DISCRETE MANUFACTURING
Root Causes,
Not Guesswork.
From Process to Part.
Failures, yield loss, quality issues – CausalDiscoverer traces each one back through every process step to the factors that truly drive it.
Manufacturing
The challenge
Modern manufacturing runs on thousands of interactions.
Traditional analytics can’t see through them.
Scattered across processes and systems
Machine, process, material, and test data sit in separate systems – each telling only part of the story.
Correlations that look like causes
Shifts, batches, and lines change together. Hidden confounders make associations look causal – and send teams chasing the wrong fix.
Only as good as the features you build
Experts turn raw process data into features by hand – time windows, aggregates, joins. Whatever nobody thought to build is never tested.
The Xplain Data way
Root-cause analysis, finally automated.
ObjectAnalytics™
unites each part’s full history – from material to final inspection – in one view.
CausalDiscoverer
tests all factors without prior assumptions and finds what truly drives failures – no feature engineering needed.
Causal DiscoveryBotAdd-on
keeps watching live production data and alerts your team when new failure drivers emerge.
Step 1 · ObjectAnalytics
Every part.
Every process step.
One object.
ObjectAnalytics assembles the complete history of the produced part into one holistic object – from the material batch through every process step to final inspection, in the right time order. So the causal analysis sees what flattened tables never show: how process steps interact across the entire production flow.
Includes the visual ObjectExplorer for fast, ad-hoc analysis from any angle.
Patented technology
Step 2 · CausalDiscoverer
Thousands of factors.
The few that truly drive failures.
CausalDiscoverer tests all factors without prior assumptions and builds a causal graph of the entire production system. It separates true causes from mere correlations – and your domain experts validate the findings before anyone changes the line.
Direct causes
Which process parameters, materials, or machine states drive a failure?
Indirect effects
Causes that act through other process steps – invisible to station-by-station analysis.
Hidden interactions
Combinations of factors that only cause problems together.
Systemic quality drivers
Patterns across lines, shifts, and batches that keep failures coming back.
up to, in non-conformance costs
up to, in root cause investigation effort
Simulate interventions before applying them on the line
Step 3 · Causal DiscoveryBot
Root cause analysis
on autopilot.
Once set up, the Causal DiscoveryBot – an add-on module for CausalDiscoverer – continuously scans live production data in the background. When new causal drivers emerge, it alerts the quality team before problems escalate.
Always-on root cause analysis across processes, machines, and products
Early detection of new and changing failure drivers
Alerts via email, Slack, Microsoft Teams, and other tools
Targeted action before issues escalate
What makes it work
Built for real production data.
Scalable by design
Built for massive, complex production data – thousands of process variables across multiple lines.
Object-centric data model
Analyze relationships between workpieces, process parameters, events, and messages across the full production flow – not just isolated stations.
No feature engineering
CausalDiscoverer tests all factors without prior assumptions – no manual feature selection.
High-performance ad-hoc analysis
Explore your data from any angle with ObjectExplorer and test what-if scenarios before you intervene.
Open integration
Python, JavaScript, and WebAPI interfaces fit Xplain Data into your analytics workflows and quality systems.
Proven in practice
Causal AI is a game-changer in electronics production. For us, this represents a breakthrough in how we can improve production.”
AI helps us identify cause-and-effect relationships in complex data.”
Reveal What Drives
Manufacturing
Performance