firm performance. Additionally, the potential risks faced by the firms in efficiently and effectively managing their supply chain significantly moderate the impact of green product innovation and green process innovation strategies on their firm performance. This study not only offers useful theoretical implications for the green innovation strategy research and for better and effective supply chain risk management. It also provides important practical guidelines and managerial actions that practicing managers can implement to accelerate their green innovation strategy, assess the effect of supply chain risks, and thus improve firm performance in the post-pandemic era.

Su-juan Xu, Mu Zhang

In order to further promote the theoretical research on the effective financial support for the high-quality development of strategic emerging industries, this paper reviews the research status at home and abroad on the mechanism and path, influence effect, support efficiency, support system, problems and countermeasures of financial support for the development of strategic emerging industries. At present, the research on financial support for the development of strategic emerging industries at home and abroad is mainly conducted from the perspective of financial support and financing. Under the two perspectives, the influence effect can be subdivided into direct financing, indirect financing and comprehensive financing, and the research on support efficiency can be divided into national and regional studies. The support system can be divided into three aspects: horizontal, vertical, and horizontal and vertical synthesis. Problems and countermeasures are subdivided into four aspects: countermeasures, policies, strategies and optimization paths. Future research directions include: Deepening the mechanism of financial support for strategic emerging industries from the perspective of financing; Expand the research of vertical support system for the development of strategic emerging industries.

Hui Zhang, Rui Yang, Jian Wang et al.

Computational combustion modeling was introduced in China in the early 1980s. Specifically, after the first computational fluid dynamics (CFD) lecture in China given by Brian Spalding, Professor of Heat Transfer and Head of the Computational Fluid Dynamics Unit at Imperial College, London, under the invitation from Professor Wei-Cheng Fan of University of Science and Technology of China, Anhui, China, in 1984. This paper presents the development history of computational combustion research led by Fan in China. During the early stages of CFD development in the 1980s and 1990s, numerous innovative models, methods, and computational tools were proposed and developed by Fan's research group to advance computational combustion research in China. Throughout the evolution of computational combustion technologies, the State Key Laboratory of Fire Science, founded and led by Fan, has played a leading role in the development and deployment of these technologies in China. Recent progress in low-pressure combustion and battery fire research and combustion modeling technologies are described.

Jiabo Geng, Zhisong Wang, Xiang Lan et al.

AbstractA significant number of ionic rare earth tailings slopes were generated during the process of rare earth mining and the factor of safety of these slopes continued to decrease under the action of rainfall. This study focussed on a specific tailings slope located in Dayu County, Ganzhou City. Through the utilization of MatDEM numerical simulation software, the variations in infiltration distribution and destabilization evolution of the tailings slope between different seasons were comparatively examined. The results indicated that during the rainy season (May to June), increased rainfall led to increased rate of water infiltration, consequently reducing the stability of the slope and exacerbating landslide damage. Furthermore, the study provided analyses of safety distances under different seasons of rainfall, taking into account the geographical location of the tailings slope in Dayu County, Ganzhou City, and the results of the analyses provided theoretical foundation into ensuring the safety of buildings and residents downstream of the slope.

Vihan Weerapura, Ranil Sugathadasa, M. Mavin De Silva et al.

The ready-mix concrete supply chain is highly disruptive due to its product perishability and Just-in-Time (JIT) production style. A lack of technology makes the ready-mix concrete (RMC) industry suffer from frequent production failures, ultimately causing high customer dissatisfaction and loss of revenues. In this paper, we propose the first-ever digital twin (DT) system in the RMC industry that can serve as a decision support tool to manage production risk efficiently and effectively via predictive maintenance. This study focuses on the feasibility of digital twins for the RMC industry in three main areas holistically: (1) the technical feasibility of the digital twin system for ready-mix concrete plant production risk management; (2) the business value of the proposed product to the construction industry; (3) the challenges of implementation in the real-world RMC industry. The proposed digital twin system consists of three main phases: (1) an IoT system to get the real-time production cycle times; (2) a digital twin operational working model with descriptive analytics; (3) an advanced analytical dashboard with predictive analytics to make predictive maintenance decisions. Our proposed digital twin solution can provide efficient and interpretable predictive maintenance insights in real time based on anomaly detection, production bottleneck identification, process disruption forecast and cycle time analysis. Finally, this study emphasizes that state-of-the-art solutions such as digital twins can effectively manage the production risks of ready-mix concrete plants by automatically detecting and predicting the bottlenecks without waiting until a production failure happens to react.

Elisa Menicucci, Guido Paolucci

Purpose The purpose of this paper is to investigate the relationship between gender diversity and the risk profile of Italian banks during the period 2015–2019. This study examines whether the presence of female board directors or top executives has any significant effect on bank risk-taking. Design/methodology/approach To explore the influence of women on bank risk-taking, the authors analyzed a sample of 387 Italian banks and developed an econometric model applying unbalanced panel data with firm fixed effects and controls per year. Within a multivariate regression model, the authors considered five risk dimensions to verify the effect of gender diversity. Findings The findings suggest that female board directors and executives are considerably more risk averse and less overconfident than their male colleagues, thus confirming a negative causality between risk-taking and gender diversity. The results reveal that banks headed by women are less risky because they report higher capital adequacy and equity to assets ratios. As credit risk in female-led banks is no different from male-led ones, higher capital adequacy does not derive from lower asset quality because it is linked to the higher risk aversion of female directors and top managers. Research limitations/implications From a theoretical standpoint, the results suggest that having women in executive positions entails different risk implications for Italian banks; from a managerial perspective, the results highlight conditions that may promote the role of women in the banking sector. The conclusions are of particular significance because they provide some support for the view that regulators should favor gender quotas in the board management of banks to reduce risk-taking behavior. Originality/value This paper offers an in-depth examination of the risk practices of banks and it attempts to bridge the gap in prior literature on the risk profile of the Italian banking industry given that few empirical studies have examined the determinants of risk-taking in this field, to date. The findings on the higher risk aversion of women directors advance the understanding of the determinants of risk-taking behavior in banks, suggesting that gender quotas in bank boards can contribute to reducing risk-taking behavior. This also unveils some policy implications for bank regulatory authorities.

Ladan Khajeh Hoseini, R. Jalilzadeh yengejeh, Maryam Mohammadi Rouzbehani et al.

Oil industries, such as oil refineries, are important sources of volatile organic compound production. These compounds have significant health effects on human health. In this study, a health risk assessment is carried out on volatile organic compounds (VOCs) in the recovery oil plant (ROP) unit of a refinery in southwest Iran. It was performed using the SQRA1 method including respiratory risk for chronic daily intake (CDI) of VOCs and cancer risk and non-cancer risk indices. Five locations in the area of oil effluents and five locations in the refinery area (control samples) were considered for evaluation. The sampling was done according to the standard NIOSH-1501 and SKC pumps. The gas chromatography/flame ionization detector (GC/FID) method was used to extract VOCs. The cancer slope factor (CSF) and respiratory reference dose (RFC) were calculated in addition to the respiratory risk (CDI). The end result shows that a significant difference was observed between the concentrations of volatile organic compounds in the two groups of air (P 1). Also, a non-cancer risk (HQ) assessment revealed that benzene had a significant health risk in the range of oil pits (2–3). Based on the results, petroleum industries, including refineries, should conduct health risk assessment studies of volatile organic compounds. The units that are directly related to the high level of VOCs should be considered sensitive groups, and their employees should be under special management to reduce the level of exposure to these compounds and other hazardous compounds.

E. Ameyaw, A. Chan

Ming Zhao, Qingjun Zeng, Ming Chang et al.

Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

Udara Ranasinghe, Marcus Jefferies, P. Davis et al.

A safe work environment is crucial in high-risk industries, such as construction refurbishment. Safety incidents caused by uncertainty and unexpected events in construction refurbishment systems are difficult to control using conventional safety management techniques. Resilience engineering (RE) is proposed as an alternative to traditional safety management approaches. It presents a successful safety management methodology designed to deal with uncertainty in high-risk work environments. Despite the fact that RE resides in the safety domain, there is no common set of RE indicators to measure and assess resilient in the work environment. The main aim of this research is to explore RE indicators that have been identified as important in developing and assessing the resilient work environment in high-risk industries, particularly in construction refurbishment. Indicators have been attained through a systematic literature review of research and scholarly articles published between the years 2004 and 2019. The literature review explored RE indicators in various industries. Descriptive analysis and co–occurrence-based network visualization were used for data analysis. The findings revealed 28 RE indicators in 11 different high-risk industries. The results show that the four commonly used indicators were: top-management commitment, awareness, learning, and flexibility, all of which have a strong relationship with RE. The findings of this study are useful for stakeholders when making decisions concerning the most important RE indicators in the context of their research or practice as this would avoid the ambiguity and disparity in the identification of RE indicators.