Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Product Life Cycle Cost Model to Solve Supplier Selection Problems in the Supply Chain Management

Version 1 : Received: 1 July 2021 / Approved: 2 July 2021 / Online: 2 July 2021 (10:11:13 CEST)
Version 2 : Received: 2 July 2021 / Approved: 6 July 2021 / Online: 6 July 2021 (10:04:31 CEST)

How to cite: Yu-Jwo, T.; Yi-Shyuan, L.; Hsuan-Shih, L.; Chang-Shu, T. Product Life Cycle Cost Model to Solve Supplier Selection Problems in the Supply Chain Management. Preprints 2021, 2021070047 (doi: 10.20944/preprints202107.0047.v1). Yu-Jwo, T.; Yi-Shyuan, L.; Hsuan-Shih, L.; Chang-Shu, T. Product Life Cycle Cost Model to Solve Supplier Selection Problems in the Supply Chain Management. Preprints 2021, 2021070047 (doi: 10.20944/preprints202107.0047.v1).

Abstract

Today’s purchasing companies demand an advanced buyer equal of enactment from their supplier while the two parties maintain a respectful connection. Although the supplier selection (SC) product life cycle cost (SCPLCC) is an important consideration among corporations, SCPLCC problem has become associated with deciding how one supplier should be selected from possible alternatives. In this study, we applied two types of goal programming, multiobjective linear programming and revised multichoice goal programming to develop a PLCC-based concept to solve the SCPLCC problem and construct a decision-making tool for application to a case of supply chain management in a Taiwanese light-emitting diode company in the high-tech industry. Our study main contribution Company managers can easily use the two approaches of the SCPLCC model with different parameters to solve SCPLCC model problems. Finally, we comparing five models found RMCGP with weighted linear goal programming had an adequate effect for application to the PLCC concept for high-technology comapny; this cloud make company decision–makers focus on low PLCC and select better supplier.

Subject Areas

supplier selection; product life cycle cost; geometric averaging-weighting; penalty weighting; multiobjective linear programming; revised multichoice goal programming.

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