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

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 fr...

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Main Authors: Siraj, Fadzilah, Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Nur Azzah, Mohd Yusof, Shahrul Azmi, Nordin, N., Yusof, S. A.
Format: Conference or Workshop Item
Language:English
Published: 2011
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Online Access:http://repo.uum.edu.my/12165/1/3.pdf
http://repo.uum.edu.my/12165/
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spelling my.uum.repo.121652014-09-14T06:30:47Z http://repo.uum.edu.my/12165/ Quality function model based on voice of customer using neural networks and statistical approaches Siraj, Fadzilah Mohamad Mohsin, Mohamad Farhan Abu Bakar, Nur Azzah Mohd Yusof, Shahrul Azmi Nordin, N. Yusof, S. A. QA76 Computer software 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 2011-11-23 Conference or Workshop Item NonPeerReviewed application/pdf en http://repo.uum.edu.my/12165/1/3.pdf 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)
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Siraj, Fadzilah
Mohamad Mohsin, Mohamad Farhan
Abu Bakar, Nur Azzah
Mohd Yusof, Shahrul Azmi
Nordin, N.
Yusof, S. A.
Quality function model based on voice of customer using neural networks and statistical approaches
description 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
format Conference or Workshop Item
author Siraj, Fadzilah
Mohamad Mohsin, Mohamad Farhan
Abu Bakar, Nur Azzah
Mohd Yusof, Shahrul Azmi
Nordin, N.
Yusof, S. A.
author_facet Siraj, Fadzilah
Mohamad Mohsin, Mohamad Farhan
Abu Bakar, Nur Azzah
Mohd Yusof, Shahrul Azmi
Nordin, N.
Yusof, S. A.
author_sort Siraj, Fadzilah
title Quality function model based on voice of customer using neural networks and statistical approaches
title_short Quality function model based on voice of customer using neural networks and statistical approaches
title_full Quality function model based on voice of customer using neural networks and statistical approaches
title_fullStr Quality function model based on voice of customer using neural networks and statistical approaches
title_full_unstemmed Quality function model based on voice of customer using neural networks and statistical approaches
title_sort quality function model based on voice of customer using neural networks and statistical approaches
publishDate 2011
url http://repo.uum.edu.my/12165/1/3.pdf
http://repo.uum.edu.my/12165/
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score 13.145126