Structural order measure of manufacturing systems based on an information-theoretic approach

Zhang, Zhifeng; David, Janet

doi:10.1016/j.eswa.2020.113636

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…However, with the addition of new nodes and the development of R&D activities, the increases in nodes and edges in the network rapidly break the order state of the initial network, slow down the flow of knowledge resources, and rapidly decrease the timeliness of network structure in a short time. Since then, due to the continuous increases in nodes and edges, knowledge flow paths increase with a growth of the network's hierarchy and the distances of knowledge interactions between nodes continue to lengthen, so that the network timeliness still exhibits a downward trend [78]; however, the rate of decline tends to slow down significantly. Until entering the 5G technology recession period, as the network scale gradually tends to zero growth, the timeliness of the network structure will also gradually become stable.…”

Section: Analysis Of Self-organizing Characteristic Of Networkmentioning

confidence: 99%

“…This is because there are fewer hierarchies and larger spans involved in knowledge flows in the initial network, which result in low quality of the initial network. However, as network edges start to increase, informational divarication decreases with a growth of the network's hierarchy and a drop of the network's span, which significantly improves the accuracy of knowledge transfer, leading to the rapid increase in network quality in a short time and then maintaining a slowly rising trend [78]. However, by the later period of 5G technology germination, the growing network scale and increasingly complex network structure causes the knowledge flow to pass through more branches, which reduces the accuracy of knowledge transfer, but each node also continuously optimizes its knowledge acquisition path in this process; thus, the quality of network structure shows a trend of fluctuating downward.…”

Section: Analysis Of Self-organizing Characteristic Of Networkmentioning

confidence: 99%

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Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Liu

Chen

et al. 2023

Systems

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With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology into the evolution dynamics of its innovation network, constructed an emerging technology temporal innovation network from a temporal network perspective, and established its evolution model in combination with the life cycle and key attributes of emerging technology. Based on this model, we took 5G technology as an example to conduct network evolution simulation, verified the rationality of the above model building, and analyzed the cyclical evolution dynamics of this network in various topological structures. The results show that the life cycle of emerging technology, as well as multiple knowledge attributes based on the key attributes of emerging technology, are important factors that affect network evolution by acting on node behaviors. Within this study, we provide a more realistic framework to describe the internal mechanism of the cyclical evolution of emerging technology innovation network, which can extend the research on innovation network evolution from the single topological dynamics to the topological–temporal dynamics containing time attributes and enrich the research dimensions of innovation network evolution from the perspective of temporal evolution.

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Section: Analysis Of Self-organizing Characteristic Of Networkmentioning

confidence: 99%

Section: Analysis Of Self-organizing Characteristic Of Networkmentioning

confidence: 99%

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Liu

Chen

et al. 2023

Systems

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“…For Bogle (2017), some challenges should be highlighted regarding the implementation of technologies in the scenario of manufacturing processes, considering professionals in the area of Process Engineering, among them is highlighted: flexibility and uncertainty; responsiveness and agility; robustness and security; prediction of properties and functions of the mixture; new paradigms of modeling and mathematics. This model aims to link disruptive technologies to manufacturing systems, combining intelligent operations and supply chain management (Zhang & David, 2020), some challenges are shown in Table 2 for the implementation of intelligent manufacturing in some industry segments in Brazil.…”

Section: State Of Digital Manufacturing At Brazil and Opportunitiesmentioning

confidence: 99%

Digital technologies review for manufacturing processes

Parente

Silva

Siqueira

et al. 2022

Ind. Jour. Manag. & Prod.

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It is apparent the industrial processes transformations caused by industry 4.0 are in advance in some countries like China, Japan, Germany and United States. But, in return, the developing countries, as the emergent Brazil, seem like to have a long way to achieve digital era. Considering manufacturing processes as the starting point the rise of industry 4.0, this research aims to show a review about the most important technologies used in smart manufacturing, including the main challenges to implement it at Brazil. The papers were collected from Web of Science (WoS), comprising 114 articles and 2 books to underpin this study. This exploratory research resulted in the presentation of some challenges faced by Brazilian industry to join the new industrial era, such as poor technological infrastructure, besides lack of investment in technologies and training of qualified people. Even though the primary motivation of this research was to present a panorama of smart manufacturing for Brazil, this study results contributes to the most of emergent countries, bringing together general concepts and addressing practical applications developed by several researchers from the international academic community.

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