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ADVANCED ANALYTICS TO MAKE BETTER PREDICTIONS IN PRODUCTION 

staalproductie-SSAB
ABOUT

About SSAB

SSAB is a highly-specialized global steel company driven by close relationships with their customers. They develop high-strength steels and are providing services for better performance and sustainability.

ssab facts & figures

PROCESS

Process

The process of steel making has been the same for thousands of years, using the traditional, coal-fired blast furnace. But SSAB is bringing steelmaking into a sustainable future. Using electricity, hydrogen and new digital tools, the highly specialized global steel manufacturer plans to produce fossil-free steel products in 2026. By 2045, SSAB’s vision is to create a complete, fossil-free value chain from customers to end-users.

To achieve this goal, almost all SSAB’s processes need to have a digital component – and many of the decisions made in daily production need to be driven by analytics.

With more than 10,000 different parameters that go into designing the final product – many monitored via Internet of Things (IoT) sensors – SSAB has a vast pool of data. Using The Grain's Data Science expertise and SAS Analytics for IoT, SSAB can streamline this data and use it to make real-time predictions during manufacturing.

Quality & Downtime
Anticipating on disruptive quality issues during various stages in the hot strip mill of a large international steel manufacturer, reducing quality issues and downtime. AI translates evolving physical properties of steel strips into real-time alerts operators can act upon.
Assest Health & Performance Optimization
Helping the global leader in air cooled condensers monitoring asset health and forecasting asset performance. Early detection of equipment failures and possible root causes lead to improved maintenance scheduling and lower maitenance costs. Genetic algorithms prescribe optimal parameters for maximing the performance of the system during all circumstances.
Advanced Analytics

Using Advanced Analytics to make better predictions in production

Safety is still of the utmost importance, but much more is possible today.

“Digital solutions are being built into the new manufacturing process from the very beginning,” Orrling says. “This approach helps us scale up from the laboratory all the way to full-scale activity. We constantly use data to simulate processes, working with so-called digital twins, and with virtual reality.”

In one use case, analytics helped reduce significant failures in a production stream. With the help of The Grain's solutions, powered by SAS Analytics for IoT, SSAB could prevent 80% of unplanned or unwanted events from happening. In addition, the use of real-time data reduces accidents, allowing users to act rather than react.

The Grain also supports SSAB in putting data to work through predictive maintenance, providing decision support to the maintenance staff. Operators can predict when a machine component needs to be replaced before it malfunctions. Instead of changing out components at a fixed interval, they can be switched at dynamic intervals based on their performance.

The most recent analytics use case is machine conditioning and modeling, which allows SSAB employees to understand the status of the equipment better, see patterns of change and pinpoint odd events within the data.

As a result, the operators can catch any irregularities and make necessary repairs.

Create value from data

A continuous quest to create value from data

With goals as ambitious as those set by SSAB, there are still plenty of analytics-driven projects to undertake and terabytes of data to explore.

Another key challenge in digitalizing every level of steel production is creating an interface that anyone from mathematicians and data scientists to factory floor managers can understand and use.

ABOUT US

About The Grain

The way we combine industrial intelligence with our data science skills is what makes us unique: our starting point is your process, not the data. We know how assets work and our domain experts work with you to understand the specifics of your operations.

Our data scientists use those insights to translate your business challenge into an analytical use case and ensure the right data and algorithms are used. We build models from scratch or configure our accelerator kits with pre-built model components to meet your specific needs – whichever will give you the best results.