Global competitiveness has recently become a big challenge for many companies around the world, which are forced to seek lower costs and higher quality for what they produce. The prosperity of manufacturing firms depends on selecting and producing products which provide customer satisfaction to meet multiple objectives.If a company is able to produce customer-oriented products at a low price and in minimum time, it can be successful.So, customer need analysis should be paid attention to in product development and the design phase.Also, the technical capabilities of a manufacturing firm and the restrictions of a company should be considered.In this way, they have one big challenge: How can they respond effectively to different and easily changing customer demands? By focusing on customer opinion, Quality Function Deployment (QFD) has been developed.Quality Function Deployment (QFD) is a robust, efficient and powerful tool in the design, development and planning of products. QFD has been used in many industries and companies over the last few decades. The main function of QFD is conversion of the voice of the customer (VOC) to Technical Characteristics (TCs).However, it is not always easy to prioritize and assess TCs during the total mass of information from the different customer attitudes. This paper provides a methodology for the development of an intelligent Quality Function Deployment (IQFD) and points for developing an intelligent system based on a fuzzy inference system, in order to capture information through the House of Quality (HOQ) matrix. The paper describes the need for development of intelligent QFD to make it easier for engineers and managers to choose between TCs and improve the quality of products and systems. This paper is composed of a background of QFD, a review of related research work, and representation of an intelligent system for its analysis. Then, it applies the proposed methodology to a case study of House of Quality for the design of a new undergraduate curriculum in the mechanical engineering department of the university of Wisconsin-Madison.

In this ever-changing business scenario, the manufacturing product industries have to be in position to recognize the ever changing pulse and demands of the market. Customer satisfaction and quality management has become a strategic issue for companies in the new millennium. Quality Function Deployment (QFD) literature suggests that building up the House of Quality (HOQ) is not a difficult task, however to analyze and interpret the information available is replete with a lot of uncertainty and presents less than optimal solutions. This paper attempts to address these twin issues of the Post-HOQ analysis and its interpretation through SWOT. The development and mechanics of QFD model is presumed to be known to the followers and the paper deals specifically with post-HOQ model through a well-defined and structured approach to comprehensive matrix analysis. The paper contributes a method for evaluating and analyzing the customer data and technical data in QFD as a function of the generation of useful information resulting in a better decision making process. The outcome of the study is a comprehensive solution which discusses post-matrix analysis through underlying concepts; requisite steps; information needed; and the computations involved. The applicability of the proposed model is demonstrated with an illustrative hypothetical example of a medical-care product – disposable syringe and needle.

Selecting the right lean tool is one of the most challenging tasks for decision-makers. This research represents an integration of two methods, Quality Function Deployment (QFD) and TOPSIS, to evaluate and prioritize the lean tools. The aim of this research is to propose an integrated QFD-TOPSIS method for finding and ranking the major wastes on a production floor and also prioritize lean tools for eliminating them. The proposed model consists of House Of Quality (HOQ) to identify major waste signs which transform them into seven wastes. A set of relative weights of the wastes were then determined which were input in the TOPSIS method to prioritize suitable lean tools.