Resin lot-to-lot variability
Resin lot-to-lot variability Every new lot behaves a little differently. Operators compensate by feel, and the compensation itself becomes a source of variation the next shift inherits.
Continuous polymer processing, held on spec.
Extrusion, film, and compounding lines run on the same physics the platform was built for: melt temperature, pressure, line speed, and resin variability, all interacting faster than offline testing can catch. The platform models your line from its own data and holds it at its best operating point.
The plastics problem set
Where continuous polymer lines lose margin
Energy-heavy, fixed setpoints
Energy-heavy, fixed setpoints Melt processes are energy-intensive, and setpoints are often left at a conservative safe maximum. Holding the line at its efficient operating point takes live feedback, not habit.
Quality found after the run
Quality found after the run Offline testing reports on product that's already made. By the time a gauge, tensile, or clarity issue is confirmed, the scrap has been produced and boxed.
01 / Between offline tests
Real-time quality prediction
Grade-specific models act as a soft sensor, predicting where quality is heading every 30 seconds and filling the blind spot between offline test results.
- Built from your line's own historical data, no industry template
- Grade logic handles product changeovers automatically
Approved visual needed
02 / The golden run
Intelligent Centerlining™
Centerlining compares live production against your best historical runs and gives operators quantified adjustments, absorbing resin lot variation instead of passing it downstream.
- Specific setpoint amounts, not vague alerts
- Separates controllable variables from uncontrollable ones
Approved visual needed
03 / When the line goes off-spec
Root cause analysis
A plastics deployment scopes roughly 800 tags. When quality breaches a limit, the platform ranks the 10–15 most likely contributing variables by influence, so the team adjusts the right lever first.
- Ranked contributing factors across linear and non-linear relationships
- Redundant signals filtered automatically
Approved visual needed
Proof from the platform
Measured where the platform runs today
Plastics is an expansion vertical for us, so we quote cross-industry results, not invented ones.
>20%
Quality variability reduction
Pulp & paper deployments
30s
Prediction cadence
Continuous, live
~800
Tags per plastics line
Configurable scope
<2 wks
To full deployment
Once connectivity is set
Figures reflect actual ProcessMiner deployments in other continuous-manufacturing verticals. Results vary by facility, process, and data quality; past performance is not a guarantee of future results.
How implementation works
Start with proof. Scale on results.
Every new customer follows the same staged path: a free analysis of the data you already collect, then a 60-day pilot on live production. From there, you expand as the results earn it. You set the pace.
Pilot program
1
Start here
Free Analysis
Includes
- Offline PM Studio dashboard from your historical data
- Data healthcheck of what you capture today
- Findings reviewed with your team
2
Live Pilot
60-Day Pilot
Includes
- Live PM Studio dashboards on your process
- Real-time monitoring of trends and drift
- Prediction, root cause & centerlining
Enterprise license
3
Stage 1
Predict at scale
Everything in the pilot, plus
- Full deployment across your operation
- Annual assessment of value delivered
4
Stage 2
Guided action
Everything in Stage 1, plus
- Prioritized recommendations operators can act on
- PM AI Agent for plain-language answers
- Value capture tracking
5
Stage 3
Autonomous optimization
Everything in Stage 2, plus
- Closed-loop control writing setpoints back to your systems
- Trial vs. production analysis validating every change
Most teams enter at the Free Analysis, but you can step in wherever fits. How far you take it depends on where your operation is today.
Start with a Free AnalysisExpansion vertical, proven mechanics
See what the models would find on your line.
Bring your quality metrics and six months of historian data. We'll map the platform to your process and decide together whether a pilot makes sense.
Historian connected in ~30 minutes. Full deployment in under two weeks.
Get in touch
Have a question?
Whether it's our platform, your process, or a pilot you'd like to see in action, let us know.