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Shaping global infrastructure with ironclad manufacturing

Gerdau uses Databricks to future-proof their steel manufacturing workflows


cost reduction for data processing


in new developments for streaming solutions


new business users onboarded

INDUSTRY: Manufacturing
SOLUTION: Digital Twins,Supply Chain Optimization
PLATFORM USE CASE: Data Intelligence Platform,Delta Lake,Delta Sharing,Photon,Unity Catalog

“Databricks has solved our data silo issue with a unified approach that brings all our data to solve our biggest challenges, from supply chain optimization to streaming industrial operations.”

— Rodrigo Silveira, Chief Data and AI Officer, Gerdau

The steel industry has undergone a digital transformation to optimize various aspects of the business, from optimizing supply chains to making decisions with the environment in mind. With a history spanning 123 years, Gerdau is Brazil’s largest steel producer, one of the leading producers of long steel in the Americas and of special steel in the world. In Brazil, Gerdau also produces flat steel and iron ore for the company’s own use.

Gerdau also has a new business division, Gerdau Next, that fosters entrepreneurship in segments adjacent to the steel industry. Guided by their purpose of empowering people who build the future, Gerdau has operations in many countries and over 30,000 employees.

Gerdau is the largest recycling company in Latin America and uses scrap as an important input, with 71% of the steel they produce made from scrap. Every year, Gerdau transforms 11 million tonnes of scrap into a variety of steel products. Gerdau also is the world’s largest charcoal producer, with over 250 hectares of planted forests in the state of Minas Gerais. As a result of their sustainable production matrix, Gerdau currently has one of the industry’s lowest average greenhouse gas emissions (CO₂e), 0.86t/CO₂e per tonne of steel, which is about half the global industry average of 1.91 t/CO₂e per tonne of steel (World Steel Association). By 2031, Gerdau’s target is to reduce their carbon emissions to 0.82t/CO₂e per tonne of steel. 

Gerdau’s shares are listed on the São Paulo (B3) and New York (NYSE) stock exchanges.

Because of the company’s size and success, Gerdau built an impressive data and analytics team of engineers, architects and scientists to develop homemade tools to manage their data. But the company was witnessing significant obstacles with cost and maintenance as their data infrastructure continued to grow in complexity while supporting the rapidly rising volumes of diverse data sources. With the Databricks Data Intelligence Platform, Gerdau was able to unify their data sources and bring new analytics and machine learning (ML) workloads to bear — putting them in position to solve pressing use cases, instill a data-first culture within the business and reduce overall operational costs. 

Complexities in managing an open source data ecosystem

As the steel industry embraces the digital era, a surge in data utilization is reshaping operations, fostering efficiency and propelling innovation to new heights. Gerdau was experiencing multiple pain points with their proprietary technology and ecosystem of open source data tools. According to Felipe Montanini, Head of Data Management, Engineering and Architecture at Gerdau, “Data analytics plays a critical role in what we can do in our mills. They help us figure out new ways to use machinery to achieve better results.” However, since most of Gerdau’s solutions were homegrown with open source in mind, they were complex, disconnected and hard to manage. “We wanted to build with open source because of its flexibility and unlimited potential,” Bruno da Silva Breder, Product Owner I4.0 at Gerdau, said. “But they required users needing to be proficient in Python and Spark. That made it difficult for engineering to maintain and drive adoptions within the business.” As if all this weren’t enough overhead for Gerdau’s technical teams, the company’s current platform couldn’t offer real-time data processing capabilities, which particularly hindered their “digital twins” use case. In the manufacturing sector, digital twins serve as virtual replicas of products, enabling companies to design, test and optimize their production lines in a virtual environment. This use case was a vital component of Gerdau’s efforts to streamline manufacturing, improve product quality and support their environmental, social and governance (ESG) strategy to reduce their carbon footprint. 

The global steel manufacturer was also in dire need of a platform that could offer fine-grained access and data lineage controls while meeting various compliance and security standards — especially if the company wanted to continue to scale at a consistent rate. These data governance roadblocks only added to Gerdau’s struggle with team collaboration and data sharing. Different teams often created duplicated or multiple versions of databases, and the lack of a unified data management system led to inefficiencies and posed risks of data inconsistencies and inaccuracies. Not only did this impact decision-making and operational effectiveness, but it also increased the total cost of ownership (TCO) of the business processes. To produce steel, you need the right chemical composition, and events could take a costly turn without the correct data at the right time.

Gerdau’s situation epitomized the intricate challenges faced by large manufacturing companies during the digital transformation process. When undertaking a project of such a massive scale on one’s own, it’s inevitable to find limitations along the way without the right partner. Since these technical hurdles were impeding Gerdau from reaching their strategic goals — particularly their commitment to ESG, desire for stronger supply chain management and vision for further AI advancement — the need for a sophisticated, integrated data platform became increasingly apparent. The Databricks Data Intelligence Platform appeared to be the missing piece in Gerdau’s digital puzzle, promising to transform their data infrastructure.

A unified approach accelerates digital transformation

Since Databricks simplified Gerdau’s data workflows by consolidating various tools into a single, user-friendly environment, the steel manufacturer has taken a significant step forward in their modernization journey. Delta Lake set the foundation for Gerdau’s new underlying data infrastructure. On top of this optimized storage layer, the company leverages Delta Sharing to easily and securely share data internally and externally with partners, which has helped to foster a more collaborative work environment within Gerdau and their ecosystem of manufacturing and distribution partners. Since data sharing is especially important in the B2B market — which typically involves a complex ecosystem of suppliers, distributors, regulators, customers and more — Databricks’ unified view and superior performance in handling large, complex datasets has helped Gerdau easily scale to the next stage of growth.

By creating this unified source of truth, Gerdau can also process data more efficiently and address the large responsibility that comes with digital twin projects and other safety measures, like composition control. Using Photon, the next-generation engine on the Databricks Platform that provides extremely fast query performance, Gerdau has reduced their average data processing time from 1.5 hours to 12 minutes — a huge performance gain and cost savings, as certain tables in their workflows are processed daily. Since data processing plays a crucial role in data governance by ensuring the accuracy, consistency and reliability of data, the company is well on their way to improved governance practices, further compounded by their use of Unity Catalog. “With Unity Catalog, we have established data governance standards across our manufacturing processes,” Eduardo Antunes Padilha, Data Governance Leader at Gerdau, said. “We have also implemented fine-grained access controls, data lineage controls and access segregations for different groups of users.” Plus, Unity Catalog has paired well with their integration with Power BI, further enabling Gerdau’s business teams to more easily access the data they need to create their own reports and dashboards. 

These implementations have not only optimized the company’s data management practices but also paved the way for future innovations. Leveraging Databricks for advanced analytics and machine learning has enabled Gerdau to further explore cutting-edge applications aside from digital twins, like predictive maintenance, image and text classification, and solutions powered by generative AI. For instance, one of their first achievements using GenAI — large language models (LLMs) — is an assistant to help people on their journey for re/upskilling. Luiz Souza Pereira, Technical Data Manager at Gerdau, explained, “Using Databricks, I can see the future very clearly, very, very quickly. In our old environment, I was unable to see it very clearly or quickly.”

Using data efficiencies to continue to innovate

After all of these very necessary implementations, Gerdau’s transition to Databricks has resulted in substantial cost savings by moving away from a mix of homemade and open source solutions. This has allowed for a more streamlined operation and reduced expenditure in both financial and labor resources. Financially, the adoption of Databricks and consolidation of Gerdau’s various tools has resulted in a remarkable 40% cost reduction for data processing and 80% in new developments for streaming solutions. These savings were a direct result of the reduced financial burdens associated with managing multiple, disparate systems and using highly manual workflows.

From an operational standpoint, Databricks’ enhanced features for compliance ensured strict adherence to data security standards and regulations. Moreover, the adoption of Databricks facilitated improved collaboration and data sharing across different teams and departments. This unified approach has helped in dismantling data silos and ensuring consistency and accuracy in data across the organization. Better governance combined with the unification of data tools under the Databricks Platform has allowed for quicker data handling and real-time data processing. 

Yet, the organizational impact of Databricks has extended beyond financial and operational efficiencies. The platform’s introduction has marked a significant shift in Gerdau’s culture toward embracing data and analytics across the entire business for innovation and growth. This was evidenced by the rapid onboarding of over 300 new global data users, including the operations in other countries such as Peru and the United States. Per Montanini, one piece of feedback he received was: “In our old environment, I had to learn five or six different tools, and now I just need to learn Databricks. Not only that, to create an ML model, I’m taking like 30% less effort now.” With Gerdau’s teams aligned on a data-driven future, Databricks has enabled the steel manufacturer to undertake more-advanced AI projects after their success. The scalability and flexibility offered by Databricks continue to support Gerdau’s growth and expansion strategies, positioning them as a forward-thinking leader in B2B digital transformation.