عنوان مقاله [English]
Today, the most important aspect of product design is based on the needs and requirements of customers, therefore, the Quality Function Deployment (QFD) approach is one of the ways that is used to increase customer satisfaction. Due to the uncertainty and inaccuracy of the properties in the relationships, the fuzzy coefficients are often used in planning QFD. In the present study, the fuzzy linear regression approach is used to identify inaccurate and ambiguous functional relationships between customer requirements and engineering indicators. In this approach, for non-fuzzy data situation, the optimal values of the parameters would be determined based on the output set of the regression model so that it has a membership degree greater than or equal to h, which indicates how much fuzzy the output of the fuzzy regression model is being. In this study, the aim of using a fuzzy linear regression model is to identify the functional relationships in QFD to optimize h value in the industrial lama electronics industry. The results of this study indicate that the values of the range of coefficients in optimal h model, when compared to zero, give better data and increase the reliability of the system.
. Andalib Ardakani, D. keshavarz, P. “Study of Green Product Development and Its Impact on Customer's Subjective Image with Structural Equation Modeling Approach”, Journal of New Research in Decision Making, 1(3), 2016, 85-112.
. Garibay, C., Gutiérrez, H., Figueroa, A. “Evaluation of a Digital Library by Means of Quality Function Deployment (QFD) and the Kano Model”, the Journal of AcademicLibrarianship, 36(2), 2010 125- 132.
. Chien, T.K., Su, C.T. “Using the QFD concept to resolve customer satisfaction strategy decisions”. Int. J. Qual. Reliab. Manag. 20 (3), 2003, 345–359.
. Karsak, E. E. "Fuzzy multiple objective programming framework to prioritize design requirements in quality function deployment”, Computers & Industrial Engineering, 47(2), 2004, 149–163.
. Liu, X., Chen Y.”A systematic approach to optimizing h value for fuzzy linear regression with symmetric triangular fuzzy numbers”, Mathematical Problems in Engineering, 2013, 1-9
. Zhong, S., Zhou, J., Chen, Y. ”Determination of target values of engineering characteristics in QFD using a fuzzy chance-constrained modelling approach”. Neurocomputing, 142 (1), 2014, 125–135.
. Ko, W.C., Chen, L.H. “An approach of new product planning using quality function deployment and fuzzy linear programming model”. Int. J. Prod. Res, 52 (6), 2014, 1728–1743.
. Liu, Y., Zhou, J., Chen, Y. ”Using fuzzy non-linear regression to identify the compensation level among customer requirements in QFD”. Neurocomputing, 142 (1), 2014, 115–124.
. Rezaei, K., Hosseini Ashtiani, H., Hoshiar, M. ”QFD A customer-oriented approach to designing and improving product quality”, Cooperative Co., Ltd. 2002
. Mottiqi, H., Safari, H., Dehghani, K, "Using Fuzzy QFD in Strategic Product Management: Case Study in Pars Electric Co. ", Management Research in Iran, 15( 2), 2011, 151-179
. Azar, A., Nahavandi, B., Rajabzadeh, Ali (), “Planning and Improvement of Expansion of Quality Performance Using Process, Fuzzy Network Analysis and Ideal Planning”, Management Research in Iran, 12( 4), 2008, 37-68
. Hosseini Ashtiani, H., Hoshiar, M. (2001) "Customer-Oriented Approach to Designing and Improving Product Quality", Tehran, Athena Publishing House.
. Nouri, I., Bakhtiari, A.”Application of QFD in order to identify the main characteristics of website design using fuzzy TOPSIS”. Industrial Management Journal of Islamic Azad University, Sanandaj Branch, , 4(9), 2009, 75-88
. Zhianpour, H., Zinipour, H., Ahmadi Kahnali, R. () ”Application of QFD in converting the demands of in-service training clients into educational requirements (Case Study: Central Astan Quds Razavi Book Organization)”, Public Management Research, Fifth year, 15, 2012, 112-85.
. Hojjati, S. M. H., Zareeniya, M. () “A New Approach to Weighting Customers' Desires and Prioritizing Product Technical Characteristics with Fuzzy QFD Approach”, Engineering and Management, Sharif, 1(30), . 1393, 150-137
. Kahraman, C., Ertay, T. "A fuzzy optimization model for QFD planning process using analytic network approach", European Journal of Operational Research, 2(1), 2006, 171-390.
. Kim, K. J., Moskowitz, H., Dhingra, A., Evans, G. "Fuzzy multicriteria models for quality function deployment”, European Journal of Operational Research, 121(3), 2000 ,504-518
. Tang, J. R., Fung, Y. K., Xu, B., Wang, D. (2002). “A new approach to quality function deployment planning with financial consideration”, Computers & Operations Research, 29: 1447-1463.
. Ahmadimanesh, M., Tavakoli, A., Naji, Z.(2013). “Product design and prototype selection using fuzzy QFD and linear assignment technique”, Msc Thesis, Faculty of Economics & Administrative sciences, Ferdowsi of mashhad university