{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T01:09:48Z","timestamp":1769908188985,"version":"3.49.0"},"reference-count":62,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UNIFEI\u2014Federal University of Itajub\u00e1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This paper presents the application of four machine learning algorithms to segment suppliers in a real case. The algorithms used were K-Means, Hierarchical K-Means, Agglomerative Nesting (AGNES), and Fuzzy Clustering. The analyzed company has suppliers that have been clustered using responses such as the number of non-conformities, location, and quantity supplied, among others. The CRISP-DM methodology was used for the work development. The proposed methodology is important for both industry and academia, as it helps managers make decisions about the quality of their suppliers and compares the use of four different algorithms for this purpose, which is an important insight for new studies. The K-Means algorithm obtained the best performance both for the metrics obtained and the simplicity of use. It is important to highlight that no studies to date have been conducted using the four algorithms proposed here applied in an industrial case, and this work shows this application. The use of artificial intelligence in industry is essential in this Industry 4.0 era for companies to make decisions, i.e., to have ways to make better decisions using data-driven concepts.<\/jats:p>","DOI":"10.3390\/computers14070271","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:38:27Z","timestamp":1752140307000},"page":"271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Decision Support System for Classifying Suppliers Based on Machine Learning Techniques: A Case Study in the Aeronautics Industry"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7806-9310","authenticated-orcid":false,"given":"Ana Claudia","family":"Andrade Ferreira","sequence":"first","affiliation":[{"name":"Production and Management Engineering Institute, Federal University of Itajub\u00e1\u2014UNIFEI, Itajub\u00e1 37500-903, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1731-5327","authenticated-orcid":false,"given":"Alexandre Ferreira","family":"de Pinho","sequence":"additional","affiliation":[{"name":"Production and Management Engineering Institute, Federal University of Itajub\u00e1\u2014UNIFEI, Itajub\u00e1 37500-903, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6300-3717","authenticated-orcid":false,"given":"Matheus Brendon","family":"Francisco","sequence":"additional","affiliation":[{"name":"Production and Management Engineering Institute, Federal University of Itajub\u00e1\u2014UNIFEI, Itajub\u00e1 37500-903, Brazil"}]},{"suffix":"Jr.","given":"Laercio Almeida","family":"de Siqueira","sequence":"additional","affiliation":[{"name":"Production and Management Engineering Institute, Federal University of Itajub\u00e1\u2014UNIFEI, Itajub\u00e1 37500-903, Brazil"}]},{"given":"Guilherme Augusto Vilas Boas","family":"Vasconcelos","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Institute, Federal University of Itajub\u00e1\u2014UNIFEI, Itajub\u00e1 37500-903, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"ref_1","first-page":"200438","article-title":"Optimization of inventory management through computer vision and machine learning technologies","volume":"24","author":"Navarro","year":"2024","journal-title":"Intell. 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