Hundreds of tags, no starting point
A single continuous process line can carry thousands of sensor tags. Without ranking, every one of them is a suspect, and engineers default to checking the ones they remember from last time.
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 10-15 most likely causes, ranked by influence. Your engineers get a clear starting point, not a historian screen full of tags.
Trusted on the floor at leading manufacturers
The problem
Manufacturing quality deviation, root cause unknown.
A lab result comes back out of spec. Somewhere among thousands of sensor tags, one or two variables caused it. Finding them by hand means opening the historian, pulling trend after trend, and comparing timestamps against a hunch. That is hours an engineer doesn't have, and product keeps running while they look.
Troubleshooting with a flashlight
Move past the reactive model of fixing a problem after the damage is done or waiting for an expert to arrive with a flashlight and a guess about where to look first.
The cost of a slow answer
An unplanned line stoppage or extended off-spec run can cost $20,000 to $200,000 per hour, depending on the process. Every hour spent scanning trends manually while quality drifts is an hour that number keeps climbing.
How root cause analysis works
From a data dump to a ranked list.
The old way treats every tag as equally suspicious. ProcessMiner's multivariate root cause analysis evaluates hundreds of process variables against the deviation, ranks them by influence and deviation severity, and hands your team a short, ordered list to start with.
Manual investigation
Every tag is a suspect
The engineer opens the historian and starts pulling trends one at a time.
- Quality flags out of spec. The lab result lands, but the cause is unknown.
- Hours of manual review. The engineer scans historian trends across hundreds of tags, hoping to spot the correlation.
- Guesswork closes the gap. Without a ranked list, experience and memory fill in where data analysis should.
Root cause analysis
Ranked, not raw
The platform does the narrowing before the engineer opens a single trend.
- Quality flags out of spec. The system detects the breach the moment it happens.
- Hundreds of variables, ranked. Multivariate analysis narrows the field to the 10-15 most likely causes, by influence.
- Engineer clicks and investigates. A clear starting point replaces a blank historian screen.
Result Pulp & paper deployments
>20%
Quality variability reduction
Downstream result of faster, more accurate root cause identification
10-15
Ranked causes
Narrowed from hundreds of variables per metric
$20k-$200k
Cost per hour of a stoppage
Industry benchmark for the cost of a slow investigation
$400k-$500k
Value per 0.1% quality gain
Per average paper mill, annually
Quality variability reduction reflects results from pulp & paper deployments measured over a defined production period. Actual results vary by facility, process, and data quality; past performance is not a guarantee of future results. The same multivariate ranking approach runs unchanged on any continuous process, regardless of industry or equipment type.
Inside the analysis
Three ways it narrows the field
Root cause analysis isn't a bigger dashboard. It's a smaller, ordered list your engineers can actually act on.
01 / Ranked, not raw
Every tag scored, not just flagged
When quality goes out of bounds, the platform evaluates hundreds of process variables and narrows them down to the 10-15 most likely causes, ranked by influence, not by guesswork. Engineers get a clear starting point, not a data dump.
- Ranks contributing factors by deviation severity and relative importance
- Recalculated the moment a new deviation is detected
Top root causes
02 / Filters the noise
Correlation isn't causation, so it doesn't get treated that way
Process tags move together constantly. A steam pressure change can drag five other tags with it, and none of the five are the actual driver. The analysis evaluates both linear and non-linear relationships across your process variables and filters redundant, correlated signals so your team acts on what actually matters.
- Works across any continuous process, regardless of industry or equipment type
- Removes duplicate signals so the ranked list stays short and specific
Signal filtering
03 / Clicks lead to answers
From ranked tag to trend, in one click
Every tag in the ranked list links straight to its trend against the deviation window. Operators and engineers click on a deviation to see ranked causes and an investigation starting point, no separate historian query required.
- One click from ranked cause to the underlying trend
- Once the cause is found, Intelligent Centerlining shows the exact adjustment back to target
Intelligent Centerlining
Integrated with PM AI Agent
Ask what caused it, in plain language.
A ranked list is the starting point. PM AI Agent takes it further: ask a direct question and get a grounded answer, in the words your team already uses on the floor.
Powered by PM AI Agent
The RCA AI assistant
Combining process data, root cause outputs, SOPs, and unstructured plant documents, the RCA AI assistant answers questions like "what drove the quality deviation on Line 3 last shift?" directly, citing the ranked causes and the procedure that applies.
- Document-only queries — search SOPs, manuals, and downtime codes directly.
- Data-only queries — ask about ranked causes and trends from the process itself.
- Hybrid queries — combine process data with plant knowledge for a single grounded answer.
Sample query (illustrative)
Example of an operator asking PM AI Agent about live production conditions, predicted limits, root causes, and plant documentation.
Simulated conversation. On your line, PM AI Agent answers from your live data, SOPs, and RCA findings.
See it on your process
Stop guessing where the quality deviation started.
Watch a live demo of root cause analysis narrowing a real deviation down to a ranked list of causes, and see how Intelligent Centerlining picks up from there.
No data science degree required. Live dashboard in hours, results in less than two weeks.
Questions, answered
Frequently asked questions
How many process variables does root cause analysis consider?
The platform evaluates hundreds of process variables for every quality metric and narrows them down to the 10-15 most likely causes, ranked by influence, whenever a quality reading breaches its limits.
Does this replace my process engineers' judgment?
No. It gives engineers a ranked starting point instead of a blank historian screen. The platform captures authentic operator and engineer expertise and applies it consistently across every shift; the final call still belongs to your team.
Can it distinguish correlation from a real driver?
The analysis evaluates both linear and non-linear relationships across process variables and filters redundant, correlated signals, so engineers investigate the tags that actually explain the deviation instead of chasing false leads.
Is there a way to just ask what caused a deviation in plain language?
Yes. PM AI Agent includes an RCA AI assistant that supports document-only, data-only, and hybrid queries, so engineers can ask questions in plain language and get a grounded answer.
What happens after root cause analysis finds the driver?
Once the cause is identified, Intelligent Centerlining shows the exact adjustment needed to bring the process back to its golden run target, so the investigation ends with a specific action rather than a diagnosis alone.
Get in touch
Have a question?
Whether it's our platform, your process, or a free in-depth process data analysis and pilot you'd like to see in action, let us know.