AI root cause analysis for manufacturing quality deviations.
When a quality reading breaches its limits, the platform narrows hundreds of process variables down to the top 10 likely causes, ranked by influence. Your engineers get a clear starting point, not a historian screen full of tags.
Read the transcript
Quality’s out of spec. Somewhere in hundreds of tags, one or two variables did it. Finding them by hand takes hours. Root Cause Analysis looks at every tag at once, drops the ones along for the ride, and ranks the top ten. One click opens the trend. PM Vector shows the move back. Your team spends the shift fixing it, not hunting for it.
How root cause analysis works
From a data dump to a ranked list.
A lab result comes back out of spec, and 1 or 2 of thousands of tags caused it. ProcessMiner’s multivariate analysis ranks every tag by influence and deviation severity and hands your team a short list before anyone opens a trend.
Pulp & paper deployments. Results vary by facility, process and data quality; past performance is not a guarantee.
Inside the analysis
3 ways it narrows the field
A short, ordered list per quality metric
It ranks the top 10 likely causes by influence and deviation severity, so your engineer starts with the most likely driver.
- Runs on every breach, for every quality metric you track
Correlation isn't causation, so it doesn't get treated that way
A steam pressure change can drag 5 other tags along, and none of the 5 is the driver. The analysis weighs linear and non-linear relationships and filters redundant signals, so your team acts on what matters.
- Works across processes and equipment types, from your own data
- Removes duplicate signals to keep the list short
One click from tag to trend
Every tag in the ranked list links to its trend against the deviation window, with no separate historian query.
- PM Vector then shows the exact adjustment back to target
PM Vector
Versus a manual investigation
How this differs from a standard root cause investigation
A manual investigation starts from a hypothesis and tests one variable at a time, over days or weeks.
Root Cause Analysis starts from all tags at once, ranks them within minutes on every breach, and removes correlated signals that send manual investigations chasing symptoms.
Engineers still make the call, starting from a short list with each trend one click away.
Free Data Health Assessment
Stop guessing where the quality deviation started.
Start with a free Data Health Assessment. We’ll take a closer look and let you know exactly what your own data can do.
No data science degree required.
Questions, answered
Frequently asked questions
How many process variables does root cause analysis consider?
Does this replace my process engineers' judgment?
Can it distinguish correlation from a real driver?
Do we have to define the causal relationships ourselves?
What happens after root cause analysis finds the driver?
Get in touch
Have a question?
Whether it's our platform, your process, or a free Data Health Assessment you'd like to start, let us know.