Production logistics digital twins: Research profiling, application, challenges and opportunities

Robotics and Computer-Integrated Manufacturing, Год журнала: 2023, Номер 84, С. 102592 - 102592

Опубликована: Май 28, 2023

Язык: Английский

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Dynamic knowledge modeling and fusion method for custom apparel production process based on knowledge graph

Advanced Engineering Informatics, Год журнала: 2023, Номер 55, С. 101880 - 101880

Опубликована: Янв. 1, 2023

Язык: Английский

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GRU-based digital twin framework for data allocation and storage in IoT-enabled smart home networks

Future Generation Computer Systems, Год журнала: 2023, Номер 153, С. 391 - 402

Опубликована: Дек. 10, 2023

Язык: Английский

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Digital Twin-based manufacturing system: a survey based on a novel reference model

Journal of Intelligent Manufacturing, Год журнала: 2023, Номер 35(6), С. 2517 - 2546

Опубликована: Июль 18, 2023

Язык: Английский

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Enhancing supply chain resilience and efficiency through internet of things integration: Challenges and opportunities

Internet of Things, Год журнала: 2024, Номер 27, С. 101324 - 101324

Опубликована: Авг. 8, 2024

Язык: Английский

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The role of digital twins in lean supply chain management: review and research directions

International Journal of Production Research, Год журнала: 2024, Номер unknown, С. 1 - 22

Опубликована: Июль 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.

Язык: Английский

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Making knowledge graphs work for smart manufacturing: Research topics, applications and prospects

Journal of Manufacturing Systems, Год журнала: 2024, Номер 76, С. 103 - 132

Опубликована: Июль 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.

Язык: Английский

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IDS-KG: An industrial dataspace-based knowledge graph construction approach for smart maintenance

Journal of Industrial Information Integration, Год журнала: 2024, Номер 38, С. 100566 - 100566

Опубликована: Янв. 28, 2024

Язык: Английский

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A review of decision support systems in the internet of things and supply chain and logistics using web content mining

Supply Chain Analytics, Год журнала: 2024, Номер 6, С. 100063 - 100063

Опубликована: Март 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.

Язык: Английский

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Spatial-temporal traceability for cyber-physical industry 4.0 systems

Journal of Manufacturing Systems, Год журнала: 2024, Номер 74, С. 16 - 29

Опубликована: Март 1, 2024

Язык: Английский

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