How is the product development process supported by data mining and machine learning techniques?

• Published in: Technology analysis & strategic management 2024-07, Vol.36 (7), p.1430-1442
• Main Authors: de Souza, Jovani Taveira, Jesus, Rômulo Henrique Gomes de, Ferreira, Mariane Bigarelli, Chiroli, Daiane Maria de Genaro, Piekarski, Cassiano Moro, de Francisco, Antonio Carlos
• Format: Article
• Language: English
• Subjects: Data mining; Machine learning; Product development; Product development process; Systematic review
• ISSN: 0953-7325; 1465-3990
• DOI: 10.1080/09537325.2022.2099262

The purpose of this paper is to present how data mining (DM) and machine learning (ML) contribute to the product development process (PDP). The Methodi Ordinatio methodology was used to identify important articles for this study, and VOSviewer software was applied to generate visual maps. A systematic review was conducted on the Web of Science, Scopus and Science Direct databases. Forty-six articles were designated for analysis in order to evaluate the most commonly used DM and ML techniques to support the PDP, as well as to demonstrate which specific phases are most often applied. In addition, the main limitations of the analyzed techniques were identified. The results show that the association rule technique was the most commonly used, followed by text mining, and the most used phases were planning and design. In this context, this study intends to stimulate companies to use computational techniques, more precisely DM and ML, to assist in the generation of knowledge and become a strategic factor in the PDP.