A novel approach to measure product quality in sustainable supplier selection. (10th April 2020)

• Record Type: Journal Article
• Authors: Negash, Yeneneh Tamirat; Kartika, Jessica; Tseng, Ming-Lang; Tan, Kimhua
• Is Part Of: Journal of cleaner production. Volume 252(2020)
• Publication Date: 2020-04-10
• Subjects: Sustainable supplier selection -- Nonlinear profiles -- Multiple comparisons with the best -- Bonferroni method -- Process yield indices; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.119838
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: A gap remains due to the intangible and qualitative criteria used to measure product quality for supplier evaluation and selection. Improving product quality is a crucial strategy for achieving reduce, reuse, recycle, and recovery. Quality characteristics are described as functional relationships (called profiles), and with the advancements in measurement technology, high dimensional data are collected. Nonetheless, prior studies have not addressed sustainable supplier selection where a nonlinear profile characterizes the product quality. Hence, this study aims to provide a novel approach to measure product quality using the process yield index, presents multiple comparisons with the best and difference test statistics and proposes a Bonferroni correction method. This study applies a Monte Carlo simulation to find the selection power and the required number of profiles. The statistical properties are investigated, and a comparison study is performed. The results show that multiple comparisons with the best outperform the Bonferroni method regarding the sample size requirement and power, and the number of levels and profiles were found to impact the power of the statistical tests. The required number of profiles and the critical value are tabulated for decision-makers. Graphical abstract: Image 1 Highlights: The process yield index is defined to measure quality characteristics. Processes where the quality is characterized by nonlinear profiles are considered. MCB was found to perform better in terms of the power and required sample size. The required profiles and critical values are tabulated for decision-makers. A case example is presented to show the real-life applicability.