Applying a hidden Markov chain model in quality function deployment to analyze dynamic customer requirements
- 166 Downloads
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.
KeywordsHidden Markov chain model Quality function deployment Customer requirement Technical measure
Unable to display preview. Download preview PDF.
- Akao Y. (1990). Quality Function Deployment: integrating Customer Requirements into Product Design. Productivity Press, Cambridge Google Scholar
- Callut, J., Dupont, P.: Learning hidden Markov models to fit long-term dependencies. Technical Report 2005-9, Universite catholique de Louvain (2005)Google Scholar
- Gryna F.M. (2001). Quality Planning and Analysis: From Product Development through Use, 4th edn. McGraw-Hill, Singapore Google Scholar
- Hauser J.R. and Clausing D. (1988). The house of quality. Harv. Bus. Rev. 66(3): 63–73 Google Scholar
- Koski T. (2001). Hidden Markov Models for Bioinformatics. Kluwer, Dordrecht Google Scholar
- MacDonald I. and Zucchini W. (1997). Hidden Markov and Other Models for Discrete-Valued Time Series. Chapman & Hall, London Google Scholar
- Poritz, A.B.: Hidden Markov model: a guided tour. In: Proceedings of the IEEE Conference of Acoustics, Speech and Signal Processing (ICASSP’88), pp. 7–13 (1988)Google Scholar
- Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech. In: Proceedings of the IEEE, vol. 77(2); pp. 257–286 (1989)Google Scholar