Mayr-Melnhof: Visualizing Cardboard Production Data with the Elastic Stack

This post is a recap of a deeper dive into how Mayr-Melnhof Group harnesses the Elastic Stack. Check out our Customers page for more in-depth Elastic Stack customer stories.

When producing more than 1.7 million tons of cardboard a year, and processing billions of consumer goods packages annually — as the Mayr-Melnhof Group does — any mistake in the complex production process can be costly.

The amount of moisture, fiber, and expensive materials in the manufacturing process required for every batch of cardboard needs to be precise.

That’s why the Vienna, Austria-based multinational has deployed more than 25,000 sensors to capture production process data on everything from pressure and temperature to machine speed and power consumption.

Enter the Elastic Stack

Mayr-Melnhof didn’t have the right tools to quickly acquire all the meaningful intelligence from the 2 TB of data these sensors were generating. Belatedly discovering a deviation in the amount of virgin fiber in the manufacturing process, for example, could be a financial headache, and even halt production.

In response, the company harnessed the Elastic Stack — specifically ElasticsearchKibana, and Logstash — to connect and visualize cardboard production data and usage of all relevant materials in real time. Four months later, the company lowered consumption of high-cost production materials by 20%.

Bridging Information Technology and Operational Technology

"The Elastic Stack makes sense of otherwise disparate data, bringing in a new real-time perspective on production processes," said Stephan Hampe, technologist for Mayr-Melnhof Karton, a unit of the Mayr-Melnhof Group. "With one feature set, we're able to bridge the two worlds of IT and OT, and optimize the process."

Watch their talk given at a recent Elastic{ON} Tour Frankfurt detailing the company’s industrial production visualization process. Check out the conference archive or find out when the Elastic{ON} Tour is coming to a city near you.

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