隨著人工智慧和機器學習的蓬勃發展，運用人工智慧改善決策可使企業提高自身競爭力，因此精準的預測對企業極其重要。在過去的研究中，運用人工智慧來進行預測的方法有很多種，然而這些方法可大致分為黑箱及白箱兩種方法，許多研究也曾比較兩種方法的優缺點，但未曾證明兩種方法是否有排他性或互補性。因此，本研究旨在提供一個混和方法，結合黑箱和白箱的優點，以改善供應鏈上的顧客需求預測。本研究結合黑箱精準預測的特性和白箱的解釋性，期望藉由白箱為黑箱提供解釋性，並藉由有意義的解釋來改善預測模型。為了檢驗本研究的混合方法，本研究採用亞太區最大半導體零組件通路商(簡稱W公司)的顧客實際取貨資料，並運用神經網路呈現黑箱方法和決策樹呈現白箱方法來驗證模型。研究結果顯示，混和方法的預測表現確實優於W公司原有的預測方法，且白箱確實提供具有意義的解釋來呈現黑箱的預測準則，例如：來自WT COWINSZ子庫存的產品適用於以週為單位的神經網絡預測。本研究對於供應鏈上的顧客需求預測有一定的貢獻，期望透過本研究可以為科技產業鏈帶來一些新的觀點。

The black-box prediction method has proved to be efficient for its predictions, while the white-box method provides an effective interpretation of outputs. Many studies have identified and compared the merits and demerits of the two methods, yet it still remains unclear whether the two methods are exclusive or complementary. In this study, we propose and develop a hybrid approach that can successfully combine the merits of two such methods in order to improve customer demand prediction in Supply Chain Management. Our novel hybrid approach combines the black-box prediction method with the white-box classification method with the aim of obtaining the accurate performance of the former and the notable interpretation of the latter. To examine the performance of the proposed approach, we use a real-world data set collected from a top distributor of semiconductors and electronics in Asia. We conclude that the novel hybrid approach is beneficial for interpreting and improving customer demand prediction. We identify several product items from a specialized sub-inventory as unsuitable for neural network prediction methods, and other low frequency product items as suitable. For example, some product items from WT COWINSZ sub-inventory are suitable for neural network prediction methods, and some low frequency product items are suitable for neural network prediction methods with integrated zero prediction model.

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