Abstract
Organizations and large companies consider “voice of customer” is an important factor for growth. The aim of our study is to achieve defined and suitable models of ranking the key criteria of the voice of customer. The method is based upon Fuzzy multiple criteria decision making (FMCDM). Fuzzy decision making trial and evaluation laboratory (Fuzzy DEMATAL) method, a useful group decision making tool, has been used to transform the complex interactions between the criteria of the problems of practical life into a visible structured model. The results indicate that in the presented case study, we could apply Fuzzy DEMATAL method to estimate the quantity of the effects of direct and indirect relations of elements with each other and promote the quality of relations and interrelations of the group. Keywords: Fuzzy DEMATEL, Fuzzy AHP, FMCDM, Voice of Customer
Highlights
Introduction developed by Bellman and Zadeh (Kahraman, 2007)
Acquire the assessments of decision makers to measure the relationships between the critical success factors which are demonstrated by C ={|i =1, 2 . . .n}
The AHP weighting is mainly determined by the decision makers who conduct the pair wise comparisons, so as to reveal the comparative importance between two criteria
Summary
Acquire the assessments of decision makers to measure the relationships between the critical success factors which are demonstrated by C ={|i =1, 2 . . .n}. The AHP weighting is mainly determined by the decision makers who conduct the pair wise comparisons, so as to reveal the comparative importance between two criteria. If there are evaluation criteria, to decide the decision making, the decision makers have to conquest C vector and vector denote the sum of rows and the sum of columns from the total-relation matrix T respectively. The concept of fuzziness, in traditional AHP, directly and without using fuzzy series has been taken into account In this method, by using linguistic terms, the concept of fuzziness is applied to determine pair comparison matrices. In this regard, we can refer to earlier models (Laarhoven & Pedrych,1983; Buckley,1985; Chang, 1992; Lin, 2010). Fuzzy AHP is described based on extent analysis method by Chang because this method has been simpler than other fuzzy AHP and similar to the method of classic AHP method
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