Applying BPN and EWMA SPC chart to cold chain temperature monitoring

Kai Ying Chen*, Yi Cheng Shaw, Mu-Chen Chen, Teh Chang Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageAmerican English
Title of host publicationProceedings of the ASME International Manufacturing Science and Engineering Conference 2009, MSEC2009
Pages247-256
Number of pages10
DOIs
StatePublished - 1 Dec 2009
EventASME International Manufacturing Science and Engineering Conference 2009, MSEC2009 - West Lafayette, IN, United States
Duration: 4 Oct 20097 Oct 2009

Publication series

NameProceedings of the ASME International Manufacturing Science and Engineering Conference 2009, MSEC2009
Volume1

Conference

ConferenceASME International Manufacturing Science and Engineering Conference 2009, MSEC2009
CountryUnited States
CityWest Lafayette, IN
Period4/10/097/10/09

Keywords

  • Back propagation network
  • LEGO®NXT
  • Radio frequency identification
  • Statistical process control
  • Temperature monitoring

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