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Research Article

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

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Pages 641-655 | Accepted 19 Jun 2021, Published online: 14 Jul 2021
 

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.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan, under Grant number MOST 106-2221-E-155-033-MY2;Ministry of Science and Technology, Taiwan [MOST 106-2221-E-155-033-MY2];

Notes on contributors

Chen-ju Lin

Chen-ju Lin, is an Associate Professor in the Department of Industrial Engineering and Management at Yuan Ze University, Taiwan. She received her BS degree in Industrial Engineering and Management from the National Chiao Tung University, and MS and PhD degrees in Industrial and Systems Engineering from the Georgia Institute of Technology. Her research interests include quality control and management, healthcare analytics and spatiotemporal statistics.

Pei-Ying Lin

Pei-Ying Lin, received her M.S. degree in Industrial Engineering and Management from Yuan Ze University, Taiwan. Her research topic focused on multiple comparisons techniques and supplier selection.

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