本研究探討之案例公司為一家高階自行車製造商，屬於委託代工（OEM，Original Equipment Manufacturing），主要生產外國競賽、極限運動用之高階自行車，是屬於多樣少量的生產模式，而因為原物料採購期冗長，且基於騎乘安全之考量，產品要求精密程度極高，使得生產過程複雜且變化程度高，這是導致案例公司交貨期長達半年之久的原因所在。因此本研究將透過精實管理概念的導入藉以改善生產流程，試圖找出適合案例公司實務可行之精實方法，期望能在不影響產出為前提，進一步降低在製品庫存及降低生產前置時間，並提供管理者進行改善時有足夠之參考依據。
首先以價值流圖繪製，找出現況之改善契機，透過合適之精實概念的導入作為控制因子，利用直交表產生數個方案，進一步以模擬為工具產生各個方案之績效值，並以田口結合TOPSIS法求取最佳因子水準組合，接著以敏感度分析以及單反應實驗來驗證所求得之最佳因子水準組合是否為整體最佳。
本研究將實際在案例公司參與改善之結果與現況進行比較，在不影響產出的情況下，能有效降低24.7%的在製品庫存，生產前置時間則改善16.2%。而根據實驗分析之結果，本研究求得之最佳因子水準組合，預期能夠進一步對於系統之績效有所提升，相比較於改善後，在產出有2.2%的提升，系統總在製品數、生產前置時間，分別有38.2%及37.1%的改善幅度，因此能夠證實該最佳水準組合能夠符合案例公司之改善目標，在保持產出不下降的情況下能有效降低在製品數、縮短前置時間之訴求。

In this study, a High-end bicycle manufacturer presented here to be example, in this case, the case company have long purchasing time of materials, in addition to this, riding safety is the most important things of High-end bicycle, therefore the product quality and precision problem will become the top issue of case company. It is the reason why that case company’s production process so complex and delivery time up to six month. Based on these reason, we used some method to find out the solution, in the first place we used Value Stream Mapping to found out the non-value-added activity and defined potential problem in the production system. Such as work-in-process inventory, overproduction, long production lead time and delivery time. After that, we proposed lean management principles to find appropriate lean concepts to be control factor and put into the Orthogonal Arrays to generate multiple scenarios. In order to discuss which one will gives the best improvement of production process, so, we used discrete-event simulation to generate value of quality characteristic performance, and we also used Taguchi method combine Technique for Order Preference by Similarity to Ideal Solution ( TOPSIS ) in Multiple Attribute Decision-Making methods ( MADM ) to measure the value of multiple quality characteristic performance to find out the best scenario of this case.
In the end of experiments, we through the sensitivity analysis to discuss whether the difference weights can cause difference result, and we also used Taguchi method to analyze single responses optimization to prove the optimized scenario is overall the best. Finally, we compare the optimal value of each quality characteristic of optimized scenario with original value.

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