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Quality function deployment analysis based on neural network and statistical results


Siraj, Fadzilah and Nordin, Norshahrizan and Yusoff, Nooraini (2008) Quality function deployment analysis based on neural network and statistical results. International Journal of Simulation Systems, Science and Technology (IJSSST), 9 (2). pp. 73-81. ISSN 1473-804x

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Abstract

QFD is a method of translating high-level objectives into concrete actions and metrics. It is one of the techniques that aims to fulfill the customers’ satisfaction at the very beginning, namely the product design phase. This study focuses on the development of general QFD for machine specification selection so that it later can be used for any kind of machine evaluation prior to purchasing the machines. A set of questionnaires was used as an instrument and was distributed to 223 respondents. NN models were generated and statistical methods were used to explain the relationship between attributes in this study. The findings from the experiments conducted exhibit that the significant correlations of QFD with customer voices help to explain the relationship between attributes. The study also indicates that NN forecasting model has been established with 12.30 percent misclassification error in determining the customer voices based on QFD versus 6.3 percent using regression. This indicates that the approach has the 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 are favourable to customer prior to acquiring the machines for their industrial usage.

Item Type: Article
Uncontrolled Keywords: Quality Function Deployment (QFD), Voice of Customer, Neural Network, Machine Planning
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Prof Madya Fadzilah Siraj
Date Deposited: 15 Dec 2010 03:38
Last Modified: 15 Dec 2010 03:38
URI: http://repo.uum.edu.my/id/eprint/1848

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