In 2024, Brazil’s manufacturing sector recorded a 3.1% increase in industrial production, the third highest in the past 15 years. This growth was largely attributed to a robust domestic market, driven by rising employment and household consumption.
Despite this positive performance, Brazil has been on a de-industrialisation trajectory since 1995, as evidenced by the decline in the sector’s contribution to GDP.
Figure 1: This graph is recreated based on data from Brazil – Manufacturing, Value Added (% Of GDP)
Against this setting, integrating artificial intelligence (AI) can be a game-changer in reversing deindustrialisation. A 2022 survey found that only 7% Brazilian companies fully utilise Industry 4.0 technologies, including AI, 3D prototyping, and automation across all stages of their production chain.
AI in action for Brazilian manufacturers
Although the survey indicated that AI adoption in Brazil is still in its infancy, adoption rates within companies are comparable to those in Europe. This suggests that Brazilian companies, particularly the more established ones, are inclined to implement practical AI solutions.
Brazilian manufacturing firms are adopting AI for several high-impact applications, including:
- Automation: The use of generative AI tools can automate repetitive or hazardous tasks on the factory floor. These technologies can also streamline internal workflows and speed up routine administrative tasks, freeing up employees’ time for high-value activities.
- Predictive maintenance: The use of AI in manufacturing enables real-time sensor data analysis where the machine learning algorithms can predict equipment failures. This allows manufacturers to improve maintenance planning and scheduling and avoid costly unplanned downtime.
- Quality control: AI-powered sensors can detect defects on a production line. This continuous, real-time monitoring can increase defect detection rates and significantly reduce waste or rework. This ensures compliance with stringent industry standards such as ISO 9001 or IATF 16949 (for the automotive industry).
- Supply chain optimisation: Predictive analytics can generate more accurate demand forecasts and optimise inventory levels. This prevents overstocking or understocking.
Webbing Maker: 30% Waste Reduction. Better Quality Product
Webbing Maker: 30% Waste Reduction. Better Quality Product
Ready for a change in your organisation?
AI-Driven Applications: What You Can Do
A significant barrier to the adoption of AI is the general resistance to new technologies and the perception that the costs of implementation are high. Some companies are reluctant to invest without a clear understanding of how these technologies can increase productivity and quality, while others do not know where or how to begin.
However, instead of hastily implementing changes all at once, companies need a focused, bespoke approach tailored to their needs. To take advantage of the opportunity presented by high-impact applications, here’s how you can get started:
Automation
Automating operational and administrative processes frees up valuable employee hours for higher-value activities. Additionally, consider implementing a lean manufacturing approach to adjust organisational structure according to demand, making operations leaner by reinforcing management software, technology and AI.
Other approaches include designing processes for capturing data on handheld devices that transmit data directly to ERP systems, and adopting an AI-driven quotation generation tool to automate routine order generation and increase sales productivity.
Predictive maintenance
Manufacturing firms should focus on improving overall equipment efficiency (OEE) and plant availability by implementing the best maintenance practices, including downtime management and failure mode and impact analysis (FMEA). To proactively identify issues, install a root cause analysis (RCA) culture and preventive maintenance (PM) calendars with real-time downtime monitoring.
Read more about the case study here
Quality control
Roll out a comprehensive training programme for OEE management and maintenance routines. Active supervision should be employed to ensure adherence to new procedures and address issues such as inadequate production records and rework. Other reliable solutions include performing Failure Mode and Effects Analysis (FMEA) and developing quality reports to closely monitor quality, reduce scrap rates and take corrective action.
Supply chain optimisation
Use value stream mapping to define processes and identify losses due to idleness and excessive displacement, which can lead to optimised fleet distribution and reduced indirect costs. Implement a market mapping system and establish systems for efficient data capture and reporting.
Seizing opportunities with government policies
In January 2024, the Brazilian government launched a series of top-down policy initiatives, including the “Nova Indústria Brasil” (New Industry Brazil) policy. A key focus of this policy is the adoption of digital transformation, such as cloud computing, IoT, big data, and AI, to integrate the country more directly into global technology supply chains.
At Renoir, we have completed over 200 digital transformation projects, scaling solutions to accommodate changing demands. We therefore understand that, rather than attempting an overall digital overhaul, manufacturers should focus on a tailored, phased approach. Our solutions enable companies to integrate new AI and IoT technologies with existing systems seamlessly.
How can AI transform my manufacturing operations?
