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

Proposal of a Classification Method for Brazilian Automotive Companies Using the Principal Components Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Proposal of a Classification Method for Brazilian Automotive Companies Using the Principal Components Analysis Paulo Sergio Gonçalves Oliveira, Luciano Ferreira Silva, Pedro Teixeira Araujo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4901600/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Dec, 2025 Read the published version in Operations Research Forum → Version 1 posted 8 You are reading this latest preprint version 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. 4th Industrial Revolution Artificial Intelligence Internet of Things advanced automation Automotive Companies Figures Figure 1 Figure 2 1. Introduction The combination of Information Technologies (IT) and the Internet has reconfigured the world, making it increasingly interconnected and digitalised. According to the National Confederation of Industry (CNI), the evolution of technology has triggered significant transformations in the operational efficiency of companies since the beginning of the Industrial Revolution. Therefore, with the arrival of powerful and autonomous computers and the recent advancement of mobile devices, we have witnessed an evolution in the industrial sector accompanied by the improvement of digital communication infrastructure. Specifically, we have seen an increase in the availability and speed of data transmission through intelligent wireless networks (Ferreira, 2017 ). Because of this Contieri et al. ( 2022 ) pointed out that understanding the phenomena of industry 4.0 in emerging countries is essential to grasp its particularities at a global level which is corroborated by Hajoary (2023) to provide guidance for policymakers and practitioners. These technological advances are bringing the cyber-physical world into reality. Also, the concept of cyberspace to operate and firms compete. This enables the networking of resources, information, objects, and people, giving rise to the Internet of Things (IoT). The Iot, can be used to create an inventory of cost and emission data, mainly because one of the industry 4.0 objectives is the waste and emission reduction (Ding et al., 2023 ). Thus, the industry is experiencing the impacts of this technological evolution, culminating in the rise of the fourth industrial revolution, also known as Industry 4.0 (Kagermann et al., 2013 ). According to Drath and Horch ( 2014 ), flexibility is one of the foundations of Industry 4.0; this flexibility is made possible through connectivity, where devices interconnect with each other and with human interfaces, providing real-time information, allowing greater productivity, faster processes fast and flexible to produce better quality products at lower costs. Since the beginning of industrialisation in the 19th century, technological advances have caused significant changes in industrial paradigms. These transformations are often referred to as "industrial revolutions". The first of these occurred with the introduction of vapour power. At the end of the 19th century, the second industrial revolution emerged with the electrification system. Subsequently, the third industrial revolution was driven by cybernetics and widespread digitalisation. We are witnessing a new industrial revolution, known as the 4.0 Industry, resulting from combining several technologies, such as the Internet and IoT (Lasi et al., 2014 ). Industry 4.0 began through a strategic project by the German government, which recognised the benefits of new manufacturing technologies, such as marketing and manufacturing (Bürkner et al., 2016 ; Capestro et al., 2023 ). This fourth revolution is transforming conventional industrial production methods, driving the manufacturing of the future to become more intelligent through the adoption of connected and digital systems. This has given rise to knowledge-based factories, significantly improving productivity levels, efficiency and, consequently, competitiveness. Rüßmann et al. ( 2015 ) and Benitez et al., ( 2023 ) highlighted that the rise of Industry 4.0, driven by digital technologies such as the IoT, Big Data and Cloud Computing (CC), had the potential to generate significant change. These changes included improvements in factory productivity, stimulating industrial growth, reconfiguring the workforce, and influencing the competitiveness of companies and regions. As mentioned previously, Germany was the pioneer in introducing the concept of Industry 4.0 and is currently the global leader in manufacturing in several sectors, including the automotive sector, which is home to renowned companies such as BMW, Mercedes-Benz, and Volkswagen (VW) (Zhou et al., 2015 ). In Brazil, VW has already implemented 3D printers in all its factories that produce materials - with maximum precision and without wasting material. Another major innovation from VW is the implementation of a technology called digital factory, which uses more than 50 pieces of software that work on the same operational platform and allows production procedures to be simulated virtually, designing new buildings, infrastructures, and offices before physical execution, while analysing the ergonomics of workplaces. The Digital Factory began in 2008 and has allowed the company to avoid numerous errors that require subsequent corrections. Considering just six significant projects implemented in the last five years, using the Digital Factory can avoid expenses exceeding R $ 100 million (Volkswagen, 2024 ). Currently, the automobile industry relies heavily on automation. According to “Revista Quatro Rodas” (Four Wheels Magazine), around 70% of tasks related to car manufacturing are performed by robots (Carvalho, 2021 ). As a result, most car manufacturers adopt Industry 4.0 principles, incorporating technology as a fundamental partner in their operations, whether in production or process management. Regarding the presented context, this article proposes a method for classifying Brazilian companies in the automotive sector according to the concepts of Industry 4.0. In order to reach this aim, a theoretical framework was developed regarding the importance of the Brazilian automobile industry. In addition to defining Industry 4.0 and its respective technologies, we present a theoretical framework section. The methodological procedures were developed in the following sections, and in the fourth section, the analysis of the collected data was followed by discussions of the results. Finally, in the last section, final remarks were shown. 2. Theory The basis of this article was structured into topics, namely: “Automobile Industry” included in the first topic, which aims to demonstrate its importance and relevance to the economy. Next, the "Industry 4.0" subsection aims to show how the concept of Industry 4.0 emerged and what the tools/technologies for this industry are. 2.1 Automotive Industry The automobile industry represents around 20% of the GDP of the Brazilian manufacturing sector, in addition to generating around 1.2 million direct and indirect jobs, making it one of the most significant sectors of the economy (Dana, 2023 ). However, it is worth highlighting that car sales in the retail sector have been falling in recent years, representing a threat to this sector, which has caused companies to look for alternatives to this reduction. For example, the car market tries to reach young people from Generation Z, who have different aspirations than other generations (Negocios SC, 2023 ). These changes in the sector point to the need for a radical transformation regarding this industry, with one of the main bets being investment in new technologies, which will undoubtedly play a fundamental role in improving the numbers in this sector, which are driven by a combination of factors such as technological advances, changing consumer preferences, government regulations, and global trends (Minas, 2023 ). Thus, due to environmental pressures, there is also pressure for the electrification of the fleet, as the mobility sector is one of the pollutants currently, causing this trend to advance worldwide. In Brazil, the first electric cars arrived in 2019; since then, a series of hybrid or fully electric models has been developed, giving the country some success stories, such as the electrification of the urban bus fleet in some states and also some car models (Bastos and Oliveira, 2023 ). These changes open space for investment in Industry 4.0, mainly due to its characteristics linked to investment in technologies that allow the integration of all technological means available by the company. Furthermore, it focuses on sustainability, including its suppliers, allowing flexibility to this type of industry, thus responding more quickly to customer needs, government regulations and the needs shaping current societal transformations. In this line, the next topic will comment on Industry 4.0 and its importance for the automobile sector. 2.2 Industry 4.0 Over the last three decades, advances in information technologies and their integration into production processes have brought comprehensive advantages throughout the value chain. The progression in the capacity of these technologies has boosted industrial productivity, resulting in reduced production costs and the offering of practical solutions to serve customers with quality, speed, and an improved cost/benefit ratio (Chen et al., 2022 ). Faced with these recent technological advances and in a context where the demand for customised products, greater sophistication, increased quality, and cost reduction is constantly growing, the discussion arises about a new industrial paradigm, which is approached as Industry 4.0 (El Kihel et al., 2023 ; Hermann et al., 2015 ; Novoszel and Wakolbinger, 2022 )​. Many scholars consider Industry 4.0 the next stage in the industrial revolution, with the potential to boost economic growth. In this new stage, the ability to integrate several technologies that facilitate the automation and digitalisation of processes is expected, offering more refined control over manufacturing methods (McClymonds et al., 2024 ). This translates into smart factories that have the potential to promote more effective, autonomous, and customisable production models, providing significant benefits to the industry (Abulibdeh et al., 2024 ; Brettel et al., 2014 ). Additionally, among these benefits, we can highlight intelligent production processes, mainly due to the creation of ecosystems that use autonomous resources, such as self-structuring and self-monitoring, which certainly increases the quality of products, in addition to productivity also allows mass customisation of these products (Harris, 2021 ; Sarı et al., 2020 ). We can also include the fact that enterprises are considering industry 4.0 as a pivotal factor for help achieve a successful transition towards a green production and more equitable future (Ocelík et al., 2023 ). Industry 4.0 was first introduced at the Hannover Fair in 2011. In the following year, in October, the group responsible for the project prepared a report that recommended the implementation of this production phase in the various industrial sectors in Germany. In April 2013, at the same fair that marked the beginning of the project, a final plan was presented in which the idea of providing the production sector with machines, cybernetic systems and intelligent networks was conceived, providing greater autonomy and efficiency in production (Bürkner et al., 2016 ; Rossit and Tohmé, 2022 ; Singh et al., 2023 . The concept of Industry 4.0 is driven by a series of technologies which allow the creation of a digital eco-system, of which we will comment on in the following paragraphs on the ones we consider paramount (Battaglia et al., 2023 ; Benitez et al., 2020 ). The first of them is Big Data, which promotes access to large amounts of data at high speed, which are complex and variable, requiring the use of advanced techniques and technologies to enable the capture, management, and efficient analysis of this data (TechAmerica Foundation Federal Big Data Commission, 2012). According to IDC (International Data Corporation), Big Data technologies represent a new generation of technologies and architectures designed to extract economic value from vast volumes and a wide variety of data, which enables the generation of information, as well as analysis in high speed, thus allowing more assertive and faster decisions. In agreement, Manyika et al. (2011) state that Big Data encompasses data sets of dimensions surpassing conventional tools' capture, storage and analysis capabilities. The main strength of Big Data lies in the fact that it combines large volumes of data, which is a combination of large, complex, and heterogeneous data generated from a wide variety of sources, such as social networks, web click streams, monitoring and action, video sharing, business processes, as well as other sources, which can be used to generate digital and smart factories. The smart factory concepts emerged with Industry 4.0, allowing human and machine interaction to synchronise and harmoniously (Sarı et al., 2020 ). In this sense, several efforts are being made by the automotive industry to integrate data from car diagnostics, thus aiming to create a knowledge base, in addition to allowing the simulation of these problems in order to anticipate critical failures throughout the entire life cycle of its products, thus allowing better service provision to customers (Rîșteiu et al., 2021 ). This data can also be used to create digital twins, both in production systems and in automobiles that are being produced, in order to be able to analyse possible problems, design flaws and process monitoring in the case of production systems (Jena and Patel, 2023 ; Wadhwa & Babbar, 2023 ). The second group of technologies that we can highlight is the IoT, which concerns the interconnection of everyday objects, often equipped with ubiquitous Intelligence, aiming to connect interactive objects in existing systems, creating a network of devices which can communicate both with humans and other devices (Xia et al., 2012). IoT expands the horizon of companies mainly using sensors spread throughout the production process, in addition to the use of embedded systems, which collect a large volume of data and make real-time data analysis possible, which can be made available in a Big Data system, opening up a wide range of applications in the analytical field (Trinks, 2018 ). IoT still has excellent room for expansion, as the development of technologies that make the flow and influx of data increasingly faster tend to leverage the possibilities that it can still deliver, among which we can highlight, for example, the popularisation of 5G and its application in an increasing number of devices, which can undoubtedly awaken new uses, in addition to unlocking a series of limitations that we currently have, in addition to enabling the integration of an increasing number of devices, which can be internal to companies or external (Atharvan et al., 2022 ). Data collected using IoT can be integrated and combined with existing production processes, enabling companies to have smart production and improving production activities through sensors and smart manufacturing systems (Manoj, 2022 ; Mokhtarzadeh et al., 2024 ). The fourth group of technologies that make up Industry 4.0 is called Cloud Computing, which aims to provide information technology services on demand, with payments according to use, without the need for specific resources allocated to a group of users, making the technology allocation process more flexible, in addition to being more in line with business demands (Buyya et al. 2009; Karatas et al., 2022 ). For this reason, both Big Data and the concept of cloud computing are considered crucial for developing a technological ecosystem, which allows flexibility for companies looking to be up-to-date concerning Industry 4.0 (Velásquez et al., 2018 ). Thus, cloud computing is crucial in this context due to its characteristic of providing scalable computing services accessible via the Internet, which may include the provision of computing as a service and software as a service, among other things as services, such as learning of machine and analytical capabilities, through five essential characteristics: essential characteristics: self-service, on-demand, comprehensive network access, resource pooling, rapid scalability, and service measurement (Zhong et al. 2017). Figure 1 shows an overview of cloud computing. The fifth group of technologies that enable Industry 4.0 is Artificial Intelligence, which can be defined as a set of skills to be developed in machines, with the aim that they can understand, reason, and learn in a way comparable to human beings, which opens up an excellent gateway to the use of computers in simulating human Intelligence (Pan, 2016). Artificial Intelligence comprises a series of disruptive technologies, some of which we can highlight, such as cognitive computing and expert systems, which can be combined with data mining/data science and advanced data analysis (Jan et al., 2023 ; Serey et al., 2023 ). For example, it enables using robots, quality data analysis, process improvement, and the development of intelligent manufacturing (de Jesus Pacheco et al., 2023 ). As previously stated, Artificial Intelligence is a sub-area of computing that aims to mimic human learning, which was previously proposed by Alan Turing, and among which we can highlight machine learning. According to Mitchell (Mitchell, 1997 ), these technologies allow algorithms to learn through the repetition of a task, making the system learn, thus improving its approach, aiming to solve it more effectively, contributing not only to existing knowledge but also to the improvement of the task in question. About machine learning, it is also important to highlight that there are some divisions, the first being between algorithms that are used for supervised learning and, therefore, need a classification label, usually applied by an expert, to classify these data. For example, when analysing a financial transaction, the specialist can decide whether the company is facing fraud and, therefore, assigns a label that represents this fraud or not, assigning, for example, a non-fraud label, making these data used for training, so that they can learn which patterns denote one label from others, generating a model which can classify data autonomously based on a success rate. The second type is unsupervised learning, where there is no label, meaning that based on some metric, the data is classified into groups and, subsequently, interpreted by an expert (Kubat, 2017 ). These two approaches can be combined, generating a third group, which we call semi-supervised learning, which uses both supervised and unsupervised techniques to promote continuous improvement of the algorithm, which can also be combined with reinforcement to evaluate error or success in decision-making (Kuncheva, 2014 ). We can also highlight bio-inspired algorithms, such as neural networks, which seek to imitate the network of neurons in our brain, and non-bio-inspired ones, such as cluster analysis, Random forests, and KNN, among others (Jordan & Mitchell, 2015 ; Marsland, 2015 ; Romeo et al., 2020 ; Sharma et al., 2019 ). One of the areas that have gained prominence in recent times is called deep learning, which is, therefore, a subfield of machine learning in which neural networks have one or more levels of hidden learning layers, which substantially improves the rate of learning the algorithm and for this reason, it opens up a series of possibilities, such as computer vision, which can be used, for example, to find a defect in a part that is being produced automatically or even guide a robot in a manufacturing factory autonomously (Allahyari et al., 2017 ; Jordan & Mitchell, 2015 ; Kuncheva, 2014 ; Lee et al., 2019 ; Marsland, 2015 ). In this sense, we can also cite natural language analysis, enabling computers to interpret texts or even oral language and communicate with human beings, among other applications ​. The sixth group of technologies linked to Industry 4.0 is called additive manufacturing due to its flexibility, which can be used for product evaluation and mass manufacturing (Kim et al., 2019 ; Nguyen Ngoc et al., 2022 ). Additive manufacturing is a production technique that uses the addition of successive layers of material until the desired shape of the element you want to produce is reached, having been proposed in the 1980s (Inácio et al., 2020 ). However, it has been evolving more quickly mainly due to advances in areas such as networked systems, including systems such as computing services and clouds, and artificial Intelligence, among others, which are driving mass production manufacturing for mass customisation through the production of highly customisable items produced through massively distributed manufacturing (Tamir et al., 2022 ). We can also add to this the fact that 3D printers can produce complex and completely functional items using a multitude of materials, from plastic, iron and even biological materials (Goh et al., 2021 ), creating space for a multitude of applications in most varied fields, such as civil construction, to the pharmaceutical industry. One example is Andreadis et al.’s ( 2022 ) research, which shows the application of the manufacture of drugs with an individualised dose and even with a degree of attractiveness for different audiences, demonstrated, such as coloured pills or in the shape of characters. In the automotive industry, additive manufacturing finds space for various applications, from producing components for prototyping to producing spare parts. Those are used in manufacturing and even producing complete automobiles and forming on-demand part of the operational process with 3D-CAD models, which can be stored in databases and shared via the cloud, thus improving business flexibility (Budzik et al., 2022 ). It should also be noted that advances in the chemical development of materials increasingly expand the range of applications and possibilities that 3D printers and the concept of additive manufacturing present both for the research area and for the industry in general, therefore being an essential tool of Industry 4.0 (Rudnik et al., 2022 ). In this context, global warming influences the most varied sectors, including agriculture, drastically reducing the production of fruits and vegetables and causing economic and food security problems and catastrophes due to extreme temperatures (Thomson et al., 2014 ). Therefore, since the use of fossil resources and energy, in addition to environmental impacts, are at the top of the list of the biggest challenges that need to be resolved in the coming years, combined with the fact that the transport sector is one of the most polluting, it was decided to include the electrification of automobiles as one of the components of the study, as this type of automobile can reduce our dependence on fossil fuels, despite the problems that still need to be resolved in this type of technology, such as battery durability, charging time and durability of these batteries (Athanasopoulou et al., 2023 ). This movement made customers pay attention to companies from the most varied sectors, pushing for a reduction in the carbon footprint in order to generate evidence of the adoption of environmental practices throughout the product's life cycle, especially those relating to the emission of greenhouse gases (Bai et al., 2020 ; Pattara et al., 2012 ). Initially, pressure caused city halls worldwide to look at electrified vehicles as an alternative for a green transition; however, this type of transport has even demonstrated economic advantages compared to vehicles powered by fossil fuels (Kruchina, 2023 ). In this way, it is understood that there is pressure for the automobile industry to promote changes in its product portfolio, offering hybrid solutions and completely electric vehicles to adapt to the pressures linked to the consumption of fossil fuels, linked to the greenhouse effect. In this way, we assume it is inevitable to incorporate Industry 4.0 technologies to overcome sustainable challenges. 3. Methods The research was classified as descriptive, as it used statistical methods to describe the behaviour of a population, in this case, the automobile industry, considering the concept of Industry 4.0 (Creswell & Creswell, 2020 ; Demo, 2000 ; Kerlinger, 1988 ). In this sense, the data was collected through a survey using secondary data from company websites, using a cross-sectional survey, as the data was collected only once (Babbie, 1999 ). The Elasticsearch software version 8.10.2 was used as a search and distributed data analysis engine based on Apache Lucene and Java, which collects unstructured and structured data, being an open and free platform. A web crawler, which uses an index to search websites, was used for this research (Elastic, 2023 ). For the search, the following keywords related to the concept of Industry 4.0 were used: 4.0 Technology, Big Data, IoT, Cloud Computing, Artificial Intelligence, Digital Factory, 3D Printer, Electrification and Electric Car. Thus, based on the websites of members of Anfavea (National Association of Motor Vehicle Manufacturers), it is an entity that brings together the leading manufacturers of automobiles, light commercial vehicles, trucks, buses, agricultural and construction machinery, 18 associates (Table 1 ) were selected, as they were more aligned with the aim of this research. Table 1 – Company websites Companies Sites Audi do Brasil Indústria e Comércio de Veículos Ltda https://www.audi.com.br Ford Motor Company Brasil Ltda https://www.ford.com.br General Motors do Brasil Ltda https://www.chevrolet.com.br Honda Automóveis do Brasil Ltda https://www.honda.com.br HPE Automotores do Brasil Ltda https://en.hpeautos.com.br Hyundai Motor Brasil Montadora de Automóveis Ltda https://www.hyundai.com.br Jaguar e Land Rover Brasil Indústria e Comércio de Veículos Ltda https://www.jaguarbrasil.com.br Mercedes-Benz do Brasil Ltda https://www2.mercedes-benz.com.br Nissan do Brasil Automóveis Ltda https://www.nissan.com.br Renault do Brasil S. A https://www.renault.com.br Toyota do Brasil Ltda https://www.toyota.com.br Volkswagen do Brasil Ltda https://www.vw.com.br Volvo do Brasil Veículos Ltda https://www.volvogroup.com Komatsu do Brasil Ltda https://www.komatsu.com.br On-Highway Brasil Ltda https://www.iveco.com Stellantis https://www.stellantis.com Scania Latin America Ltda https://www.scania.com Source: Anfavea (2023). The scan carried out on the website of these 18 associated companies resulted in approximately 137,382 documents that presented at least one incidence of keywords linked to Industry 4.0, as demonstrated in Table 2 , composing the research sample. Table 2 – Number of Occurrences in Documents on Company Websites Companies 4.0 Tech. Big Data IoT Cloud Computing (CC) Artificial Intelligence (AI) Digital Factory 3D Printer Electr. Electric Car Audi do Brasil Indústria e Comércio de Veículos Ltda 448 466 441 466 460 44 44 44 44 Ford Motor Company Brasil Ltda. 0 0 0 0 0 0 0 0 0 General Motors do Brasil Ltda. 258 208 208 208 207 208 208 208 208 Honda Automóveis do Brasil Ltda. 91 91 91 91 91 91 91 91 91 HPE Automotores do Brasil Ltda. 1 1 1 1 1 1 1 1 1 Hyundai Motor Brasil Montadora de Automóveis Ltda. 122 122 122 123 122 123 123 123 123 Jaguar e Land Rover Brasil Indústria e Comércio de Veículos Ltda. 119 121 85 64 64 64 64 64 64 Mercedes-Benz do Brasil Ltda. 1 1 1 1 1 1 1 1 1 Nissan do Brasil Automóveis Ltda. 1 202 1 1 1 1 1 1 202 Renault do Brasil S.A. 202 364 202 203 202 203 203 203 364 Toyota do Brasil Ltda. 364 93 364 355 334 353 364 364 86 Volkswagen do Brasil Ltda. 6699 172 6200 6291 6429 4458 3941 3945 69 Volvo do Brasil Veículos Ltda 93 20222 86 87 86 94 94 94 4834 Komatsu do Brasil Ltda. 502 33 1066 1066 1066 1065 1065 1066 1 On-Highway Brasil Ltda 117 8617 63 65 63 69 67 69 6490 Stellantis 1111 1097 4059 4592 81 2859 3551 3170 76 Scania Latin America Ltda. 869 10462 2510 1343 432 1068 198 1002 197 BMW do Brasil Ltda. 0 0 0 0 0 0 0 0 0 18 10998 42272 15500 14957 9640 10702 10016 10446 12851 Total 137382 Source: Elaborated with Research Data (2023) The data was collected by counting the documents related to the keywords, which were arranged in columns, indicating a possible engagement of that company with that type of technology through the communication of these attributes. The collected data was subjected to factor analysis with the main components extraction method in order to summarise the variables raised and subsequently generate a classification of companies according to their engagement with the Industry 4.0 concept (Hair et al., 2009 ; Malhotra & Menezes, 2019; Pestana & Gageiro, 2014 ). 4. Results Data analysis began with the descriptive analysis of the data, which is demonstrated in Table 3 . Table 3 – Descriptive Data Analysis Keywords Companies number Occurrence number Minimum Maximum Mean Standard deviation 4.0 Technologies 18 10998 0 6699 611.00 1551.063 Big Data 18 42272 0 20222 2348.44 5396.493 IoT 18 15500 0 6200 861.11 1705.657 Cloud Computing 18 14957 0 6291 830.94 1742.913 AI 18 9640 0 6429 535.56 1493.993 Digital factory 18 10702 0 4458 594.56 1193.406 3D Printer 18 10016 0 3941 556.44 1188.199 Electrification 18 10446 0 3945 580.33 1136.303 Electric car 18 12851 0 6490 713.94 1824.918 Source: Elaborated with Research Data (2023) The data were then subjected to exploratory factor analysis using the principal components method, without any rotation method, through which the quality of the analysis was initially verified through the KMO, whose acceptance limit is > = 0.5, which presented a value of 0.627. The Bartlet Sphericity Test presented X 2 = 489; df = 36 and α < 0.0001; therefore, the use of factor analysis is appropriate (Hair et al., 2009 ; Malhotra & Menezes, 2019; Pestana & Gageiro, 2014 ). The next test was the application of commonalities whose values should be more significant, ranging from 0 to 1, with the acceptable limit being > = 0.5; through Table 4 , it is observed that all values exceeded these limits, being, therefore, acceptable (Pestana & Gageiro, 2014 ). Table 4 – Communalities Initial Extraction 4.0 Technologies 1.000 0.873 Big Data 1.000 0.882 IoT 1.000 0.963 Cloud Computing 1.000 0.977 AI 1.000 0.758 Digital Factory 1.000 0.990 3D Printer 1.000 0.913 Electrification 1.000 0.959 Electric Car 1.000 0.874 * Extraction Method: Principal Component Analysis. Source: Elaborated with Research Data (2023) The explained variance of the components was 71.90% for component 1 and 19.08 for component 2 of the study, which totalled 90.98% of the total explanation of the sum of these two components, therefore being a good explanation of variations using these two components (Hair et al., 2009 ; Pestana & Gageiro, 2014 ). Table 4 demonstrates the components matrix, which denotes the variables that belong to the first component and the variables that comprise the second component, both highlighted in grey. Table 4 – Components Extracted with Component Analysis Analysis Components Matrix Components Tools Innovations Digital Factory 0.994 0.049 Cloud Computing 0.988 0.042 IoT 0.979 0.072 Electrification 0.978 0.043 3D Printer 0.956 0.011 4.0 Technologies 0.933 0.055 AI 0.870 0.032 Big Data -0.120 0.931 Electric Car -0.197 0.914 Extraction Method: Principal Component Analysis. *. 2 extracted components. Source: Elaborated with Research Data (2023) Based on the variables that were concentrated in Component 1, it was named Tools because they are concrete means of use in manufacturing products in the automotive sector. In contrast, Component 2 was named Innovations because it determines a new result of an existing product. 5. Discussion The Fig. 2 shows companies according to their positioning concerning the concept of Industry 4.0. Based on Fig. 2 , we will begin to analyse the first lower left quadrant, which means that companies in this region have low adherence to the innovation components and tools of Industry 4.0, thus needing to develop actions related to both components. It is worth noting that in this quadrant, we have two companies that significantly impact the national automobile market. Ford Motor Company left Brazil in 2021 and started to have a portfolio of imported automobiles. This may explain the low number of documents linked to Industry 4.0, as the company no longer has manufacturing units in the country. Regarding Renault, the strategy adopted by the group from 2021 onwards suggests that there will be a significant investment in electrification and simplification through platforms, which explains the positioning at the upper limit of the lower quadrant. However, this company perhaps could migrate to the upper quadrant to what it is now, with a greater focus on innovations linked to Industry 4.0. In this sense, we can point out that this company situation is worth highlighting, as some actions occur outside Brazil, perhaps for this reason not being captured by the analysis (Quatro Rodas, 2021 ). In the Brazilian market, the idea is to invest in Turbo 1.0 engines, in addition to restyling some models, in addition to starting to introduce electric vehicles in order to be in tune with the demands for environmental sustainability through electrification, reducing the carbon footprint and reduction of greenhouse emissions (Athanasopoulou et al., 2023 ; Pattara et al., 2012 ; Thomson et al., 2014 ). Moving forward with the analysis in the lower right quadrant, which includes only one company, Komatsu do Brazil, whose products are mainly used in mining and civil construction, with products such as hydraulic excavators, tracked and wheeled tractors, mechanical trucks and electrical (Komatsu, 2024 ). This quadrant is characterised by being high in the Industry 4.0 tools dimension and low in innovation, with the company being close to the upper limit, as there are products linked to electrification, which is linked more to the Y innovation axis, and close to the lower limit of the axis X - Tools, as it has a complete factory park, as well as implementing innovations in the area of construction, such as the use of monitoring drones, real-time equipment monitoring system and preventive and predictive maintenance called Komtrax, besides use augmented reality (Komatsu do Brasil, 2023 ). The Industry 4.0 tools used by Komatsu have been used in the automotive industry to integrate diagnostic data through the implementation of the IoT, which is embedded in equipment, such as trucks, excavators, etc., in addition to the use of cloud computing (Atharvan et al., 2022 ; Manoj, 2022 ; Rîșteiu et al., 2021 ; Trinks, 2018 ; Wadhwa & Babbar, 2023 ). The third quadrant represents companies with a high degree of innovation (Y axis) and Industry 4.0 tools (x), including the companies Stellantis, Volkswagen and Scania. This quadrant by the automobile company Volkswagen, we can highlight the following actions: the use of digital factory and intelligent manufacturing, which began in 2019 with the inauguration of the São Bernado Campo factory, the virtual prototyping laboratory, aiming to generate virtual models of automobiles that will be launched in the future. Which begins with the development of a virtual prototype, which is fed with information from the company's product design and engineering area and consolidates the project using augmented and virtual reality software so that it can, using this model, carry out safety simulations (crash-test), aerodynamic acoustics, durability, among other points, which dramatically reduces the number of project failures. Tests are then carried out with physical vehicles, thus generating savings and project reliability (Volkswagen, 2024 ). This highlighted aspect allows the company to meet the demand for customised products, which present a greater degree of sophistication, in addition to achieving significant cost reduction, as it uses the integration of various technologies, such as networks, cloud computing, big data, among others, to generate simulations with a high degree of assertiveness, thus generating smart factories which are customisable, autonomous and more effective (Brettel et al., 2014 ; Gavilanes et al., 2018 ; Hermann et al., 2015 ). With the digital factory concept, the company Stellantis, together with the company Comau Industrial Automation and Robotics, implemented virtual process simulations, aiming to optimise the steps involved in the manufacture of automobiles, thus allowing the performance of the process as a whole to be evaluated, enabling virtually anticipate problems and identify areas for improvement. This optimisation also resulted in the possibility of scheduling the production of different types of automobiles on the same line. This innovation allowed the integration of 74 automatic and 10 manual lines with advanced vision and robotics services to optimise the process (COMAU, 2024 ). Concerning intelligent manufacturing, Scania made a significant investment in its São Bernado do Campo factory to modernise the industrial complex, and the area that underwent the most modification was the welding area, with welding carried out by robots. In addition to quality verification carried out by 3D scanning, improvements are also being made in the production of parts, mainly regarding review and reevaluation (Automotive Business, 2019 ). In the case of Volkswagen, it is interesting to analyse the use of additive manufacturing, as all factories in Brazil already use 3D printers for prototyping, which generates greater precision and leads to a reduction in waste since reliable and functional parts are generated with the liquid resin and laser technology, of future vehicles (Volkswagen, 2024 ). In this case, Volkswagen uses 3D printers for prototyping, which, in our understanding, is in an initial state, as this type of technology has been evolving rapidly, opening up space for mass customisation of customisable items and parts, thus allowing widely distributed manufacturing (Andreadis et al., 2022 ; Goh et al., 2021 ; Inácio et al., 2020 ; Kim et al., 2019 ). About additive manufacturing, Stellantis created the Mopar 3D Lab, which aims to allow customers to print car accessories using a 3D printer by making the files available on their website, which has already had to start with the Rampage automobile, which is produced in South America, offering the possibility of customisation (Stellantis, 2023 ). Concerning Stellantis, the concept of additive manufacturing is being used to initially print accessories by customers themselves, which is currently available free of charge, which we imagine could be charged for in the future, in addition to new applications emerging regarding the use of 3D printers (Andreadis et al., 2022 ; Inácio et al., 2020 ; Volpe et al., 2021 ). The Volkswagen Automotive Cloud (VW.AC) supports all of these processes, focusing on integrating all digital services. Additionally, to mobility proposals, with its team based in Seattle in the United States, using the services of Microsoft's Azure Cloud, which allows the company to deliver software updates and installations to vehicles, partners, and the manufacturing plant itself, substantially improving the customer experience (Volkswagen, 2024 ). Due to the flexibility provided by cloud services, Velásquez et al. ( 2018 ) state that both Big Data and cloud computing are crucial for developing a technological ecosystem for companies that want to be up to date with the concept of Industry 4.0. It is also worth highlighting that this concept is crucial in providing scalable computing services through a computing cloud's five essential characteristics: self-service, on-demand, comprehensive network access, resource pooling, rapid scalability, and service measurement (Zhong et al., 2017). Regarding cloud Computing and IoT, Stellantis Shared Services South America, the company's cloud is supported by Oracle Cloud Infrastructure (OCI), which replaced IBM Power machines in 8 physical locations, achieving a reduction in reporting processes and performing calculations with a 50% speed gain (Oracle, 2022 ). Concerns to IoT, Stellantis participates in the Smart City dos Carajás project together with the Federal University of Pará and the city hall for the installation of devices in cars, in addition to the use of AI to filter daily situations such as the condition of the roads, vehicles parked in prohibited places, accumulation of rubbish in the streets, animals on the road, as well as disease outbreaks, such as stagnant water, with all information sent to a monitoring centre, which allows city halls from each region to propose solutions to solve the problems (Ascom, 2022 ). Furthermore, Scania uses Amazon's AWS solution for their cloud computing demand, mainly aiming to achieve better sustainability rates and reduction in carbon emissions by proposing more sustainable transport systems through data capture and sharing (Scania Group, 2023 ). The fourth quadrant, which is represented by companies that have a high application of innovation in Industry 4.0 (Y axis) and low application of Industry 4.0 tools, is represented by ON – HIGHWAY companies that represent the Iveco brand and Volvo, which are more linked to the use of Big Data for the collection, analysis and integration of data in order to improve decision-making and the search for electrification, with the use of a technological ecosystem provided by the IoT and cloud computing (Cooke, 2021 ; Horváthová et al., 2019 ; Lv et al., 2022 ). The first of the technologies to be analysed is Big Data, which Iveco uses through the Iveco Over the Air functionality, which provides features for both customers, such as automation of software updates for all Daily and Iveco S-WAY, in addition to its use in its manufacturing plants and dealerships. The vehicles are equipped with a connectivity box, which allows software updates remotely, which enhances the customer experience (IVECO, 2023 ). In the case of Volvo, the beginning of the use of Big Data took place through its line of trucks, which provided a series of connectivity solutions, mainly about operation management, aiming to reduce consumption and increase comfort and driver safety through the provision of a series of information regarding tolls, availability of gas stations, nearby dealerships and road safety (Premium Papers, 2023 ). Big Data is also used to speed up the delivery of parts using Machine Learning (Fagarassi, 2023 ). Regarding the application of Big Data in both companies, the main strength of this technology is evident, which is the integration and combination of large volumes of complex and heterogeneous data obtained from a wide variety of sources, therefore being one of the pillars of Industry 4.0 for providing diagnoses, simulations through the creation of a knowledge base (Rîșteiu et al., 2021 ; Sarı et al., 2020 ). Concerning electrification of the produced fleet, IVECO Group is one of the leading companies in the energy transition, providing electric and natural gas alternatives. Regarding electrification, Nikola Corporation is developing batteries for trucks with 500 km or 800 km of autonomy, depending on the technology used. Iveco also presented a prototype for Daily and hybrid buses (Alves & Estradão, 2023 ). Concerning Volvo, the company's strategic plan is for it to become an electric car company by the year 2030 through targets linked to the amount of CO 2 emitted during the life cycle of the cars, which is called life cycle assessment (LCA), making it focus on electrification to reduce emissions, combined with the sale of products online (Volvo Cars, 2021 ). It can be seen from the example of both companies that the electrification of their automobile portfolio is closely linked to reducing the use of fossil resources through the search for cleaner energy, as the transport sector is one of the most pollutants today, causing a significant impact on public opinion, since there is a direct relationship between these gases and the greenhouse effect, which causes significant changes in the Earth's climate, affecting everything from crops to natural catastrophes, due to drastic changes in the climate., such as rising temperatures, torrential rains and hurricanes, which makes electrification an alternative for reducing the carbon footprint throughout the life cycle of products (Athanasopoulou et al., 2023 ; Kruchina, 2023 ; Pattara et al., 2012 ; Thomson et al., 2014 ). 6. Conclusions The study's objective was achieved by proposing a method of classifying car manufacturing companies according to terms linked to Industry 4.0 through a scan of the websites of companies belonging to Anfavea. To do so, we adopted a survey that used the ElasticSearch software, and the data was subsequently classified using the principal components method. Through this classification, it can be observed that many companies have not yet paid attention to or have not disclosed applications of 4.0 technology, emphasising Ford and Renault, which, in the case of Ford, can be explained by its departure from the country in 2021. Thus, focusing mainly on imported models, perhaps for this reason, gives a more significant focus concerning commercialisation; as far as Renault is concerned, we noticed that there is a relative approach to the fourth quadrant, indicating that it may be developing actions towards to implement this type of technology, with a focus on generating innovations based on this concept. In the second quadrant, only the company Komatsu was classified, which, as it focuses on heavy machinery, uses Industry 4.0 mainly to seek to reduce operating costs through electrification and the use of monitoring tools through the IoT, mainly aiming to create a technological ecosystem that allows supporting the activities of its customers. In the third quadrant, companies seek both the application of tools in order to create a technological ecosystem through computational clouds and by collecting and classifying data to later apply them in innovations, such as the use of prototyping or even in finished products, in order to be up to date with customer needs, there is also a concern with the electrification of the fleet, but without leaving aside technologies linked to combustion, through obtaining more efficient engines and technologies embedded in its vehicles by providing the integration of usage data and provision of services to customers. Additionally, the company invest in smart manufacturing, which allows a high degree of mass customisation. In the fourth quadrant, some companies have reached a certain degree of maturity about 4.0 technology, therefore having the capacity to focus more on innovations and to be aligned with customer needs, such as high investment in electrification and reduction of carbon footprint, topics that raise current concerns in society, both concerning the vehicle throughout its life cycle, in terms of pollutant emissions, and regarding production methods aimed at reducing the carbon footprint, mainly through the use of product life cycle indicators. The companies On-Highway and Volvo are classified in this quadrant, the latter of which aims to become a company entirely focused on electric vehicles by the year 2030. However, it is worth highlighting that despite the results found, the study needs to be improved, mainly due to its limitations, for example, the application only in the national market, in addition to the fact that there may be better keywords that could be considered for the development of new studies, because due to the dynamics existing in the market, new demands and therefore new appeals will undoubtedly emerge over time, meaning that a panel can be developed based on this study, therefore serving as an indicator for industries regarding investments planned by them. Therefore, future researchers could propose new studies based on applications in other countries, perhaps using the exact keywords or adding new ones and adapting them to the context in which they are inserted. There is also the possibility of developing comparative studies between countries. Of course, the model developed here can be adapted to other types of industry and other concepts present in these scenarios. The paper's main contribution was a proposition of a framework to evaluate how automobile manufacturing companies are positioned according to the 4.0 industry. It provided some clues about why such companies belong to such groups and which actions can be performed to improve their performance in this scenario. Declarations Author Contribution P.S.G.O - Data analysis and. prepared the figuresL.F.S - Paper review, data analysisP.T.A - Data collect, Elastic Search DevelopmentG.F.G.R - Theorical reference preparation, Data collectM.A.S.G.O - Theorical reference preparation, Data collect Data Availability “Data is provided within the manuscript or supplementary information files” References Abulibdeh A, Zaidan E, Abulibdeh R (2024) Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. 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International Journal of Computer Integrated Manufacturing 35, 1172–1187. https://doi.org/10.1080/0951192X.2021.2022760 Rudnik M, Hanon MM, Szot W, Beck K, Gogolewski D, Zmarzły P, Kozior T (2022) Tribological Properties of Medical Material (MED610) Used in 3D Printing PJM Technology. Tehnicki vjesnik /. Tech Gaz 29:1100–1108. https://doi.org/10.17559/TV-20220111154304 Rüßmann M, Lorenz M, Gerbert P, Waldner M, Engel P, Harnisch M, Justus J (2015) Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries [WWW Document]. BCG Global. URL https://www.bcg.com/publications/2015/engineered_products_project_business_industry_4_future_productivity_growth_manufacturing_industries (accessed 1.10.24) Sarı T, Güleş HK, Yiğitol B (2020) Awareness and readiness of Industry 4.0: The case of Turkish manufacturing industry. Adv Prod Eng Manage 15:57–68. https://doi.org/10.14743/apem2020.1.349 Scania Group (2023) Scania collaborates with Amazon Web Services on green-IT initiative [WWW Document]. Scania Group. URL https://www.scania.com/group/en/home/newsroom/news/2021/Scania-collaborates-with-Amazon-Web-Services-on-green-IT-initiative.html (accessed 1.9.24) Serey J, Alfaro M, Fuertes G, Vargas M, Durán C, Ternero R, Rivera R, Sabattin J (2023) Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research. Symmetry (20738994) 15, 535. https://doi.org/10.3390/sym15020535 Sharma D, Kumar B, Chand S (2019) A Trend Analysis of Machine Learning Research with Topic Models and Mann-Kendall Test. IJISA 11, 70–82. https://doi.org/10.5815/ijisa.2019.02.08 Singh A, Madaan G, Hr S, Kumar A (2023) Smart manufacturing systems: a futuristics roadmap towards application of industry 4.0 technologies. Int J Comput Integr Manuf 36:411–428. https://doi.org/10.1080/0951192X.2022.2090607 Stellantis (2023) Mopar 3D Lab inova com impressão de acessórios [WWW Document]. URL https://www.media.stellantis.com/br-pt/parts-services/press/mopar-3d-lab-inova-com-impressao-de-acessorios (accessed 1.9.24) Tamir TS, Xiong G, Fang Q, Dong X, Shen Z, Wang F-Y (2022) A feedback-based print quality improving strategy for FDM 3D printing: an optimal design approach. Int J Adv Manuf Technol 120:2777–2791. https://doi.org/10.1007/s00170-021-08332-4 Thomson G, McCaskill M, Goodwin I, Kearney G, Lolicato S (2014) Potential impacts of rising global temperatures on Australia’s pome fruit industry and adaptation strategies. New Z J Crop Hortic Sci 42:21–30. https://doi.org/10.1080/01140671.2013.838588 Trinks S (2018) A Classification of Real Time Analytics Methods. an Outlook for the Use Within the Smart Factory. Scientific Papers of Silesian University of Technology. Organization & Management / Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie 313–329 Velásquez N, Estevez E, Pesado P (2018) J Comput Sci Technol (JCS&T) 18:258–266. https://doi.org/10.24215/16666038.18.e29 . Cloud Computing, Big Data and the Industry 4.0 Reference Architectures: Cloud Computing, Big Data y las Arquitecturas de Referencia de la Industria 4.0. Volkswagen (2024) Inovação VW: Uma Nova Volkswagen para um novo mundo. [WWW Document]. URL https://www.vw.com.br/pt/volkswagen/tecnologia/inovacao.html (accessed 1.8.24) Volpe S, Sangiorgio V, Petrella A, Coppola A, Notarnicola M, Fiorito F (2021) Building Envelope Prefabricated with 3D Printing Technology. Sustain (2071 – 1050) 13:8923–8923. https://doi.org/10.3390/su13168923 Volvo Cars (2021) Volvo Cars será totalmente elétrica até 2030 [WWW Document]. URL https://www.media.volvocars.com/lat/pt-br/media/pressreleases/294982/volvo-cars-sera-totalmente-eletrica-ate-2030-brazil-only (accessed 1.9.24) Wadhwa K, Babbar H (2023) Digital Twin in the Motorized (Automotive / Vehicle) Industry. Int J Perform Eng 19:568–578. https://doi.org/10.23940/ijpe.23.09.p2.568578 Zhou K, Liu T, Zhou L (2015) Industry 4.0: Towards future industrial opportunities and challenges, in: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). Presented at the 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), IEEE, Zhangjiajie, China, pp. 2147–2152. https://doi.org/10.1109/FSKD.2015.7382284 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4901600","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343080468,"identity":"910cc22e-9531-4c95-963b-63d08fc8c2d4","order_by":0,"name":"Paulo Sergio Gonçalves Oliveira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYLCCBwYMDGwMDIyPwTxm5gbCWhIgWpiNGRiALGZGYrRAKDZpsBYGAlrM+Y9ffJBQcFiOT/rws+qCij/R/O1ALT8qtuHUYjkjp9ggweCwMRtfmtntGWcMcmccZmxg7DlzG6cWgxs8aRIJBmmJbTwMZrd52wxyG4BamBnb8Gg5fwaspb6Nh/1bMUjLfIJaDqQfA2qxSWDj4TFjBmnZQEgL0C/MQL/YGLbx8BRL85wxzt0I1HIQn1+AIfbwwYc/EvLyPewbP/NUyOXOO3/44IMfFXgcxsBjgCl6AKd6sBb2B/jkR8EoGAWjYBQwMAAAOTpSeowM5cYAAAAASUVORK5CYII=","orcid":"","institution":"Universidade Anhembi Morumbi","correspondingAuthor":true,"prefix":"","firstName":"Paulo","middleName":"Sergio Gonçalves","lastName":"Oliveira","suffix":""},{"id":343080469,"identity":"d9fe2a95-4755-40ae-841f-94b90e13464d","order_by":1,"name":"Luciano Ferreira Silva","email":"","orcid":"","institution":"Universidade Nove de 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Horizonte","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"Antônios Soares Gomes","lastName":"Otero","suffix":""}],"badges":[],"createdAt":"2024-08-12 15:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4901600/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4901600/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43069-025-00552-8","type":"published","date":"2025-12-03T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64232814,"identity":"a7d00569-8207-4a91-86e7-94be94591b1e","added_by":"auto","created_at":"2024-09-10 15:31:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCloud Computing Overview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Prepared by the authors (2023).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4901600/v1/11841a6490233e75c27c90fc.png"},{"id":64232816,"identity":"142aa730-43ec-42cd-8844-9349fcc2d594","added_by":"auto","created_at":"2024-09-10 15:31:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60599,"visible":true,"origin":"","legend":"\u003cp\u003ePositioning of Companies concerning Industry 4.0\u003c/p\u003e\n\u003cp\u003eSource: Elaborated with research data (2023)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4901600/v1/96bc640f036efdb873c3bf5d.png"},{"id":97723739,"identity":"0f1403c1-8df3-4dc5-9f34-5f6b83fb584b","added_by":"auto","created_at":"2025-12-08 16:00:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1113026,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4901600/v1/e66291f8-4216-4670-8ae6-931fc4ca1d3c.pdf"},{"id":64233189,"identity":"ad2381a3-caa6-487c-b5a2-1009996f8dab","added_by":"auto","created_at":"2024-09-10 15:39:07","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20995,"visible":true,"origin":"","legend":"","description":"","filename":"DadosATCC.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4901600/v1/8d0eaf9ddfacd588934ff765.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Proposal of a Classification Method for Brazilian Automotive Companies Using the Principal Components Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe combination of Information Technologies (IT) and the Internet has reconfigured the world, making it increasingly interconnected and digitalised. According to the National Confederation of Industry (CNI), the evolution of technology has triggered significant transformations in the operational efficiency of companies since the beginning of the Industrial Revolution. Therefore, with the arrival of powerful and autonomous computers and the recent advancement of mobile devices, we have witnessed an evolution in the industrial sector accompanied by the improvement of digital communication infrastructure. Specifically, we have seen an increase in the availability and speed of data transmission through intelligent wireless networks (Ferreira, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Because of this Contieri et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) pointed out that understanding the phenomena of industry 4.0 in emerging countries is essential to grasp its particularities at a global level which is corroborated by Hajoary (2023) to provide guidance for policymakers and practitioners.\u003c/p\u003e \u003cp\u003eThese technological advances are bringing the cyber-physical world into reality. Also, the concept of cyberspace to operate and firms compete. This enables the networking of resources, information, objects, and people, giving rise to the Internet of Things (IoT). The Iot, can be used to create an inventory of cost and emission data, mainly because one of the industry 4.0 objectives is the waste and emission reduction (Ding et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, the industry is experiencing the impacts of this technological evolution, culminating in the rise of the fourth industrial revolution, also known as Industry 4.0 (Kagermann et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to Drath and Horch (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), flexibility is one of the foundations of Industry 4.0; this flexibility is made possible through connectivity, where devices interconnect with each other and with human interfaces, providing real-time information, allowing greater productivity, faster processes fast and flexible to produce better quality products at lower costs.\u003c/p\u003e \u003cp\u003eSince the beginning of industrialisation in the 19th century, technological advances have caused significant changes in industrial paradigms. These transformations are often referred to as \"industrial revolutions\". The first of these occurred with the introduction of vapour power. At the end of the 19th century, the second industrial revolution emerged with the electrification system. Subsequently, the third industrial revolution was driven by cybernetics and widespread digitalisation. We are witnessing a new industrial revolution, known as the 4.0 Industry, resulting from combining several technologies, such as the Internet and IoT (Lasi et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndustry 4.0 began through a strategic project by the German government, which recognised the benefits of new manufacturing technologies, such as marketing and manufacturing (B\u0026uuml;rkner et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Capestro et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This fourth revolution is transforming conventional industrial production methods, driving the manufacturing of the future to become more intelligent through the adoption of connected and digital systems. This has given rise to knowledge-based factories, significantly improving productivity levels, efficiency and, consequently, competitiveness.\u003c/p\u003e \u003cp\u003eR\u0026uuml;\u0026szlig;mann et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Benitez et al., (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlighted that the rise of Industry 4.0, driven by digital technologies such as the IoT, Big Data and Cloud Computing (CC), had the potential to generate significant change. These changes included improvements in factory productivity, stimulating industrial growth, reconfiguring the workforce, and influencing the competitiveness of companies and regions. As mentioned previously, Germany was the pioneer in introducing the concept of Industry 4.0 and is currently the global leader in manufacturing in several sectors, including the automotive sector, which is home to renowned companies such as BMW, Mercedes-Benz, and Volkswagen (VW) (Zhou et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Brazil, VW has already implemented 3D printers in all its factories that produce materials - with maximum precision and without wasting material. Another major innovation from VW is the implementation of a technology called digital factory, which uses more than 50 pieces of software that work on the same operational platform and allows production procedures to be simulated virtually, designing new buildings, infrastructures, and offices before physical execution, while analysing the ergonomics of workplaces. The Digital Factory began in 2008 and has allowed the company to avoid numerous errors that require subsequent corrections. Considering just six significant projects implemented in the last five years, using the Digital Factory can avoid expenses exceeding R\u003cspan\u003e$\u003c/span\u003e100\u0026nbsp;million (Volkswagen, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, the automobile industry relies heavily on automation. According to \u0026ldquo;Revista Quatro Rodas\u0026rdquo; (Four Wheels Magazine), around 70% of tasks related to car manufacturing are performed by robots (Carvalho, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a result, most car manufacturers adopt Industry 4.0 principles, incorporating technology as a fundamental partner in their operations, whether in production or process management. Regarding the presented context, this article proposes a method for classifying Brazilian companies in the automotive sector according to the concepts of Industry 4.0. In order to reach this aim, a theoretical framework was developed regarding the importance of the Brazilian automobile industry. In addition to defining Industry 4.0 and its respective technologies, we present a theoretical framework section. The methodological procedures were developed in the following sections, and in the fourth section, the analysis of the collected data was followed by discussions of the results. Finally, in the last section, final remarks were shown.\u003c/p\u003e"},{"header":"2. Theory","content":"\u003cp\u003eThe basis of this article was structured into topics, namely: \u0026ldquo;Automobile Industry\u0026rdquo; included in the first topic, which aims to demonstrate its importance and relevance to the economy. Next, the \"Industry 4.0\" subsection aims to show how the concept of Industry 4.0 emerged and what the tools/technologies for this industry are.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Automotive Industry\u003c/h2\u003e \u003cp\u003eThe automobile industry represents around 20% of the GDP of the Brazilian manufacturing sector, in addition to generating around 1.2\u0026nbsp;million direct and indirect jobs, making it one of the most significant sectors of the economy (Dana, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, it is worth highlighting that car sales in the retail sector have been falling in recent years, representing a threat to this sector, which has caused companies to look for alternatives to this reduction. For example, the car market tries to reach young people from Generation Z, who have different aspirations than other generations (Negocios SC, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese changes in the sector point to the need for a radical transformation regarding this industry, with one of the main bets being investment in new technologies, which will undoubtedly play a fundamental role in improving the numbers in this sector, which are driven by a combination of factors such as technological advances, changing consumer preferences, government regulations, and global trends (Minas, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, due to environmental pressures, there is also pressure for the electrification of the fleet, as the mobility sector is one of the pollutants currently, causing this trend to advance worldwide. In Brazil, the first electric cars arrived in 2019; since then, a series of hybrid or fully electric models has been developed, giving the country some success stories, such as the electrification of the urban bus fleet in some states and also some car models (Bastos and Oliveira, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese changes open space for investment in Industry 4.0, mainly due to its characteristics linked to investment in technologies that allow the integration of all technological means available by the company. Furthermore, it focuses on sustainability, including its suppliers, allowing flexibility to this type of industry, thus responding more quickly to customer needs, government regulations and the needs shaping current societal transformations. In this line, the next topic will comment on Industry 4.0 and its importance for the automobile sector.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Industry 4.0\u003c/h2\u003e \u003cp\u003eOver the last three decades, advances in information technologies and their integration into production processes have brought comprehensive advantages throughout the value chain. The progression in the capacity of these technologies has boosted industrial productivity, resulting in reduced production costs and the offering of practical solutions to serve customers with quality, speed, and an improved cost/benefit ratio (Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Faced with these recent technological advances and in a context where the demand for customised products, greater sophistication, increased quality, and cost reduction is constantly growing, the discussion arises about a new industrial paradigm, which is approached as Industry 4.0 (El Kihel et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hermann et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Novoszel and Wakolbinger, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)​.\u003c/p\u003e \u003cp\u003eMany scholars consider Industry 4.0 the next stage in the industrial revolution, with the potential to boost economic growth. In this new stage, the ability to integrate several technologies that facilitate the automation and digitalisation of processes is expected, offering more refined control over manufacturing methods (McClymonds et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This translates into smart factories that have the potential to promote more effective, autonomous, and customisable production models, providing significant benefits to the industry (Abulibdeh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Brettel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, among these benefits, we can highlight intelligent production processes, mainly due to the creation of ecosystems that use autonomous resources, such as self-structuring and self-monitoring, which certainly increases the quality of products, in addition to productivity also allows mass customisation of these products (Harris, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sarı et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We can also include the fact that enterprises are considering industry 4.0 as a pivotal factor for help achieve a successful transition towards a green production and more equitable future (Ocel\u0026iacute;k et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndustry 4.0 was first introduced at the Hannover Fair in 2011. In the following year, in October, the group responsible for the project prepared a report that recommended the implementation of this production phase in the various industrial sectors in Germany. In April 2013, at the same fair that marked the beginning of the project, a final plan was presented in which the idea of providing the production sector with machines, cybernetic systems and intelligent networks was conceived, providing greater autonomy and efficiency in production (B\u0026uuml;rkner et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rossit and Tohm\u0026eacute;, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e. The concept of Industry 4.0 is driven by a series of technologies which allow the creation of a digital eco-system, of which we will comment on in the following paragraphs on the ones we consider paramount (Battaglia et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Benitez et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe first of them is Big Data, which promotes access to large amounts of data at high speed, which are complex and variable, requiring the use of advanced techniques and technologies to enable the capture, management, and efficient analysis of this data (TechAmerica Foundation Federal Big Data Commission, 2012). According to IDC (International Data Corporation), Big Data technologies represent a new generation of technologies and architectures designed to extract economic value from vast volumes and a wide variety of data, which enables the generation of information, as well as analysis in high speed, thus allowing more assertive and faster decisions. In agreement, Manyika et al. (2011) state that Big Data encompasses data sets of dimensions surpassing conventional tools' capture, storage and analysis capabilities.\u003c/p\u003e \u003cp\u003eThe main strength of Big Data lies in the fact that it combines large volumes of data, which is a combination of large, complex, and heterogeneous data generated from a wide variety of sources, such as social networks, web click streams, monitoring and action, video sharing, business processes, as well as other sources, which can be used to generate digital and smart factories. The smart factory concepts emerged with Industry 4.0, allowing human and machine interaction to synchronise and harmoniously (Sarı et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this sense, several efforts are being made by the automotive industry to integrate data from car diagnostics, thus aiming to create a knowledge base, in addition to allowing the simulation of these problems in order to anticipate critical failures throughout the entire life cycle of its products, thus allowing better service provision to customers (R\u0026icirc;șteiu et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This data can also be used to create digital twins, both in production systems and in automobiles that are being produced, in order to be able to analyse possible problems, design flaws and process monitoring in the case of production systems (Jena and Patel, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wadhwa \u0026amp; Babbar, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second group of technologies that we can highlight is the IoT, which concerns the interconnection of everyday objects, often equipped with ubiquitous Intelligence, aiming to connect interactive objects in existing systems, creating a network of devices which can communicate both with humans and other devices (Xia et al., 2012). IoT expands the horizon of companies mainly using sensors spread throughout the production process, in addition to the use of embedded systems, which collect a large volume of data and make real-time data analysis possible, which can be made available in a Big Data system, opening up a wide range of applications in the analytical field (Trinks, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIoT still has excellent room for expansion, as the development of technologies that make the flow and influx of data increasingly faster tend to leverage the possibilities that it can still deliver, among which we can highlight, for example, the popularisation of 5G and its application in an increasing number of devices, which can undoubtedly awaken new uses, in addition to unlocking a series of limitations that we currently have, in addition to enabling the integration of an increasing number of devices, which can be internal to companies or external (Atharvan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Data collected using IoT can be integrated and combined with existing production processes, enabling companies to have smart production and improving production activities through sensors and smart manufacturing systems (Manoj, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mokhtarzadeh et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fourth group of technologies that make up Industry 4.0 is called Cloud Computing, which aims to provide information technology services on demand, with payments according to use, without the need for specific resources allocated to a group of users, making the technology allocation process more flexible, in addition to being more in line with business demands (Buyya et al. 2009; Karatas et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For this reason, both Big Data and the concept of cloud computing are considered crucial for developing a technological ecosystem, which allows flexibility for companies looking to be up-to-date concerning Industry 4.0 (Vel\u0026aacute;squez et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, cloud computing is crucial in this context due to its characteristic of providing scalable computing services accessible via the Internet, which may include the provision of computing as a service and software as a service, among other things as services, such as learning of machine and analytical capabilities, through five essential characteristics: essential characteristics: self-service, on-demand, comprehensive network access, resource pooling, rapid scalability, and service measurement (Zhong et al. 2017). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows an overview of cloud computing.\u003c/p\u003e \u003cp\u003eThe fifth group of technologies that enable Industry 4.0 is Artificial Intelligence, which can be defined as a set of skills to be developed in machines, with the aim that they can understand, reason, and learn in a way comparable to human beings, which opens up an excellent gateway to the use of computers in simulating human Intelligence (Pan, 2016). Artificial Intelligence comprises a series of disruptive technologies, some of which we can highlight, such as cognitive computing and expert systems, which can be combined with data mining/data science and advanced data analysis (Jan et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Serey et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, it enables using robots, quality data analysis, process improvement, and the development of intelligent manufacturing (de Jesus Pacheco et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAs previously stated, Artificial Intelligence is a sub-area of computing that aims to mimic human learning, which was previously proposed by Alan Turing, and among which we can highlight machine learning. According to Mitchell (Mitchell, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), these technologies allow algorithms to learn through the repetition of a task, making the system learn, thus improving its approach, aiming to solve it more effectively, contributing not only to existing knowledge but also to the improvement of the task in question.\u003c/p\u003e\u003cp\u003eAbout machine learning, it is also important to highlight that there are some divisions, the first being between algorithms that are used for supervised learning and, therefore, need a classification label, usually applied by an expert, to classify these data. For example, when analysing a financial transaction, the specialist can decide whether the company is facing fraud and, therefore, assigns a label that represents this fraud or not, assigning, for example, a non-fraud label, making these data used for training, so that they can learn which patterns denote one label from others, generating a model which can classify data autonomously based on a success rate. The second type is unsupervised learning, where there is no label, meaning that based on some metric, the data is classified into groups and, subsequently, interpreted by an expert (Kubat, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese two approaches can be combined, generating a third group, which we call semi-supervised learning, which uses both supervised and unsupervised techniques to promote continuous improvement of the algorithm, which can also be combined with reinforcement to evaluate error or success in decision-making (Kuncheva, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We can also highlight bio-inspired algorithms, such as neural networks, which seek to imitate the network of neurons in our brain, and non-bio-inspired ones, such as cluster analysis, Random forests, and KNN, among others (Jordan \u0026amp; Mitchell, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Marsland, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Romeo et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne of the areas that have gained prominence in recent times is called deep learning, which is, therefore, a subfield of machine learning in which neural networks have one or more levels of hidden learning layers, which substantially improves the rate of learning the algorithm and for this reason, it opens up a series of possibilities, such as computer vision, which can be used, for example, to find a defect in a part that is being produced automatically or even guide a robot in a manufacturing factory autonomously (Allahyari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jordan \u0026amp; Mitchell, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kuncheva, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marsland, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this sense, we can also cite natural language analysis, enabling computers to interpret texts or even oral language and communicate with human beings, among other applications ​.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe sixth group of technologies linked to Industry 4.0 is called additive manufacturing due to its flexibility, which can be used for product evaluation and mass manufacturing (Kim et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nguyen Ngoc et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additive manufacturing is a production technique that uses the addition of successive layers of material until the desired shape of the element you want to produce is reached, having been proposed in the 1980s (In\u0026aacute;cio et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, it has been evolving more quickly mainly due to advances in areas such as networked systems, including systems such as computing services and clouds, and artificial Intelligence, among others, which are driving mass production manufacturing for mass customisation through the production of highly customisable items produced through massively distributed manufacturing (Tamir et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe can also add to this the fact that 3D printers can produce complex and completely functional items using a multitude of materials, from plastic, iron and even biological materials (Goh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), creating space for a multitude of applications in most varied fields, such as civil construction, to the pharmaceutical industry. One example is Andreadis et al.\u0026rsquo;s (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) research, which shows the application of the manufacture of drugs with an individualised dose and even with a degree of attractiveness for different audiences, demonstrated, such as coloured pills or in the shape of characters.\u003c/p\u003e \u003cp\u003eIn the automotive industry, additive manufacturing finds space for various applications, from producing components for prototyping to producing spare parts. Those are used in manufacturing and even producing complete automobiles and forming on-demand part of the operational process with 3D-CAD models, which can be stored in databases and shared via the cloud, thus improving business flexibility (Budzik et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It should also be noted that advances in the chemical development of materials increasingly expand the range of applications and possibilities that 3D printers and the concept of additive manufacturing present both for the research area and for the industry in general, therefore being an essential tool of Industry 4.0 (Rudnik et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, global warming influences the most varied sectors, including agriculture, drastically reducing the production of fruits and vegetables and causing economic and food security problems and catastrophes due to extreme temperatures (Thomson et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, since the use of fossil resources and energy, in addition to environmental impacts, are at the top of the list of the biggest challenges that need to be resolved in the coming years, combined with the fact that the transport sector is one of the most polluting, it was decided to include the electrification of automobiles as one of the components of the study, as this type of automobile can reduce our dependence on fossil fuels, despite the problems that still need to be resolved in this type of technology, such as battery durability, charging time and durability of these batteries (Athanasopoulou et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis movement made customers pay attention to companies from the most varied sectors, pushing for a reduction in the carbon footprint in order to generate evidence of the adoption of environmental practices throughout the product's life cycle, especially those relating to the emission of greenhouse gases (Bai et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pattara et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Initially, pressure caused city halls worldwide to look at electrified vehicles as an alternative for a green transition; however, this type of transport has even demonstrated economic advantages compared to vehicles powered by fossil fuels (Kruchina, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this way, it is understood that there is pressure for the automobile industry to promote changes in its product portfolio, offering hybrid solutions and completely electric vehicles to adapt to the pressures linked to the consumption of fossil fuels, linked to the greenhouse effect. In this way, we assume it is inevitable to incorporate Industry 4.0 technologies to overcome sustainable challenges.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cp\u003eThe research was classified as descriptive, as it used statistical methods to describe the behaviour of a population, in this case, the automobile industry, considering the concept of Industry 4.0 (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Demo, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kerlinger, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). In this sense, the data was collected through a survey using secondary data from company websites, using a cross-sectional survey, as the data was collected only once (Babbie, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The Elasticsearch software version 8.10.2 was used as a search and distributed data analysis engine based on Apache Lucene and Java, which collects unstructured and structured data, being an open and free platform. A web crawler, which uses an index to search websites, was used for this research (Elastic, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the search, the following keywords related to the concept of Industry 4.0 were used: 4.0 Technology, Big Data, IoT, Cloud Computing, Artificial Intelligence, Digital Factory, 3D Printer, Electrification and Electric Car. Thus, based on the websites of members of Anfavea (National Association of Motor Vehicle Manufacturers), it is an entity that brings together the leading manufacturers of automobiles, light commercial vehicles, trucks, buses, agricultural and construction machinery, 18 associates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were selected, as they were more aligned with the aim of this research.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Company websites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAudi do Brasil Ind\u0026uacute;stria e Com\u0026eacute;rcio de Ve\u0026iacute;culos Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.audi.com.br\u003c/span\u003e\u003cspan address=\"https://www.audi.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFord Motor Company Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ford.com.br\u003c/span\u003e\u003cspan address=\"https://www.ford.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Motors do Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.chevrolet.com.br\u003c/span\u003e\u003cspan address=\"https://www.chevrolet.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHonda Autom\u0026oacute;veis do Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.honda.com.br\u003c/span\u003e\u003cspan address=\"https://www.honda.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPE Automotores do Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://en.hpeautos.com.br\u003c/span\u003e\u003cspan address=\"https://en.hpeautos.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyundai Motor Brasil Montadora de Autom\u0026oacute;veis Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hyundai.com.br\u003c/span\u003e\u003cspan address=\"https://www.hyundai.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJaguar e Land Rover Brasil Ind\u0026uacute;stria e Com\u0026eacute;rcio de Ve\u0026iacute;culos Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jaguarbrasil.com.br\u003c/span\u003e\u003cspan address=\"https://www.jaguarbrasil.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMercedes-Benz do Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www2.mercedes-benz.com.br\u003c/span\u003e\u003cspan address=\"https://www2.mercedes-benz.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNissan do Brasil Autom\u0026oacute;veis Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nissan.com.br\u003c/span\u003e\u003cspan address=\"https://www.nissan.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenault do Brasil S. A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.renault.com.br\u003c/span\u003e\u003cspan address=\"https://www.renault.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToyota do Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.toyota.com.br\u003c/span\u003e\u003cspan address=\"https://www.toyota.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolkswagen do Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.vw.com.br\u003c/span\u003e\u003cspan address=\"https://www.vw.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolvo do Brasil Ve\u0026iacute;culos Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.volvogroup.com\u003c/span\u003e\u003cspan address=\"https://www.volvogroup.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKomatsu do Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.komatsu.com.br\u003c/span\u003e\u003cspan address=\"https://www.komatsu.com.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-Highway Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iveco.com\u003c/span\u003e\u003cspan address=\"https://www.iveco.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStellantis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stellantis.com\u003c/span\u003e\u003cspan address=\"https://www.stellantis.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScania Latin America Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scania.com\u003c/span\u003e\u003cspan address=\"https://www.scania.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Anfavea (2023).\u003c/p\u003e \u003cp\u003eThe scan carried out on the website of these 18 associated companies resulted in approximately 137,382 documents that presented at least one incidence of keywords linked to Industry 4.0, as demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, composing the research sample.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Number of Occurrences in Documents on Company Websites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003cp\u003eTech.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBig Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIoT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCloud Computing (CC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArtificial Intelligence (AI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigital Factory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3D Printer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eElectr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eElectric Car\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAudi do Brasil Ind\u0026uacute;stria e Com\u0026eacute;rcio de Ve\u0026iacute;culos Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFord Motor Company Brasil Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Motors do Brasil Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHonda Autom\u0026oacute;veis do Brasil Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPE Automotores do Brasil Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyundai Motor Brasil Montadora de Autom\u0026oacute;veis Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJaguar e Land Rover Brasil Ind\u0026uacute;stria e Com\u0026eacute;rcio de Ve\u0026iacute;culos Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMercedes-Benz do Brasil Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNissan do Brasil Autom\u0026oacute;veis Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenault do Brasil S.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToyota do Brasil Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolkswagen do Brasil Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolvo do Brasil Ve\u0026iacute;culos Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKomatsu do Brasil Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-Highway Brasil Ltda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStellantis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScania Latin America Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMW do Brasil Ltda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e137382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eSource: Elaborated with Research Data (2023)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data was collected by counting the documents related to the keywords, which were arranged in columns, indicating a possible engagement of that company with that type of technology through the communication of these attributes. The collected data was subjected to factor analysis with the main components extraction method in order to summarise the variables raised and subsequently generate a classification of companies according to their engagement with the Industry 4.0 concept (Hair et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Malhotra \u0026amp; Menezes, 2019; Pestana \u0026amp; Gageiro, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eData analysis began with the descriptive analysis of the data, which is demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Descriptive Data Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeywords\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompanies number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOccurrence number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4.0 Technologies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e611.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1551.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBig Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2348.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5396.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIoT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e861.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1705.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCloud Computing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e830.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1742.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e535.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1493.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDigital factory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e594.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1193.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3D Printer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e556.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1188.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectrification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e580.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1136.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectric car\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e713.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1824.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource: Elaborated with Research Data (2023)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data were then subjected to exploratory factor analysis using the principal components method, without any rotation method, through which the quality of the analysis was initially verified through the KMO, whose acceptance limit is \u0026gt;\u0026thinsp;=\u0026thinsp;0.5, which presented a value of 0.627. The Bartlet Sphericity Test presented X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;489; df\u0026thinsp;=\u0026thinsp;36 and α\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; therefore, the use of factor analysis is appropriate (Hair et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Malhotra \u0026amp; Menezes, 2019; Pestana \u0026amp; Gageiro, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The next test was the application of commonalities whose values should be more significant, ranging from 0 to 1, with the acceptable limit being \u0026gt;\u0026thinsp;=\u0026thinsp;0.5; through Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it is observed that all values exceeded these limits, being, therefore, acceptable (Pestana \u0026amp; Gageiro, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Communalities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtraction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4.0 Technologies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBig Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIoT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCloud Computing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDigital Factory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3D Printer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectrification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectric Car\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e* Extraction Method: Principal Component Analysis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Elaborated with Research Data (2023)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe explained variance of the components was 71.90% for component 1 and 19.08 for component 2 of the study, which totalled 90.98% of the total explanation of the sum of these two components, therefore being a good explanation of variations using these two components (Hair et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pestana \u0026amp; Gageiro, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates the components matrix, which denotes the variables that belong to the first component and the variables that comprise the second component, both highlighted in grey.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Components Extracted with Component Analysis Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eComponents Matrix\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eComponents\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInnovations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDigital Factory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCloud Computing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIoT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectrification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3D Printer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4.0 Technologies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBig Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectric Car\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eExtraction Method: Principal Component Analysis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e*. 2 extracted components.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Elaborated with Research Data (2023)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the variables that were concentrated in Component 1, it was named Tools because they are concrete means of use in manufacturing products in the automotive sector. In contrast, Component 2 was named Innovations because it determines a new result of an existing product.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows companies according to their positioning concerning the concept of Industry 4.0.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we will begin to analyse the first lower left quadrant, which means that companies in this region have low adherence to the innovation components and tools of Industry 4.0, thus needing to develop actions related to both components. It is worth noting that in this quadrant, we have two companies that significantly impact the national automobile market. Ford Motor Company left Brazil in 2021 and started to have a portfolio of imported automobiles. This may explain the low number of documents linked to Industry 4.0, as the company no longer has manufacturing units in the country.\u003c/p\u003e \u003cp\u003eRegarding Renault, the strategy adopted by the group from 2021 onwards suggests that there will be a significant investment in electrification and simplification through platforms, which explains the positioning at the upper limit of the lower quadrant. However, this company perhaps could migrate to the upper quadrant to what it is now, with a greater focus on innovations linked to Industry 4.0. In this sense, we can point out that this company situation is worth highlighting, as some actions occur outside Brazil, perhaps for this reason not being captured by the analysis (Quatro Rodas, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Brazilian market, the idea is to invest in Turbo 1.0 engines, in addition to restyling some models, in addition to starting to introduce electric vehicles in order to be in tune with the demands for environmental sustainability through electrification, reducing the carbon footprint and reduction of greenhouse emissions (Athanasopoulou et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pattara et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Thomson et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoving forward with the analysis in the lower right quadrant, which includes only one company, Komatsu do Brazil, whose products are mainly used in mining and civil construction, with products such as hydraulic excavators, tracked and wheeled tractors, mechanical trucks and electrical (Komatsu, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This quadrant is characterised by being high in the Industry 4.0 tools dimension and low in innovation, with the company being close to the upper limit, as there are products linked to electrification, which is linked more to the Y innovation axis, and close to the lower limit of the axis X - Tools, as it has a complete factory park, as well as implementing innovations in the area of construction, such as the use of monitoring drones, real-time equipment monitoring system and preventive and predictive maintenance called Komtrax, besides use augmented reality (Komatsu do Brasil, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Industry 4.0 tools used by Komatsu have been used in the automotive industry to integrate diagnostic data through the implementation of the IoT, which is embedded in equipment, such as trucks, excavators, etc., in addition to the use of cloud computing (Atharvan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Manoj, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; R\u0026icirc;șteiu et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Trinks, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wadhwa \u0026amp; Babbar, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe third quadrant represents companies with a high degree of innovation (Y axis) and Industry 4.0 tools (x), including the companies Stellantis, Volkswagen and Scania. This quadrant by the automobile company Volkswagen, we can highlight the following actions: the use of digital factory and intelligent manufacturing, which began in 2019 with the inauguration of the S\u0026atilde;o Bernado Campo factory, the virtual prototyping laboratory, aiming to generate virtual models of automobiles that will be launched in the future. Which begins with the development of a virtual prototype, which is fed with information from the company's product design and engineering area and consolidates the project using augmented and virtual reality software so that it can, using this model, carry out safety simulations (crash-test), aerodynamic acoustics, durability, among other points, which dramatically reduces the number of project failures.\u003c/p\u003e \u003cp\u003eTests are then carried out with physical vehicles, thus generating savings and project reliability (Volkswagen, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This highlighted aspect allows the company to meet the demand for customised products, which present a greater degree of sophistication, in addition to achieving significant cost reduction, as it uses the integration of various technologies, such as networks, cloud computing, big data, among others, to generate simulations with a high degree of assertiveness, thus generating smart factories which are customisable, autonomous and more effective (Brettel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gavilanes et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hermann et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith the digital factory concept, the company Stellantis, together with the company Comau Industrial Automation and Robotics, implemented virtual process simulations, aiming to optimise the steps involved in the manufacture of automobiles, thus allowing the performance of the process as a whole to be evaluated, enabling virtually anticipate problems and identify areas for improvement. This optimisation also resulted in the possibility of scheduling the production of different types of automobiles on the same line. This innovation allowed the integration of 74 automatic and 10 manual lines with advanced vision and robotics services to optimise the process (COMAU, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConcerning intelligent manufacturing, Scania made a significant investment in its S\u0026atilde;o Bernado do Campo factory to modernise the industrial complex, and the area that underwent the most modification was the welding area, with welding carried out by robots. In addition to quality verification carried out by 3D scanning, improvements are also being made in the production of parts, mainly regarding review and reevaluation (Automotive Business, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the case of Volkswagen, it is interesting to analyse the use of additive manufacturing, as all factories in Brazil already use 3D printers for prototyping, which generates greater precision and leads to a reduction in waste since reliable and functional parts are generated with the liquid resin and laser technology, of future vehicles (Volkswagen, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this case, Volkswagen uses 3D printers for prototyping, which, in our understanding, is in an initial state, as this type of technology has been evolving rapidly, opening up space for mass customisation of customisable items and parts, thus allowing widely distributed manufacturing (Andreadis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Goh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; In\u0026aacute;cio et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAbout additive manufacturing, Stellantis created the Mopar 3D Lab, which aims to allow customers to print car accessories using a 3D printer by making the files available on their website, which has already had to start with the Rampage automobile, which is produced in South America, offering the possibility of customisation (Stellantis, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConcerning Stellantis, the concept of additive manufacturing is being used to initially print accessories by customers themselves, which is currently available free of charge, which we imagine could be charged for in the future, in addition to new applications emerging regarding the use of 3D printers (Andreadis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; In\u0026aacute;cio et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Volpe et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Volkswagen Automotive Cloud (VW.AC) supports all of these processes, focusing on integrating all digital services. Additionally, to mobility proposals, with its team based in Seattle in the United States, using the services of Microsoft's Azure Cloud, which allows the company to deliver software updates and installations to vehicles, partners, and the manufacturing plant itself, substantially improving the customer experience (Volkswagen, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to the flexibility provided by cloud services, Vel\u0026aacute;squez et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) state that both Big Data and cloud computing are crucial for developing a technological ecosystem for companies that want to be up to date with the concept of Industry 4.0. It is also worth highlighting that this concept is crucial in providing scalable computing services through a computing cloud's five essential characteristics: self-service, on-demand, comprehensive network access, resource pooling, rapid scalability, and service measurement (Zhong et al., 2017).\u003c/p\u003e \u003cp\u003eRegarding cloud Computing and IoT, Stellantis Shared Services South America, the company's cloud is supported by Oracle Cloud Infrastructure (OCI), which replaced IBM Power machines in 8 physical locations, achieving a reduction in reporting processes and performing calculations with a 50% speed gain (Oracle, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Concerns to IoT, Stellantis participates in the Smart City dos Caraj\u0026aacute;s project together with the Federal University of Par\u0026aacute; and the city hall for the installation of devices in cars, in addition to the use of AI to filter daily situations such as the condition of the roads, vehicles parked in prohibited places, accumulation of rubbish in the streets, animals on the road, as well as disease outbreaks, such as stagnant water, with all information sent to a monitoring centre, which allows city halls from each region to propose solutions to solve the problems (Ascom, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, Scania uses Amazon's AWS solution for their cloud computing demand, mainly aiming to achieve better sustainability rates and reduction in carbon emissions by proposing more sustainable transport systems through data capture and sharing (Scania Group, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fourth quadrant, which is represented by companies that have a high application of innovation in Industry 4.0 (Y axis) and low application of Industry 4.0 tools, is represented by ON \u0026ndash; HIGHWAY companies that represent the Iveco brand and Volvo, which are more linked to the use of Big Data for the collection, analysis and integration of data in order to improve decision-making and the search for electrification, with the use of a technological ecosystem provided by the IoT and cloud computing (Cooke, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Horv\u0026aacute;thov\u0026aacute; et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe first of the technologies to be analysed is Big Data, which Iveco uses through the Iveco Over the Air functionality, which provides features for both customers, such as automation of software updates for all Daily and Iveco S-WAY, in addition to its use in its manufacturing plants and dealerships. The vehicles are equipped with a connectivity box, which allows software updates remotely, which enhances the customer experience (IVECO, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the case of Volvo, the beginning of the use of Big Data took place through its line of trucks, which provided a series of connectivity solutions, mainly about operation management, aiming to reduce consumption and increase comfort and driver safety through the provision of a series of information regarding tolls, availability of gas stations, nearby dealerships and road safety (Premium Papers, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Big Data is also used to speed up the delivery of parts using Machine Learning (Fagarassi, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the application of Big Data in both companies, the main strength of this technology is evident, which is the integration and combination of large volumes of complex and heterogeneous data obtained from a wide variety of sources, therefore being one of the pillars of Industry 4.0 for providing diagnoses, simulations through the creation of a knowledge base (R\u0026icirc;șteiu et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sarı et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Concerning electrification of the produced fleet, IVECO Group is one of the leading companies in the energy transition, providing electric and natural gas alternatives. Regarding electrification, Nikola Corporation is developing batteries for trucks with 500 km or 800 km of autonomy, depending on the technology used. Iveco also presented a prototype for Daily and hybrid buses (Alves \u0026amp; Estrad\u0026atilde;o, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConcerning Volvo, the company's strategic plan is for it to become an electric car company by the year 2030 through targets linked to the amount of CO\u003csup\u003e2\u003c/sup\u003e emitted during the life cycle of the cars, which is called life cycle assessment (LCA), making it focus on electrification to reduce emissions, combined with the sale of products online (Volvo Cars, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It can be seen from the example of both companies that the electrification of their automobile portfolio is closely linked to reducing the use of fossil resources through the search for cleaner energy, as the transport sector is one of the most pollutants today, causing a significant impact on public opinion, since there is a direct relationship between these gases and the greenhouse effect, which causes significant changes in the Earth's climate, affecting everything from crops to natural catastrophes, due to drastic changes in the climate., such as rising temperatures, torrential rains and hurricanes, which makes electrification an alternative for reducing the carbon footprint throughout the life cycle of products (Athanasopoulou et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kruchina, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pattara et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Thomson et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe study's objective was achieved by proposing a method of classifying car manufacturing companies according to terms linked to Industry 4.0 through a scan of the websites of companies belonging to Anfavea. To do so, we adopted a survey that used the ElasticSearch software, and the data was subsequently classified using the principal components method. Through this classification, it can be observed that many companies have not yet paid attention to or have not disclosed applications of 4.0 technology, emphasising Ford and Renault, which, in the case of Ford, can be explained by its departure from the country in 2021. Thus, focusing mainly on imported models, perhaps for this reason, gives a more significant focus concerning commercialisation; as far as Renault is concerned, we noticed that there is a relative approach to the fourth quadrant, indicating that it may be developing actions towards to implement this type of technology, with a focus on generating innovations based on this concept.\u003c/p\u003e \u003cp\u003eIn the second quadrant, only the company Komatsu was classified, which, as it focuses on heavy machinery, uses Industry 4.0 mainly to seek to reduce operating costs through electrification and the use of monitoring tools through the IoT, mainly aiming to create a technological ecosystem that allows supporting the activities of its customers.\u003c/p\u003e \u003cp\u003eIn the third quadrant, companies seek both the application of tools in order to create a technological ecosystem through computational clouds and by collecting and classifying data to later apply them in innovations, such as the use of prototyping or even in finished products, in order to be up to date with customer needs, there is also a concern with the electrification of the fleet, but without leaving aside technologies linked to combustion, through obtaining more efficient engines and technologies embedded in its vehicles by providing the integration of usage data and provision of services to customers. Additionally, the company invest in smart manufacturing, which allows a high degree of mass customisation.\u003c/p\u003e \u003cp\u003eIn the fourth quadrant, some companies have reached a certain degree of maturity about 4.0 technology, therefore having the capacity to focus more on innovations and to be aligned with customer needs, such as high investment in electrification and reduction of carbon footprint, topics that raise current concerns in society, both concerning the vehicle throughout its life cycle, in terms of pollutant emissions, and regarding production methods aimed at reducing the carbon footprint, mainly through the use of product life cycle indicators. The companies On-Highway and Volvo are classified in this quadrant, the latter of which aims to become a company entirely focused on electric vehicles by the year 2030.\u003c/p\u003e \u003cp\u003eHowever, it is worth highlighting that despite the results found, the study needs to be improved, mainly due to its limitations, for example, the application only in the national market, in addition to the fact that there may be better keywords that could be considered for the development of new studies, because due to the dynamics existing in the market, new demands and therefore new appeals will undoubtedly emerge over time, meaning that a panel can be developed based on this study, therefore serving as an indicator for industries regarding investments planned by them.\u003c/p\u003e \u003cp\u003eTherefore, future researchers could propose new studies based on applications in other countries, perhaps using the exact keywords or adding new ones and adapting them to the context in which they are inserted. There is also the possibility of developing comparative studies between countries. Of course, the model developed here can be adapted to other types of industry and other concepts present in these scenarios.\u003c/p\u003e \u003cp\u003eThe paper's main contribution was a proposition of a framework to evaluate how automobile manufacturing companies are positioned according to the 4.0 industry. It provided some clues about why such companies belong to such groups and which actions can be performed to improve their performance in this scenario.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP.S.G.O - Data analysis and. prepared the figuresL.F.S - Paper review, data analysisP.T.A - Data collect, Elastic Search DevelopmentG.F.G.R - Theorical reference preparation, Data collectM.A.S.G.O - Theorical reference preparation, Data collect\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e\u0026ldquo;Data is provided within the manuscript or supplementary information files\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbulibdeh A, Zaidan E, Abulibdeh R (2024) Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. 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Presented at the 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), IEEE, Zhangjiajie, China, pp. 2147\u0026ndash;2152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/FSKD.2015.7382284\u003c/span\u003e\u003cspan address=\"10.1109/FSKD.2015.7382284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"operations-research-forum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Operations Research Forum](https://link.springer.com/journal/43069)","snPcode":"43069","submissionUrl":"https://submission.nature.com/new-submission/43069/3","title":"Operations Research Forum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"4th Industrial Revolution, Artificial Intelligence, Internet of Things, advanced automation, Automotive Companies","lastPublishedDoi":"10.21203/rs.3.rs-4901600/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4901600/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis 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\u0026rsquo; 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 \u0026ldquo;high tools\u0026rdquo; and \u0026ldquo;high in innovations\u0026rdquo;, 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.\u003c/p\u003e","manuscriptTitle":"Proposal of a Classification Method for Brazilian Automotive Companies Using the Principal Components Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-10 15:31:01","doi":"10.21203/rs.3.rs-4901600/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-11T14:40:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T13:03:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295926381881280635324655024854083406520","date":"2025-04-04T10:57:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216951441966814379168028884155317930077","date":"2025-04-03T21:57:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T02:55:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-12T23:42:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-12T23:40:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Operations Research Forum","date":"2024-08-12T15:21:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"operations-research-forum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Operations Research Forum](https://link.springer.com/journal/43069)","snPcode":"43069","submissionUrl":"https://submission.nature.com/new-submission/43069/3","title":"Operations Research Forum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"77565450-0ba8-429b-8d3a-10820cdaafe9","owner":[],"postedDate":"September 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T15:58:43+00:00","versionOfRecord":{"articleIdentity":"rs-4901600","link":"https://doi.org/10.1007/s43069-025-00552-8","journal":{"identity":"operations-research-forum","isVorOnly":false,"title":"Operations Research Forum"},"publishedOn":"2025-12-03 15:56:54","publishedOnDateReadable":"December 3rd, 2025"},"versionCreatedAt":"2024-09-10 15:31:01","video":"","vorDoi":"10.1007/s43069-025-00552-8","vorDoiUrl":"https://doi.org/10.1007/s43069-025-00552-8","workflowStages":[]},"version":"v1","identity":"rs-4901600","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4901600","identity":"rs-4901600","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}