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http://dx.doi.org/10.6109/jkiice.2021.25.11.1525

Manufacturing Data Aggregation System Design for Applying Supply Chain Optimization Technology  

Hwang, Jae-Yong (LINC+, Wonkwang University)
Shin, Seong-Yoon (School of Computer Information & Communication Engineering, Kunsan National University)
Kang, Sun-Kyoung (Department of Computer Software Engineering, Wonkwang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.11, 2021 , pp. 1525-1530 More about this Journal
Abstract
By applying AI-based efficient inventory management and logistics optimization technology using the smart factory's production plan and manufacturing data, the company's productivity improvement and customer satisfaction can be expected to increase. In this paper, we proposed a system that collects data from the factory's production process, stores it in the cloud, and uses the manufacturing data stored there to apply AI-based supply chain optimization technology later. While the existing system supported approximately 10 to 20 data types, the proposed system is designed and developed to support more than 100 data types. In addition, in the case of the collection cycle, data can be collected 1-2 times per second, and data collection in TB units is possible. Therefore This system is designed to be applied to the existing factory of past in addition to the smart factory.
Keywords
AI-based supply chain optimization; Data aggregation; Edge gateway; Industrie 4.0; Smart factory;
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