Brazilian Journal of Operations and Production Management | 2026
Authors: Cordeiro L.N.; Lordsleem A.C.; Costa T.C.D.A.; Rabbani E.R.K.
DOI: 10.14488/BJOPM.3022.2026
Journal: Brazilian Journal of Operations and Production Management
Year: 2026
Publisher: Associacao Brasileira de Engenharia de Producao
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
Goal: This study systematically reviews the literature on the application of machine learning algorithms in the selection, evaluation, and prediction of supplier performance in the context of supply chain management, identifying trends and opportunities for future research. Design/methodology/approach: A systematic literature review was conducted, conducted, following the PRISMA protocol. The search was conducted in Scopus, ScienceDirect, Web of Science, and Engineering Village databases, considering articles published between 2015 and 2025. After applying the inclusion and exclusion criteria, 27 studies were selected and analyzed regarding the type of learning, performance metrics, application sectors, and methodological characteristics. Results: The results indicate a predominance of supervised models (89.7%), with emphasis on algorithms based on decision trees and neural networks. The reported accuracies range from 46% to 99%, with an overall average of 91.97%, highlighting advances in the use of data-driven models for supplier classification and ranking. Limitations: The main limitations relate to the heterogeneity of the databases, the lack of standardization of evaluation metrics, and the incomplete methodological description in some of the analyzed studies. Practical implications: The study demonstrates that machine learning can support strategic decisions in purchasing and supply, increasing the accuracy in supplier selection and evaluation. Originality/Value: The study offers a systematic and structured synthesis of recent literature, organizing evidence by algorithmic families, performance metrics, and application sectors, in addition to identifying relevant gaps for future research. © 2026, Associacao Brasileira de Engenharia de Producao. All rights reserved.
Machine Learning; Supplier Qualification; Supplier Selection; Supply Chain Management