Multiple criteria supplier selection: A fuzzy approach

Hasan, MM; Shohag, MAS; Azeem, A; Paul, SK

Multiple criteria supplier selection: A fuzzy approach

Hasan, MM Shohag, MAS Azeem, A Paul, SK

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Publication Type:: Journal Article
Citation:: International Journal of Logistics Systems and Management, 2015, 20 (4), pp. 429 - 446
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Hasan, MM	en_US
dc.contributor.author	Shohag, MAS	en_US
dc.contributor.author	Azeem, A	en_US
dc.contributor.author	Paul, SK https://orcid.org/0000-0001-9523-179X	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	International Journal of Logistics Systems and Management, 2015, 20 (4), pp. 429 - 446	en_US
dc.identifier.issn	1742-7967	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119256
dc.description.abstract	Copyright © 2015 Inderscience Enterprises Ltd. A company must purchase a lot of diverse components and raw materials from different upstream suppliers to manufacture or assemble its products. However, it is not only a very complicated and perplexing task to select outstanding suppliers for decision-makers of strategic purchasing, but also it involves uncertainty and produces erroneous results while considering single criteria. For this reason, the decision-makers of strategic purchasing greatly require an efficient, valid and fair tool to assist them in selecting appropriate suppliers forthwith. This paper proposes a supplier selection model for any kind of company by using MATLAB fuzzy logic toolbox to help the purchasing department in selecting the most appropriate supplier. The main task in the proposed model involves determining the numerical score for different suppliers considering their respective performance in various qualitative and quantitative evaluation criteria and then selecting the best supplier having highest score. Fuzzy control is used to determine the best supplier by calculating the score in selected evaluation criteria which are provided in numerical values for the convenience of calculation.	en_US
dc.relation.ispartof	International Journal of Logistics Systems and Management	en_US
dc.relation.isbasedon	10.1504/IJLSM.2015.068488	en_US
dc.subject.classification	Business & Management	en_US
dc.title	Multiple criteria supplier selection: A fuzzy approach	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	20	en_US
utslib.for	1503 Business and Management	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Management Discipline
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

Copyright © 2015 Inderscience Enterprises Ltd. A company must purchase a lot of diverse components and raw materials from different upstream suppliers to manufacture or assemble its products. However, it is not only a very complicated and perplexing task to select outstanding suppliers for decision-makers of strategic purchasing, but also it involves uncertainty and produces erroneous results while considering single criteria. For this reason, the decision-makers of strategic purchasing greatly require an efficient, valid and fair tool to assist them in selecting appropriate suppliers forthwith. This paper proposes a supplier selection model for any kind of company by using MATLAB fuzzy logic toolbox to help the purchasing department in selecting the most appropriate supplier. The main task in the proposed model involves determining the numerical score for different suppliers considering their respective performance in various qualitative and quantitative evaluation criteria and then selecting the best supplier having highest score. Fuzzy control is used to determine the best supplier by calculating the score in selected evaluation criteria which are provided in numerical values for the convenience of calculation.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/119256