General
General information that applies to all features
Demo Center
General information about demos
Release Notes
Provides information on what is new in the Noreja tool
Tech-Blog
Gives a deep dive into the challenges and wonders of the Noreja development process
Smart Tools
Information on how to use Noreja porductivity apps
Minerva-AI
Information on how Minerva-AI is integrated into Noreja
Featured Articles
Purchase-To-Pay (Purchasing for special-purpose machine manufacturer)
From internal demand to payment: how Causal Process Mining makes approval bypasses, rework, capacity dependencies and supplier performance in the Purchase-to-Pay process visible and explainable.
Lead-To-Activation (District Heating Connection)
How to Analyze a District Heating Connection Using an Event Knowledge Graph, Causal Process Logic, and AI
The Origin of Causal Process Mining
Causal Process Mining (CPM) offers a new way to discover processes by going beyond directly-follows relations and flat event logs. Unlike traditional process mining, which can produce spurious relations, self-loops, and complex “spaghetti models” in 1:N and N:M data structures, CPM uses the causal knowledge embedded in relational databases. By transforming relational schemas into Causal Event Graphs (CEGs) with a Causal Process Template (CPT), it preserves cardinalities and foreign key relationships, resulting in clearer and more accurate process models. A case study from European food production shows that integrating causal domain knowledge can greatly improve process transparency and analytical quality.
Noreja Smart Data Forge
Model process structures and causal relationships and generate realistic synthetic data for your PM Use Case.