TY - JOUR
T1 - Production Logistics in Industry 3.X: Bibliometric Analysis, Frontier Case Study, and Future Directions
AU - Yi, Honglin
AU - Qu, Ting
AU - Zhang, Kai
AU - Li, Mingxing
AU - Huang, George Q.
AU - Chen, Zefeng
N1 - Funding information:
This paper was financially supported by National Key Research and Development Program of China (2021YFB3301701), 2019 Guangdong Special Support Talent Program–Innovation and Entrepreneurship Leading Team (China) (2019BT02S593), 2018 Guangzhou Leading Innovation Team Program (China) (201909010006), and the Science and Technology Development Fund (Macau SAR) (0078/2021/A). We also appreciate the sponsorships from the industry, including but not limited to Carpoly Chemical Group Co., Ltd., Guangzhou Ink Stone Technology, Inc., Sendwant Logistic Ltd., Zhuhai Top Cloud Tech Co., Ltd. Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics by the Department of Science and Technology of Guangdong Province, thanks to which the international collaboration was effectively conducted.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/7
Y1 - 2023/7
N2 - At present, the development of the global manufacturing industry is still in the transition stage from Industry 3.0 to Industry 4.0 (i.e., Industry 3.X), and the production logistics system is becoming more and more complex due to the individualization of customer demands and the high frequency of order changes. In order to systematically analyze the research status and dynamic evolution trend of production logistics in the Industry 3.X stage, this paper designed a Log-Likelihood ratio-based latent Dirichlet allocation (LLR-LDA) algorithm based on bibliometrics and knowledge graph technology, taking the literature of China National Knowledge Infrastructure and Web of Science database as the data source. In-depth bibliometric analysis of literature was carried out from research progress, hotspot evolution, and frontier trends. At the same time, taking the case of scientific research projects overcome by our research group as an example, it briefly introduced the synchronized decision-making framework of digital twin-enabled production logistics system. It is expected to broaden the research boundary of production logistics in the Industry 3.X stage, promote the development and progress of the industry, and provide valuable reference for steadily moving towards the Industry 4.0 stage.
AB - At present, the development of the global manufacturing industry is still in the transition stage from Industry 3.0 to Industry 4.0 (i.e., Industry 3.X), and the production logistics system is becoming more and more complex due to the individualization of customer demands and the high frequency of order changes. In order to systematically analyze the research status and dynamic evolution trend of production logistics in the Industry 3.X stage, this paper designed a Log-Likelihood ratio-based latent Dirichlet allocation (LLR-LDA) algorithm based on bibliometrics and knowledge graph technology, taking the literature of China National Knowledge Infrastructure and Web of Science database as the data source. In-depth bibliometric analysis of literature was carried out from research progress, hotspot evolution, and frontier trends. At the same time, taking the case of scientific research projects overcome by our research group as an example, it briefly introduced the synchronized decision-making framework of digital twin-enabled production logistics system. It is expected to broaden the research boundary of production logistics in the Industry 3.X stage, promote the development and progress of the industry, and provide valuable reference for steadily moving towards the Industry 4.0 stage.
KW - Industry 3.X
KW - LLR-LDA algorithm
KW - production logistics
KW - scientific knowledge graph
KW - synchronization
UR - http://www.scopus.com/inward/record.url?scp=85175110063&partnerID=8YFLogxK
U2 - 10.3390/systems11070371
DO - 10.3390/systems11070371
M3 - Journal article
AN - SCOPUS:85175110063
SN - 2079-8954
VL - 11
JO - Systems
JF - Systems
IS - 7
M1 - 371
ER -