企業在面對國際市場激烈的競爭時，為了滿足客戶需求及產品多樣化的趨勢；企業界已體認到，持續不斷的新產品開發設計，才能有效的掌握市場變化並且迅速提供符合顧客需求之產品。新產品的開發可歸納為三個階段：1. 規劃階段。2. 產品發展與設計階段。3. 商品化與應用階段，經由此三個階段並在結合各方的智慧與精力付出之後，才得以完成。在新產品開發過程中，顧客之聲音或其感受通常是具模糊現象的，同時由於企業資源有限，如何評估各種策略機會亦考驗決策者之智慧，本研究擬以新產品開發過程為研究對象，整合應用品質機能展開（QFD）、（TRIZ）方法及失效模式與效應分析(FMEA)應用於新產品開發各階段，並建構一具模糊處理能力之決策支援系統，以協助新產品開發過程中各階段各種策略選擇機會，於本研究中，首先發展以模糊理論為基礎之品質機能展開（QFD）應用於顧客聲音之收集與分析並轉換為產品規格，接著，整合TRIZ 與模糊決策方法將應用於發展具創新特色之產品構想，最後，整合模糊理論之失效模式與效應分析(FMEA)將應用於各種商品化方案之最佳選擇，期望藉由理論的研究與研究者本身的新產品開發與管理的實務經驗，釐清新產品開發的邏輯，整理的新產品開發的脈絡，讓新產品開發工作成為一件有跡可循的工作，並提供產業界做為創造企業價值的利器。 Facing with intensive global competition, more demanding customers, rapidly shrinking product life cycles, and shorter responsive time, organizations are now pursuing competitiveness by achieving higher level of product value and accordingly, higher customer satisfaction. In order to doing this, research and development of new products as well as innovation and quick response to the market must be emphasized and focused on. The development of a new product can be summarized into three stages: 1. the planning stage; 2. the design and development stage; and 3. commercializing and application stage. It is only by efforts of these three stages that one new product can finally and successfully go to the market. It always begins with listening to voice of customers during the new product development process. Customer voice usually is vague and possesses characteristic of fuzzy. Meanwhile, how to make decisions among many conflict and competing conditions as well as limited resources is also critical and possesses characteristic of fuzzy, too. This research plans to investigate the new product development process, integrating tools and methods such as quality function deployment (QFD), TRIZ method, failure mode effect analysis (FMEA) into a fuzzy-based decision support system. In this research framework, a fuzzy based QFD will be developed to collect and analyze voice of customers and turn it into product specification; then the TRIZ method integrated with fuzzy decision method will help to propose some design features; finally a fuzzy-based FEMA will be employed to choose optimal alternative among all options proposed. The result would, hopefully, help practitioners improve quality and performance in their new product development process.
