Hasil untuk "Risk in industry. Risk management"

Adel Badri, Bryan Boudreau-Trudel, A. Souissi

Abstract Real-time communication, Big Data, human–machine cooperation, remote sensing, monitoring and process control, autonomous equipment and interconnectivity are becoming major assets in modern industry. As the fourth industrial revolution or Industry 4.0 becomes the predominant reality, it will bring new paradigm shifts, which will have an impact on the management of occupational health and safety (OHS). In the midst of this new and accelerating industrial trend, are we giving due consideration to changes in OHS imperatives? Are the OHS consequences of Industry 4.0 being evaluated properly? Do we stand to lose any of the gains made through proactive approaches? Are there rational grounds for major concerns? In this article, we examine these questions in order to raise consciousness with regard to the integration of OHS into Industry 4.0. It is clear that if the technologies driving Industry 4.0 develop in silos and manufacturers’ initiatives are isolated and fragmented, the dangers will multiply and the net impact on OHS will be negative. As major changes are implemented, previous gains in preventive management of workplace health and safety will be at risk. If we are to avoid putting technological progress and OHS on a collision course, researchers, field experts and industrialists will have to collaborate on a smooth transition towards Industry 4.0.

N. Paltrinieri, L. Comfort, G. Reniers

Abstract Risk assessment has a primary role in safety-critical industries. However, it faces a series of overall challenges, partially related to technology advancements and increasing needs. There is currently a call for continuous risk assessment, improvement in learning past lessons and definition of techniques to process relevant data, which are to be coupled with adequate capability to deal with unexpected events and provide the right support to enable risk management. Through this work, we suggest a risk assessment approach based on machine learning. In particular, a deep neural network (DNN) model is developed and tested for a drive-off scenario involving an Oil & Gas drilling rig. Results show reasonable accuracy for DNN predictions and general suitability to (partially) overcome risk assessment challenges. Nevertheless, intrinsic model limitations should be taken into account and appropriate model selection and customization should be carefully carried out to deliver appropriate support for safety-related decision-making.

Hongfang Lu, Kun Huang, Mohammadamin Azimi et al.

Blockchain technology has been developed for more than ten years and has become a trend in various industries. As the oil and gas industry is gradually shifting toward intelligence and digitalization, many large oil and gas companies were working on blockchain technology in the past two years because of it can significantly improve the management level, efficiency, and data security of the oil and gas industry. This paper aims to let more people in the oil and gas industry understand the blockchain and lead more thinking about how to apply the blockchain technology. To the best of our knowledge, this is one of the earliest papers on the review of the blockchain system in the oil and gas industry. This paper first presents the relevant theories and core technologies of the blockchain, and then describes how the blockchain is applied to the oil and gas industry from four aspects: trading, management and decision making, supervision, and cyber security. Finally, the application status, the understanding level of the blockchain in the oil and gas industry, opportunities, challenges, and risks and development trends are analyzed. The main conclusions are as follows: 1) at present, Europe and Asia have the fastest pace of developing the application of blockchain in the oil and gas industry, but there are still few oil and gas blockchain projects in operation or testing worldwide; 2) nowadays, the understanding of blockchain in the oil and gas industry is not sufficiently enough, the application is still in the experimental stage, and the investment is not enough; and (3) blockchain can bring many opportunities to the oil and gas industry, such as reducing transaction costs and improving transparency and efficiency. However, since it is still in the early stage of the application, there are still many challenges, primarily technological, and regulatory and system transformation. The development of blockchains in the oil and gas industry will move toward hybrid blockchain architecture, multi-technology combination, cross-chain, hybrid consensus mechanisms, and more interdisciplinary professionals.

Mukesh Kumar, Rakesh D. Raut, S. Mangla et al.

Abstract Artificial intelligence (AI) has been used in various industries to provide innovative and intelligent features in micro, small and medium enterprises (MSMEs). Big industries started adopting AI for their HR and workforce management (WFM) activities, however, past literature suggests a lack of AI adoption in MSMEs. In the ongoing pandemic, a large number of job loss is reported in the literature. Thus, artificial intelligence-powered intelligent workforce management (WFM) may be critical during or post-pandemic to manage the huge employment in MSMEs. In this study, we develop and test a conceptual framework based on three areas where AI-powered WFM adoption for MSMEs revenue development has been highlighted. These are (a) risk management (workforce), (b) business and marketing, and (c) information exchange. Six hypotheses have been proposed and tested using structural equation modelling (SEM) with responses from 307 employees. According to the research findings, all of the offered hypotheses are significant. The findings suggest to MSME decision-makers that AI-powered WFM may help revenue growth, workforce risk reduction, intelligent business and marketing, and thoughtful, innovative, and safe information exchange. MSMEs are required to use AI in the information sharing that help in workforce risk management, business and marketing, and intelligent workforce management that scale-up the economic growth.

Min-Ho Park, Won-Ju Lee

Maintenance optimization is essential in reducing a ship’s operating costs and ensuring its safety. This review aims to introduce existing literature related to shipboard maintenance management strategies and products developed in the maritime industry, and provide an overall understanding of the same by discussing both the relevant technologies and applications. Accordingly, the following maintenance strategies are extensively discussed: 1) corrective; 2) predetermined (inspection-, usage-, and time-based); 3) proactive (risk-based and reliability-centered); 4) predictive (condition-based and prognostics and health management); and 5) prescriptive. The basic concepts, application examples, and related studies for each strategy are introduced, and appropriate figures are inserted to help readers understand. Next, system diagrams, components, expected effects, and use cases for seven maintenance management products in the maritime industry are explained. Finally, literature references and products are summarized chronologically, and a discussion based on shipboard experience and future works is presented. From this review, potential readers can gain insights into the history, status, and future directions in shipboard maintenance management.

Dongyeop Lee, Daesik Lim, Joonwon Lee

The electric power industry poses significant risks to workers with a wide range of hazards such as electrocution, electric shock, burns, and falls. Regardless of the types and characteristics of these hazards, electric power companies should protect their workers and provide a safe and healthy working environment, but it is difficult to identify the potential health and safety risks present in their workplace and take appropriate action to keep their workers free from harm. Therefore, this paper proposes a novel safety autonomous platform (SAP) for data-driven risk management in the electric power industry. It can automatically and precisely provide a safe and healthy working environment with the cooperation of safety mobility gateways (SMGs) according to the safety rule and risk index data created by the risk level of a current task, a worker profile, and the output of an on-site artificial intelligence (AI) engine in the SMGs. We practically implemented the proposed SAP architecture using the Hadoop ecosystem and verified its feasibility through a performance evaluation of the on-site AI engine and real-time operation of risk assessment and alarm notification for data-driven risk management.

Israr Ullah, Klaus Reicherter, Tomáš Pánek et al.

Landslides are a major geological hazard causing significant loss of life and infrastructure damage worldwide. Landslide susceptibility mapping is a crucial, though developing, tool for understanding the spatial distribution of landslide hazard. This study addresses the absence of a comprehensive landslide inventory, limited understanding of causative factors and the lack of regional-scale susceptibility maps for the Lesser Caucasus and Kura Basin (LC-KB). A landslide inventory was created for the Lesser Caucasus of Azerbaijan and compiled with other inventories, documenting 3,659 landslide polygons. Sixteen causative factors were analysed, and multicollinearity tests confirmed no significant correlations. Three Machine Learning (ML) models—Logistic Regression (LGR), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost)—were fine-tuned to create landslide susceptibility maps. Slope is consistently the most influential factor across all models. Results suggest stronger influence of seismic factors than climatic ones. XGBoost achieves the highest accuracy (0.81) on the testing data set, followed by SVM (0.80) and LGR (0.73). The first two models show strong validation performance, with AUC values of 0.89 and 0.87, respectively, while LGR shows a lower AUC of 0.78. The results are vital for planning and disaster management, highlighting areas needing urgent mitigation.

Lu Chen, Jianming Zhu, Guoqing Wang

Considering the information interaction in the virtual–real network, this paper introduces a novel three-layer model that explores the integrated influence between virtual and real networks. Existing models often fail to capture the dynamic feedback between these networks and do not effectively simulate integrated decision-making processes. Focusing on the Facility Service Balance Problem, we aim to optimize resource allocation and information diffusion in response to real-world events like natural disasters or large-scale activities. Based on the Linear Threshold model, the Feedback Linear Threshold model, which incorporates feedback mechanisms between virtual and real networks and integrates both original and feedback information in the activation function of nodes, has been proposed to better simulate the information feedback and integrated decision-making process. Then, combined with location-based interpersonal and online social networks, a comprehensive framework that models decision-making processes without direct influence between decision-makers has been provided, focusing on the decision-making of individuals influenced by cumulative information, ultimately maximizing the facility service efficiency. Finally, conduct experiments have been conducted, using two types of data to test the general effectiveness of the feedback mechanism.

Vivek Soni, Vedansh Verma

Ancillary services are an integral part to support challenges such as intermittent generation, risk, and large-scale (LS) integration of renewable energy sources (RESs). New changing environments, such as system response and variation, as well as customer behavior, demonstrate the need to frame new regulations that encourage new types of products in the Indian ancillary services of the electricity market. These market conditions lead to tri-fold gaps in research: In conventional ancillary services, frequency variations and voltage limits pose limitations on RE uses in the grid, as they become slow and costly. Furthermore, the supply pattern of RESs often does not match the demand from consumers, causing significant issues for system operators and, in turn, connected consumers, industries, and regulators. For an increased penetration of distributed RESs, the current research objectively studies and compares the Indian electricity market and assesses the impact of systems parameters (frequency, voltage, and demand—f, v, d/s) on large-scale RE integration. Secondly, we aim to understand the cost–benefit influence of such systems’ levels and parameters, which may offer new ancillary services and any amendments required in regulations, regimes, and policies. An initial understanding is developed by reviewing past studies, based on secondary sources of databases of the international energy market, the IEA, IRENA, the World Bank, and Indian regulatory agencies. Using a combined methodology (qualitative synthesis used for comparative studies of the electricity market and cost–benefit analysis), this study provides insights into new ancillary products and services (fast frequency response mechanism and voltage support initiatives/methods, with some related to demand-side management). This study concludes with offering different perspectives to adopt new services, based on a comparative assessment of ancillary services, and suggests changes in regulations (f, v, and d/s) and methods for cheaper services as value propositions, offered to customers of select energy markets of Australia, the USA, the UK, and Germany. A few implications for stakeholders, such as policymakers, industry representatives, and consumers, for their better coordination to develop futuristic ancillary services within the future scope of this work are presented to support the future energy of markets and a greener energy system.

Huai-Wei Lo, J. Liou

Abstract Failure mode and effect analysis (FMEA) is a forward-looking risk-management technique used in various industries for promoting the reliability and safety of products, processes, structures, systems, and services. However, FMEA has many defects in practical experimentation. Therefore, this paper proposes a new model that uses multiple-criteria decision-making in combination with grey theory for FMEA. This approach has several advantages, such as being able to add the expected cost into the original risk priority number (RPN) to reflect the actual resource limitations, consider the different weights of severity, occurrence, detectability, and cost based on the best–worst method in RPN element calculation, and use the grey interval linguistic variables to manage information uncertainty. Furthermore, this study applied probability-based grey relational analysis to calculate the RPN, which preserves the information of prioritized failure modes through interval analysis. To demonstrate the usefulness and effectiveness of the proposed model, real data from an international electronics company were applied. The proposed model can provide an alternative risk priority solution for product development.

Tao Wang, Zishuo Liu, Liyuan Liu

Acoustic emission predicts coal sample failure, vital for early warnings and monitoring. The study emphasizes using acoustic emission to predict coal sample failure patterns and establish a discriminative model for monitoring. It employs a lightweight 3D convolutional model combining DenseNet with Group Convolution (GC) and the "squeeze-and-excitation" (SE) module. Coal samples from Xinglongzhuang Coal Mine underwent uniaxial compression, and a subset of experiments with prominent features was selected. The resulting acoustic emission parameters were then transformed into spatiotemporal image sequences for input to the model. Successfully identifying hazardous coal damage stages, all four network structures (DenseNet, DenseNet + GC, DenseNet + SE, DenseNet + GC + SE) achieved over 98.29% predictive accuracy in verification samples. The model exhibited a recall rate of over 98.02% for high-risk samples, effectively capturing spatiotemporal information. DenseNet + GC + SE showed a probability distribution focusing on different risk levels. By integrating group convolution and SE modules, this model significantly reduced both model and time complexity while preserving precision, enhancing efficiency. It effectively discerns diverse acoustic emission features through group convolution and evaluates their significance using the SE module. Compared to the traditional C3D neural network, this model greatly reduces complexity and time requirements while improving efficiency in distinguishing between various damage stages.

Wen Si, Lixia Niu

With the advent of Industry 4.0, the prevalence of tower cranes equipped with hook visualization is increasing. However, the introduction of new interface management tasks has led to novel patterns of human errors for operators. The Simplified Plant Analysis Risk–Human Reliability Assessment (SPAR-H) method has emerged as a relevant approach for the prediction of human reliability in smart construction tower crane operations. However, the current SPAR-H method is only partially applicable and does not fully meet the requirements of this study. Initially, a text mining approach (TF-IDF-TruncatedSVD-ComplementNB) was employed to identify operator error-specific terms in tower crane operations. These terms were then correlated with the eight Performance Shaping Factors (PSFs) of the SPAR-H method, and corresponding failure modes and potential causes were determined from the literature. This ensured a more objective selection of influencing factors and PSFs during the stratification process, which was validated through questionnaire surveys. Furthermore, standards for SPAR-H PSF levels were established based on the characteristics of tower crane operators. Given the inherent complexity of relationships among SPAR-H PSFs, the DEMATEL-ANP method was applied. This involved analyzing logical interactions and causal relationships between first-level and second-level indicators of PSFs, obtaining weights, and integrating these with the SPAR-H method to determine human reliability. Finally, an analysis and validation were conducted using a case study of an accident involving a smart construction tower crane, confirming the subsequent reliability of operator actions. The result of the accident case study yielded a reliability measure of 4.2 × 10⁻⁵. These findings indicate that the evaluation process of this method aligns with scenarios encountered in smart construction tower crane operations.

S. Campbell, S. George, E.A. Sharp et al.

Wildlife is at risk of exposure to rodenticides used in pest management. An industry-led stewardship scheme introduced new rules on use and sale of products across the UK in 2016, with the aim of reducing this risk. To determine if the scheme had achieved this, exposure to second generation anticoagulant rodenticides (SGARs) was measured in foxes. Liver samples from 406 foxes collected between 2011 and 2022 were analysed and the percentage presence and concentrations of SGARs, where present, from pre-stewardship and post-stewardship samples were compared. There was no statistically significant change in the percentage of foxes exposed to bromadiolone, difenacoum or summed SGAR residues after the introduction of stewardship. The percentage of foxes exposed to brodifacoum increased significantly post-stewardship, from 18% to 43%. There were no significant changes of either summed or individual SGAR concentrations post-stewardship.These findings suggest that the industry-led stewardship scheme has not yet had the intended impact of reducing SGAR exposure in non-target wildlife, and they highlight a substantial increase in foxes encountering brodifacoum, together with weak statistical evidence of an increase in the percentage of foxes exposed to multiple SGARs.

Dingchao Chen, Xiangyu Wang, Jianbiao Bai et al.

Gob-side roadways are excavations created during the mining process, serving to alleviate mining-induced stress. Previous studies have predominantly focused on the caving method, neglecting the examination of failure mechanisms in gob-side roadway associated with continuous mining and continuous backfilling (CMCB). In this study, focusing on Sima Coal Mine, we employed numerical simulation method to thoroughly investigate the full-cycle stress evolution patterns of the gob-side roadway. The numerical simulation results reveal that during the upper working face mining period, severe stress concentration phenomena occur within the coal pillars, with peak stresses reaching 36.0 MPa. Throughout the mining period of next working face, as the CMCB process progressed, the coal inside the working face was gradually replaced by the filling body, forming various coal pillars with decreasing widths. The alternant load between the coal pillar and filling body led to the gradual increase of the internal stress of the coal pillar, with the peak stress reaching 45.0 MPa. To mitigate stress concentration in the surrounding rock of the roadways, this study proposes a control methodology that incorporates "directional control of hydraulic fracturing slots" in conjunction with "reinforcement support of coal pillars" and "temporary support of single hydraulic props" as core components. Industrial experiments were conducted to validate the efficacy of the proposed control techniques. Field monitoring results demonstrate significant improvements in the stress environment, effectively mitigating deformation of roadway.

M. Akhtaruzzaman, Sabri Boubaker, Mardy Chiah et al.

This study investigates oil price risk exposure of financial and non-financial industries around the world during the COVID–19 pandemic. The empirical results show that oil supply industries benefit from positive shocks to oil price risk in general, whereas oil user industries and financial industries react negatively to positive oil price shocks. The COVID–19 outbreak appears to moderate the oil price risk exposure of both financial and non-financial industries. This brings important implications in risk management of energy risk during the pandemic.

Karim Farghaly, Ranjith K. Soman, William H. Collinge et al.

Michael Wang, Sobhan Asian, Lincoln C. Wood et al.

The purpose of the paper is to present an empirical study on the logistics innovation capability and its impacts on the supply chain risk in the Australian courier firms. Based on the resource-based review, logistics innovation capability provides valuable insight into mitigating supply chain risks in the Industry 4.0 era.,The research model focuses on the relationships between logistics innovation capability and supply chain risk. Partial least squares approach for structural equation modelling is used to validate the research model by empirically analysing survey data.,The empirical result shows negative relationships between logistics innovation capability and supply chain risks. These relationships may imply that firms can mitigate the negative impacts of supply chain risks by developing logistics innovation capabilities. The findings demonstrate the applicability of logistics innovation capability for mitigating supply chain risks in the Australian courier firms.,There are very few empirical studies on the mitigating supply chain risk through logistics innovation capability. The empirical results provide an insight into innovation management and risk management in logistics and supply chain. This insight offers practical guidance for developing and deploying logistics innovation capability to support and enable supply chain risk management strategies in the Industry 4.0 era.

Jie Zhou, Shaoqi Zuo, Yang Wang et al.

Over a third of the global chemical production and sales occurred in China, which make effective assessment and management for chemicals produced by China’s chemical industry essential not just for China but for the world. Here, we systematical assessed the persistence (P), bioaccumulation (B), mobility (M) and toxicity (T) potency properties for the chemicals listed in Inventory of Existing Chemical Substances of China (IECSC) via experimental data retrieved from large scale databases and in silico data generated with well-established models. Potential PBT, PMT and PB&MT substances were identified. High risk potentials were highlighted for groups of synthetic intermediates, raw materials, as well as a series of biocides. The potential PBT and PMT synthetic intermediates and/or raw materials unique to the IECSC were dominated with organofluorines, for example, the intermediates used as electronic light-emitting materials. Meanwhile, the biocides unique to the IECSC were mainly organochlorines. Some conventional classes of insecticides, such as organochlorines and pyrethroids, were classified as being of high concern. We further identified a group of PB&MT substances that were considered to be both “bioaccumulative” and “mobile”. Their properties and common substructures for several major clusters were characterized. The present results prioritized groups of substances with high potentials to cause adverse effects to the environment and humans, many of which have not yet been fully recognized.

Alireza Arabameri, Amir Seyed Danesh, M. Santosh et al.

Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important for flood management measures. We compute the flood susceptibility map for the Kaiser watershed in Iran using machine learning models such as support vector machine (SVM), Particle swarm optimization (PSO), and genetic algorithm (GA) along with ensembles (PSO-GA and SVM-GA). The application of such machine learning models in flood susceptibility assessment and mapping is analyzed, and future research suggestions are presented. The model of flood susceptibility model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, and land cover, normalize differences vegetation index (NDVI), convergence index (CI), topographical wetness index (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness index (TRI), terrain surface texture (TST), geology and stream power index (SPI) and flood inventory data which later is divided by 70% for training the model and 30% for validated the model. The model output was evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, and receiver operating curve (ROC). The evaluation of flood susceptibility mapping through the receiver operating curve method along with flood density shows robust results from support vector machine (0.839), particle swarm optimization (0.851), genetic algorithm (0.874), SVM-GA (0.886), and PSO-GA (0.902). Compared have done with some methods commonly used in this susceptibility assessment. A high-quality, informative database is essential for the classification of flood types in flood susceptibility mapping that is very important and helpful to improve the model performances. The performance of the ensemble PSO-GA is better than that of the machine learning model, yielding a high degree of accuracy (AUC-0.902%). Our approach, therefore, provides a novel method for flood susceptibility studies in other watersheds.

Chiara Antonelli, Margherita Biscontri, Dania Tabet et al.

Plant trade coupled with climate change has led to the increased spread of well-known and new <i>Phytophthora</i> species, a group of fungus-like organisms placed in the Kingdom Chromista. Their presence in plant nurseries is of particular concern because they are responsible for many plant diseases, with high environmental, economic and social impacts. This paper offers a brief overview of the current status of <i>Phytophthora</i> species in European plant nurseries. Focus was placed on Italian sites. Despite the increasing awareness of the risk of <i>Phytophthora</i> spread and the management strategies applied for controlling it, the complexity of the <i>Phytophthora</i> community in the horticulture industry is increasing over time. Since the survey carried out by Jung et al. (2016), new <i>Phytophthora</i> taxa and <i>Phytophthora</i>-host associations were identified. <i>Phytophthora</i><i>hydropathica</i>, <i>P. crassamura</i>, <i>P. pseudocryptogea</i> and <i>P. meadii</i> were reported for the first time in European plant nurseries, while <i>P. pistaciae</i>, <i>P. mediterranea</i> and <i>P. heterospora</i> were isolated from Italian ornamental nurseries. Knowledge of Phytophthora diversity in plant nurseries and the potential damage caused by them will help to contribute to the development of early detection methods and sustainable management strategies to control <i>Phytophthora</i> spread in the future.