A decision support tool for sustainable supplier selection in manufacturing firms

Ifeyinwa Orji, Sun Wei

Abstract


Purpose: Most original equipment manufacturers (OEMs) are strategically involved in supplier base rationalization and increased consciousness of sustainable development thus, reinforcing need for accurately considering sustainability in supplier selection to improve organizational performance. In real industrial case, imprecise data, ambiguity of human judgment, uncertainty among sustainability factors and the need to capture all subjective and objective criteria are unavoidable and pose huge challenge to accurately incorporate sustainability factors into supplier selection.

Methodology: This study develops a model based on integrated multi- criteria decision making (MCDM) methods to solve such problems. The developed model applies Fuzzy logic, DEMATEL and TOPSIS to effectively analyze the interdependencies between sustainability criteria and to select the best sustainable supplier in fuzzy environment while capturing all subjective and objective criteria. A case study is illustrated to test the proposed model in a gear manufacturing company, an OEM to provide insights and for practical applications.

Findings: Results show that social factors of sustainability ranks as the most important in supplier selection. However, the most influential sustainability sub- criteria are work safety (WS) and quality.

Originality/Value: The model is capable of capturing all subjective and objective criteria in fuzzy environment to accurately incorporate sustainability factors in supplier selection. It is decision support tool relevant for providing insights to managers while implementing sustainable supplier selection.


Keywords


supplier selection; sustainability; supply chain; Original equipment manufacturers (OEM).

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DOI: http://dx.doi.org/10.3926/jiem.1203


Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2019

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience