現在的社會處於運動風氣興盛的時代，市場對於運動商品的需求增加，商品不僅要實用更需兼具時尚，而對於製作運動用品的製造商來說，除了增加自身生產效率，仍需以大量客製化的方式以滿足不同顧客需求。面臨環境競爭與國際化的趨勢，製造商朝向高品質（Quality）、低成本（Cost）、交期準（Delivery）與高彈性（Flexibility）等目標持續改善，製造商也紛紛傾向導入精實生產方式（Lean Production）來縮短生產前置時間與增加製造彈性以提升產業競爭力。
本研究探討之案例公司是一家光學眼鏡製造商，屬於接單式生產方式（Make to order，MTO），當所有零件到齊而經過最終組裝製程即可出貨至顧客手中，但產品種類多樣且生產流程變化程度高，為典型的零工式生產環境（Job Shop environment）。零件之生產前置時間差異大，進而影響物料到齊狀況，將造成最終組裝製程無法依據生產計劃進行。本研究欲藉由將精實管理概念應用於流程改善，產生出合適且實務可行的流程設計方案，讓管理者在進行流程改善時，有一參考之依據。
首先以價值流圖定義問題並找出浪費所在，以此作為改善之契機，接著導入精實管理概念作為控制因子發展多種精實系統設計，以模擬工具產生多層面的績效指標來衡量改善成效。為了評估各設計方案之整體績效，本研究以田口方法結合多屬性決策方法來求解多重品質特性之最佳化問題，尋找最佳因子水準組合，並以驗證實驗來比較最佳設計與現況之改善效益，最後利用單反應實驗來探討求得的解是否為整體最佳。
經過實驗分析可以得知，本研究求解之最佳因子水準組合為 ，表示採用小批量與平準化之精實管理概念將有效改善系統績效，比較於現況之服務水準改善了6.6%、產出數量改善3.4%、組裝產能利用率改善14.3%，而週期時間與備料倉庫之WIP數量亦分別有7.5%與9.1%的改善幅度。根據單反應實驗結果，我們可以發現服務水準與週期時間等兩項指標皆能達到最佳情況，對於案例公司屬於接單式生產且大量客製化之環境，符合案例公司之目標，表示以該最佳設計將能夠滿足縮短週期時間以提升服務水準之訴求。

In this study, an optical glasses manufacturer presented here to be example, we used value stream mapping to found waste and defined potential problem in the production system. Such as material shortage, overproduction, high variability of shop floor. Based on these reason, we proposed lean management principles to discuss and identify which lean design will gives better improvement. However, how to measure the performance when we looking forward to optimizing overall performance index? We used discrete-event simulation to generate value of performance index, and also used multiple attribute decision-making methods to found best design.
When we evaluated single response of performance index, most academics likely used Taguchi methods. However, traditional Taguchi method focus on single quality characteristic problem is not enough to analysis multiple quality characteristic problem. This study presented an optimizing procedure to solving the multiple quality characteristic problem. By using the proposed approach and the overall performance index for multiple responses to optimize multiple quality characteristics problems, and best combination of parameters can be found. In the end of experiments, we also used traditional Taguchi method to analysis single responses optimization. Through the experiments to find out optimal value of each quality characteristic, and compare with the best combination.

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