Gestamp redefines supply chain management for Industry 4.0.article

In automotive logistics, the concept of connectivity has transcended traditional boundaries, and digital networks have become the backbone of innovation and collaboration. For some, the concept of connectivity only refers to connecting systems, but this limits understanding the true potential of digital networks and the capabilities they have for automotive logistics. His Gestamp, a parts manufacturer, is able to see beyond this parochial view.

To take networking applications to the next logical step, Gestamp embraced the idea of ​​a “federated network,” where separate networks or locations share resources. Global Federation Software Diego Marada says his framework's concepts are primarily about people. automotive logistics He spoke in a video interview after speaking at the 1st Automotive Logistics & Supply Chain Digital Strategy Conference last year.

“What we're trying to do is create a global community,” he says. “And these communities are made up of experts in their respective technical fields.”

Through this global federated network, Gestamp is creating a global digital environment where people from all regions of operation can contribute 'beyond borders'. In other words, contrary to common assumptions, this federated network focuses on building global collaboration in the automotive supply chain. For example, the company may have experts in China with expertise who can contribute to the creation of mobile applications. Through networks, these professionals connect with people in Germany, Spain or, in some cases, other parts of the world.

It is important that Gestamp's logistics data is standardized and understandable across all locations. Diego Marada, Gestamp

Mallada said the federated network is the foundation of a kind of open source community that “allows Gestamp to further advance global skills development in all areas of expertise.” The applications of this open source community are almost endless. It will also give you intimate experience and work across our global factory footprint, as our Industry 4.0 strategy ultimately aims to transform every factory in the world. ”

This is also one of the reasons why Gestamp joined the Catena-X project in 2023, committed to exchanging data in a reliable and secure manner. For Gestamp and Mallada, implementing a federated network in automotive logistics is key to strengthening not only systems but also people.

Data quality and semantics in digital networks

With over 100 plants and many networks, Gestamp has plenty of data to work with. The question is how to capture, interpret, and implement that data to power Industry 4.0 strategies in automotive supply chain management. A recurring theme, and one that data-driven enterprises must understand, is data quality and semantics as the virtual “backbone” of digital networks. To truly realize a federated network, it is important that Gestamp's logistics data is standardized and understandable across all locations. Improving the quality and standardization of data in the supply chain is critical to the global automotive ecosystem.

Mallada points out that achieving this data standardization is not an easy task, but Gestamp is actively working on developing its own solutions. For example, in logistics terms, enterprise resource planning (ERP) data cannot be considered at the expense of data related to the Internet of Things (IoT), edge, cloud, or any aspect of an industrial process. They use different protocols. ”

Gestamp also has some aspects of its architecture that are more “real-time” and behave “closer” to industrial processes. “We have different infrastructure, so we're not all at the same stage,” he says. This requires the collaboration of industry experts as well as all parts of data governance, and all these components are connected and working together to standardize data to achieve semantic uniformity across all layers. This means that data office supervision is required in order to operate. ”

Gestamp's innovative approach to supply chain data: The global lake house model

Another important goal concerns further data integration and visibility across the broader supply chain. In addition to building the necessary global framework through software design to enable interoperability, Gestamp is also building a global “lake house” for supply chain data.

“For example, data from the Internet of Things. Data collected from machines feeds into a broader picture of the entire industrial process, enhancing data quality and wider logistics maps.”

According to Mallada, this structure is on a Venn diagram between Gestamp's teams, such as Gestamp's data office on one side and the infrastructure team on the other. This model helps Gestamp exchange internal data between departments and eliminate potential data silos.

“This is also a way to exchange data with other members throughout the supply chain,” Marada says. This architecture enables end-to-end supply chain control. From a technical perspective, there is usually a disconnect between real-time (or streaming) data and data from “lagging” components. This can be a problem. However, Gestamp can read that (very large) data in “batches” while providing robust data governance and security, enabling digital synchronization for a streamlined data environment.

“We use the lifecycle of machine learning operations, technology, and artificial intelligence and implement them into production systems.”

And all this data makes cybersecurity a necessary focus. Mallada says cybersecurity is at the heart of his overall Gestamp strategy, and cutting supply chains poses a lot of risk and missed opportunities. There is growing concern about cybersecurity challenges in digital supply chain networks. By working with initiatives such as his Catena X, Gestamp hopes to strengthen data reliability and security and enhance traceability across his supply chain. “For example, data from the IoT. Data collected from machines feeds into a broader picture of the entire industrial process, enhancing data quality and broader logistics maps. Tracking part IDs across streams. can.”

Gestamp leverages AI for supply chain efficiency and decision-making

However, in modern automotive logistics, we cannot talk about data without talking about artificial intelligence (AI). Mallada says that 70-80% of his time spent developing AI algorithms is spent on data preparation and quality control, and Gestamp employs the same strategy for AI as it does for software development. says. So what is the role of machine learning in supply chain optimization? “We use the lifecycle of machine learning operations, technology, and artificial intelligence and implement them into production systems,” the company says. We also use AI for “clustering.” It aims to determine the optimal path across Gestamp's global factory footprint. “Also, as I mentioned earlier, we want our plants to be able to learn from plants that are performing better in other parts of the world, and we are using AI to help us achieve that.” We are further training our models and algorithms.”

Looking to the future: Gestamp, Catena-X, and the connected supply chain

Big data can solve big problems, but looking to the future, Marada says there are also big challenges. First, federated ecosystems, like natural ecosystems, are not static and require continuous improvement. Marada said it's important that communities connected through the system continue to exchange data and “connect the dots to see how we can improve things internally, externally and globally.” states that it is important. He also believes that Catena X will start having more use cases in 2024. “In fact, we're talking about developing entirely new use cases for his Catena X, where it's not just about how a single organization does things internally.” Data – Overall How we exchange data to change things is important. ”