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Case Studies
Quality Leap Powered by Causal AI Analysis of SW Machine Data
By deploying the Xplain Data solution in a project with our partner Schwäbische Werkzeugmaschinen GmbH (SW) at automotive supplier Schabmüller, we were able to reduce scrap and rework in discrete manufacturing by 84%. Our Causal AI methods uncovered previously unrecognized root causes of defects, enabling the underlying problems to be successfully resolved.
Maximizing Equipment Uptime: AI-Driven Root Cause Analysis at Laser Specialist TRUMPF
In a successful project with TRUMPF SE + Co. KG, our Causal AI methods helped decode complex cause-and-effect relationships in production and operational data. The goal was to precisely identify the true root causes of machine failures. Ongoing monitoring through smart algorithms will now help detect emerging failure causes early and alert the responsible teams in time.
How Siemens Sets New Quality Standards in PCB Assembly
The application of Causal AI methods at Siemens Motion Control in Erlangen demonstrates the potential of innovative data analysis in electronics manufacturing: By seamlessly linking and analyzing all data across the entire value chain, we were able to precisely identify complex root causes of defects.
As a result, the plant was able to overcome the historical 5-Sigma threshold and achieve the desired Six-Sigma quality level.
White Paper
From Correlation to Causation to Artificial Intelligence
Correlation does not equal causation. Discover how an algorithmic search for alternative explanations reveals direct and potential causal factors — illustrated through predicting depressive episodes and the effects of specific drug groups.
From Observational Data to Causal Discovery
How do you get from “Big Data” to valuable insights? Learn the difference between observational and experimental data, and what it takes to identify potential cause-and-effect relationships from purely observational “Big Data.”
From Relational Tables to ObjectAnalytics™
Relational databases split objects like “The Patient” across many tables, making holistic analysis hard. Discover our patented ObjectAnalytics paradigm and the analytic operations it enables on whole objects.
Vision4Health: The Potential of Causal Discovery for the Future of Medicine
This vision paper explores how aggregating real-world patient data at scale and applying Causal AI can accelerate the future of medicine — outlining why a 3D view of data is key to understanding the causal chains behind disease and recovery.