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Quality function model based on voice of customer using neural networks and statistical approaches

Siraj, Fadzilah and Mohamad Mohsin, Mohamad Farhan and Abu Bakar, Nur Azzah and Mohd Yusof, Shahrul Azmi and Nordin, N. and Yusof, S. A. (2011) Quality function model based on voice of customer using neural networks and statistical approaches. In: International Soft Science Conference 2011 (ISSC 2011), 23-25 November 2011, Ho Chi Minh, Vietnam. (Unpublished)

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Quality Function Deployment or QFD is a flexible and comprehensive group decision making technique used in product or service development, brand marketing, and product management. QFD can strongly help an organization focuses on the critical characteristics of a new or existing product or service from the separate viewpoints of the customer marketsegments, company, or technology development needs.While the structure provided by QFD can be significantly beneficial, it is not a simple method touse. This study focuses on the development of general QFD for customer requirement so that it later can be used for any kind of machine evaluation prior to purchasing the machines.Based on a set of questionnaires with 228respondents, NN models were generated and statistical methods were used toexplain the relationship between attributes in this study.In addition tosignificant correlation between attributes, NN has shown satisfactory result with12.3 percent misclassification error.Therefore when NN is complimented with statistical techniques, 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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Quality Function Deployment, Neural Network, Statistics, Voice of Customer
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Prof Madya Fadzilah Siraj
Date Deposited: 14 Sep 2014 06:30
Last Modified: 14 Sep 2014 06:30
URI: http://repo.uum.edu.my/id/eprint/12165

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