An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment

Z. X. Guo, Wai Ting Ngai, Can Yang, Xuedong Liang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

130 Citations (Scopus)

Abstract

Global manufacturing companies have some pressing needs to improve production visibility and decision-making performance by implementing effective production monitoring and scheduling. This paper proposes a radio frequency identification (RFID)-based intelligent decision support system architecture to handle production monitoring and scheduling in a distributed manufacturing environment. A pilot implementation of the architecture is reported in a distributed clothing manufacturing environment. RFID and cloud technologies were integrated for real-time and remote production capture and monitoring. Intelligent optimization techniques were also implemented to generate effective production scheduling solutions. A prototype system with remote monitoring and production scheduling functions was developed and implemented in a distributed manufacturing environment, which demonstrated the effectiveness of the architecture. The proposed architecture has good extensibility and scalability, which can easily be integrated with production decision-making as well as production and logistics operations in the supply chain. Lastly, this paper discusses the difficulties encountered and lessons learned during system implementation and the managerial implications of the proposed architecture.
Original languageEnglish
Pages (from-to)16-28
Number of pages13
JournalInternational Journal of Production Economics
Volume159
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Cloud technology
  • Distributed monitoring and scheduling
  • Intelligent decision-making
  • Managerial implications

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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