Abstract

Quality function deployment (QFD) is a method commonly used in manufacturing industries to identify customer requirements (CRs) and the manufacturing capacity to meet CRs. Importance rate (IR) of CRs plays an important role in the QFD method. The existing methods in determining IR of CRs cannot consider all related factors of customer satisfaction, need importance, personal information, and relationship between customer satisfaction and function implementation. This paper proposes a method to improve the IR accuracy. Based on comments of customers for a product, importance rates of CRs are defined using integrated importance-performance analysis (IPA) and Kano models. IPA and Kano models are integrated by spectral clustering where a similarity matrix W is formed to balance the influence proportion between the IPA and Kano models considering comments of different customers for the product. IR of CRs is used in the QFD method to define functions and structures of the product. The proposed method is compared with several existing methods in case studies of designing an upper limb rehabilitation device and a feed drive system of the CNC machine. Results show that the proposed method has improved accuracy of IR of CRs for the product design and manufacturing.

中文翻译：

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一种提高QFD中客户需求重要性评级准确性的光谱聚类方法

摘要

质量功能部署（QFD）是制造业中常用的一种方法，用于识别客户需求（CR）和满足CR的制造能力。CR的重要性率（IR）在QFD方法中起着重要作用。确定CR的IR的现有方法无法考虑客户满意度，需求重要性，个人信息以及客户满意度与功能实现之间的关系的所有相关因素。本文提出了一种提高红外精度的方法。根据客户对产品的评论，使用集成的重要性－绩效分析（IPA）和Kano模型定义CR的重要性率。IPA和Kano模型通过频谱聚类进行集成，其中相似矩阵W考虑到不同客户对产品的评论，形成了IPA和Kano模型之间的影响比例平衡的模型。QFD方法中使用CR的IR来定义产品的功能和结构。在设计数控机床的上肢康复装置和进给驱动系统的案例研究中，将所提出的方法与几种现有方法进行了比较。结果表明，所提出的方法提高了产品设计和制造中CR的IR精度。