Implementing Fuzzy Linear Regression Model Using Optimized H Value to Identify Functional Relationships in Qfd

Document Type : Original Article

Authors

1 Ph.D. Student of, Management- Operation Research, School of Administrative and Economic Sciences, Ferdowsi of Mashhad University, Mashhad, Iran

2 Associate Professor of Management, School of Administrative and Economic Sciences, Ferdowsi of Mashhad University, Mashhad, Iran

Abstract

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.

Keywords

References

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