在全球競爭的環境中，服務或產品品質是非常重要的。好的服務或產品品質將導致顧客的高度滿意和企業的持續利潤與永續經營。 目前兩階流程管理的觀念與方法只應用在製造業品質的管制上，而本研究將此觀念與方法推廣並應用在個案公司的採購與銷售作業流程管理上。經由收集個案公司的採購成本和銷售額的資料，再分別據以建立兩階流程的個別管制圖和選控圖以管制採購成本和利潤變異情形。分析結果顯示採購成本和利潤變異並不穩定，而造成的原因經由分析發現後，分別提出適當的改善策略。預期未來實施後應能達到有效的改善作業流程、降低成本並提昇市場之競爭力。

Today, most products are produced by several dependent subprocesses. This paper considers the economic-statistical process control for two dependent subprocesses with two assignable causes following Weibull shock distributions. We construct the individual X control chart to monitor the in-coming quality produced by previous process, and use the cause-selecting control chart to monitor the specific quality produced by current process. By using the charts, we can effectively and economically distinguish which subprocess is out of control. The renewal theorem approach is extended to construct the cost model for our proposed control charts, and the successive quadratic programming algorithm and a finite difference gradient method in the Fortran IMSL subroutine (dnconf) is used to determine the optimal design parameters of the proposed control charts. Finally, we give an example to show how to construct and apply the proposed control charts. Furthermore, the sensitivity analysis illustrates the effects of cost and process parameters on the optimal design parameters and the minimal expected cost per unit time for the proposed control charts.

在子製程相關下所產生的品質特性資料使Shewhart管制圖無法對各別的製程狀態加以解釋，而選控圖可診斷前後製程的責任歸屬。本文提出有n個子製程時，建立選控圖的方法及在製程上之應用，以追蹤製程變異之發生，明確地劃分出子製程的責任歸屬。文中並以模擬資料和實際例證說明，在相關品質特性的資料中，選控圖的建立及診斷效果。在實務上，若產品是由許多相關的子製程共同製造而成時，對於受到前製程影響的品質特性，即可用選控圖進行管制，以獲得正確的製程狀態訊息，並進一步採取正確的管制行動。

由近期的研究中發現變動所有參數的管制圖在偵測小幅度偏移時的速度比起傳統的舒華特管制圖來的快，許多文獻也討論到利用調適性管制技術同時監控製程的平均數和變異數。而在這份研究中，為了改善現有管制圖的偵測效率，依序提出了U-V管制圖以及Max-M管制圖來偵測單一製程與兩相依製程的平均數和變異數。採用AATS及ANOS來衡量管制圖的偵測績效，並利用馬可夫鏈推導計算得之。透過兩階段的範例來介紹所提出的管制圖的應用方法並將VP U-V管制圖、VP Max-M管制圖與FP Z(X-bar)-Z(Sx^2)管制圖加以比較。從所研究的數值分析中發現VP Max-M管制圖比另兩種管制圖的表現來的好，再加上只需要單一管制圖在使用上對工程師來說也較為簡便，因此建議Max-M管制圖値得在實務上被使用。 / Recent studies have shown that the variable parameters (VP) charts detect small process shifts faster than the traditional Shewhart charts. There have been many papers discussed adaptive control schemes to monitor process mean and variance simultaneously. In the study, to improve the efficiency and performance of the existing control charts, the U-V control charts and Max-M control charts are respectively proposed to monitor the process mean and variance for a single process and two dependent process steps. The performance of the proposed control charts is measured by using adjusted average time to signal (AATS) and average number of observations to signal (ANOS). The calculation of AATS and ANOS is derived by Markov chain approach. The application of the proposed control charts is illustrated by a numerical example for two dependent process steps, and the performance of VP U-V control charts, VP Max-M control charts and FP Z(X-bar)-Z(Sx^2) control charts is compared. From the results of data analyses, it shows that the VP Max-M control charts have better performance than VP U-V control charts and FP Z(X-bar)-Z(Sx^2) control charts. Furthermore, using a single chart to monitor a process is easier than using two charts for engineers. Hence, Max-M control charts are recommended in real industrial process.