品質機能展開(Quality Function Deployment, QFD)是為企業廣泛運用的產品開發工具。透過品質機能展開系統能夠將抽象的顧客需求，轉換為具體的生產或改良步驟，以達到更大的顧客滿意度目標，且為了能對市場的變化做出快速且適當的回應，競爭分析也是一個重要的議題，將這些由顧客所獲得的資訊、其他競爭者及企業本身於市場上的表現等市場資訊，分析運用在執行品質機能展開的過程中。決定生產計劃時亦需考量許多因素，如顧客滿意度、成本、技術困難度等。而在資源有限的情況下，如何進行資源的分配，是一個重要的議題。在過去關於資源分配的研究中，大都有將需求缺口的概念納入考量，以避免造成資源的浪費，但在品質機能展開的決策流程中，尚未有研究考慮到此概念。因此，本研究將需求缺口限制納入執行度決策模式中，以避免產生執行度大於需求的現象，而造成資源的浪費。
　　為了能更充分的表達顧客需求意見之不確定性，本研究建構一個以模糊數作為評估值的品質機能展開決策流程，共分為三個階段。第一階段為整合專家意見，考慮專家意見的模糊性質，並利用群體決策的方法將專家們的意見加以整合；第二階段為計算各個顧客需求之設計需求執行度上限；第三階段為計算考量需求缺口之設計需求執行度。而第二、三階段皆藉由模糊品質屋方法及設計需求執行度的決策模式完成，模糊品質屋方法將顧客需求轉換為相對應的設計需求，並整合顧客需求與設計需求的競爭分析之相關文獻；設計需求執行度的決策模式則以模糊目標規劃來完成，最後透過演算來驗證本方法的可行性。藉由此品質機能展開決策流程，可以達到新產品開發與品質改良之目的，並因加入需求缺口之考量，改進資源分配結果，以提高顧客滿意程度。

　　Quality function deployment (QFD) is a customer-driven approach to achieve higher level of customer satisfaction, in which the design requirements affecting product performance are established to match customer requirements. This study proposed a QFD decision process considering the customer requirement gap to avoid resource wasting.
　　The first phase of QFD is the development of a so-called house of quality (HOQ). This study presents a three-stage QFD decision-making process, as follows: (1) The integration of expert advice, based on an entropy method. In this, the weight of each expert in the HOQ depends on how much information they supply. (2) The calculation of the design requirements’ (DRs) fulfillment level upper bound, in which the maximum expectation of the customer requirements is set as the improvement goal, and then execute repeatedly the HOQ and a fuzzy mathematical programming model to convert the customer requirements (CRs) to the corresponding upper bound of the DRs’ fulfillment level, and also consider the competitive analysis at the HOQ step. (3) The calculation of the final fulfillment level of the DRs, in which the steps are similar to stage (2) to obtain the final fulfillment level of the DRs. The differences are replacing the maximum expectation of the customer requirements with the experts given improvement goal at the HOQ step and consider the limitation of the DRs’ fulfillment level from (2) during the progress of the mathematical programming model.
　　Unlike previous models, which only focus on maximizing the final fulfillment level of the DRs, the proposed model avoids the wasting of resources caused by a fulfillment level exceeds customer requirements, thus the resulting product plan can be more resource-effective. A numerical example is used to demonstrate the rationality and superiority of the proposed model. The results of the case study verify the reasonableness of the QFD decision-making model. Thus it is important that decision-makers must consider the customer requirement gap when using the QFD approach.

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