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A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain

Liu, Yan; Eckert, Claudia; Yannou-Le Bris, Gwenola and Petit, Gaëlle (2019). A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain. Computers & Industrial Engineering, 127 pp. 196–212.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1016/j.cie.2018.12.022
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Abstract

Sustainable supply chain management has received much attention from both academia and industry due to various issues such as economic stability, environment conservation, and social ethics. To improve the sustainable performance of a value chain, its members need to carefully select their suppliers in relation to their own strategy. Thus, an effective tool for sustainable supplier selection and evaluation is essential, which considers the triple bottom line (TBL) of economic, environmental and social aspects by means of criteria adapted to the situation analysed. This paper develops a fuzzy decision tool to evaluate the sustainable performance of suppliers according to TBL. Sustainability criteria are identified to take into account the real hotspots in a food value chain. The proposed model integrates triangular fuzzy numbers (TFN), AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) in a novel way to consider quantitative and qualitative criteria as well as objective and subjective data. This is missing in most existing research when building their fuzzy models for supplier selection, but critical in dealing with the heterogeneous data available for TBL assessment. The application in a sustainable agrifood value chain illustrates the effectiveness of the proposed tool.

Item Type: Journal Item
Copyright Holders: 2018 Elsevier Ltd.
ISSN: 0360-8352
Project Funding Details:
Funded Project NameProject IDFunding Body
studentship for Yan LiuNot SetThe Open University (OU)
Keywords: sustainable supply chain; supplier selection; sustainable performance; fuzzy decision-making methods; heterogeneous data
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Engineering and Innovation
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Design and Innovation
Item ID: 58222
Depositing User: Claudia Eckert
Date Deposited: 12 Dec 2018 09:38
Last Modified: 04 May 2019 10:07
URI: http://oro.open.ac.uk/id/eprint/58222
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