Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 198, P. 110680 - 110680
Published: Nov. 5, 2024
Language: Английский
Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 99, P. 104930 - 104930
Published: Sept. 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.
Language: Английский
Citations
35
Robotics and Computer-Integrated Manufacturing, Journal Year: 2023, Volume and Issue: 86, P. 102682 - 102682
Published: Oct. 22, 2023
Language: Английский
Citations
25
Mathematics, Journal Year: 2023, Volume and Issue: 11(15), P. 3350 - 3350
Published: July 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.
Language: Английский
Citations
23
Robotics and Computer-Integrated Manufacturing, Journal Year: 2023, Volume and Issue: 86, P. 102649 - 102649
Published: Sept. 16, 2023
Language: Английский
Citations
22
International Journal of Precision Engineering and Manufacturing-Smart Technology, Journal Year: 2023, Volume and Issue: 1(2), P. 187 - 200
Published: July 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.
Language: Английский
Citations
15
Sustainability, Journal Year: 2024, Volume and Issue: 16(9), P. 3552 - 3552
Published: April 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.
Language: Английский
Citations
6
Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102597 - 102597
Published: May 20, 2024
Language: Английский
Citations
6
Supply Chain Analytics, Journal Year: 2024, Volume and Issue: 7, P. 100075 - 100075
Published: July 29, 2024
Language: Английский
Citations
6
Journal of Innovation & Knowledge, Journal Year: 2024, Volume and Issue: 9(3), P. 100516 - 100516
Published: July 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.
Language: Английский
Citations
4
International Journal of Logistics Research and Applications, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 43
Published: Jan. 18, 2025
Language: Английский
Citations
0