對於自行車的製造商除了增加生產效率外還須以大量客製化的生產方式來滿足不同的顧客需求，且面臨環境競爭的趨勢下，製造商漸漸地朝向高品質、低成本、交期準等目標持續改善。本研究以一間位在台南具有50年歷史的自行車工廠為案例，實際到現場實習一年，參與改善，該公司屬於接單式生產，目前案例公司從買料到完成品出貨長達半年的前置時間，其中焊接製程所耗費的時間最長為兩個月，且屬於大量的人力作業，因此本研究欲藉由精實管理原則的導入及少人化的觀點，打破焊接現況方案（定員制），以模擬最佳化的工具求解最適人員配置及數量，讓管理者在進行改善或是遇到在製品堆積時於人員數量調整上有一個改善的參考依據。首先以現況方案價值流圖找出所要分析的問題點，再根據問題點進行精實管理原則的導入，因為本研究是筆者實際到現場參與改善，也看到實際現場實際的改善，在產出不受影響情況下，改善比例分別為：在製品和週期時間為16%；現場空間16%；瓶頸站人員也不需再加班，大約可以比以往提早1.5個小時下班，因此本研究也將改善的過程加入方案之一，並利用模擬工具建立模式產生不同方案之績效指標值，觀察方案間改善的狀況，最後少人化的部分則利用模擬最佳化工具求解最適人員配置及數量，並達到少人化的效益，經由實驗分析結果，證實導入精實管理原則及做少人化對於系統來說有最佳的績效表現，然而在產出不受影響的情況下，績效指標提升代表系統內部的效率提升，各指標改善比例分別為：在製品55%、週期時間57%、服務水準78%、人力數量22%。

In this case, high-end bicycle manufacture presented here to be example, first, we use Value Stream Mapping（VSM）to find out the potential improvement opportunities in the system. The case company has long lead time, every products from the beginning of purchasing to finished product up to six months. From VSM that we can find out material took most of time in waiting, therefore center about the reason of these problems, we proposed appropriate lean principle after discuss with experts and line leader to design the scenario, there are three scenario in this study. And how to measure the performance of every scenario? We used discrete-event simulation to generate the value of performance index, and also used optimization tool to find out better operator allocation and number of each scenario in order to reach advantage of Shojinka.
Because this case study was actually the author to participate the progress of improvement, the improvement rates were: work-in-process （WIP） and cycle time by 16%; field of space by 16% ; bottleneck station personnel do not need to work overtime, by approximately 1.5 hours earlier than in the past to work when throughput didn’t impacted, we also taken into account the improvement situation to one of our scenarios. Then we used discrete-event simulation to measure the performance of each scenario. After experiment, it proved that lean principles could improve the system performances, such as WIP、cycle time、service level when throughput didn’t impacted. Based on these reasons, it represents the true efficiency in the system. And at the end of the experiment, we use sensitivity analysis to see that when demand change if system need to meet the service level, the operator number of system and the throughput of system will how to change. From sensitivity analysis can conclude that the number of operator in system increase when demand increase, on the contrary, it decrease when demand decrease. From this point of view, the system have reached the advantage of Shojinka.

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