A Heterogeneous Data Analytics Framework for RFID-Enabled Factories

Ray Y. Zhong, Goran D. Putnik, Stephen T. Newman

Research output: Contribution to journalArticlepeer-review

6 Citations (SciVal)


As the wide use of various smart sensors in the manufacturing environment, traditional factories have been upgraded and transformed into an intelligent level. Smart manufacturing factory thus has been enabled by some advanced technologies, such as Internet of Things (IoT) which could facilitate production operations and decision-makings on the one hand. On the other hand, enormous data will be created by the IoT devices. Manufacturing companies are facing some challenges when attempting to make full use of the huge datasets which are heterogeneous in format, complex in logic, unstructured in storage, and abstract in interpretation. In order to address these challenges, this article proposes a data heterogeneous analytics framework for a radio-frequency identification (RFID) enabled factory. RFID captured data from a real-life company is used for validating the proposed framework. Specifically, the performance of machining processes, logistics operations, and inspection behavior are examined from the RFID captured data.

Original languageEnglish
Article number8931748
Pages (from-to)5567-5576
Number of pages10
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number9
Early online date12 Dec 2019
Publication statusPublished - 18 Aug 2021


  • Data analytics
  • framework
  • heterogeneity
  • radio-frequency identification (RFID)
  • smart manufacturing

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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