Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing

Ray Y. Zhong, Shulin Lan, Chen Xu, Qingyun Dai, George Q. Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

208 Citations (Scopus)

Abstract

Cloud Manufacturing twining with Internet of Things (IoT) has been waked up to achieve final intelligent manufacturing. With the IoT technologies such as radio frequency identification (RFID) implemented in manufacturing sites, enormous data will be generated. Such data are so complex, abstract, and variable so that it is difficult to make full use of the data which carry great myriad of useful information and knowledge. This paper presents a visualization approach for the RFID-enabled shopfloor logistics Big Data from Cloud Manufacturing. An innovative RFID-Cuboid model is used for reconstructing the RFID raw data given the production logic and time series. Several contributions are highlighted. Firstly, a possible approach to integrate IoT and Cloud Manufacturing is introduced to upgrade and transform the traditional industry for an intelligent future. Secondly, an RFID-Cuboid model is proposed by using the production logic and time stamps to chain the RFID data so that the data could be interpreted. Thirdly, a real-life case is reported to show the feasibility and practicality of the proposed visualization approach to help different end-users to ease their daily operations. Lessons and insights from this case are meaningful for the implementation of IoT-enabled Cloud Manufacturing and Big Data analytics in industry field.

Original languageEnglish
Pages (from-to)5-16
Number of pages12
JournalInternational Journal of Advanced Manufacturing Technology
Volume84
Issue number1-4
DOIs
Publication statusPublished - 1 Apr 2016
Externally publishedYes

Keywords

  • Big Data visualization
  • Cloud Manufacturing
  • Logistics
  • RFID
  • Shopfloor

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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