科技日新月異，產品生命週期愈來愈短，如何快速地掌握顧客需求，是企業在產品開發時所必須重視的問題。品質機能展開是企業常用以掌握顧客需求並將顧客需求轉換成具體設計規格的方法，而品質屋是品質機能展開中最重要的工具。品質屋的第一步是蒐集顧客需求及其相對重要性，而在過去的文獻中，大多是以直接給予整數值(point scale)或者讓顧客以語意變數來描述，再將其轉換成模糊數(Fuzzy number)的方法。再者，在競爭激烈的市場環境下，企業在進行產品開發時，也必須考慮到競爭者的情況，以及企業在生產產品時所受到的實際限制，像是成本、技術困難度等等。為能更充分的表達顧客需求意見之不確定資訊，因此，本研究將利用直覺式模糊數做為顧客需求的評估值而發展出品質機能展開模式，藉由直覺式模糊數中的屬於資訊、不屬於資訊及猶豫資訊，來模擬人類思維中不確定性的部分，以提高顧客需求表達之真實性。
本研究使用直覺式模糊數作為顧客需求之評估值，並將模式分為三個階段。第一階段為蒐集並整合專家意見，利用群體決策的方法，由專家對產品的顧客需求進行語意評估，並整合專家意見；第二階段為品質屋階段，將所得到的語意變數評估值進行競爭分析、以及將相對應的顧客需求轉換成設計需求，幫助企業了解市場情況，以及避免設計需求間的相互影響，更準確的得到設計需求重要性；第三階段則是考慮設計需求在實際執行時，可能面臨的成本、技術困難度限制，利用多目標規劃求得各項設計需求的實際執行度。最後，本研究再以Chan and Wu (2005)所提出之範例進行比較與分析，並驗證模式之合理性。

With the advance and innovation of technology, product life-cycles have progressively grown shorter. Therefore, firms need to quickly determine customer requirements during product development. Quality function deployment (QFD) is a product development process which maximizes customer satisfaction. The first phase of QFD, usually called house of quality (HOQ), translates the voice of customer (VoC) into engineering characteristics. The first step of HOQ is to identify customers’ needs and their relative ranking of those needs. Most of the existing literature uses point scales or linguistic variables to describe customer opinions, and transforms linguistic variables into fuzzy numbers. In addition to customer needs, firms also need to consider their competitors and their own limitations. In order to increase the accuracy of customers’ requirements, this research mainly uses intuitionistic fuzzy numbers to describe customer opinions, denoting uncertainty by membership, non-membership and information regarding hesitancy of intuitionistic fuzzy numbers.
This research uses intuitionistic fuzzy numbers to deal with the uncertainty of customer requirements, and the proposed model consists of three steps: (1) using the group decision method to collect and aggregate experts’ linguistic opinions (2) implementing competitive analysis as well as converting the corresponding customer requirements into design requirements in order to assist firms in understanding the competitive market, avoiding the correlation among design requirements and getting more accurate design requirements (3)using multi-objective programming to determine the fulfillment level of each design requirement based upon resource limitations and technical difficulties .Finally, we use a practical example to demonstrate the rationality and superiority of the proposed model by comparing it to the method used in Chan and Wu(2005).

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