Partner selection in sustainable supply chains: a fuzzy ensemble learning model

Wu, C., Lin, C., Barnes, D. and Zhang,Y. 2020. Partner selection in sustainable supply chains: a fuzzy ensemble learning model. Journal of Cleaner Production. 275 123165. https://doi.org/10.1016/j.jclepro.2020.123165

TitlePartner selection in sustainable supply chains: a fuzzy ensemble learning model
TypeJournal article
AuthorsWu, C., Lin, C., Barnes, D. and Zhang,Y.
Abstract

With the increasing demands on businesses to operate more sustainably, firms must ensure that the performance of their whole supply chain in sustainability is optimized. As partner selection is critical to supply chain management, focal firms now need to select supply chain partners that can offer a high level of competence in sustainability. This paper proposes a novel multi-partner classification model for the partner qualification and classification process, combining ensemble learning technology and fuzzy set theory. The proposed model enables potential partners to be classified into one of four categories (strategic partner, preference partner, leverage partner and routine partner), thereby allowing distinctive partner management strategies to be applied for each category. The model provides for the simultaneous optimization of both efficiency in its use of multi-partner and multi-dimension evaluation data, and effectiveness in dealing with the vagueness and uncertainty of linguistic commentary data. Compared to more conventional methods, the proposed model has the advantage of offering a simple classification and a stable prediction performance. The practical efficacy of the model is illustrated by an application in a listed electronic equipment and instrument manufacturing company based in southeastern China.

KeywordsPartner selection
Sustainable supply chains
Ensemble learning
Fuzzy set theory
Machine learning
Article number123165
JournalJournal of Cleaner Production
Journal citation275
ISSN0959-6526
Year2020
PublisherElsevier
Accepted author manuscript
License
CC BY-NC-ND 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jclepro.2020.123165
Publication dates
Published online16 Jul 2020
Published in print01 Dec 2020

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