RFID supply chain data deconstruction method based on artificial intelligence technology

被引:3
作者
Zhang, Huiying [1 ]
Li, Ze [1 ]
机构
[1] Chongqing Vocat Coll Transportat, Business Coll, Chongqing 402247, Peoples R China
关键词
artificial intelligence; supply chain; supply chain system; radio frequency identification; ETHICAL CONSIDERATIONS; IMPLEMENTATION;
D O I
10.1515/comp-2022-0265
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Radio frequency identification (RFID) is a broad rapidly evolving skill in the past few years. It is characterized by non-contact identification, fast read and write speed, small label size, large data storage capacity, and other technical advantages. RFID technology for goods movement has completely changed the traditional supply chain management, greatly improved the operational efficiency of enterprises, and has become an important method for the development of supply chain logistics. This work mainly studies and analyzes the RFID supply chain, introduces the development and application of RFID supply chain sector technology, and discusses the operation of the supply chain in detail. Then, according to the existing RFID supply chain, a RFID supply chain artificial intelligence (AI) based approach to technology is proposed, and the data analysis of RFID supply chain is introduced in detail. In this work, through the research experiment of AI technology RFID supply chain data analysis, the experimental data show that there are several time-consuming links in the supply chain system. The time consumed in the AI RFID system is 9.9, 3.4, 3.5, and 29.9 min, respectively, while each link in the original system takes 13.4, 4.9, 4.9, and 34.9 min. It can be seen from the above data that the amount of time in each system link of the AI RFID supply chain system is less than that of the original supply chain system, which shortens the entire product passing cycle and greatly improves work efficiency.
引用
收藏
页数:11
相关论文
共 23 条
[11]  
Hutson M, 2018, SCIENCE, V359, P725, DOI 10.1126/science.359.6377.725
[12]   Deep Learning for Consumer Devices and Services Pushing the limits for machine learning, artificial intelligence, and computer vision. [J].
Lemley, Joseph ;
Bazrafkan, Shabab ;
Corcoran, Peter .
IEEE CONSUMER ELECTRONICS MAGAZINE, 2017, 6 (02) :48-56
[13]   INTELLIGENT 5G: WHEN CELLULAR NETWORKS MEET ARTIFICIAL INTELLIGENCE [J].
Li, Rongpeng ;
Zhao, Zhifeng ;
Zhou, Xuan ;
Ding, Guoru ;
Chen, Yan ;
Wang, Zhongyao ;
Zhang, Honggang .
IEEE WIRELESS COMMUNICATIONS, 2017, 24 (05) :175-183
[14]   Artificial intelligence for fault diagnosis of rotating machinery: A review [J].
Liu, Ruonan ;
Yang, Boyuan ;
Zio, Enrico ;
Chen, Xuefeng .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 108 :33-47
[15]  
Marouni H., 2017, QUAL PROG, V50, P60
[16]  
Meng Y., 2022, PEERJ COMPUT SCI, V8, P984
[17]  
Pichoff S, 2019, SUPPLY CHAIN BRAIN, V23, P45
[18]   RFID Implementation in Supply Chain Management [J].
Podduturi, Pooja Reddy ;
Maco, Trevor ;
Ahmadi, Pouyan ;
Islam, Khondkar .
INTERNATIONAL JOURNAL OF INTERDISCIPLINARY TELECOMMUNICATIONS AND NETWORKING, 2020, 12 (02) :34-45
[19]   Computational Support for Academic Peer Review: A Perspective from Artificial Intelligence [J].
Price, Simon ;
Flach, Peter A. .
COMMUNICATIONS OF THE ACM, 2017, 60 (03) :70-79
[20]   A survey on 5G/6G, AI, and Robotics [J].
Qiao, Liang ;
Li, Yujie ;
Chen, Dongliang ;
Serikawa, Seiichi ;
Guizani, Mohsen ;
Lv, Zhihan .
COMPUTERS & ELECTRICAL ENGINEERING, 2021, 95