Applying a hidden Markov chain model in quality function deployment to analyze dynamic customer requirements

Applying a hidden Markov chain model in quality function deployment to analyze dynamic customer... This study applies a hidden Markov chain model in quality function deployment to analyze dynamic customer requirements from probabilities viewpoints. In reality, the needed probabilities can be computed based upon the experts’ opinions for economic conditions as well as the customers’ surveys by asking customers’ preferences under different economic conditions. Each customer requirement can be analyzed as time goes by. In addition, the changes for each technical measure can be closely examined from time to time. More importantly, when new customers’ surveys are conducted and available as well as the new economic conditions analyzed by experts have been updated, both customer requirements and technical measures can be adjusted in a timely basis to reflect and fulfill the dynamic customer requirements. As a result, this proposed approach provides a decision maker to analyze and satisfy both past and present customer needs early on such that a better strategy can be made based upon the most updated customers’ surveys and economic conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Applying a hidden Markov chain model in quality function deployment to analyze dynamic customer requirements

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Publisher
Springer Netherlands
Copyright
Copyright © 2007 by Springer Science+Business Media B.V.
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-007-9153-8
Publisher site
See Article on Publisher Site

Abstract

This study applies a hidden Markov chain model in quality function deployment to analyze dynamic customer requirements from probabilities viewpoints. In reality, the needed probabilities can be computed based upon the experts’ opinions for economic conditions as well as the customers’ surveys by asking customers’ preferences under different economic conditions. Each customer requirement can be analyzed as time goes by. In addition, the changes for each technical measure can be closely examined from time to time. More importantly, when new customers’ surveys are conducted and available as well as the new economic conditions analyzed by experts have been updated, both customer requirements and technical measures can be adjusted in a timely basis to reflect and fulfill the dynamic customer requirements. As a result, this proposed approach provides a decision maker to analyze and satisfy both past and present customer needs early on such that a better strategy can be made based upon the most updated customers’ surveys and economic conditions.

Journal

Quality & QuantitySpringer Journals

Published: Jan 5, 2008

References

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