存貨管理為各領域普遍的共通議題，為了能夠維持在市場上的競爭力，許多公司都致力於不斷改善存貨管理模式。本研究採用存貨水準及服務水準做為為存貨管理模式績效衡量標準。然而存貨水準與服務水準乃是一個兩難的議題，其目標會相互衝突，但公司仍需在兩者間相互權衡。同時為了解決傳統的ABC分類在實務中存貨單位(Stock Keeping Unit, SKU)分類效果的限制，結合以需求變異程度分類的XYZ分類，導入ABC-XYZ分類做為SKU的分類方法。
第一，本研究將建立一合適的ABC-XYZ動態補貨決策模型。使用ABC-XYZ分類法將SKU分類，並且在定期訂購(R, S)庫存政策的框架下分別使用四種參數計算方法(簡單移動平均法、神經網路、機率分配擬合、經驗法則)，並進行實驗，再來利用績效指標進行實驗結果分析，最後得出各類別建議的補貨方式。
第二是討論服務水準與存貨水準在此案例中之權衡，將先對結果先進行確定性決策，使用單目標決策法在現有服務水準下進行決策，再利用多準則評估方法中的TOPSIS分析結合敏感度分析，評估服務水準與存貨水準兩項績效指標的表現，並觀察在不同權重下實驗結果的變化。最後會結合兩者的結果，得出各SKU類別明確的庫存管理模式供參考。

Inventory management is a common issue in all fields. Many companies are striving to improve their inventory management models in order to remain competitive in the marketplace. In this study, inventory level and service level are used as the performance measures of inventory management methods. However, inventory levels and service levels are a dilemma, with conflicting objectives, but companies still need to balance them against each other. In order to solve the limitation of the traditional ABC analysis in the practice of Stock Keeping Unit (SKU) classification, the ABC-XYZ analysis is introduced as the classification method of SKU by combining the XYZ analysis with the demand variation classification.
First, this study will establish an appropriate ABC-XYZ dynamic replenishment decision model. The SKUs are classified using the ABC-XYZ analysis, and four parameter calculation methods (Simple Moving Average, Neural Network, Probability Distribution Fitting, and Rule of Thumb) are used in the framework of the periodic ordering (R, S) inventory policy, and experiments are conducted.
Second, we will discuss the trade-off between service level and inventory level in this case. We will first make a decision by using the single-objective decision making method under the existing service level, and then evaluate the performance of the two performance indicators, service level and inventory level, by using TOPSIS analysis in the multi-criteria evaluation method combined with sensitivity analysis, and observe the changes of the experimental results under different weights of performance measures. Finally, the two results will be combined to derive a clear inventory management method for each SKU category.

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