Use Cases

From Data to Impact – Use Cases That Work

Achenbach – Digital Aluminium Milling

Achenbach Buschhütten, a leading manufacturer of aluminium rolling mill technology, set out to deliver more value beyond machine delivery. Together with SOTEC, we  set up OPTILINK – a cloud-based platform that turns machine data into actionable insights. scitis.io ensures smooth and reliable operation of the platform, helping Achenbach enable smarter processes, remote monitoring, and new service-driven business models.

key facts
connected machines
0 +
Global active Use
of data per machine/day
~ 0 GB

Challenge

In order to ensure customer satisfaction even after a machine has been delivered, Achenbach needs to improve the insight into the operation of their products.

Solution

scitis helped Achenbach set up OPTILINK – a cloud platform that continuously collects live data from the production line. It delivers insights into process efficiency and detects potential interruptions.

Result

OPTILINK now allows Achenbach to remotely monitor and maintain their delivered machines – eventually providing their customers with a knowledge base for process optimization and helping them to reach their product goals.

“With the technology behind the scitis.io platform, we can connect not only our own machines but the entire supply chain — a game-changing opportunity for us as a machinery manufacturer, and for our customers and their customers: efficient and sustainable production, reduced waste, and real-time feedback for better end products.”
André E. Barten
CEO, Achenbach Buschhütten GmbH & Co. KG

Prinzhorn Group: AI-Based Quality Control in Paper Production

within the Prinzhorn Group, Hamburger Containerboard implemented an AI-driven quality control framework that improves efficiency, reduces waste, and enables energy-conscious paper and cardboard production. scitis.io ensures stable operation and integration of the virtual sensor infrastructure.
key facts
prediction error
< 0 %

Live in-production quality monitoring

Reduced waste and energy consumption

Challenge

Product quality is critical in paper roll production. Until recently, it could only be assessed through samples taken after the process — too late to intervene effectively in case of deviations.

Solution

Available sensor data from the machines is used to build a virtual sensor that predicts key quality parameters during production — with a <5% error margin.

Result

The AI's enhanced prediction capabilities allow for parameters to be changed more promptly and throughout production. This enables resource-efficient production which precisely matches the required quality.

Lorenz: Data-Driven Optimization in Snack Production

Lorenz leverages a data-driven approach to modernize its snack production lines. With scitis.io, they’ve unlocked valuable insights from machine data — laying the foundation for smarter operations and strategic product decisions
key facts
data points
> 0
PLC systems connected
> 0
multiple locations

Challenge

Lorenz had already exhausted traditional methods of cost optimization on their snack production lines. A new approach was needed — one driven by digital insights.

Solution

Cloud-based machine data collection was implemented to detect faulty machine behavior, overdue maintenance, and component failures in real time.

Result

Lorenz’s management now leverages these insights for strategic decisions across production and product portfolio. The result: single-digit percentage savings and a more efficient manufacturing process.

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