Multiple Comparisons With the Best and Difference Test Statistic for Supplier Selection for Nonlinear Profiles

Abstract

It is vital to choose the right supplier to reduce cost and provide high-quality products. However, a gap remains because the supplier’s process is mainly measured using qualitative and intangible criteria. Further, with technological advances, the measurement of quality characteristics is transforming through smart data sensors. With a specific time or space measurements can be done at high frequency. The functional relationships between the measures or profiles can be established. The profiles indicate the pattern in the data. The literature focused on the case when quality characteristics are described by linear profile and consider symmetric tolerance. However, in a real-world application, nonlinear profiles and asymmetric tolerance is frequently found. This study proposed multiple comparisons with the best and the difference test statistic methods to select the best suppliers when the quality characteristics are described by nonlinear profile with asymmetric tolerances. A Monte Carlo simulation study is conducted, computer programs are written in the R programming language. The result indicated in terms of rejecting inferior suppliers, the multiple comparisons with the best method perform better than the difference test statistics. With the proposed methods, managers can make decisions using a single, easy-to-understand index. Also, these methods can handle any number of suppliers. For the convenience of a decision-maker, critical values, and profile size requirements are provided. An illustrative example is included to give a better insight into the proposed methods.