Proposal of a Classification Method for Brazilian Automotive Companies Using the Principal Components Analysis

Author:

Oliveira Paulo Sergio Gonçalves1,Silva Luciano Ferreira2,Araujo Pedro Teixeira3,Reis Guilherme Fernandes Gomes3,Otero Marco Antônios Soares Gomes3

Affiliation:

1. Universidade Anhembi Morumbi

2. Universidade Nove de Julho

3. Centro Universitário de Belo Horizonte

Abstract

Abstract

This article proposes a method for classifying Brazilian companies according to the concepts of Industry 4.0, to do so, research was carried out on the websites of automotive companies affiliated with Anfavea (Brazilian Association of Motor Vehicle Manufacturers), using the ElasticSearch software. This tool allows scanning large textual databases, including websites. The search found 137,382 occurrences in documents belonging to the companies’ websites. To develop the classification, principal component analysis was used, by limiting it to two components, which together explain 90.98% of the total variation. The components are named tools and innovations using this, data was divided into quadrants represented by the x and y axes of the chart. The first quadrant is considered "low in tools (y) and low in innovations (x)", where 12 companies were classified, with highlights being Renault and Ford. In the second quadrant, "low in tools and high in innovations (x), only the company Komatsu was classified. In The third quadrant, companies that have "high classification" were classified as “high tools” and “high in innovations”, represent by Volkswagen, Stellantis, and Scania. In the fourth quadrant, companies were classified as on-highway and Volvo, with high use of innovations and low use of industry 4.0 tools.

Publisher

Springer Science and Business Media LLC

Reference93 articles.

1. Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions;Abulibdeh A;J Clean Prod,2024

2. Allahyari M, Pouriyeh S, Assefi M, Safaei S, Trippe ED, Gutierrez JB, Kochut K (2017) A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques. arXiv:1707.02919 [cs]

3. Alves W, Estradão R (2023) Iveco revela dados dos novos caminhões elétricos | Mobilidade Estadão |. Mobilidade Estadão. URL https://mobilidade.estadao.com.br/meios-de-transporte/iveco-anuncia-dados-de-novos-caminhoes-eletricos/ (accessed 1.9.24)

4. Andreadis II, Gioumouxouzis CI, Eleftheriadis GK, Fatouros DG (2022) The Advent of a New Era in Digital Healthcare: A Role for 3D Printing Technologies in Drug Manufacturing? Pharmaceutics 14, 609. https://doi.org/10.3390/pharmaceutics14030609

5. Ascom (2022) Canaã do Carajás ganha ferramenta tecnológica para ações municipais - Belém.com.br [WWW Document]. URL https://belem.com.br/noticia/6525/canaa-do-carajas-ganha-ferramenta-tecnologica-para-acoes-municipais (accessed 1.9.24)

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