Quality
Cut quality variability by up to 20% in specific deployments — without adding headcount. Quality predictions every 30 seconds, grade/SKU-specific limits.
Keep the line running. Catch the drift before it becomes a stop.
Most unplanned stops start as slow quality drift that nobody sees until a lab result finally makes it obvious. ProcessMiner holds quality inside its limits continuously, so the conditions that lead to a stop never get the chance to build.
Why lines stop
Stops don't come out of nowhere. They come out of drift.
A break or an off-spec run is usually the last step in a chain: a process variable wanders, quality trends toward its limit, and the deviation goes unnoticed until the lab, or the line itself, reports it. Keeping throughput up isn't about predicting the stop. It's about maintaining quality continuously so the conditions that produce one don't accumulate.
Reactive operation
Drift discovered, line stopped
Quality drifts between lab samples. The problem announces itself.
- Blind between samples. Drift builds for minutes or hours before a lab result flags it.
- The line reports the problem. A break or off-spec run is the first clear signal, at $20k–$200k per hour of downtime.
- Recovery eats the shift. Rethread, restabilize, requalify. Throughput lost twice: the stop, then the ramp back.
Continuous quality maintenance
Drift corrected, line keeps running
Quality is predicted every 30 seconds and held inside its limits.
- Drift seen early. A live prediction every 30 seconds shows the trend well before any lab result would.
- Correction applied in time. A specific setpoint recommendation, issued only when the trend heads out of bounds.
- The line keeps moving. Quality stays inside its limits, so the conditions that lead to a stop never build up.
How the platform delivers it
Three capabilities that keep quality, and the line, on track
Throughput isn't a separate feature. It's the result of holding quality inside its limits, continuously, across every grade/SKU you run. Three parts of the platform do that work together.
01 / Real-Time Prediction
See the drift while there's still time to act
The platform predicts quality every 30 seconds, grade by grade, filling the blind spot between lab samples with a live trend. Your team sees where quality is heading, not where it was an hour ago.
- Predictions refresh every 30 seconds, continuously
- Corrective recommendations every 15 minutes, only when the trend is out of bounds
- Grade-specific limits applied automatically, 5 to 40+ grades without reconfiguration
Live quality trend
02 / Root Cause Analysis
When quality breaches a limit, know why in minutes, not shifts
Chasing the cause of a deviation is where hours of throughput go. When a prediction crosses a grade limit, the platform surfaces the top 10–15 influencing process tags, ranked by influence, so operators fix the right variable the first time instead of experimenting on a running line.
- Triggered automatically the moment quality breaches its limits
- Top 10–15 contributing tags, ranked by influence
- Shorter investigations mean shorter deviations, and fewer of them turning into stops
Ranked contributing factors
03 / Intelligent Centerlining
Run every shift like your best shift
Every machine has runs where everything held: quality on target, no surprises, full output. Centerlining captures those golden run conditions and keeps key process variables aligned to them in real time, so the process stays in the zone where stops do not happen.
- Key variables held to golden-run targets in real time
- Radar-style live dashboard shows alignment at a glance
- Consistency through grade transitions, where drift most often starts
Golden run alignment
What throughput is worth
The math that makes this a C-suite conversation
Every hour of unplanned stop is a six-figure event on a paper machine, and even a fraction of a percent of quality improvement is worth hundreds of thousands per year. Small, continuous corrections compound.
$20–200K
Cost of an unplanned stop
Per hour of downtime, industry benchmark
$400–500K
Value of a 0.1% quality gain
Per year, average paper mill
~50%
Fiber share of production cost
Every off-spec run wastes it
30s
Prediction cadence
Continuous, live, grade-aware
Figures reflect actual ProcessMiner deployments in pulp and paper and published industry benchmarks. Results vary by facility, process, and data quality; past performance is not a guarantee of future results.
Prove it on your data
See what your line looks like without the drift.
We'll walk through how the platform would hold quality on your grades/SKUs, connect to your historian, and quantify what fewer stops are worth on your machines.
Already seen the platform in action? If you're ready to start a pilot, click below.
~30 min
to connect your historian
Hours
to a first live quality prediction model
Under 2 weeks
to full deployment
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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.