Will this batch meet the expected quality?
Probability that the batch stays within the expected quality range.
Predictive Quality
Detect quality riskbefore the batch is finished.
INVO combines bill of materials, supplier, batch, production, machine and quality data to estimate whether a process may deviate from expected quality — before the problem becomes visible at final inspection.
- Product
- Airframe shell
- Line
- Line 3
- Supplier batch
- Prepreg lot #CF-8842
Progress68%
Quality risk · High
Probability of variance
76%
Most significant signals
- Component variance
- Cure stage longer than expected
- Supplier batch with an elevated rejection history
Run an intermediate quality check before the next stage
Quality forms during the process
Quality problems rarelycome from a single factor.
The final result can be affected by the component, the supplier batch, the bill of materials, process parameters, equipment, operator context and variances that occurred earlier in the production sequence.
- 01
Components
Different supplier batches can behave differently in production.
- 02
Supplier quality
The historical rejection rate and quality consistency matter.
- 03
Process parameters
Time, temperature, sequence and handling affect the result.
- 04
Equipment
Machine settings and actual performance can change process stability.
- 05
Production context
Batch size, line load and earlier operations can affect execution.
- 06
Human / procedural factors
Deviating from the expected process steps can raise the risk.
INVO analyzes these signals together instead of treating each one on its own.
Quality prediction
Know wherequality risk is rising.
Which batches need an extra check?
Prioritize quality control based on predicted risk.
Is supplier quality deteriorating?
Identify batches and suppliers with worsening historical patterns.
Which process stage creates the highest risk?
Estimate where in the production process quality is most likely to deviate.
Is this result unusual for this product?
Compare current process behavior against historical production of the same or a similar bill of materials.
Quality Intelligence
See quality risk across productionin one place.
- Batches in production
- 42
- High risk
- 4
- Medium risk
- 7
- Recommended checks
- 6
- Supplier batches to review
- 3
| Batch | Product | Progress | Quality risk | Main signal |
|---|---|---|---|---|
| #28491 | Airframe shell | 68% | 76% High | Supplier batch |
| #28492 | Tail assembly | 42% | 18% Low | — |
| #28493 | Battery pack | 81% | 61% Medium | Process time |
Needs attention
Batch #28491 · Airframe shell
Predicted quality deviation
76%
Run a quality check before assembly
Real-time quality risk
Don't wait for the final inspectionto find the problem.
Expected process
- Cure stage
- 42–48 min
- Expected yield
- 94–97%
- Temperature range
- configured target
Current batch
- Cure stage
- 55 min
- Yield
- 91.8%
- Supplier batch history
- below average
55 min
- Plan
- 45 min
- Expected range
- 42–48 min
- Actual
- 55 min
AI quality risk
Recommended check: Verify the product before the next stage.
76%
Predictive Quality does not replace quality control. It helps decide where and when an extra check has the most value.
Why is the risk rising?
See which signals weigh moston the prediction.
- Supplier batch
- +22%
- Process duration
- +18%
- Yield
- +11%
- Machine
- +6%
- Supplier batch+22%
This supplier batch has a higher rejection history than comparable batches.
- Process duration+18%
The current cure stage is 17% longer than the historical range.
- Yield+11%
The current yield is below the expected range.
- Machine+6%
Line 3 has shown higher variance for this product family.
These signals explain what influenced the risk score. They are not automatic proof of a root cause.
How it works
The model learns what a normalquality result looks like.
- 01
We collect production and quality data
The INVO data warehouse combines:
- Bills of materials
- Components
- Supplier batches
- Process parameters
- Machines
- Production batches
- Quality checks
- Complaints / rejections
- Yield
- Waste
- 02
Models learn the normal patterns
The system analyzes which combinations of parameters historically led to:
- good quality
- rejection
- rework
- unusual yield
- a complaint
- an out-of-spec result
- 03
Every new batch gets a risk score
- Batch
- #28491
- Predicted risk
- 76%
- Confidence
- High
- Suggested action
- Extra check
Models & technology
Different models answerdifferent quality questions.
ERP + MES + WMS + Quality → INVO Data Warehouse → AI models
Quality risk prediction
Will this batch meet the expected quality criteria?
The model compares current production conditions against historical batches.
Expected risk
12%
Current batch
76%
Gradient Boosting · Classification Models
Earlier identification of batches that may need attention.
Process anomaly detection
Is something unusual happening for this product?
The model learns the normal range of process behavior.
Expected cure stage
42–48 min
Actual
55 min
Anomaly Detection · Statistical Models
Catch unusual process behavior even when no static threshold was crossed.
Quality trend forecasting
Is quality performance deteriorating over time?
The model analyzes trends for suppliers, materials, machines, products and lines.
Supplier rejection rate
1.2%
Now
3.8%
Time-Series Models
Identify deterioration before it becomes a recurring operational problem.
Explainability
What influenced the risk score most?
The system shows the factors that had the biggest effect on the prediction.
Supplier batch
+22%
Process duration
+18%
Feature Attribution · Explainable ML
Quality teams can understand why a batch was flagged.
From risk to action
A risk score only mattersif it changes what happens next.
01 · Alert
Batch #28491 · quality risk 76%
02 · Recommended check
An extra quality check before assembly.
03 · Decision
- Approve
- Hold
- Rework
- Reject
- Escalate
04 · Learn
The final quality result returns to the data warehouse as another verified example for future model training.
Critical release decisions stay with the quality team — the model prioritizes attention, it does not approve product on its own.
Supplier quality
Predict quality risk beforethe material enters production.
Incoming material quality is one of the strongest predictors of the production result. Predictive Quality links supplier batches with what actually happened on the line.
Supplier A — carbon fiber batch #8821
- Historical acceptance rate
- 98.4%
- Current supplier trend
- ↓ declining
- Predicted rejection risk
- 6.8%
- Category average
- 1.9%
Predicted rejection risk
Category average
Recommended action
Raise the incoming inspection level
Business impact
Catch quality risk earlier —while there is still time to react.
- Rework ↓
Fewer batches going to late-stage rework
Identify risky production earlier.
- Waste ↓
Lower cost of rejected product
Intervene before assembly, flight test and logistics raise the value at risk.
- QC efficiency ↑
Focus checks where risk is highest
Apply risk-based inspection instead of treating every batch identically.
- Complaint risk ↓
Reduce the chance that problems reach the client
Identify variances before release.
- Supplier quality ↑
See supplier deterioration earlier
Connect incoming material quality with actual production results.
- Traceability ↑
Understand what was present when quality changed
Connect batch, supplier, component and process data.
ROI calculator
The cost of poor quality — an illustrative simulation.
Work out how much of the cost of poor quality and of inspection effort can realistically be addressed by catching risk earlier.
Potential annual impact165,000 USD
Potential productivity impact33,000 USD
Total illustrative potential
/ year
198,000 USD
An illustrative simulation for production at $14–55M revenue scale — the values are not a guaranteed result.
Predictive Quality
Know which batches need attention,before quality problems get expensive.
See how INVO combines production, component, supplier and quality data to predict quality risk, prioritize checks and detect unusual process behavior.