為了幫助首屈一指的電子零組件供應商解決其訂貨決策問題，本研究試圖去找出優於公司現有訂貨法則的訂貨政策。本研究將兩種學術上知名的訂貨政策比較於公司現有訂貨法則，以得到表現相對較佳的訂貨政策。兩種訂貨政策包含critical fractile solution以及Scarf’s rule。本研究首先比較在需求分配資訊已知下的訂貨政策表現，其次比較了在需求分配資訊發生錯誤預期時的訂貨政策表現。在本研究中，我們採納了貝塔二項分配去捕捉需求，並設計了兩個模擬實驗分別比較上述兩種情境中的訂貨政策表現，以了解在錯誤預期發生前後三種訂貨政策將如何被影響。本研究的目標在於找出在完整資訊下及錯誤預期下的最適訂貨政策，以幫助焦點公司改善其營運績效。

Motivated by the ordering decision problem at the largest high-tech electronic distribution company in the world, this research aims to find a better ordering policy for company managers. To ensure that the new ordering policies can lower the loss incurred by ordering decisions, we compare two well-known theoretical ordering policies, critical fractile and Scarf’s rule, to the simple rule used by managers in order to assess the performance of the three ordering policies. We also consider the performance of these three ordering policies when managers misjudge the risk of demand distribution. We use a beta-binomial distribution to capture the perceived demand and design a simulation experiment to observe how wrong beliefs affect the performance of different policies. We aim to identify ordering policies that are robust to wrong beliefs and can help the focal company to improve its operational performance, which has been compromised by excess inventory and demand uncertainty.

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