RFID-BASED SYNCHRONIZATION OF INFORMATION FLOW AND MATERIAL FLOW

2008 ◽  
Vol 07 (02) ◽  
pp. 271-274
Author(s):  
HUA-LIN ZHENG ◽  
YUE-PAI WANG ◽  
XI-YUAN WAN

RFID (Radio Frequency Identification) technology is put forward as a new data collection method to bridge the gap between information flow and material flow. The data achieved by RFID can be shared by both MES and ERP simultaneously. A simulated WIP (Work In Process) machining process application case study is used in the paper to show how the synchronization is realized.

Author(s):  
Chandana Unnithan ◽  
Arthur Tatnall

Australian hospitals had begun exploring Radio Frequency Identification, a wireless automatic identification and data capture technology for improving the quality of their services towards the end of 2000s. After many an unsuccessful pilots, a breakthrough for large hospitals came in 2010, with a key learning rendered by a large regional hospital that not only experimented with the technology, but also have made it all pervasive in their operations. In this chapter, we present the case study, through an innovation translation perspective, focusing on the socio-technical factors captured through elements of Actor-Network Theory.


PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0210148 ◽  
Author(s):  
Rachael M. Milwid ◽  
Terri L. O’Sullivan ◽  
Zvonimir Poljak ◽  
Marek Laskowski ◽  
Amy L. Greer

Author(s):  
V. E. Taratun ◽  

The article reveals the issues of the relevance of the use of radio frequency identification technology both for organizing the production of an aerospace enterprise and for a single supply chain of material flow to the International Space Station. The article lists the main types of automatic identification. The functional possibilities for organizing the production of an aerospace enterprise using radio frequency identification technology are determined.


2012 ◽  
Vol 2012 ◽  
pp. 1-12
Author(s):  
Shigeaki Sakurai

This paper deals with transactions with their classes. The classes represent the difference of conditions in the data collection. This paper redefines two kinds of supports: characteristic support and possible support. The former one is based on specific classes assigned to specific patterns. The latter one is based on the minimum class in the classes. This paper proposes a new method that efficiently discovers patterns whose characteristic supports are larger than or equal to the predefined minimum support by using their possible supports. Also, this paper verifies the effect of the method through numerical experiments based on the data registered in the UCI machine learning repository and the RFID (radio frequency identification) data collected from two apparel shops.


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