Establishing AI-supported quality monitoring on a semantic data foundation.
A shared data semantics connects isolated quality systems and makes deviations detectable centrally, consistently and AI-supported for the first time.
The question
How can quality deviations be detected early when relevant process data is spread across isolated systems and not semantically comparable?
Our answer
An ontology-based process and data model unifies the meaning of distributed quality data. Building on this, we developed a scalable cloud platform and a central user interface that integrates data across systems, makes quality processes transparent and enables AI-supported anomaly detection as well as future automation.
What we delivered
Shared semantics for distributed quality data
Processes, data objects and relationships modelled across systems and unambiguously linked
A platform connects existing quality systems
Distributed process and quality data integrated in the cloud and made centrally accessible
Detect quality deviations centrally and early
A unified user interface creates transparency and supports AI-supported anomaly detection
What it achieved
- Unified access to distributed quality and process data
- Central process transparency improves well-founded decisions in quality management
- Earlier detection of quality deviations supports faster countermeasures
- A scalable data foundation enables further AI and automation cases
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