A step-up approach for selecting substitute suppliers under nonlinear profile data

Abstract

ABSTRACT Selecting qualified suppliers from a group of candidates is important to procurement. Having several qualified suppliers to choose from can increase production flexibility and bargaining power. This research aims at selecting the substitute suppliers that can produce the parts with the same quality as the current supplier. Most of the existing supplier selection methods focused on univariate or linear profile data, which are insufficient to solve the supplier selection problem under nonlinear profile data. This research develops a step-up approach to select qualified suppliers for substitution under nonlinear profile data where there exists a nonlinear functional relationship between the quality characteristic and the explanatory variable. The proposed approach uses polynomial regression followed by the sequential test procedure to compare profile differences. The simulation results show that the proposed approach can reject the suppliers with different profile functions from the current supplier with satisfying power levels. To illustrate the effectiveness and practicality, the proposed approach is applied to select qualified voice coil motor (VCM) suppliers for a digital camera module manufacturer. Procuring the VCM from the selected suppliers can fulfill a higher demand, while maintaining the operational function and quality of the digital camera module.