01[seconds]
Catch it live.
A reading leaves its learned band, and the line knows before the part is scrap.
Flagged 14:32 · part 4 117 held for check
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Use case · Quality Loop
A process that learns from every part.
Inducloud correlates measured quality with every process parameter. It catches deviations in real time, shows where problems start, and feeds the outcome back to planning and costing. Every run makes the next one better.
Reading outside the normal band · flagged 14:32
The problem
The answer was in the data. Nobody connected it.
Quality teams usually find out that a process drifted when the batch is already finished. And the lesson rarely makes it back to the people who set up the next order.
The signals that explain the deviation were there all along, in the machine data. They were just never connected to the outcome they shaped.
Self-learning process
Catch. Improve. Plan. One self-learning process.
Correlate what you measured on the part with how the machine made it. The same data then works on three time scales, and your people close each loop with better facts.
02[weeks]
Improve the process.
See which parameter, tool or shift the defects trace back to, and narrow the process window.
03[every order]
Plan on facts.
The outcome flows back to process planning and costing, so the next order starts from what actually works.
Melt temperature setpoint680 °C676 °C
Cycle time in routing44.0 s41.5 s
Scrap allowance in costing4.0 %1.5 %
Updated from 1 840 measured parts
Catch / improve / plan / repeat
How it works
From signal to action, step by step.
[01]
Capture every process signal at full resolution.
[02]
Link each part's measured quality to the parameters that made it.
[03]
Learn each signal's normal band. No thresholds to set.
[04]
Flag a deviation the moment it happens, before it becomes scrap.
[05]
Find where problems start and narrow the process window.
[06]
Feed the outcome back to process planning and costing.
What you get
Measurable effect on the floor and in the P&L.
Less scrapLess reworkRoot cause in minutesNarrower process windowBetter first-time-rightSelf-learning processFull traceability
In production
−⅓
scrap at a global surface-treatment supplier, live since 2023
From assumptions to measurements
Next step
Make your process self-learning. Start with one machine.
Then we scale on proof, not promises.
Results in 4–8 weeks