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Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices

At present, environmental issues become real critical barriers for many supply chain corporations concerning the sustainability of their businesses. In this context, several studies have been proposed from both academia and industry trying to develop new measurements related to green supply chain ma...

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Autores principales: Boutkhoum, Omar, Hanine, Mohamed, Boukhriss, Hicham, Agouti, Tarik, Tikniouine, Abdessadek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899393/
https://www.ncbi.nlm.nih.gov/pubmed/27350904
http://dx.doi.org/10.1186/s40064-016-2233-2
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author Boutkhoum, Omar
Hanine, Mohamed
Boukhriss, Hicham
Agouti, Tarik
Tikniouine, Abdessadek
author_facet Boutkhoum, Omar
Hanine, Mohamed
Boukhriss, Hicham
Agouti, Tarik
Tikniouine, Abdessadek
author_sort Boutkhoum, Omar
collection PubMed
description At present, environmental issues become real critical barriers for many supply chain corporations concerning the sustainability of their businesses. In this context, several studies have been proposed from both academia and industry trying to develop new measurements related to green supply chain management (GSCM) practices to overcome these barriers, which will help create new environmental strategies, implementing those practices in their manufacturing processes. The objective of this study is to present the technical and analytical contribution that multi-criteria decision making analysis (MCDA) can bring to environmental decision making problems, and especially to GSCM field. For this reason, a multi-criteria decision-making methodology, combining fuzzy analytical hierarchy process and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS), is proposed to contribute to a better understanding of new sustainable strategies through the identification and evaluation of the most appropriate GSCM practices to be adopted by industrial organizations. The fuzzy AHP process is used to construct hierarchies of the influential criteria, and then identify the importance weights of the selected criteria, while the fuzzy TOPSIS process employs these weighted criteria as inputs to evaluate and measure the performance of each alternative. To illustrate the effectiveness and performance of our MCDA approach, we have applied it to a chemical industry corporation located in Safi, Morocco.
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spelling pubmed-48993932016-06-27 Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices Boutkhoum, Omar Hanine, Mohamed Boukhriss, Hicham Agouti, Tarik Tikniouine, Abdessadek Springerplus Research At present, environmental issues become real critical barriers for many supply chain corporations concerning the sustainability of their businesses. In this context, several studies have been proposed from both academia and industry trying to develop new measurements related to green supply chain management (GSCM) practices to overcome these barriers, which will help create new environmental strategies, implementing those practices in their manufacturing processes. The objective of this study is to present the technical and analytical contribution that multi-criteria decision making analysis (MCDA) can bring to environmental decision making problems, and especially to GSCM field. For this reason, a multi-criteria decision-making methodology, combining fuzzy analytical hierarchy process and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS), is proposed to contribute to a better understanding of new sustainable strategies through the identification and evaluation of the most appropriate GSCM practices to be adopted by industrial organizations. The fuzzy AHP process is used to construct hierarchies of the influential criteria, and then identify the importance weights of the selected criteria, while the fuzzy TOPSIS process employs these weighted criteria as inputs to evaluate and measure the performance of each alternative. To illustrate the effectiveness and performance of our MCDA approach, we have applied it to a chemical industry corporation located in Safi, Morocco. Springer International Publishing 2016-05-20 /pmc/articles/PMC4899393/ /pubmed/27350904 http://dx.doi.org/10.1186/s40064-016-2233-2 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Boutkhoum, Omar
Hanine, Mohamed
Boukhriss, Hicham
Agouti, Tarik
Tikniouine, Abdessadek
Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices
title Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices
title_full Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices
title_fullStr Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices
title_full_unstemmed Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices
title_short Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices
title_sort multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899393/
https://www.ncbi.nlm.nih.gov/pubmed/27350904
http://dx.doi.org/10.1186/s40064-016-2233-2
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