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Social sustainable supplier evaluation and selection: a group decision-support approach

Social sustainable supplier evaluation and selection: a group decision-support approach
Social sustainable supplier evaluation and selection: a group decision-support approach
Organisational and managerial decisions are influenced by corporate sustainability pressures. Organisations need to consider economic, environmental and social sustainability dimensions in their decisions to become sustainable. Supply chain decisions play a distinct and critical role in organisational good and service outputs sustainability. Sustainable supplier selection influences the supply chain sustainability allowing many organisations to build competitive advantage. Within this context, the social sustainability dimension has received relatively minor investigation; with emphasis typically on economic and environmental sustainability. Neglecting social sustainability can have serious repercussions for organisational supply chains. This study proposes a social sustainability attribute decision framework to evaluate and select socially sustainable suppliers. A grey-based multi-criteria decision-support tool composed of the ‘best-worst method’ (BWM) and TODIM (TOmada de Decisão Interativa e Multicritério – in Portuguese ‘Interactive and Multicriteria Decision Making’) is introduced. A grey-BWM approach is used to determine social sustainability attribute weights, and a grey-TODIM method is utilised to rank suppliers. This process is completed in a group decision setting. A case study of an Iranian manufacturing company is used to exemplify the applicability and suitability of the proposed social sustainability decision framework. Managerial implications, limitations, and future research directions are introduced after the application of the model.
0020-7543
1-22
Bai, Chunguang
2b9d3a6f-e955-4a13-82c4-2b2a2c67a944
Kusi-Sarpong, Simonov
a7e68240-2b34-456e-9849-c01bd10c68f7
Badri Ahmadi, Hadi
10b7cb9f-4f39-45ac-94b2-a03abcffa1cf
Sarkis, Joseph
8dd9610c-26f9-4504-bfe1-0d4a99c2dc88
Bai, Chunguang
2b9d3a6f-e955-4a13-82c4-2b2a2c67a944
Kusi-Sarpong, Simonov
a7e68240-2b34-456e-9849-c01bd10c68f7
Badri Ahmadi, Hadi
10b7cb9f-4f39-45ac-94b2-a03abcffa1cf
Sarkis, Joseph
8dd9610c-26f9-4504-bfe1-0d4a99c2dc88

Bai, Chunguang, Kusi-Sarpong, Simonov, Badri Ahmadi, Hadi and Sarkis, Joseph (2019) Social sustainable supplier evaluation and selection: a group decision-support approach. International Journal of Production Research, 1-22. (doi:10.1080/00207543.2019.1574042).

Record type: Article

Abstract

Organisational and managerial decisions are influenced by corporate sustainability pressures. Organisations need to consider economic, environmental and social sustainability dimensions in their decisions to become sustainable. Supply chain decisions play a distinct and critical role in organisational good and service outputs sustainability. Sustainable supplier selection influences the supply chain sustainability allowing many organisations to build competitive advantage. Within this context, the social sustainability dimension has received relatively minor investigation; with emphasis typically on economic and environmental sustainability. Neglecting social sustainability can have serious repercussions for organisational supply chains. This study proposes a social sustainability attribute decision framework to evaluate and select socially sustainable suppliers. A grey-based multi-criteria decision-support tool composed of the ‘best-worst method’ (BWM) and TODIM (TOmada de Decisão Interativa e Multicritério – in Portuguese ‘Interactive and Multicriteria Decision Making’) is introduced. A grey-BWM approach is used to determine social sustainability attribute weights, and a grey-TODIM method is utilised to rank suppliers. This process is completed in a group decision setting. A case study of an Iranian manufacturing company is used to exemplify the applicability and suitability of the proposed social sustainability decision framework. Managerial implications, limitations, and future research directions are introduced after the application of the model.

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More information

Accepted/In Press date: 17 January 2019
e-pub ahead of print date: 7 February 2019

Identifiers

Local EPrints ID: 434515
URI: https://eprints.soton.ac.uk/id/eprint/434515
ISSN: 0020-7543
PURE UUID: 39cd66d8-d3cc-46b5-b271-5d1163982811

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Date deposited: 25 Sep 2019 16:30
Last modified: 02 Dec 2019 17:32

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