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Sustainable supplier performance scoring using audition check-list based fuzzy inference system: A case application in automotive spare part industry

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dc.contributor.author Ghadimi, Pezhman
dc.contributor.author Dargi, Ahmad
dc.contributor.author Heavey, Cathal
dc.date.accessioned 2017-08-04T10:19:27Z
dc.date.issued 2017
dc.identifier.uri http://hdl.handle.net/10344/5942
dc.description peer-reviewed en_US
dc.description The full text of this article will not be available on ULIR until the embargo expires on the 04/1/2020
dc.description.abstract With the global awareness of sustainability issues, sustainable development is being increasingly recognized by governments and industries. In addressing these issues, organizations worldwide have taken initiatives in adopting sustainability practices in their supply chain transferring it to sustainable supply chain management. In order to establish a responsible sustainable supply chain management, an effective way would be to make sure that the potential suppliers for procuring required components are precisely assessed and evaluated based on sustainable criteria. Therefore, this paper proposes a practical decision making approach to evaluate and select the most sustainable suppliers for an automotive spare part manufacturer licensed under a France-based automotive organization. Firstly, a requirement gathering approach, the audition check-list approach, is designed to facilitate the process of data gathering for supplier evaluation based on three pillars of sustainability. Next, the gathered data are processed using a proposed fuzzy inference system to remove impreciseness and vagueness in the gathered sustainability related data. The strength of this model falls into its applicability in data gathering phase which helps decision makers in manufacturing company to perform a fast audition of a typical supplier. Secondly, the final sustainable ranking of suppliers using the proposed fuzzy inference system provide a precise and less uncertain sustainability performance scoring which makes the developed approach a reliable system for making sustainable sourcing decisions. Comparison and sensitivity analysis are performed to evaluate the proficiency of the developed approach. Finally, theoretical and managerial implications together with conclusions of the study are presented. en_US
dc.language.iso eng en_US
dc.publisher Elsevier en_US
dc.relation info:eu-repo/grantAgreement/EC/FP7/285171 en_US
dc.relation.ispartofseries Computers and Industrial Engineering;105, pp. 12-27
dc.relation.uri http://dx.doi.org/10.1016/j.cie.2017.01.002
dc.rights This is the author’s version of a work that was accepted for publication in Computers and Industrial Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Industrial Engineering, 2017, 105, pp. 12-27,http://dx.doi.org/10.1016/j.cie.2017.01.002 en_US
dc.subject supply chain management en_US
dc.subject sustainability en_US
dc.subject supplier selection en_US
dc.title Sustainable supplier performance scoring using audition check-list based fuzzy inference system: A case application in automotive spare part industry en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2017-08-04T10:09:30Z
dc.description.version ACCEPTED
dc.identifier.doi 10.1016/j.cie.2017.01.002
dc.contributor.sponsor ERC en_US
dc.relation.projectid 285171 en_US
dc.date.embargoEndDate 2020-01-04
dc.embargo.terms 2020-01-04 en_US
dc.rights.accessrights info:eu-repo/semantics/embargoedAccess en_US
dc.internal.rssid 2705400
dc.internal.copyrightchecked Yes
dc.identifier.journaltitle Computers & Industrial Engineering
dc.description.status peer-reviewed


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