Research Article

The automotive recall data search and its analysis applying machine learning

Bruno Fernandes Maione; Paulo Carlos Kaminski; Emilio Carlos Baraldi

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Paper aims: This article investigates the worldwide trend of growth in the number of recalls, as well as in the number of products involved in each campaign.

Originality: To investigate these facts, a study of the automotive recall was developed, comprising Brazil, the European Union, and the United States of America.

Research method: Due to the different availabilities between the locations, search tools and software were developed to obtain and group hidden data from 2010 to 2019.

Main findings: In this work, the impacts of the recall were analyzed using three categories of algorithms: clustering, classification, and regression. Analyzes were made about the results obtained and discussions were built about the importance of applying the machine learning technique.

Implications for theory and practice: The use of search tools and software to obtain and group hidden data in databases and opens the opportunity for new research in various areas.

Supplementary Material


Recall, Automotive Industry, Machine Learning, Product Quality


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