Analyzing Quality Function Development (QFD) Based On Voice of Customer (VoC)

• Main Author: Norsharizan, Nordin
• Format: Thesis
• Language: English
• Published: 2006
• Subjects: T Technology (General)
• Online Access: https://etd.uum.edu.my/1922/1/ANALYZING_QUALITY_FUNCTION_DEVELOPMENT_%28QFD%29_BASED_ON..._NORSHAHRIZAN_BT._NORDIN.pdf; https://etd.uum.edu.my/1922/

Analyzing Quality Function Deployment (QFD) based on voice of customer aims to provide an advanced machine planning methodology based on QFD principles, for identifying and minimize the risks of project failures due to failure in complying with the voice of the customers. The methodology was developed by reviewing current design product definition and QFD tools that have been applied to a number of industry-based machine design projects in academic institution as well as an in-depth study at selected industry organization. This study focuses on the development of general QFD for machine specification selection where it later can be used for any kind of machine evaluation before buying is made. NN models were generated and statistical methods were used to explain the relationship between attributes used in this study. A set of questionnaires was used as an instrument comprises of two main sections; a Customer Profile and Possible Customer Requirements. For Customer Profile, there are three parameters used namely, Name of Company or Institution, Type of Customer and Type of Work piece material Used. For Possible Customer Requirements, there are six (6) sections according to Machine Standard Specification, Machine Control, Machine Safety, Machine Performance, Machine Maintenance and Machine after Sales Services. The important subject to focus is the customer voices selected to model QFD for industry; including Professional, Management level, Maintenance and Operator. In summary, the findings from the experiments conducted indicate that the significant correlations of QFD with customer voices help to explain the relationship between attributes used in the study. The study also indicates that NN forecasting model has been established with 87.696% accuracy in determining the customer voices based on QFD. The study indicates that the approach has potential in explaining the relationship between QFD and the customers, as well as predicting the type of customer if QFD information is provided. Hence, the study reveals the type of machine and type of operation that we favorable to customer prior to acquiring the machines for their industrial usage.