The world’s largest brick maker’s path to sustainable manufacturing
SAS provides AI insights for reducing energy consumption.
A single platform
for analytics and AI
wienerberger achieved this using SAS® Viya and SAS® Visual Analytics on Microsoft Azure
wienerberger AG uses SAS® Viya® on Microsoft Azure to create a connected factory and reduce waste
When you think of a factory, energy use and emissions might come to mind. It’s no secret: Historically, manufacturing companies have had high environmental impacts. However, the world’s largest brick manufacturer, wienerberger, is working to change this. Its goal? To set a new standard for sustainable manufacturing using data, analytics and artificial intelligence.
Founded in 1819 and headquartered in Vienna, wienerberger operates more than 200 production sites across 28 countries. It produces materials for every stage of the building process, such as clay blocks for walls, roof tiles and paving stones. Beyond bricks, wienerberger also produces ceramic and plastic pipes and other solutions for water management and energy efficiency.
In alignment with the sustainability targets of the European Green Deal, wienerberger has announced plans to be a climate-neutral manufacturer by 2050. “By meeting our ambitious short-, medium- and long-term sustainability targets, we will give future generations the same opportunities we have today,” says Heimo Scheuch, CEO of wienerberger.
Our ESG goals are very ambitious and are driving toward carbon-neutral brick production. SAS is helping us move in this direction by allowing us to continuously reduce energy, and respectively CO2.Florian Zittmayr Team Lead for Data Science wienerberger
The path to climate neutrality
In pursuit of this goal, the company developed the wienerberger Sustainability Program, which clearly outlines targets for 2023-26 such as a 25% reduction in CO2 emissions, 15% renewable energy used in its own production, 90% of products sold being recyclable or reusable, and more.
Thanks to these efforts, wienerberger has been awarded with a variety of sustainability awards and recognitions, including five consecutive Austrian Sustainability Reporting Awards (ASRA) in the category for large companies and organizations, and the Microsoft Intelligent Manufacturing Award for Sustainability.
Transformation at this scale is no small feat. It requires a clear vision, coordinated effort across the organization and practical data insights. Equipped with a clear vision and goals, wienerberger partnered with SAS to coordinate its efforts across teams and use its data to gain valuable insights with advanced technologies like machine learning, artificial intelligence and digital twins.
“Our ESG goals are very ambitious and are driving toward carbon-neutral brick production,” says Florian Zittmayr, Team Lead for Data Science at wienerberger. “SAS is helping us move in this direction by allowing us to continuously reduce energy, and respectively CO2.”
Optimizing energy use in brick production
As a first order of business, wienerberger set out to identify key changes it could make in its production process. Reducing energy consumption in its own production plants was an essential piece of the puzzle.
“Essentially, you have two options,” Zittmayr says. “The first is investing in new production equipment designed for lower energy use. Of course, the whole plant network is continuously maintained in that regard, but these new technologies are also very investment intensive and time consuming. The second is further improving your existing process with completely new tools like analytics and artificial intelligence. This shows enormous potential without heavy investments needed or changing the current production drastically.”
The team needed to find a way to track all existing data points to determine baseline energy use throughout the manufacturing process. Without clean, relevant, integrated data, it’s not possible to build effective models or get reliable AI insights. Zittmayr says, “In our case, the energy need is the target variable that we want to lower. So, you must monitor this properly. Otherwise, optimization is difficult.”
wienerberger – Facts & Figures
200+
production sites in 28 countries
20,000+
employees
#1
in worldwide brick production
Organizing and streamlining data
wienerberger’s data scientists began collecting data from order forms, machinery in the plant, raw materials and quality control checks, as well as data about environmental factors that impact production needs like weather and humidity. “We are producing building materials out of natural raw material, clay,” says Zittmayr. “That means our production is exposed to a lot of fluctuations and environmental factors that have a severe impact on the process stability.”
Once they collected this data, it was stored in the Microsoft Azure cloud environment. Then using SAS Viya, the team linked all data streams together to begin analyzing and monitoring energy use. With SAS Viya, the team was able to do a deep analysis of the complicated process and use artificial intelligence to get insights and recommendations.
“The analysis of manufacturing data was a very cumbersome process previously,” says Zittmayr. “Data was flying around here and there and fragmented in different systems. Now we have it available all in one platform.”
Democratizing data and creating a connected factory
Next the team set its sights on democratizing access across the company. With a limited number of dedicated data professionals, having more than one team monitoring energy use was essential for wienerberger to achieve its goals.
“SAS helps us to democratize analytics,” remarks Zittmayr. “We don’t have data scientists sitting in every factory, but we have a lot of experienced process engineers who know the production and the process by heart. To use their capabilities is key for us, and SAS helps us here a lot.”
By creating a connected factory and making data available across roles, wienerberger saw improvements in monitoring and energy reduction. It also allowed team members across roles to contribute ideas on how to continue improving the energy efficiency of the manufacturing process.
“With help from SAS, we have been able to install ‘citizen data scientists,’ who’ve helped us to continuously improve our data models with their precise knowledge about the process,” says Zittmayr.
The analysis of manufacturing data was a very cumbersome process previously. Data was flying around here and there and fragmented in different systems. Now we have it available all in one platform.Florian Zittmayr Team Lead for Data Science wienerberger
Testing sustainable manufacturing processes with digital twins
With this knowledge at its fingertips, wienerberger has also been able to test innovative ideas with digital twins. Digital twins create virtual replicas of physical manufacturing systems, processes or products. These digital counterparts are created using real-time data and advanced simulations, allowing manufacturers to monitor, analyze and optimize their operations.
For wienerberger, this technology has been invaluable. Zittmayr remarks, “The concept of the digital twin plays an absolutely crucial role in manufacturing, especially when use cases get a bit more complicated and they’re not focusing on an isolated production step.”
By having a virtual model of the entire production process, the team can test new ideas without risking operational delays or failures. So far they have been able to test strategies for improving key production elements like brick firing and brick drying in a virtual environment.
Pressing ahead toward carbon neutrality
Overall, wienerberger has accomplished huge strides toward its goal of becoming carbon neutral by 2050. But it is not slowing down anytime soon. wienerberger is continuing to find innovative ways to implement sustainable manufacturing practices and minimize waste. “I see analytics in the production area as only the starting phase,” says Zittmayr. “So if a company is doing its homework and preparing its data for analytics, the potential is endless.”
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.