Quality Defect Prediction
“Will this production run have defects?”
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A real-world example
Will this production run have defects?
Defective production runs cost manufacturers 5-15% of revenue in scrap, rework, and warranty claims. SPC charts catch process drift but miss the multi-variable interactions that cause defects: when material batch variance combines with equipment wear and ambient conditions. For a manufacturer producing $500M in goods annually, reducing defect rates from 3% to 1% saves $10M in direct costs and prevents $25M in downstream warranty exposure.
How KumoRFM solves this
Graph-powered intelligence for manufacturing
Kumo connects production runs, process parameters, materials, inspections, and equipment into a manufacturing graph. The GNN learns the combinatorial defect patterns that SPC misses: specific material-parameter-equipment triplets that produce defects only under certain ambient conditions. PQL predicts defect probability per production run before it starts, enabling parameter adjustments or material substitutions that prevent defects at the source.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
PRODUCTION_RUNS
| run_id | product | equipment_id | material_id | start_time |
|---|---|---|---|---|
| RUN601 | Widget-A | EQ001 | MAT101 | 2025-03-01 06:00 |
| RUN602 | Widget-B | EQ002 | MAT102 | 2025-03-01 06:00 |
| RUN603 | Widget-A | EQ003 | MAT101 | 2025-03-01 14:00 |
PARAMETERS
| run_id | temperature_c | pressure_bar | speed_rpm | humidity_pct |
|---|---|---|---|---|
| RUN601 | 185 | 42 | 1,200 | 45% |
| RUN602 | 210 | 38 | 800 | 52% |
| RUN603 | 188 | 41 | 1,180 | 48% |
MATERIALS
| material_id | supplier | batch | mfi | tensile_mpa |
|---|---|---|---|---|
| MAT101 | PolySupply Co | B2025-042 | 19.8 | 540 |
| MAT102 | ResinWorks | B2025-038 | 21.5 | 520 |
INSPECTIONS
| inspection_id | run_id | defect_count | defect_type | date |
|---|---|---|---|---|
| INS401 | RUN590 | 0 | None | 2025-02-28 |
| INS402 | RUN591 | 12 | Surface crack | 2025-02-28 |
| INS403 | RUN592 | 0 | None | 2025-02-28 |
EQUIPMENT
| equipment_id | type | hours_since_service | condition_score |
|---|---|---|---|
| EQ001 | Injection Molder | 480 | 87% |
| EQ002 | Extruder | 1,200 | 72% |
| EQ003 | Injection Molder | 120 | 95% |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(INSPECTIONS.defect_count > 0, 0, 1, days) FOR EACH PRODUCTION_RUNS.run_id
Prediction output
Every entity gets a score, updated continuously
| RUN_ID | PRODUCT | DEFECT_PROB | TOP_RISK_FACTOR |
|---|---|---|---|
| RUN601 | Widget-A | 0.38 | Material batch MFI drift |
| RUN602 | Widget-B | 0.71 | Equipment condition + humidity |
| RUN603 | Widget-A | 0.08 | Within tolerance |
Understand why
Every prediction includes feature attributions — no black boxes
Production Run RUN602 -- Widget-B on Extruder EQ002
Predicted: 71% defect probability
Top contributing features
Equipment hours since service
1,200 hrs (high)
28% attribution
Humidity above optimal range
52% (target: <48%)
24% attribution
Material MFI at upper spec limit
21.5 g/10min
21% attribution
Similar run on EQ002 had defects last week
12 defects
16% attribution
Equipment condition score below threshold
72%
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A manufacturer producing $500M in goods saves $35M annually by predicting defects before production runs start. Kumo's manufacturing graph catches the material-equipment-parameter combinations that statistical process control monitors in isolation.
Related use cases
Explore more manufacturing use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
