| Summary: | 碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 97 === 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 are aimed to apply EWMA control chart and artificial neural network technologies to collect 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 appened, the control center of an enterprise can do some actions immediately to prevent further disaster. Finally, construct a system with back-propagation neural network, statistical process control chart. Simulations and demonstrations of real environment would be implemented by LEGO bricks at last.
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