Developing a Fuzzy Verification Method of Performance Improvement Using Satisfaction Index

Abstract

Performance evaluation matrix (PEM) has captured many scholars’ attention and has been widely used to a variety of industry fields to evaluate their efficiency performance. The data mainly come from the collection of questionnaires in which customers’ varied opinions are enquired; however, Likert scale will be modified to fuzzy linguistic scale due to some uncertainty aspect in the linguistic data. In contrast, the major flaw of some researches was that they failed to present a verification approach with statistical inference to confirm the effectiveness of the improvement. Regarding to the related issues, Chen et al. (2018a) use discrimination index in reliance section to establish fuzzy membership function; subsequently, they propose fuzzy hypothesis testing to locate critical-to-quality (CTQ) so as to make further improvement. Following the same logic, this paper uses the identical process to develop the fuzzy verification method of the improvement efficiency for CTQ. Finally, this paper puts forward a numerical example to demonstrate application of the proposed verification model. The result means that there is sufficient evidence showing that performance improvement is poor and non-effective. Thus, manager should adopt otherwise measures to improve. Obviously, the method proposed particularly highlights the reduction of sampling error, shies away from the complexity inherent in fuzzy semantic collection, overcomes the uncertainty in the collected data, and increases the reliability of evaluation method.