Digital engineering., Journal Year: 2024, Volume and Issue: 3, P. 100020 - 100020
Published: Oct. 23, 2024
Language: Английский
Robotics and Computer-Integrated Manufacturing, Journal Year: 2023, Volume and Issue: 84, P. 102592 - 102592
Published: May 28, 2023
Language: Английский
Citations
40
Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 55, P. 101880 - 101880
Published: Jan. 1, 2023
Language: Английский
Citations
36
Future Generation Computer Systems, Journal Year: 2023, Volume and Issue: 153, P. 391 - 402
Published: Dec. 10, 2023
Language: Английский
Citations
26
Journal of Intelligent Manufacturing, Journal Year: 2023, Volume and Issue: 35(6), P. 2517 - 2546
Published: July 18, 2023
Language: Английский
Citations
24
International Journal of Production Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 22
Published: July 7, 2024
Digital twins in Industry 4.0 enhance lean supply chains by optimizing efficiency, reducing costs, and ensuring responsiveness to consumers. However, their relationship with remains understudied. We address this gap through a systematic literature review, analyzing 33 selected papers from 759 articles. Utilizing the supply-chain operations reference (SCOR) framework, we assess digital twins' impact on chain processes performances. Our findings indicate that are primarily used plan, make, delivery processes, limited exploration source return processes. They practices improving information flow, eliminating waste, logistics, enabling just-in-time production. top management commitment, supplier management, customer understudied areas. also recognize two additional areas where contribute: enhancing coordination, bolstering resilience, particularly against disruptions such as COVID-19 geopolitical events. Additionally, propose framework for twin-driven smart highlight importance of future research (SCDT) mapping, convergence, interaction, cognition service. This study pioneers exploring motivations, applications, contributions management.
Language: Английский
Citations
16
Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 76, P. 103 - 132
Published: July 29, 2024
Smart manufacturing (SM) confronts several challenges inherently suited to knowledge graphs (KGs) capabilities. The first key challenge lies in the synthesis of complex and varied data surrounding context, which demands advanced semantic analysis inference second main limitation is contextualization systems exploitation domain knowledge, requires a dynamic holistic representation knowledge. last major obstacle arises from facilitation intricate decision-making processes towards correlated ecosystems, benefit interconnected structures that KGs excel at organizing. However, existing survey studies concentrated on distinct facets SM offered isolated insights into KG applications while overlooking interconnections between various technologies their application across multiple domains. What specific role should play aforementioned challenges, how effectively harness for these essential topics methodologies required make functional remain underexplored. To explore potential SM, this study adopts systematic approach investigate, evaluate, analyse current research KGs, identifying core advancements implications future practices. Firstly, cutting-edge developments challenge-driven roles techniques are identified, extraction mining construction updates, further extending embedding, fusion, reasoning—central driving ecosystems. Specifically, depicted holistically, emphasizing interplay diverse with comprehensive framework. Subsequently, foundation outlines discusses scenarios engineering design predictive maintenance, covering representative stages life cycle. Lastly, explores practical advantages systems, pointing emerging avenues.
Language: Английский
Citations
15
Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: 38, P. 100566 - 100566
Published: Jan. 28, 2024
Language: Английский
Citations
13
Internet of Things, Journal Year: 2024, Volume and Issue: 27, P. 101324 - 101324
Published: Aug. 8, 2024
Language: Английский
Citations
12
Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 74, P. 16 - 29
Published: March 1, 2024
Language: Английский
Citations
10
Supply Chain Analytics, Journal Year: 2024, Volume and Issue: 6, P. 100063 - 100063
Published: March 19, 2024
The Internet of Things (IoT) has attracted the attention researchers and practitioners in supply chains logistics (LSCs). IoT improves monitoring, controlling, optimizing, planning LSCs. Several have reviewed IoT-based LSCs publications indexed by academic journals focusing on decision-making. Decision support systems (DSS) are infancy stage This paper reviews IoT-LSCs from DSS perspective. We propose a new framework for helping decision-makers implement based decisions that need to be made describing transition scheme simple, if-then analytical decision-making approaches IoT-LSCs. Adopter II is an extension framework, which layer called 'decision' been added enable implementing improve list predefined processes Although literature review analysis provides valuable insights, wide range related information available online. study also utilizes web content mining approach first time analyze context. results show IoT-LSC field involves two emerging themes, blockchain chain 5.0, mainstream i.e., big data analytics management.
Language: Английский
Citations
10