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.
- Manufacturing Engineering Division
Applying BPN and EWMA SPC Chart to Cold Chain Temperature Monitoring
Chen, K, Shaw, Y, Chen, M, & Wu, T. "Applying BPN and EWMA SPC Chart to Cold Chain Temperature Monitoring." Proceedings of the 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. pp. 247-256. ASME. https://doi.org/10.1115/MSEC2009-84209
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