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A dynamic low carbon supplier preference model based on Taguchi method.

Authors :
Wang, Feng
Zhang, Lingrong
Source :
Journal of Cleaner Production. Feb2024, Vol. 442, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

The dynamic nature of the supply chain due to personalised demand and technological advancements has made supplier selection a challenging issue in supply chain management. This research paper aims to tackle the supplier selection problem in a multi-period, multi-sourced, and multi-product environment by creating a process that places the manufacturer at the centre. Firstly, this paper has constructed a multi-period supplier dynamic preference model that takes into account environmental protection, to minimise the total cost and carbon emissions. Secondly, the Taguchi method is extended to the supplier selection problem in the field of quality engineering. The parameter optimisation process is then designed based on the Taguchi method, aiming to optimise the main key parameters that affect supplier selection and determine the optimisation level combinations upon which the model is solved. Finally, the model's feasibility and evolution are verified through examples of its application. The paper's methodology enhances the model's relevant parameters' robustness, augments decision-making efficiency, and offers suppliers guidance to boost their performance and substantially cut the manufacturer's total procurement expenditure and carbon dioxide emissions. [Display omitted] • Propose a supplier selection process under multi-cycle multi-parameter variation. • Research results can provide decision-making tools for suppliers and manufacturers. • Decision makers should balance short-term benefits with sustainability goals. • Companies need to continually adapt business models to face up to the challenges. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09596526
Volume :
442
Database :
Academic Search Index
Journal :
Journal of Cleaner Production
Publication Type :
Academic Journal
Accession number :
175453928
Full Text :
https://doi.org/10.1016/j.jclepro.2024.140763