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Applying BPN and EWMA SPC Chart to Cold Chain Temperature Monitoring

[+] Author Affiliations
Kai-Ying Chen, Yi-Cheng Shaw

National Taipei University of Technology, Taipei, Taiwan

Mu-Chen Chen

National Chiao Tung University, Taipei, Taiwan

Teh-Chang Wu

Industrial Technology Research Institute, Hsinchu, Taiwan

Paper No. MSEC2009-84209, pp. 247-256; 10 pages
doi:10.1115/MSEC2009-84209
From:
  • ASME 2009 International Manufacturing Science and Engineering Conference
  • ASME 2009 International Manufacturing Science and Engineering Conference, Volume 1
  • West Lafayette, Indiana, USA, October 4–7, 2009
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4361-1 | eISBN: 978-0-7918-3859-4
  • Copyright © 2009 by ASME

abstract

Recently, with the development of urbanization, the enhancement efficiency of contactless, real-time features and high data transmission rate in supply chain management are widely discussed. The cold chain is one part of the supply chain, and especially the temperature monitoring plays a vital role in cold chain system. In this paper, we apply EWMA control chart and artificial neural network technologies to monitor temperature data. The back-propagation neural network is used to predict temperature shifts and trend. EWMA control chart is adopted to monitor temperature variation. As there’re something wrong happened, the control center of an enterprise can do some actions immediately to prevent further disaster. Finally, we construct a system with back-propagation neural network and statistical process control chart. A simulations and demonstrations environment using LEGO® bricks is also implemented.

Copyright © 2009 by ASME

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