面對全球經濟劇烈變動，半導體產業隨著景氣的波動更為敏銳。面對總體經濟環境的不確定性，企業如何及早預測景氣波動，因應訂單的轉變進行決策及計畫擬定，為目前企業所重視的議題。因此，面對未來全球化經營及景氣波動，預測對於企業主而言是不可或缺的經營工具之一。
現今預測的問題屬於非線性模式較多，且近年來人工智慧的方法漸漸受到重視。因此，本研究利用灰關聯分析在各種環境因子中篩選出關聯度較高之因子，再利用資料探勘的決策樹手法找出各關聯因子；將灰關聯因子置入倒傳遞類神經網路預測模式，進行訂單量訓練與預測。期待此模式於業界中能提升預測準確度，提高訂單生產預測，並提供半導體IC封裝產業做為生產預測之參考。

The semiconductor industry has been subject to tremendous flux amidst a period of severe global economic change. Therefore, how to predict economic change and plan for order shifting in an uncertain production environment are important topics for most semiconductor companies. As globalization is a defining trend of the future, prediction becomes one of the necessary managing tools for entrepreneurs.

Most predictions nowadays are developed using nonlinear models, and employing artificial intelligence (AI) to engage in prediction is becoming more popular. Therefore, this research employs fuzzy-gray correlation analysis to sift factors which have higher correlations from different types of environmental factors, and find out the factor weight using the decision tree method of data mining, then placing those factors and weights into the back-propagation neural network (BPN) to engage in practices and predictions. This process is expected to increase the accuracy of predictions in the industry and improve order production predictions, and become a good reference for production prediction in the IC packaging industry.

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