Evaluating machine learning models compared to traditional supplier qualification process techniques in Supply Chain Management
a systematic literature review
DOI:
https://doi.org/10.14488/BJOPM.3022.2026Keywords:
Machine learning, Supplier management, Supply Chain Management, Supplier SelectionAbstract
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.
Downloads
References
Abdulla, A.; Baryannis, G. (2024), “A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection”, Supply Chain Analytics, Vol. 7, p. 100074. https://doi.org/10.1016/j.sca.2024.100074
Abdulla, A.; Baryannis, G.; Badi, I. (2023), “An integrated machine learning and MARCOS method for supplier evaluation and selection”, Decision Analytics Journal, Vol. 9, pp. 100342. https://doi.org/10.1016/j.dajour.2023.100342.
Ali, M.R.; Nipu, A.; Khan, S.A. (2023), “A decision support system for classifying supplier selection criteria using machine learning and random forest approach”, Decision Analytics Journal, Vol. 7, No. 2, 100238. https://doi.org/10.1016/j.dajour.2023.100238.
Ali, S.M.; Burney, S.M.A.; Khan, S.Y. (2020), “Fuzzy-AHP-TOPSIS: An integrated multi-criteria decision support system for supplier selection in Pakistan’s textile industry”, International Journal of Computer Science and Network Security, Vol. 20, No. 4, pp. 91–100.
Apriliani, R.; Wibirama, S. (2024), “Classification of vendor categories using machine learning in industrial decision support systems”, IEEE, pp. 888–893. https://doi.org/10.1109/ISRITI64779.2024.10963453.
Asrol, M.; Nipu, A.; Khan, S. A. (2023), “Improving supplier evaluation model using ensemble-method machine learning for food industry”, Procedia Computer Science, Vol. 227, pp. 307-315. https://doi.org/10.1016/j.procs.2023.10.529.
Azarakhsh, S.; Ferrari, S. (2025), “Bayesian Optimization in Hybrid Data Envelopment Analysis and Stacking Approach for Supplier Selection”, IFAC-PapersOnLine, Vol. 36, pp. 812-829. https://doi.org/10.1016/j.ifacol.2025.09.458.
Badakhshan, E.; Mustafee, N.; Bahadori, R. (2024), “Application of simulation and machine learning in supply chain management: A synthesis of the literature using the Sim-ML literature classification framework”, Computers & Industrial Engineering, Vol. 198, pp. 110649. https://doi.org/10.1016/j.cie.2024.110649.
Beske, P. (2012), “Dynamic capabilities and sustainable supply chain management”, International Journal of Physical Distribution & Logistics Management, Vol. 42, No. 4, pp. 372-387.
Bishop, C.M. (2006), Pattern Recognition and Machine Learning, Springer, New York.
Brans, J.-P.; Vincke, P. (1985), “A preference ranking organisation method: The PROMETHEE method”, Management Science, Vol. 31, No. 6, pp. 647-656.
Cavalcante, I.M.; Frazzon, E.M.; Forcellini, F.A.; Ivanov, D. (2019), “A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing”, International Journal of Information Management, Vol. 49, pp. 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004.
Chopra, S.; Meindl, P. (2021), Supply Chain Management: Strategy, Planning, and Operation, 8th ed., Pearson, Boston.
De Boer, L.; Labro, E.; Morlacchi, P. (2001), “A review of methods supporting supplier selection”, European Journal of Purchasing & Supply Management, Vol. 7, No. 2, pp. 75–89.
DiMaggio, P.J.; Powell, W.W. (1983), “The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields”, American Sociological Review, Vol. 48, No. 2, pp. 147–160.
Dweiri, F.; Kumar, S.; Khan, S. A.; Jain, V. (2016), “Designing an integrated AHP-based decision support system for supplier selection in automotive industry”, Expert Systems with Applications, Vol. 62, pp. 273–283. https://doi.org/10.1016/j.eswa.2016.06.030.
Ferreira, A. C. A., de Pinho, A. F., Francisco, M. B., de Siqueira, L. A., Jr., & Vasconcelos, G. A. V. B. (2025), “A decision support system for classifying suppliers based on machine learning techniques: a case study in the aeronautics industry”, Computers, Vol. 14, No. 7. DOI: 10.3390/computers14070271.
Ghaleb, A.M.; Kaid, H.; Alsamhan, A.; Mian, S.H.; Hidri, L. (2020), “Assessment and comparison of various MCDM approaches in the selection of manufacturing process”, Advances in Materials Science and Engineering, Vol. 2020, Art. 4039253. https://doi.org/10.1155/2020/4039253
Goodfellow, I.; Bengio, Y.; Courville, A. (2016), Deep Learning, MIT Press, Cambridge, MA.
Govindan, K.; Rajendran, S.; Sarkis, J. (2015), “Multi criteria decision making approaches for green supplier evaluation and selection: A literature review”, Journal of Cleaner Production, Vol. 98, pp. 66–83.
Guneri, B.; Deveci, M. (2023), “Evaluation of supplier selection in the defense industry using q-rung orthopair fuzzy set based EDAS approach”, Expert Systems with Applications, Vol. 222, Art. 119846. https://doi.org/10.1016/j.eswa.2023.119846.
Gupta, I.; Martinez, A.; Correa, S.; & Wicaksono, H. (2025), “A comparative assessment of causal machine learning and traditional methods for enhancing supply chain resiliency”, Supply Chain Analytics, Art. 100116. https://doi.org/10.1016/j.sca.2025.100116
Harzing, A.-W.; Alakangas, S. (2016), “Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison”, Scientometrics, Vol. 106, No. 2, pp. 787–804. https://doi.org/10.1007/s11192-015-1798-9.
Hastie, T.; Tibshirani, R.; Friedman, J. (2009), The Elements of Statistical Learning, 2nd ed., Springer, New York.
Hayati, E.N.; Jauhari, W.A.; Damayanti, R.W. & Rosyidi, C.N. (2025), “An integrated analytics model for supplier selection and order allocation with machine learning and multi-criteria optimization”, Decision Analytics Journal, Vol. 16, pp. 100599. https://doi.org/10.1016/j.dajour.2025.100599.
Ho, W.; Xu, X.; Dey, P. K. (2010), “A review of multi-criteria decision-making approaches for supplier evaluation and selection”, European Journal of Operational Research, Vol. 202, No. 1, pp. 16–24. https://doi.org/10.1016/j.ejor.2009.05.009.
Huang, A.; Zhuang, J.; Ren, Y.; Rao, Y.; Tsai, S. (2024), “Supply chain management in the digital economy: Case studies of deep learning technology applications”, Journal of Global Information Management, Vol. 32, No. 1. https://doi.org/10.4018/JGIM.361589.
Husna, A.; Amin, S.H.; Ghasempoor, A. (2025), “Machine learning techniques and multi-objective programming to select the best suppliers and determine the orders”, Machine Learning with Applications, Vol. 19, Art. 100623. https://doi.org/10.1016/j.mlwa.2025.100623.
Ivanov, D.; Dolgui, A. (2021), “A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0”, Production Planning & Control, Vol. 32, No. 9, pp. 775–788.https://doi.org/10.1080/09537287.2020.1768450.
International Organization for Standardization (2015), ISO 9001:2015 – Quality management systems — Requirements, ISO, Geneva.
International Organization for Standardization (2017), ISO 20400:2017 – Sustainable procurement — Guidance, ISO, Geneva.
International Organization for Standardization (2018a), ISO 28000:2018 – Security and resilience — Security management systems — Requirements, ISO, Geneva.
International Organization for Standardization (2018b), ISO 31000:2018 – Risk management — Guidelines, ISO, Geneva.
James, G.; Witten, D.; Hastie, T.; Tibshirani, R. (2013), An Introduction to Statistical Learning, Springer, New York.
Jebbor, I.; Benmamoun, Z.; Hachimi, H. (2024), “Forecasting supply chain disruptions in the textile industry using machine learning: A case study”, Ain Shams Engineering Journal, Vol. 15, Issue 12, Art. 103116. DOI: 10.1016/j.asej.2024.103116.
Kang, P. S.; Bhawna, B. (2025), “Enhancing supply chain resilience through supervised machine learning: supplier performance analysis and risk profiling for a multi-class classification problem”, Business Process Management Journal, DOI: 10.1108/BPMJ-03-2024-0174.
Kumaar, S.J. & Maryam, Z. (2025), “Smart sustainability: A theory-guided convolutional neural network with gazelle optimization framework for supplier classification”, Proceedings of the 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI-2025), IEEE, pp. 1–8. http://doi.org/10.1109/ICDICI66477.2025.11135188
Mahin, M.P.R.; Shahriar, M.; Das, R.R.; Roy, A. & Reza, A.W. (2025), “Enhancing sustainable supply chain forecasting using machine learning for sales prediction”, Procedia Computer Science, Vol. 252, pp. 470–479. https://doi.org/10.1016/j.procs.2025.01.006.
Mchopa, A. D.; Kimario, H. F.; Panga, F. P. (2025), “Green procurement practices and performance of small scale manufacturing firms in Dar es Salaam Region, Tanzania”, African Journal of Applied Social Sciences.
https://www.ajol.info/index.php/ajasss/article/view/286049
Mirasçı, S.; Aksoy, A. (2025), “Data-Driven purchasing strategies: Price prediction models and strategy development”, Expert Systems with Applications, Vol. 266, Art. 125986. https://doi.org/10.1016/j.eswa.2024.125986.
Monczka, R.M.; Handfield, R.B.; Giunipero, L.C.; Patterson, J.L. (2020), Purchasing and Supply Chain Management, 7th ed., Cengage, Boston.
Palakshappa, A.; Maradithaya, S., & V, C. (2025). A Machine Learning Method to improve Supplier Delivery Appointments in Supply Chain Industries : A Case Study. Brazilian Journal of Operations & Production Management, 22(1), 2040. https://doi.org/10.14488/BJOPM.2040.2025
Ravichandran, I.; Sharma, S.; Rajendran, P.; Kumar, A.; Rahman, M. (2025), “Enhancing Supply Chain Transparency and Efficiency Through Hybrid Machine Learning Models”, Proceedings of the 2025 Global Conference in Emerging Technology (GINOTECH), IEEE, pp. 1–6. https://doi.org/10.1109/GINOTECH63460.2025.11076956.
Saaty, T.L. (1980), The Analytic Hierarchy Process, McGraw-Hill, New York.
Santis, R. B. de, Golliat, L., & Aguiar, E. P. de. (2017). Multi-criteria supplier selection using fuzzy analytic hierarchy process: case study from a Brazilian railway operator. Brazilian Journal of Operations & Production Management, 14(3), 428–437. https://doi.org/10.14488/BJOPM.2017.v14.n3.a15
Shidpour, H.; Mousavi, S.; Ghadami, A.; Hashemi, A. (2025), “A multi-phase analytics framework for supply chain supplier selection and order allocation with delay risks and Industry 4.0 readiness”, Supply Chain Analytics, Vol. 3, Art. 100172. https://doi.org/10.1016/j.sca.2025.100172.
Tao, J.; Aamir, M.; Shoaib, M.; Yasir, N.; Babar, M. (2025), “Bridging the Gap Between Supply Chain Risk and Organizational Performance Conditioning to Demand Uncertainty”, Sustainability, Vol. 17, No. 6, Art. 2462. https://doi.org/10.3390/su17062462
Teece, D.J.; Pisano, G.; Shuen, A. (1997), “Dynamic capabilities and strategic management”, Strategic Management Journal, Vol. 18, No. 7, pp. 509–533.
Teece, D.J. (2007), “Explicating dynamic capabilities”, Strategic Management Journal, Vol. 28, No. 13, pp. 1319–1350.
Vahdani, B.; Mousavi, S. M.; Tavakkoli-Moghaddam, R.; Hashemi, H. (2017), “A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study”, International Journal of Computational Intelligence Systems, Vol. 10, No. 1, pp. 293–311. http://doi.org/10.2991/ijcis.2017.10.1.20.
Wei, Y. (2022), “A machine learning algorithm for supplier credit risk assessment based on supply chain management”, International Transactions on Electrical Energy Systems, Vol. 2022, No. 1, Art. 4766597. http://doi.org/10.1155/2022/4766597
Zheng, G., & Brintrup, A. (2025). A machine learning approach for enhancing supply chain visibility with graph-based learning. Supply Chain Analytics, 11, 100135. https://doi.org/10.1016/j.sca.2025.100135
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Laura Nogueira Cordeiro, Alberto Casado Lordsleem Junior, Thais Cohen de Almeida Costa, Emília Rahnemay Kohlman Rabbani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors must have a written permission from any third-party materials used in the article, such as figures and graphics. The permission must explicitly allow authors to use the materials. The permission should be submitted with the article, as a supplementary file.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after BJO&PM publishes it (See The Effect of Open Access).
