需求預測與企業營運緊密連結，並對庫存決策具有關鍵性的影響，對於產品數量龐大的零售業更是如此，若不妥善管理庫存，將會產生額外的持有及缺貨成本，進而影響企業獲利。然而庫存單位普遍存在無需求頻繁發生的間歇性需求特徵，導致數據不具有明顯的趨勢性與季節性變化，預測時除了要關注需求規模外，還需額外探討需求的不規則發生頻率。而零售業的銷售環境複雜，有眾多影響零售需求預測的外部因子存在，更加劇了零售需求預測問題的困難度。為了能有效改善上述兩種會影響預測表現的關鍵面向，本研究使用類神經網路方法，透過其優秀的學習及運算能力，建構出一套具間歇性需求特性的零售需求預測系統，先以注意力機制長短期記憶網路學習需求數據的時間性與間歇性特徵，再利用隨機森林方法的高維度數據處理能力、不容易過擬合等優勢，處理外部因子對需求數據的影響，並透過模型集成概念，將上述兩模型的預測結果結合以發揮各別優勢，以期望此方法框架能提高需求預測的準確率。而本研究使用Walmart零售公司的公開資料集驗證所提出之系統績效表現，結果顯示，在不同模型比較中，無論是間歇性特徵處理亦或是外部因子處理面向，本研究主要使用的Attention-LSTM及RF模型皆有最好的績效表現，集成模型的結果更是比單一模型來的優異，特別是使用MLP非線性集成方法。而本研究之系統亦能在不同的預測時長範圍下，保有極小預測表現差距變化，突顯出系統具有良好的穩定性及適用性，同時系統亦具備有市場淘汰風險識別和外部因子重要性分析的功能，其識別間歇性產品市場淘汰風險的能力，能提供零售業者提前兩個月進行庫存及銷售策略修正的機會，於變數重要性分析中也識別出價格與產品差異是最重要的外部影響因子，為零售業者提供營運策略的首要方針為定價策略的管理見解，上述等結果皆充分展現出系統之實務價值，最終達成了透過預測結果支持企業營運決策的最終目標。

Demand forecasting is crucial for retailers as it enables them to make well-informed decisions regarding operations and inventory management. However, many retail stock keeping units (SKUs) exhibit intermittent demand, characterized by numerous periods of zero demand. This inconsistency in trends and seasonal patterns complicates the forecasting process. Additionally, external factors such as price, holidays, and other environmental variables further increase the complexity of forecasting. In this study, we propose a retail demand forecasting system that leverages artificial intelligence and ensemble methods to improve prediction accuracy. The component of the system includes an Attention-LSTM network to capture temporal and intermittent characteristics, and a random forest model to analyze the high-dimensional relationships of external factors. After obtaining the prediction results from both methods, we integrate them using model ensembled techniques to leverage the individual strengths of each model. The results demonstrate that Attention-LSTM and random forest model outperform other methods in handling intermittent characteristics and the impact of external factors, respectively. Furthermore, the comparison between individual models and the final ensembled system reveals that the ensembled system achieves the highest accuracy in predictions, demonstrating the superior efficacy of the proposed approach. Additionally, the final demand results will be provided to retailing managers, enabling them to conduct further analysis of market elimination risk for products with intermittent demand characteristics and the importance of external factors. This empowers enterprises to make data-driven decisions, ultimately enhancing their strategic planning and operational efficiency.

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