An intelligent fuzzy logic–based system to support quality function deployment analysis

Quality function deployment is an efficient and powerful tool in design, development, and planning of products. The main function of quality function deployment is conversion of VOC to technical characteristics or voice of designer. However, it is not always easy to prioritize and assess technical characteristics during the total mass of information from the different customer attitudes. This article provides a methodology for the development of an intelligent quality function deployment based on fuzzy inference system in order to capture information through house of quality. As quality function deployment integrates different components of design and development, intelligent quality function deployment would be the best replacement for human expertise and can support decision-makers in wide range of design and development. This methodology applied on a classic sample for the design of a new undergraduate curriculum in an engineering department of a university as an illustrative example shows capability of this method. This article is composed of the background of quality function deployment, review of related research works, and representation of an intelligent system for analyzing it.

[1]  Zuhair A. Ahmed,et al.  An Intelligent Quality Function Deployment (IQFD) for Manufacturing Process Environment , 2009 .

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

[3]  E. Aspinwall,et al.  Quality function deployment: An empirical study in the UK , 2001 .

[4]  Y. Chen,et al.  A methodology of determining aggregated importance of engineering characteristics in QFD , 2007, Comput. Ind. Eng..

[5]  Deok-Hwan Kim,et al.  Robustness indices and robust prioritization in QFD , 2009, Expert Syst. Appl..

[6]  E. Ertugrul Karsak,et al.  A QFD-based fuzzy MCDM approach for supplier selection , 2013 .

[7]  R. Y. K. Fung,et al.  Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD , 2005 .

[8]  Yu-Jie Wang,et al.  A criteria weighting approach by combining fuzzy quality function deployment with relative preference relation , 2014, Appl. Soft Comput..

[9]  Dinesh Verma,et al.  A fuzzy set paradigm for conceptual system design evaluation , 1994 .

[10]  H. Brian Hwarng,et al.  Translating customers’ voices into operations requirements ‐ A QFD application in higher education , 2001 .

[11]  K. S. Lau,et al.  Application of Function Deployment Model in Decision Making for New Product Development , 2006, Concurr. Eng. Res. Appl..

[12]  Paulo Augusto Cauchick Miguel,et al.  Review, analysis and classification of the literature on QFD—Types of research, difficulties and benefits , 2008 .

[13]  Chang Hee Han,et al.  Prioritizing engineering characteristics in quality function deployment with incomplete information: A linear partial ordering approach , 2004 .

[14]  James L. Bossert,et al.  Quality Function Deployment , 2021, Quality Function Deployment.

[15]  Ali Emrouznejad,et al.  Strategic logistics outsourcing: An integrated QFD and fuzzy AHP approach , 2012, Expert Syst. Appl..

[16]  Li Pheng Khoo,et al.  A prototype genetic algorithm-enhanced rough set-based rule induction system , 2001, Comput. Ind..

[17]  Nancy C. Praizier,et al.  The Qfd Book: The Team Approach to Solving Problems and Satisfying Customers Through Quality Function Deployment , 1993 .

[18]  V. Selladurai,et al.  Fuzzy logic approach to prioritise engineering characteristics in quality function deployment (FL‐QFD) , 2004 .

[19]  Tijen Ertay,et al.  AN INTEGRATED FUZZY APPROACH FOR DETERMINING ENGINEERING CHARACTERISTICS IN CONCRETE INDUSTRY , 2011, Appl. Artif. Intell..

[20]  Min Xie,et al.  The implementation of quality function deployment based on linguistic data , 2001, J. Intell. Manuf..

[21]  Shun Takai,et al.  Sensitivity analysis of relative worth in quality function deployment matrices , 2012, Concurr. Eng. Res. Appl..

[22]  Hsin-Yu Lin,et al.  A hybrid approach to develop an analytical model for enhancing the service quality of e-learning , 2012, Comput. Educ..

[23]  Alina Crişan,et al.  Designing customer oriented courses and curricula in higher education. A possible model , 2011 .

[24]  Ali Firat Cabalar,et al.  Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering , 2012 .

[25]  Majid Azadi,et al.  A combination of QFD and imprecise DEA with enhanced Russell graph measure: A case study in healthcare , 2013 .

[26]  Liang-Hsuan Chen,et al.  A fuzzy model for exploiting quality function deployment , 2003 .

[27]  Biren Prasad,et al.  Review of QFD and related deployment techniques , 1998 .

[28]  Li Pheng Khoo,et al.  Towards a QFD-based expert system: A novel extension to fuzzy QFD methodology using rough set theory , 2010, Expert Syst. Appl..

[29]  W. Shin,et al.  Quality Function Deployment: A Practitioner's Approach , 1993 .

[30]  Seyed Hossein Iranmanesh,et al.  Design Parameter Estimation using a Modified QFD Method to Improve Customer Perception , 2005, Concurr. Eng. Res. Appl..

[31]  Kwai-Sang Chin,et al.  Technical importance ratings in fuzzy QFD by integrating fuzzy normalization and fuzzy weighted average , 2011, Comput. Math. Appl..

[32]  Semiha Kiziltas,et al.  Strategic use of quality function deployment (QFD) in the construction industry , 2005 .

[33]  Fiorenzo Franceschini.  Advanced Quality Function Deployment , 2001 .

[34]  S. Sahney,et al.  A SERVQUAL and QFD approach to total quality education , 2004 .

[35]  Zaifang Zhang,et al.  An integrated approach for rating engineering characteristics' final importance in product-service system development , 2010, Comput. Ind. Eng..