Applying a markov chain model in quality function deployment

The relationships between customer requirements and technical measures are typically resolved by a cross-functional team with the assumption that the relationships are able to be identified objectively. However, due to the limited knowledge and experiences, determining the appropriate relationship could be difficult since the decision makers might not have enough information to evaluate the actual relationship. Moreover, the importance of technical measures is typically expressed in the current time period. It would be of interest to trace the future trends of technical measures since customer needs are fulfilled by technical measures. Under such circumstances, a Markov chain model could be an approach to model the relationship and monitor the trends of technical measures from probabilities viewpoints. With the needed probabilities, the dynamic relationships as well as the trends of technical measures can be performed by different time periods. Finally, the relationships and future trends of technical measures can be updated when the new information is available.

[1]  Dennis J. Sweeney,et al.  Study guide to accompany an introduction to management science : quantitative approaches to decision making , 1985 .

[2]  Hamdy A. Taha,et al.  Operations Research an Introduction , 2007 .

[3]  Kenneth D. Lawrence,et al.  Using Markov Chains to Identify Potential Large Donors , 1994 .

[4]  Ming-Lu Wu,et al.  Quality function deployment: A literature review , 2002, Eur. J. Oper. Res..

[5]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[6]  Ping Wang,et al.  Using grey theory in quality function deployment to analyse dynamic customer requirements , 2005 .

[7]  Barış Tan Markov chains and the RISK board game , 1997 .

[8]  Hsin-Hung Wu,et al.  A Comparative Study of Using Grey Relational Analysis in Multiple Attribute Decision Making Problems , 2002 .

[9]  Ming-Lu Wu,et al.  Quality Function Deployment: A Comprehensive Review of Its Concepts and Methods , 2002 .

[10]  P. Pfeifer,et al.  Modeling customer relationships as Markov chains , 2000 .

[11]  Ming-Lu Wu,et al.  A systematic approach to quality function deployment with a full illustrative example , 2005 .

[12]  Lai K. Chan,et al.  PRIORITIZING THE TECHNICAL MEASURES IN QUALITY FUNCTION DEPLOYMENT , 1998 .

[13]  Don Clausing Total quality development : a step-by-step guide to world class concurrent engineering , 1994 .

[14]  Luis Betancourt,et al.  Using Markov Chains to Estimate Losses from a Portfolio of Mortgages , 1999 .

[15]  Miyoung Jeong,et al.  Quality function deployment: An extended framework for service quality and customer satisfaction in the hospitality industry , 1998 .

[16]  Jeffrey J. Hunter,et al.  Mathematical techniques of applied probability , 1985 .

[17]  Rong Chen,et al.  Convergence analyses and comparisons of Markov chain Monte Carlo algorithms in digital communications , 2002, IEEE Trans. Signal Process..

[18]  Hsin-Hung Wu,et al.  Applying grey model to prioritise technical measures in quality function deployment , 2006 .

[19]  Jr. Hanna Stair,et al.  Quantitative Analysis for Management , 1982 .

[20]  Philip Sedgwick,et al.  Probability Theory: An Introductory Course , 1992 .

[21]  J. Hauser,et al.  The House of Quality , 1988 .

[22]  Hsin-Hung Wu,et al.  Using a Markov chain model in quality function deployment to analyse customer requirements , 2006 .