| Author: | Wan, Kwok-leung Kevin |
| Title: | A QFD system for optimizing product design |
| Degree: | M.Sc. |
| Year: | 2001 |
| Subject: | New products Quality function deployment Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies Department of Manufacturing Engineering |
| Pages: | 105, [20] leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | Today, many companies are facing with the rapid transformation stimulated by technological innovations and changing customer demands. These companies realize that the effort to rapidly develop new products that customer wants is crucial for their survival. New product development is a complex process involving multiple functional groups, each with a different perspective. Quality function deployment (QFD), first proposed by Akao in late 1960s, is a concept and mechanism for translating the "voice of the customer" through the various stages of product planning, engineering, and manufacturing into a final product. A QFD contains information on: -Customer attributes and their relative importance; -The engineering characteristics, and how they affect the customer attribute levels as well as each other; -Customer's perception of the degree of satisfaction of the customer attributes for the company's product, as well as with respect to its competitors; and -Current measures of engineering characteristic levels for the company's product as well as the competitors. Based upon the information contained in a house of quality, "target levels" for engineering characteristics of a new or revised product can be determined. The process in the conventional QFD currently is accomplished in subjective, ad hoc manner. In this project, a QFD system for optimizing product design was developed by using the following steps: (1) Prioritize the customer attributes weighting by AHP. (2) Determine the relationships between the customer attributes and the engineering characteristics, and among the engineering characteristics by using Fuzzy Logic approach. (3) Introduce the regression parameter estimation to a set of relationship data so that the data can be used as the input to an analytical model. (4) Use the linear programming technique to determine the optimal target values of engineering characteristics. The optimal target values obtained from the system can yield higher customer satisfaction. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15995951.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.85 MB | Adobe PDF | View/Open |
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