品質機能展開(Quality Function Deployment, QFD)為企業開發新產品時，用以掌握顧客需求，並將之轉換為具體設計規格的方法。隨著市場日趨競爭，為能對市場變化及顧客需求做出及時且恰當的回饋，競爭分析亦為一項重要考量，將這些來自顧客、相關競爭者，及企業在市場上的表現等資訊，加入品質機能展開的過程。訂定生產計劃時，亦須考量諸多因素，如成本、顧客滿意度、技術上限等。而在有限資源的情形下，如何將資源做最有效的分配，是一重要議題。以往品質機能展開研究文獻中，對於品質屋的各項輸入值，大多以整數值或語意變數來評估。但歸納現行品質機能展開評估方式所採用的方法，其評估方式為針對單一準則下評估值。但此法對於決策而言，因為只考量單一準則，並無參考準則間的相對關係，不利決策結果，因此造成準確性值得商榷。而偏好關係可以將準則間的相對關係，有系統地聯結，並將評估準則間兩兩成對比較；一方面可以減輕決策之複雜度，一方面可以減少判斷之失誤，讓決策者僅需專注於兩兩決策準則間的相對關係。得以提升整體評估的正確性。
本研究利用多種偏好關係(preference relation)作為評估值的品質機能展開決策流程，共分為三階段。第一階段利用偏好關係蒐集及整合專家意見，由專家對產品的顧客需求進行評估後，整合專家意見；第二階段為品質屋階段將第一階段所得到的評估值進行競爭分析，並避免設計需求的相互影響，求得具有競爭資訊的顧客需求重要性與正規化關係矩陣。並以此進入第三階段設計需求執行度求解，考量預算、技術的條件下，並加入Kano概念，以期能得一較佳之執行度解。

Quality Function Deployment (QFD) is a method that transforms customer demand into specific designs. In previous studies of QFD, House of Quality (HOQ) Matrix is frequently set based on integer values or linguistic variables, which makes it a single criterion method that can result in inaccurate evaluation and fault decisions. Whereas in a preference relations-based method, correlations are systematically sorted and compared with evaluation criterions in pairs. On one hand, it eases the complexity of the decision making process; on the other, it reduces the chances of false judgments, enabling decision makers to solely focus on the binary comparison. Therefore, accuracy of the overall evaluation is enhanced.

This research adopts preference relations in a three-phase QFD process. In the first phase, experts are sought to inquire and evaluate on customer demand of a product. Results are gathered and integrated with the experts’ opinions based on preference relation. In the second phase, the HOQ takes the values derived from the first phase and conducts a competitive analysis. It is important that design requirements do not conflict with one another and a normalized relationship matrix that considers customer demand is obtained. Based on the results of the previous phase, it is in phase three where implementation efficiency is closely monitored under budget and technical conditions using the Kano model. A figure is submitted as a model to justify the rationality and superiority of this research. In the findings, the Kano model is proven to be of higher efficiency.

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