This paper presents an analytical literature review on fuzzy quality function deployment (FQFD) of papers published between 2000 and 2011. In this review, publications were divided into two main groups. First group included publications which proposed some models to develop FQFD. The second one was related to new applications of FQFD models. Next, publications were analyzed and research gaps and future directions were presented. We reached some conclusions including the following. (i) Most of studies were focused on quantitative methods to accomplish phase 1 of QFD or House of Quality (HoQ). The most employed techniques were multicriteria decision making (MCDM) methods. (ii) Although main purpose of using QFD was product development, other factors such as risk and competiveness analysis should be considered in product development process. (iii) A promising approach is using of metaheuristic methods for solving complicated problems of FQFD. (iv) There are a few studies on completing all phases of FQFD. 1. Introduction Quality function deployment (QFD) is a customer-driven product development tool to achieve higher customer satisfaction through translating customer needs (CNs) into design requirements (DRs), part characteristics (PCs), and production plans and control [1]. Chan and Wu [2] defined QFD as “a system to assure that customer needs drive product design and production process.” QFD is used essentially in order to design product according to customer favorites. A general QFD process consists of 4 phases. First phase, which is called House of Quality (HoQ), is an important stage in deploying QFD process. In this stage, after determining CNs and technical characteristics (TCs), relationships between CNs (Whats) and TCs (Hows) as well as their interdependencies are established and their importance weight is calculated [1]. In second phase TCs are translated to important PCs. Critical parameters of process are established in third stage and finally production requirements are specified (fourth phase) [3]. Most of required data in QFD processes and activities are expressed in natural language. Customers, for example, say their expectations from product by using expressions such as “easy to use,” “safe,” and “comfortable” which all of them have ambiguity. Computing these ambiguities in a requirement is an important issue [4]. Using tools from fuzzy sets and their concepts, we can approximate linguistic data to a numeric precision [5]. This review, consisting of a bank with more than 70 papers, divided publications in two main groups. First was
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