Descrições de produtos:
A indústria siderúrgica passou por uma transformação digital para otimizar vários aspectos do negócio, desde a otimização das cadeias de suprimentos até a tomada de decisões tendo em mente o meio ambiente. Com 123 anos de história, a Gerdau é a maior produtora de aço do Brasil, uma das principais na produção de aços longos nas Américas e de aços especiais no mundo. No Brasil, a Gerdau também produz aços planos e minério de ferro para uso próprio.
A Gerdau também possui uma nova divisão de negócios, a Gerdau Next, que fomenta o empreendedorismo em segmentos adjacentes à indústria siderúrgica. Guiada pelo propósito de empoderar pessoas que constroem o futuro, a Gerdau opera em diversos países e conta com mais de 30 mil funcionários.
A Gerdau é a maior empresa de reciclagem da América Latina e utiliza a sucata como um importante insumo, sendo que 71% do aço que produz é feito a partir da sucata. Todos os anos, a Gerdau transforma 11 milhões de toneladas de sucata em uma variedade de produtos siderúrgicos. A Gerdau também é a maior produtora de carvão vegetal do mundo, com mais de 250 hectares de florestas plantadas no Estado de Minas Gerais. Como resultado de sua matriz de produção sustentável, a Gerdau tem atualmente uma das menores médias de emissões de gases de efeito estufa (CO₂e) da indústria, de 0,86t/CO₂e por tonelada de aço, que é cerca de metade da média global da indústria de 1,91 t/CO₂e por tonelada de aço (World Steel Association). Até 2031, a meta da Gerdau é reduzir suas emissões de carbono para 0,82t/CO₂e por tonelada de aço.
As ações da Gerdau estão listadas na Bolsa de Valores de São Paulo (B3) e na Bolsa de Valores de Nova Iorque (NYSE).
Devido ao tamanho e ao sucesso da empresa, a Gerdau construiu uma equipe impressionante de engenheiros, arquitetos e cientistas de dados e analytics para desenvolver ferramentas internas para gerenciar seus dados, mas a empresa estava enfrentando obstáculos significativos com custos e manutenção à medida que sua infraestrutura de dados continuava a crescer em complexidade enquanto suportava os volumes crescentes de diversas fontes de dados. Com a Data Intelligence Platform da Databricks, a Gerdau conseguiu unificar suas fontes de dados e trazer novos workloads de analytics e machine learning (ML) - colocando-a em posição de resolver casos de uso urgentes, instilar uma cultura de dados em primeiro lugar na empresa e reduzir os custos operacionais gerais.
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 beyond digital twins, such as predictive maintenance, image and text classification, and other solutions powered by generative AI. For instance, one of their first achievements using 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 possibilities very clearly and very quickly.”
Using data efficiencies to continue innovation
After all of these 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, as Gerdau embraces data and analytics for innovation and growth across their entire business. This was evidenced by the rapid onboarding of over 300 new global data users, including operations in other countries such as Peru and the United States. According to Montanini, he has already received positive feedback from users like: “In our old environment, I had to learn five or six different tools, and now I just need to learn Databricks. Not only that, creating an ML model takes 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. 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.