Modelling the relationship between digital twins implementation barriers and sustainability pillars: Insights from building and construction sector

Sustainable Cities and Society, Год журнала: 2023, Номер 99, С. 104930 - 104930

Опубликована: Сен. 10, 2023

A Digital Twin (DT) is a digital copy of real-world object or process. Although DT has gained traction in construction, its relationship with sustainable success remains insufficiently studied. This research addresses this gap by investigating barriers to implementing construction. The study employs hybrid approach involving literature review, expert interviews, and modeling techniques, data collected from 108 construction experts based on number criteria, including the experience, degree, familiarity about Hong Kong building sector Kong. findings reveal 45 categorized into six clusters, notable obstacles such as "legacy systems," "data uncertainties," "connectivity." key clusters identified are "performance" "security," while "social" aspect least supported. Recognising these challenges assists decision-makers navigating utilising for environmentally conscious streamlined processes, positive societal impacts. Future could delve integrating sustainability throughout project lifecycle using technology adoption theories.

Язык: Английский

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Multi-objective coupling optimization of electrical cable intelligent production line driven by digital twin

Robotics and Computer-Integrated Manufacturing, Год журнала: 2023, Номер 86, С. 102682 - 102682

Опубликована: Окт. 22, 2023

Язык: Английский

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A Bibliometric Analysis of Digital Twin in the Supply Chain

Mathematics, Год журнала: 2023, Номер 11(15), С. 3350 - 3350

Опубликована: Июль 31, 2023

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis in supply chain to help researchers, industry practitioners, academics understand trend, development, focus areas chain. found several key clusters research, including designing model, integration application quality control, digitalization. In embryonic stage was tested production line with limited optimization. development stage, importance 4.0 observed, as big data, machine learning, Industrial Internet Things, blockchain, edge computing, cloud-based systems complemented models. applied improve sustainability manufacturing logistics. current prosperity high annual publications, recent trends this topic on deep data models, artificial intelligence for also that COVID-19 pandemic drove start research Researchers field are slowly moving towards applying human-centric mass personalization prepare transit 5.0.

Язык: Английский

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An ontology-based data-model coupling approach for digital twin

Robotics and Computer-Integrated Manufacturing, Год журнала: 2023, Номер 86, С. 102649 - 102649

Опубликована: Сен. 16, 2023

Язык: Английский

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Digitalization in Production Logistics: How AI, Digital Twins, and Simulation Are Driving the Shift from Model-based to Data-driven Approaches

International Journal of Precision Engineering and Manufacturing-Smart Technology, Год журнала: 2023, Номер 1(2), С. 187 - 200

Опубликована: Июль 1, 2023

The paradigm shift from model-based to data-driven approaches in production logistics is radically transforming the manufacturing landscape. This paper delves into profound implications of this transition, emphasizing instrumental role simulation and digital twins. Through an exhaustive literature review, emerging trends driving forces behind change are elucidated. A comparative case study presented, contrasting approach, which employs predefined models principles simulations, with innovative utilizes real-time data machine learning for system monitoring predictions logistics. analysis reveals heightened efficiency, adaptability, effectiveness offered by showcasing their superiority. Additionally, prospective roles AI, particularly large language like ChatGPT, enhancing investigated. Exploratory scenarios envision future trajectories twin applications rapidly evolving field. provides academia industry a comprehensive overview digitalization logistics, immense promise approach AI.

Язык: Английский

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A Topic Modeling Approach to Determine Supply Chain Management Priorities Enabled by Digital Twin Technology

Sustainability, Год журнала: 2024, Номер 16(9), С. 3552 - 3552

Опубликована: Апрель 24, 2024

Background: This paper examines scientific papers in the field of digital twins to explore different areas application supply chains. Methods: Using a machine learning-based topic modeling approach, this study aims provide insights into key chain management that benefit from twin capabilities. Results: The research findings highlight priorities infrastructure, construction, business, technology, manufacturing, blockchain, and agriculture, providing comprehensive perspective. Conclusions: Our confirm several recommendations. First, model identifies new are not addressed human review results. Second, while results put more emphasis on practicality, such as activities, processes, methods, learning pay attention macro perspectives, business. Third, is able extract granular information; for example, it core technologies beyond twins, including AI/reinforcement learning, picking robots, cybersecurity, 5G networks, physical internet, additive cloud manufacturing.

Язык: Английский

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A multi-level digital twin construction method of assembly line based on hybrid worker digital twin models

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102597 - 102597

Опубликована: Май 20, 2024

Язык: Английский

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Unlocking the potential of digital twins in supply chains: A systematic review

Supply Chain Analytics, Год журнала: 2024, Номер 7, С. 100075 - 100075

Опубликована: Июль 29, 2024

Язык: Английский

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Towards Scientists and Researchers Classification Model (SRCM)-based machine learning and data mining methods: An ISM-MICMAC approach

Journal of Innovation & Knowledge, Год журнала: 2024, Номер 9(3), С. 100516 - 100516

Опубликована: Июль 1, 2024

This study introduces an innovative automated model, the Scientists and Researchers Classification Model (SRCM), which employs data mining machine-learning techniques to classify, rank, evaluate scientists researchers in university settings. The SRCM is designed foster environment conducive creativity, innovation, collaboration among academics augment universities' research capabilities competitiveness. model's development roadmap, depicted Figure 1, comprises four pivotal stages: preparation, empowerment strategies, university-recognised ID, evaluation re-enhancement. implementation structured across three layers: input, ranking, recommendations assessments. An extensive literature review identifies ten principal procedures further evaluated by experts. utilises Interpretive Structural Modelling (ISM) analyse these procedures' interactions hierarchical relationships, revealing a high degree of interdependence complexity within framework. Key with significant influence include determining input sources collecting comprehensive lists researchers. Despite its approach, faces challenges, such as quality, ethical considerations, adaptability diverse academic contexts. Future developments collection methodologies, addressing privacy issues, will enhance long-term effectiveness environments. contributes theoretical understanding systems offers practical insights for universities that aim implement sophisticated data-centric classification models. For example, implementing models, can objectively assess faculty performance promotion or tenure. These models enable evaluations based on publication records, citation counts, teaching evaluations, fostering culture excellence guiding initiatives. limitations, has emerged promising tool transforming higher education institutions' management processes.

Язык: Английский

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Integrating circular economy while adopting digital twin for enhancing logistics efficiency: a hybrid Fuzzy Delphi-FUCOM based approach

International Journal of Logistics Research and Applications, Год журнала: 2025, Номер unknown, С. 1 - 43

Опубликована: Янв. 18, 2025

Язык: Английский

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