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Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology

Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology
Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology
Environmental issues from the extractive industries and especially mining are prevalent and maleficent. An effective way to manage these pernicious environmental problems is through organizational practices that include the broader supply chain. Green supply chain practices and their role in mining industry strategy and operations have not been comprehensively addressed. To address this gap in the literature, and building upon the literature in general green supply chain management and environmental decision tools, we introduce a comprehensive framework for green supply chain practices in the mining industry. The framework is categorized into six areas of practice, with detailed practices described and summarized. The green supply chain practices framework is useful for practical managerial decision making purposes such as programmatic evaluation. The framework may also be useful as a theoretical construct for empirical research on green supply chain practices in the mining industry. To exemplify the practical utility of the framework we introduce a multiple criteria evaluation of green supply programs using a novel multiple criteria approach that integrates rough set theory elements and fuzzy TOPSIS. Using illustrative data we provide an example of how the methodology can be used with the green supply chain practices framework for the mining industry. This paper sets the foundation for significant future research in green supply chain practices in the mining industry.
0301-4207
86-100
Kusi-Sarpong, Simonov
a7e68240-2b34-456e-9849-c01bd10c68f7
Bai, Chunguang
2b9d3a6f-e955-4a13-82c4-2b2a2c67a944
Sarkis, Joseph
8dd9610c-26f9-4504-bfe1-0d4a99c2dc88
Wang, Xuping
23293fd8-784d-4134-803b-3f24b10fb78d
Kusi-Sarpong, Simonov
a7e68240-2b34-456e-9849-c01bd10c68f7
Bai, Chunguang
2b9d3a6f-e955-4a13-82c4-2b2a2c67a944
Sarkis, Joseph
8dd9610c-26f9-4504-bfe1-0d4a99c2dc88
Wang, Xuping
23293fd8-784d-4134-803b-3f24b10fb78d

Kusi-Sarpong, Simonov, Bai, Chunguang, Sarkis, Joseph and Wang, Xuping (2015) Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology. Resources Policy, 46, 86-100. (doi:10.1016/j.resourpol.2014.10.011).

Record type: Article

Abstract

Environmental issues from the extractive industries and especially mining are prevalent and maleficent. An effective way to manage these pernicious environmental problems is through organizational practices that include the broader supply chain. Green supply chain practices and their role in mining industry strategy and operations have not been comprehensively addressed. To address this gap in the literature, and building upon the literature in general green supply chain management and environmental decision tools, we introduce a comprehensive framework for green supply chain practices in the mining industry. The framework is categorized into six areas of practice, with detailed practices described and summarized. The green supply chain practices framework is useful for practical managerial decision making purposes such as programmatic evaluation. The framework may also be useful as a theoretical construct for empirical research on green supply chain practices in the mining industry. To exemplify the practical utility of the framework we introduce a multiple criteria evaluation of green supply programs using a novel multiple criteria approach that integrates rough set theory elements and fuzzy TOPSIS. Using illustrative data we provide an example of how the methodology can be used with the green supply chain practices framework for the mining industry. This paper sets the foundation for significant future research in green supply chain practices in the mining industry.

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More information

Published date: 1 December 2015

Identifiers

Local EPrints ID: 434273
URI: http://eprints.soton.ac.uk/id/eprint/434273
ISSN: 0301-4207
PURE UUID: dfeb0813-1b2a-4988-a643-356977e6d809
ORCID for Simonov Kusi-Sarpong: ORCID iD orcid.org/0000-0003-1618-2518

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Date deposited: 18 Sep 2019 16:30
Last modified: 27 Apr 2022 02:15

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Contributors

Author: Chunguang Bai
Author: Joseph Sarkis
Author: Xuping Wang

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