近年來，我國對於田口式品質工程方法的運用，已漸漸普及於各個生產事 業。然而，在以往田口方法的應用實例中大多只使用一種數據分析法，即 傳統實驗設計的變異數分析法，或田口方法的信號雜音比( S/N ratio )，對於二者之間的比較及異同處，則較少提及﹔且絕大部分是只探討單 一品質特性最佳化的問題，對於同一製程中同時具有多個品質特性需最佳 化的問題，亦較少觸及。本研究首先分別引用二種數據分析法進行提昇自 動光學檢驗(AOI)偵測能力」}的數據解析、比較二種分析法在理論上及應 用上的差別，並依保守原則，初步決定各品質特性(本研究有三個品質特 性)的最佳因子水準組合。其次，針對各品質特性間因子水準互相矛盾的 情形，本文將引用下述3種分析方法於研究案例，並綜合各種方法的優缺 點及適用狀況，藉以找出多品質特性同時最佳化的因子水準組合。此三種 分析方法為: (1) 畫出各因子對個別品質特性之影響效果總調查表，再經 由人為的比較判斷後選取最佳的條件組合 (2) 以個別品質特性之信號雜 音比的加權和作分析 (3) 使用品質特性值標準化的方法做分析由分析結 果，吾人可知現有各種方法均有其優缺點，唯有熟悉各種方法並配合豐富 經驗與專業知識，視實驗狀況而選擇性的加以應用，方能獲致良好實驗效 果。

在全球化競爭的條件之下，企業之專業分工漸趨精細，企業為了發展自己本身的核心能力，選擇將非核心能力委外給專業的第三方，以求獲得更佳的服務品質且為企業減少成本支出。其中物流相關費用占企業營運費用約5%~35%，物流在整體企業營運扮演重要角色，近年來供應鏈管理的興起，企業更加重視與上下游廠商間協調關係，物流管理受到重視，物流關係到整體供應鏈的穩定度，因此在選擇物流委外供應商時，企業需審慎的評估供應商各方面的能力，以選出最適物流委外供應商。 　　過去評選物流委外供應商，多數藉由專家主觀判斷，本研究提出一套完整的物流委外供應商評選模式，以客觀的角度評選物流委外供應商。過去文獻中多數假設評估準則間關係獨立，此假設與現實狀況不符，評估準則通常存在相互關聯。企業在評選過程中，針對特定評選準則會有限定標準，若單以績效值評估，不符企業之需求，本研究將其納入考量，建立出更加完整的物流委外供應商評選模式。此評選模式可給予參與評選之候選廠商改善建議，為雙方建立良好的關係，創造雙贏的局面。 　　本研究透過決策實驗室法建立指標之網路關係，以網路程序分析法計算指標之權重，於評分處理部份，針對企業特定評選指標，透過田口品質損失函數轉換成與目標之損失值，最後藉由VIKOR排序法評選出最佳物流委外供應商。受限於成本考量，本研究透過系統動態學模擬運送情形，藉由所獲得之營運資料驗證此評選模式。 / Under the global competitive conditions, enterprises become more sophisticated division of labor. Enterprises want to develop their own core competencies. Enterprises choose to outsource non-core capabilities to third-party professional,because of better service quality and reducing costs for enterprises. In recent years, enterprises pay more attention to coordination with the relationship between upstream and downstream firms. The logistics in the overall business operations play an important role. Therefore, in the choice of logistics outsourcing providers, enterprises evaluate all aspects of supplier's abilities to select the most suitable supplier of logistics outsourcing. 　　This study provides a complete model to evaluate logistic service providers. Enterprises in the selection process, selection criteria will be qualified for a particular standard. Selection criteria usually have interrelationship. Under the condition, this study use DEMATEL and ANP to create criteria relationship and compute criteria weights. Taguchi loss function is used to calculate some criteria loss value, which is the distance between enterprise’s target values. Finally, VIKOR combines loss value with performance value to select the best logistics service providers. These models also give candidates some suggestions to improve their performance.

The CUSUM control charts have been widely used in detecting small process shifts since it was first introduced by Page (1954). And recent studies have shown that adaptive charts can improve the efficiency and performance of traditional Shewhart charts. To monitor the process mean and variance in a single chart, the loss function is used as a measure statistic in this article. The loss function can measure the process quality loss while the process mean and/or variance has shifted. This study combines the three features: adaption, CUSUM and the loss function, and proposes the optimal VSSI, VSI, and FP CUSUM Loss chart. The performance of the proposed charts is measured by using Average Time to Signal (ATS) and Average Number of Observations to Signal (ANOS). The ATS and ANOS calculations are based on Markov chain approach. The performance comparisons between the proposed charts and some existing charts, such as X-bar+S^2 charts and CUSUM X-bar+S^2 charts, are illustrated by numerical analyses and some examples. From the results of the numerical analyses, it shows that the optimal VSSI CUSUM Loss chart has better performance than the optimal VSI CUSUM Loss chart, optimal FP CUSUM Loss chart, CUSUM X-bar+S^2 charts and X-bar+S^2 charts. Furthermore, using a single chart to monitor a process is not only easier but more efficient than using two charts simultaneously. Hence, the adaptive CUSUM Loss charts are recommended in real process. / The CUSUM control charts have been widely used in detecting small process shifts since it was first introduced by Page (1954). And recent studies have shown that adaptive charts can improve the efficiency and performance of traditional Shewhart charts. To monitor the process mean and variance in a single chart, the loss function is used as a measure statistic in this article. The loss function can measure the process quality loss while the process mean and/or variance has shifted. This study combines the three features: adaption, CUSUM and the loss function, and proposes the optimal VSSI, VSI, and FP CUSUM Loss chart. The performance of the proposed charts is measured by using Average Time to Signal (ATS) and Average Number of Observations to Signal (ANOS). The ATS and ANOS calculations are based on Markov chain approach. The performance comparisons between the proposed charts and some existing charts, such as X-bar+S^2 charts and CUSUM X-bar+S^2 charts, are illustrated by numerical analyses and some examples. From the results of the numerical analyses, it shows that the optimal VSSI CUSUM Loss chart has better performance than the optimal VSI CUSUM Loss chart, optimal FP CUSUM Loss chart, CUSUM X-bar+S^2 charts and X-bar+S^2 charts. Furthermore, using a single chart to monitor a process is not only easier but more efficient than using two charts simultaneously. Hence, the adaptive CUSUM Loss charts are recommended in real process.

This article proposes the algorithm of a new control chart (loss function control chart) based on the Taguchi loss function with an adaptive scheme for binomial data. The loss function control chart is able to monitor cost variation from the process by applying loss function in the design. This new angle economically explores production cost. This research provides designs of the loss function control chart with specified VSI, optimal VSI, VSS and VP, respectively. Numerical analyses show that the specified VSI loss function chart, the optimal VSI loss function chart, the optimal VSS loss function chart and the optimal VP loss function chart outperform the Fp loss function chart significantly and show costs can be controlled systematically.