汽車維修業為具有高度客製化生產(Make-to-Order)的特性，不同維修車其維修車大小及維修項目等皆不同。其不同維修車種類會有其不同之維修時間，且維修時間會呈現較大的變異，因此在需求不確定、產品差異大且不易預測之環境下，常會導致技工本身工作負荷不均或是無法準時交貨之局面，在此情況下，常會導致顧客抱怨事件增加、維修總成本上升、服務水準下降等情形。
本研究以精實觀點，針對汽車維修廠之無形性(Intangibility)、變異性(Heterogeneity)、不可分割性(Inseparability)及不可儲存性(Perishability)之四大服務特性，提出結合精實服務系統與價值流圖(Value Stream Mapping，VSM)分析方法找出浪費所在，以此為改善契機並繪製價值流圖作為改善方案之依據，也就是提出在維修流程前設置等待區以及利用維修技工取代原有接待人員，使其維修服務流程能更加連續，並利用模擬最佳化之方式進一步找出維修廠之最適人員配置，改善維修廠之總維修成本及服務水準。
最後導入精實服務系統至案例，其結果比較發現，現況圖與未來圖在汽車到達間隔時間不變之情況下，以汽車維修廠之總成本以及服務水準作為衡量標準之下，發現情境：未來情境下做人員決策所表現的績效最佳，比原始情境：不做人員決策及流程改善之維修總成本從$2723272減少至$1806600，改善33.7%，服務水準由74.05%增加至95.3%，改善28.7%，此現象說明，如能同時採用兩種改善方式將會比只單一採用精實服務系統或是只單一採用模擬最佳化求解最適人員配置更能夠為公司帶來效益，改善系統中的浪費，進而增加企業的競爭力。

The auto repair industry is in Make-to-order environment. Different orders need different resources and service items to repair. The different types of car maintenance take different length of time to repair and the time of each type of maintenance also fluctuates a lot. With demand uncertainty, different types of car maintenance and various repair time can make technicians’ workload uneven and fail to complete the service on time. Consequently, it can lead to more complaints, rising cost and worse service level. Therefore, we need a way to avoid the situation.
This paper considers intangibility, heterogeneity, inseparability and perishability of the four service characteristics on auto repair facility. We propose a Lean service system and value stream mapping analysis to find out where the waste is in order to find the opportunities as a basis of improvement. In other words, we reconfigure the waiting area before maintenance process and use maintenance technicians to replace the original reception staff to make the process flow as the future value stream mapping. Then, we use simulation optimization to solve the allocation of technician problem to reduce the total cost and enhance the service level.
Finally, we implement the proposed procedure for an auto repair facility as a case study. After comparing current-state map with allocation of technicians and future-state map, the results indicate that the total maintenance cost can be reduced by 33.7%. Meanwhile, the service level can be improved by 28.7%.

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