Open AccessImproving Customers Satisfaction through Significance of Technical Attribute in QFD Studies
Author(s) -
Zafar Iqbal,
Lubna Waheed,
Muhammad Waheed,
Muhammad Rajab
Publication year - 2021
Publication title -
journal of business and social review in emerging economies
Language(s) - English
Resource type - Journals
eISSN - 2519-089X
pISSN - 2519-0326
DOI - 10.26710/jbsee.v7i2.1706
Subject(s) - quality function deployment , statistic , selection (genetic algorithm) , statistics , statistical significance , originality , mathematics , quality (philosophy) , population , prioritization , operations management , computer science , psychology , engineering , process management , artificial intelligence , social psychology , medicine , philosophy , value engineering , environmental health , epistemology , creativity
Purpose: Quality Function Deployment, (QFD) is a methodology which helps to satisfy customer requirements through the selection of appropriate Technical Attributes (TAs). The rationale of this article is to provide a method lending statistical support to the selection of TAs. The purpose is to determine the statistical significance of TAs through the derivation of associated significance (P) values.
Design/Methodology/Approach: We demonstrate our methodology with reference to an original QFD case study aimed at improving the educational system in high schools in Pakistan; and then with five further published case studies obtained from literature. Mean weights of TAs are determined. Considering each TA mean weight to be a Test Statistic, a weighted matrix is generated from the VOCs’ importance ratings, and ratings in the relationship matrix. Finally using R, P-values for the means of original TAs are determined from the hypothetical population of means of TAs.
Findings: Each TA’s P-value evaluates its significance/insignificance in terms of distance from the grand mean. P-values indirectly set the prioritization of TAs.
Implications/Originality/Value: The novel aspect of this study is extension of mean weights of TAs, to also provide P-values for TAs. TAs with significant importance can be resolved on priority basis, while other can be fixed with appropriateness.