Failure prioritization and control using the neutrosophic best and worst method

Abstract

Failure prioritization process is described by identifying potential failures and its effects, quantifying their priorities and determining appropriate ways to mitigate or control. In the literature, many approaches are suggested to prioritize failures and associated effects quantitatively. Multicriteria decision-making (MCDM) approaches are forefront that they can express the failures verbally based on decision-makers' judgments. They explain different types of uncertainties, which are generally modeled by fuzzy sets. However, fuzzy sets focus only on one membership value in decision-making. At this point, neutrosophic sets are more suitable than classical fuzzy sets by proposing three membership values named truth-membership, indeterminacy-membership and falsity-membership. Therefore, in this study, a novel approach based on the neutrosophic best and worst method (NBWM) is proposed and a case study is also performed in the implant production. The best and worst method (BWM) is merged with neutrosophic sets since it has fewer pairwise comparisons while determining the importance weights of failures. To show the applicability of the approach, a case study in an implant manufacturing plant that produces many products, including implants in different shapes and sizes in Turkey is carried out. Besides the case study, a comparative study is performed to test the validity of the proposed NBWM approach. This approach can make the decision-making process more dynamic in real-world problems with indeterminate and inconsistent information, considering the benefits of BWM and neutrosophic sets either individually or in integration. The present study contributes to the knowledge both methodologically and in an application by proposing NBWM for failure assessment problems for the first time in the literature and creating an adaptive model for manufacturing and other industries.