Machine learning in Brazilian industry

Machine learning (ML) is increasingly transforming industries worldwide, including within the Brazilian industrial sector, highlighting key areas where ML drives innovation and efficiency. The Brazilian industrial landscape is actively engaging with machine learning and industry 4.0 technologies, driven by a desire for increased productivity, competitiveness, and innovation. While still navigating challenges such as the high cost of implementation, a skills gap, and the need for robust digital infrastructure and regulatory frameworks, significant progress is being made.
Machine learning (ML), a subset of artificial intelligence (AI), refers to computational methods that enable systems to learn from data and improve performance over time without being explicitly programmed for each task. These methods are increasingly embedded in everyday technologies, often operating behind the scenes in applications such as recommendation systems, voice assistants, fraud detection, and autonomous vehicles. ML models are capable of identifying patterns, making predictions, and adapting to new data, which makes them especially valuable in dynamic and complex environments.
Machine learning and the digital transformation of brazil's industrial sector
In recent years, ML has become a cornerstone of digital transformation strategies worldwide. Its integration into industrial processes is reshaping how companies operate, enabling predictive maintenance, quality control, supply chain optimization, and real-time decision-making. The ability to process vast amounts of data and extract actionable insights is revolutionizing traditional manufacturing and service models, contributing to increased efficiency, reduced costs, and enhanced competitiveness.
In Brazil, the industrial sector is undergoing significant digital transformation by integrating advanced technologies such as ML into production and management processes.
Despite its promising potential, Brazil faces structural challenges in fully embracing this new industrial paradigm. A considerable portion of the country’s industrial base still operates at technological levels associated with the Second or early third industrial revolutions, characterized by limited automation and low digital integration. This technological gap is particularly evident among micro and small enterprises (MSEs), which make up the majority of Brazil’s industrial landscape but often lack the resources and expertise to adopt advanced digital solutions.
Nevertheless, there is growing national commitment to modernizing the industrial sector. Public and private initiatives have been launched to foster innovation, support digital infrastructure development, and promote workforce training in emerging technologies. Programs such as the “Plano IA para o Bem de Todos” (AI Plan for the Good of All) and the “Estratégia Brasileira para a Transformação Digital” (Brazilian Digital Transformation Strategy) reflect the government’s strategic vision to position Brazil as a competitive player in the global digital economy.
Furthermore, Brazilian research institutions, development banks, and industry associations play a crucial role in this transformation. Organizations such as BNDES, CGEE, and CNI are actively engaged in funding research, conducting sectoral studies, and developing policy recommendations to accelerate the adoption of industry 4.0 technologies. These efforts are supported by international collaborations and knowledge exchange programs aimed at aligning Brazil’s industrial capabilities with global best practices. — demonstrating its versatility and transformative potential. As Brazil continues to navigate its digital transformation journey, the effective integration of ML will be essential for enhancing productivity, fostering sustainability, and ensuring long-term industrial resilience.
Historical context and industry 4.0 in brazil
The concept of industry 4.0 was first introduced in Germany as a strategic initiative to reinforce the country’s technological leadership and enhance its global industrial competitiveness.
One of the defining features of industry 4.0 is its holistic impact across all levels of industrial activity. It not only transforms production lines but also reshapes logistics, supply chain management, product development, and customer service. The convergence of physical and digital systems enables companies to create "smart factories" where machines, systems, and humans interact in increasingly sophisticated ways. This transformation is supported by technologies such as cloud computing, big data analytics, robotics, and additive manufacturing, which collectively enable greater flexibility, customization, and efficiency in industrial processes.
In Brazil, the adoption of industry 4.0 has been gradual and uneven. Early assessments revealed a significant gap in digital readiness among industrial firms. A 2016 survey conducted by the Confederação Nacional da Indústria (CNI) found that 42% of Brazilian companies were unaware of the relevance of digital technologies for industrial competitiveness, and 46% either did not use or were unsure about their use. These findings highlighted a widespread lack of awareness and preparedness for digital transformation.
However, more recent data suggest a shift in this landscape. By 2022, the same institution reported a noticeable increase in the adoption of industry 4.0 technologies, although the maturity level of implementation remained relatively low.
In the Brazilian context, the term “digital transformation” is often preferred over “industry 4.0” because it encompasses a broader scope of organizational change. It reflects the understanding that digital technologies should not be confined to the factory floor but should permeate all aspects of business operations, including marketing, finance, human resources, and customer engagement. This broader perspective is essential for fostering a culture of innovation and agility in a rapidly evolving global economy.
Several enabling technologies are central to Brazil’s digital transformation agenda. These include:
* : facilitates real-time monitoring and control of industrial processes through interconnected sensors and devices.
* : supports predictive analytics, quality control, and decision-making based on large datasets.
* Big data: enables the collection and analysis of vast amounts of structured and unstructured data to optimize operations.
* Additive manufacturing (3D printing): allows for rapid prototyping and customized production.
* Cloud computing: provides scalable infrastructure for data storage and processing.
* Advanced robotics: enhances automation and precision in manufacturing tasks. The "Centro de Gestão e Estudos Estratégicos" (CGEE) has contributed through research on technological trends and workforce development needs. Meanwhile, the "Confederação Nacional da Indústria" (CNI) has conducted extensive surveys and advocacy to promote digital adoption among Brazilian firmsds.
A major milestone in Brazil’s digital strategy is the launch of the national AI plan, "Plano IA para o Bem de Todos". This initiative outlines 31 strategic actions across sectors such as healthcare, agriculture, education, and industry. It includes substantial investments in AI infrastructure, research centers, and workforce training programs. The plan also emphasizes ethical AI development, data governance, and international cooperation, positioning Brazil as a proactive participant in the global AI ecosystem.
Key sectors embracing machine learning in brazil
The implementation of industry 4.0 technologies, including machine learning, offers substantial benefits across various industrial sectors in Brazil, such as increased productivity, improved product quality, reduced operational costs, enhanced industrial safety, and optimized supply chain management.
Here are examples of ML and digital transformation applications in specific Brazilian industrial sectors:
Chemicals & pharmaceuticals (c&p)
In 2016, the C&P sector ranked 9th and 10th in Brazil for the use of digital technologies in process and development, respectively. A survey conducted by UNICAMP and ABTCP revealed that the cellulose industry, for instance, has a high level of digitalisation and vertical integration, while the paper (integrated with cellulose) and paper (non-integrated with cellulose) sectors show medium levels. industry 4.0 concepts like "Digital Twin" (simulation/mixed reality) are highlighted as key enabling technologies for this sector.
Healthcare
The healthcare sector is crucial for both social development and economic growth, contributing to job creation and technological advancement. Brazil, like many other countries, faces challenges related to an aging population and the increasing prevalence of chronic and non-communicable diseases, which drive up healthcare costs and demand new care models. ML and AI applications in the public healthcare system (SUS) are being actively pursued under the "Plano IA para o Bem de Todos":
# "Otimização dos Diagnósticos no SUS": a system to enhance the precision and agility of medical diagnoses for critical conditions like strokes, pneumonia, breast cancer, tuberculosis, and melanoma.
# "IA em Saúde Bucal no SUS": technologies to improve the quality of oral healthcare services and the prognosis of oral cancer.
# "IA e Big Data para tratamento de câncer": a platform utilizing AI for peritoneal cancer treatment through ultrasound technology for chemotherapeutic aerosolization, aiming to increase treatment response and patient survival.
Agrifood
The swine farming industry, for example, faces environmental challenges related to high water consumption and waste generation, where technological solutions are being explored for mitigation. Genetic improvement has significantly advanced swine farming in Brazil, reducing the time to reach slaughter weight and decreasing fat content, enhancing meat quality. IoT and precision agriculture technologies are being deployed to collect vast amounts of data on soil quality, irrigation levels, climate, and pest presence. This data enables predictive maintenance and improved production, harvesting practices, and tool usage, potentially leading to significant productivity gains and reduced consumption of inputs like fertilizers, herbicides, and fuel.
Oil & gas
The oil and gas sector is making significant investments in digital technologies, with Big Data and analytics being particularly crucial due to the immense volume of data generated by sensors. For instance, modern offshore drilling platforms can have up to 80,000 sensors generating 15 petabytes of data over an asset's lifespan. Digitalisation initiatives in this sector focus on digital asset lifecycle management, collaborative ecosystems, creating new services "beyond the barrel," and supporting new energy sources. Machine learning and artificial intelligence are also central to forecasting in the oil and gas industry, enabling predictive maintenance, reservoir modeling, and operational optimization.
Aerospace and defense
This sector is traditionally a significant developer of new technologies and makes extensive use of advanced analytics, AI, and Big Data for threat identification and advanced manufacturing. Cybersecurity is a primary concern for Aerospace and Defense, given the complex demands of national security. Blockchain can enhance supply chain tracking and provisioning through shared databases, improve supplier performance validation, and reduce fraud by recording timestamps for every movement of physical objects.
Native forests
Brazil faces a substantial environmental restoration challenge, with the estimating a need to restore 10.3 million hectares of native vegetation by 2030, particularly in the (2.7 million hectares) and Amazon (3.5 million hectares) biomes. This requires massive investments in expanding native seedling production capacity, ranging from R$ 160 million to R$ 540 million nationally, depending on the restoration techniques and scale. The need for hundreds of new nurseries, especially in the Amazon region, is evident due to the high demand and limited existing capacity. While not explicitly stated in the sources, the immense scale and data complexity of managing such an extensive restoration effort, from seedling production to monitoring, present a clear opportunity for ML applications in optimization, logistics, and predictive modeling.
Software development
Machine learning for software defect prediction (ML SDP) is a promising area in software engineering, with increasing validation in industrial settings. Common ML classifiers used in SDP include Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and various neural networks. Features for SDP typically involve process metrics, source code characteristics, and historical defect data, though their industrial availability can be challenging. Popular frameworks for building ML SDP models include WEKA, Scikit-Learn, Keras, and TensorFlow. Industrial datasets are crucial for validating ML SDP solutions, but the lack of public access to proprietary data hinders further research and adoption. Studies on the cost-effectiveness of ML SDP are rare, yet some evidence shows significant benefits, such as a 30% reduction in quality assurance costs and a remarkable 7300% Return on Investment (ROI) in a simulated industrial project. Key learnings for practitioners include the importance of gathering structured feedback, comparing multiple methods and features, validating solutions on diverse datasets (research, open-source, and industry), and conducting ROI calculations.
Micro, small and medium enterprises
MSMEs constitute 98% of Brazil's industrial park, yet most operate at levels closer to the Second Industrial Revolution, with low digital maturity. This limits their participation in digitalized markets and data-driven business models Key challenges for MSMEs include a lack of awareness about digital technologies, slow adoption of management digitalization tools like Enterprise Resource Planning (ERP) systems, the need for customisation in advanced automation, and challenges in developing and training their workforce for new digital paradigms. Larger companies generally exhibit higher adoption rates of industry 4.0 technologies.
* AI Adoption: many AI solutions are available as open-source components, reducing the need for large capital or human resources investments for adoption.
* Blockchain: can facilitate secure financial transactions and improve supply chain transparency for MSMEs by offering a decentralized ledger for digital records.
* : can positively transform business areas like internal communication, collaboration, training, development, and customer service simulations.
* Cloud computing: allows MSMEs to access necessary IT infrastructure and software packages at reduced costs, improving efficiency, accessibility, and security while minimizing physical storage and software update requirements. Government programs, such as the "Programa Brasil Mais" are designed to support MSMEs in adopting digital solutions that are quickly implementable, low-cost, and high-impact, aiming to increase productivity and competitiveness. The key impacts of this transformation can be observed in several critical areas:
* Increased productivity and efficiency gains: these are the most direct and tangible benefits of adopting new technologies, enabling better resource utilization and faster production cycles.
* Decentralised process control and optimised logistics: enabling greater flexibility and responsiveness in production through real-time data and automation.
* Improved industrial safety and quality assurance: through precision in procedures and efficient use of inputs, reducing human error and enhancing compliance.
* New business models and customisation: the ability to offer customized products at scale, reducing costs and differentiating offerings in competitive markets.
* Enhancing global value chain integration: new technologies allow for a better insertion of Brazil into global value chains by improving traceability, compliance, and responsiveness.
Policy and regulation of AI
The debate around regulating AI in Brazil, particularly the proposed bill [https://www.camara.leg.br/proposicoesWeb/prop_mostrarintegra?codteor2881712&filenameAvulso+PL+2338%2F2023 PL 2.338/2023], highlights significant concerns from industry stakeholders. The "Confederação Nacional da Indústria" (CNI) which stands for National Confederation of Industry, for instance, worries that the bill's excessively broad scope - regulating not only AI use but also its conception and development - could stifle innovation and research by applying to even low- and medium-risk AI systems. Other concerns include the fixation of user rights over clear company obligations, potential regulatory overlap, disproportionate governance requirements for smaller companies, and risks to intellectual property (trade secrets) due to external auditing provisions.
* Creation of science, technology, and innovation poles in AI: following models like the Franco-German research and development network to foster regional innovation ecosystems.
* Public education and digital literacy in AI: to reduce apprehension and increase adoption, society needs to be informed about the basic concepts, applications, risks, and rights related to AI.
* Balancing innovation and social responsibility: finding an equilibrium between the speed of innovation and the social responsibilities derived from new technologies, ensuring reliability, transparency, and accountability.
* Standardising terminology and principles: creating a universal glossary for AI terminology and harmonizing principles across companies and entities for a cohesive approach.
* Risk-specific approaches: developing differentiated assessment and testing procedures for AI applications based on their specific risk levels.
The ai plan for the good of all
The "Plano IA para o Bem de Todos" reflects this commitment, allocating substantial resources for:
Challenges and limitations
Despite the promising potential of machine learning in Brazilian industry, several challenges and limitations hinder its widespread adoption and full impact., with a large portion, especially , still operating at levels characteristic of the Second Industrial Revolution. This limits their integration into digital markets and data-driven business models. Compared to OECD countries, Brazil lags in the adoption of technologies like cloud computing, IoT, and AI.. The need to create and enhance national databases for AI training is a recognised challenge in Brazil’s digital transformation agenda.
Scarcity of specialised professionals
A critical barrier is the lack of qualified labour capable of implementing and managing digital transformation processes. This includes a need for workforce requalification and alignment of educational programmes with industry 4.0 demands. The perception of a "low" or "medium" availability of industry 4.0 specialists in the market highlights the necessity for significant professional training.
Cultural and financial barriers
* High investment costs: The implementation of digital technologies often requires substantial investment, which is a major internal barrier for Brazilian companies. There is also a need for greater awareness among companies regarding the opportunities and risks presented by industry 4.0.
Regulatory uncertainty
The regulatory environment for AI in Brazil, particularly the bill [https://www.camara.leg.br/proposicoesWeb/prop_mostrarintegra?codteor2881712&filenameAvulso+PL+2338%2F2023 2.338/2023], has raised concerns within the industry regarding potential over-regulation that could stifle innovation and deter investments. This contrasts with more flexible approaches in other leading countries. The current proposal is considered by some to be the most restrictive globally, regulating the technology from conception to adoption, rather than just its high-risk applications.
Adoption of ml in industrial policy
Brazil is actively developing and implementing national strategies to accelerate digital transformation and industry 4.0. The Brazilian Digital Transformation Strategy (e-Digital) and the National IoT Plan are key initiatives aiming to stimulate informatisation, dynamism, productivity, and competitiveness.
Emerging trends
Future trends in ML applications in Brazilian industry align with global industry 4.0 advancements:
* Predictive automation and maintenance: this involves using ML algorithms to predict equipment failures and optimise maintenance schedules, leading to increased efficiency and reduced costs. This capability extends to complex systems, allowing for adaptation to sudden changes in production processes.
* Autonomous systems and robotics: the development of autonomous robots and cyber-physical systems is a key aspect, enabling smart factories to operate with greater self-organisation and decision-making capabilities. Brazil, despite lagging in robotic density compared to global leaders, is seeing increased investment in industrial automation.
* Digital twins: the ability to create virtual representations of physical systems and processes (digital twins) allows for real-time monitoring, simulation, and optimisation of production. This significantly minimises product development time by enabling simulation during the early stages of design.
* Enhanced connectivity (5G and IoT): the rollout of 5G technology is expected to significantly impact industry 4.0 by providing high-speed, low-latency connectivity for massive numbers of IoT devices, crucial for real-time data exchange and control in smart factories. The industrial internet of things (IIoT), a specific application of IoT in industry, focuses on maximising data capture for improved traceability and real-time decision-making.
* Mass customisation: Technologies enable a shift from mass production to mass customisation, where products can be individually tailored to customer preferences, leading to reduced costs and enhanced product differentiation.
* Sustainable and Efficient Production: future applications of ML will also focus on environmental benefits, such as optimising energy consumption, reducing waste, and promoting the use of renewable energy sources in industrial operations.

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