AI watches scrap and alerts you in time
A traditional report tells you how much scrap you made yesterday. Instead, we analyze the data in real time: AI recognizes an anomaly - a sudden spike, a slow creep, or their combination - and alerts the right person before the impact grows. The goal is simple: shorten the reaction time and significantly reduce scrap.
Tested on real production data
The summary number arrives too late
- Scrap is measured, but it's only noticed once the damage is done.
- The daily report shows the workstation had a bad day. The shift is already lost.
- Slow growth is the most dangerous. For weeks nobody notices it, then comes a jump and the quarter is ruined.
- Before the information reaches the right person, the problem keeps running.
- Causes are argued over at the quality meeting. Nobody has hard data on exactly when it started.
The cost: a late reaction means whole shifts of scrap, the fix targets the symptom instead of the cause, and recurring incidents mean there's no timely alert. The key isn't more data, but timely information for the person who can intervene.
From data to a timely notification
AI continuously watches your scrap data and tells you itself when something is off. Five steps, and the last one makes the biggest difference.
Three patterns the AI recognizes
Every dashboard sees sudden jumps. A slow creep it doesn't. AI watches both and their combination, and for drift it alerts hours to days earlier than it shows up in the daily report.
Something just went wrong
A bad tool change, a shifted parameter, a different material batch. A sharp jump above normal.
Slow drift
Mold wear, gradual calibration drift, seasonal influence. The eye misses it in reports.
Several signals at once
For example a sudden jump on top of a slow creep. The most serious situation, to be handled first.
Four views of one module
The “Scrap” module has four tabs. From live incidents through history and backtest to a look into the model’s quality.
Active scrap
Live incidents right now, with severity, status and context. What you need at the morning meeting.
History
The scrap-rate and runtime over time, with incidents marked in the chosen range.
Backtest
Virtual incidents: what the model would have caught on your history, by type and duration. Export to CSV.
Detection status
Model quality (a score 0 to 100), training and a watch on whether detection is actually running.
| Type | Context | Level | Duration | Severity | Signif. |
|---|---|---|---|---|---|
| Sudden | Welding shop | Workstation | 2 h 15 min | 18.5% | 12 |
| Slow creep | Assembly | Workstation | 7 h 30 min | 6.6% | 8 |
| Combination | Press shop | Workstation | 13 h 15 min | 28.2% | 14 |
An illustrative example. Each incident has a type, context, severity (how far above normal) and statistical significance.
Try it on your own history
Before you put anything into operation, you let the model replay your history. You see virtual incidents: how many there'd be, of what type (sudden vs. slow) and how long they'd last. From that we tune the sensitivity so it neither floods you nor misses things. No black box.
What you would catch
An overview of virtual incidents on your history: count, the split into sudden and slow, average duration.
Sensitivity tuning
More alerts versus fewer false alarms. We set it for each series separately.
Export to CSV
You take the backtest results out and we go through them together, on your data.
You see how the model is doing
Detection works unsupervised, so it has no pre-labeled “correct” answers. That’s why we openly show how good the model’s data is. Where data is scarce, we tell you the result is less reliable.
0 to 100 for each series
The score blends data volume, day coverage, scrap density and history length. Rated from “Excellent” to “Insufficient”.
Automatic and manual
The model is retrained regularly (monthly) and whenever you run it manually. Training is idempotent and can be repeated safely.
It knows when data stops coming
If no new bins arrive, the system reports it (Detection not running). You won’t find out only when an incident is missing.
A notification where you can intervene
Detection on its own won't reduce scrap. Timely action will. That's why the notification is the core of the module: it goes to the person who'll do something about it, at a moment when it still matters.
To the right person
The foreman on shift, the workstation's process engineer. Not into a generic report nobody reads.
With context
Where, when, what type, how far above normal and how confident the detection. You immediately know what to address.
On a channel you watch
Mobile push (Android and iOS), SMS and email. The alert reaches you even when you're not at a screen.
What the AI does NOT do
What the module needs
Technical prerequisites: a deployed TPM&M platform with TPM Analytics and a MES with a continuous data feed (bins). Detection quality grows with the volume and length of history, so the model openly shows a quality score for each series. The module is now in pilot operation, with release expected in summer 2026.
What we're asked most often
It's now running in pilot operation on real production data. We plan release for summer 2026. If you want to be among the first, get in touch and we'll arrange pilot testing.
The model learns without labeled data, but it needs enough history to recognize your normal behavior. Where data is scarce, it openly shows a lower quality score.
Yes. The model has built-in thresholds for a minimum number of non-zero bins, so it doesn't raise false alarms on series with negligible output.
Sensitivity is configurable per series. Before launch we run a backtest on your history and show how many alerts you'd get. We tune the balance together.
Complementarily. Your SPC thresholds stay. AI catches what statistical control misses: drift below the limit and combined signals.
Automatically at a regular interval (monthly) and whenever you run it manually. If data stops coming, the system warns that detection isn't running.
Want to be among the first?
We test the module on real production data. We'll show it to you live and arrange how to try it at your site before it ships.
Book a presentation