The extension of fuzzy QFD: From product planning to part deployment
The extension of fuzzy QFD: From product planning to part deployment
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DOI：
10.1016/j.eswa.2009.02.070
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发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
Hao-Tien Liu
中科院分区：
文献类型：
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作者：
Hao-Tien Liu
质量功能展开（QFD）通过关注倾听顾客的意见，已经成为产品设计和开发中一种成功的分析工具。为了解决QFD中的不确定性或不精确性，许多研究者试图将模糊集理论应用于QFD，并发展了各种模糊QFD方法。他们的模型通常集中在产品规划，质量功能展开的第一阶段。QFD的后续阶段（零件配置、工艺规划和生产规划）很少被提及。此外，他们的模型经常使用模糊数的代数运算来计算QFD中的模糊集。经过几次乘法或除法运算后，很容易产生有偏差的结果。针对这两个问题，本研究的目的是开发一种扩展的模糊质量功能配置方法（E-QFD），扩大了研究范围，从产品规划到零件部署。在产品规划中，提出了一种更先进的收集客户需求的方法，同时考虑了竞争分析。在部件部署中，通过包括部件特征（PC）的重要性和PC的瓶颈级别来增强原始部件部署表。提出了一种改进的模糊k-均值聚类方法来对PC机的各种瓶颈（或重要性）组进行分类。通过模糊推理方法，对高瓶颈（或高重要性）PC组进行失效模式和影响分析（FMEA）。此外，E-QFD采用了一种更精确的方法，α-割运算，而不是模糊数的代数运算来计算QFD中的模糊集。最后，通过一个实例说明了该方法的分析过程.
By focusing on listening to the customers, quality function deployment (QFD) has been a successful analysis tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have attempted to apply the fuzzy set theory to QFD and have developed various fuzzy QFD approaches. Their models usually concentrate on product planning, the first phase of QFD. The subsequent phases (part deployment, process planning, and production planning) of QFD are seldom addressed. Moreover, their models often use algebraic operations of fuzzy numbers to calculate the fuzzy sets in QFD. Biased results are easily produced after several multiplicative or divisional operations. Aiming to solve these two issues, the objective of this study is to develop an extended fuzzy quality function deployment approach (E-QFD) which expands the research scope, from product planning to part deployment. In product planning, a more advanced method for collecting customer requirements is developed while the competitive analysis is also considered. In part deployment, the original part deployment table is enhanced by including the importance of part characteristics (PCs) and the bottleneck level of PCs. A modified fuzzy k-means clustering method is proposed to classify various bottleneck (or importance) groups of PCs. The failure mode and effects analysis (FMEA) is conducted for the high bottleneck (or high importance) group of PCs through the fuzzy inference approach. Moreover, E-QFD employs a more precise method, α-cut operations, to calculate the fuzzy sets in QFD instead of algebraic operations of fuzzy numbers. Finally, a case study is given to explain the analysis process of the proposed method.