結合聯合分析與Kano模式於新產品開發之整合研究

Abstract

新產品開發(NPD: new product development)是近年來非常熱門的研究議題，不論是在工 業工程、工業設計、行銷與生產管理等領域；探討的研究議題包括顧客偏好的衡量與獲 取、顧客需求與產品設計的連結、市場區隔與產品設計方案的評選等，大量融入了多變 量分析、作業研究、甚至資料採礦的技巧。雖然策略行銷上所倚賴的STP 三步驟 (segmentation-targeting-positioning)被廣泛地應用在目標行銷或產品組合管理；然而針對 消費者偏好進行區隔的研究文獻明顯不足，對消費者偏好的調查與掌握方式也莫衷一 是。因此，本研究提出一個結合模糊多準則決策(MCDM: multi-criteria decision making) 與線性整數規劃(linear integer programming)的整合模型，以模糊尺度的語意量表；將灰 關聯法(GRA: grey relational analysis)、聯合分析法(CA: conjoint analysis)、決策實驗分析 法(DEMATEL: decision making and trial laboratory)、品質機能展開(QFD：quality function deployment)、Kano 模型加以融合彙整以突破傳統模式在實務應用的困境，同時以平板 電腦或智慧型手機的設計實證資料來印證所提研究架構之有效性。本研究預期提出一個 架構，先將顧客依其對產品偏好進行市場區隔；隨後針對每個區隔的特性開發其最符合 需求的產品以進行目標行銷，並協助品牌製造商來達成產品組合管理的目的。

Recently, NPD (new product development) has been very popular in many domains, including industrial engineering, industrial design, marketing, and production management. Its research issues cover acquisition of customer preference, connecting customer requirement with product design, market segmentation, and evaluation of product alternatives, particularly it incorporates many techniques such as multivariate analysis, operation research, and data mining. Despite a three-phase approach named STP (segmentation-targeting-positioning) was commonly applied to target marketing and product portfolio management, however, past studies that focused on customer preference based segmentation are rarely addressed. Moreover, an effective way to acquire customer preference is also deficient. Thus, this study attempts to propose a hybrid approach which incorporates several fuzzy MCDM (multi-criteria decision making) schemes like GRA (grey relational analysis), CA (conjoint analysis), DEMATEL (decision making and trial laboratory) into an integrated framework to overcome the difficulties encountered by the conventional approach. More importantly, we plan to use “PAD computer” or “smart phone” to validate our proposed approach. Initially, customers will be segmented according to their preferences. Thereafter, based on each segment, an optimal product configuration will be developed and recommended.