Incoming Quality Prediction
“Will this incoming batch pass inspection?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
Will this incoming batch pass inspection?
100% incoming inspection is expensive and slow. Skip-lot strategies save time but miss quality escapes that cause downstream defects costing 10-50x the incoming material value. For an automotive manufacturer receiving 5,000 batches per month, predicting which batches need full inspection vs. skip-lot saves $12M in inspection costs while reducing quality escapes by 70%, preventing $35M in warranty claims.
How KumoRFM solves this
Graph-powered intelligence for supply chains
Kumo connects suppliers, materials, inspections, production runs, and specs into a quality graph. The GNN learns which supplier-material-production combinations produce defects: when a supplier's process changed, when raw material specs drifted, or when a production run stressed equipment beyond normal parameters. PQL predicts pass/fail per incoming batch, directing inspection resources to high-risk shipments.
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
SUPPLIERS
| supplier_id | name | quality_tier | last_audit |
|---|---|---|---|
| SUP101 | MetalWorks Inc | A | 2025-01-15 |
| SUP102 | PlastiForm Co | B | 2024-11-20 |
| SUP103 | ChemSynth Ltd | A+ | 2025-02-10 |
MATERIALS
| material_id | name | spec_range | supplier_id |
|---|---|---|---|
| MAT201 | Steel Alloy 7075 | Tensile: 570-590 MPa | SUP101 |
| MAT202 | ABS Resin Batch | MFI: 18-22 g/10min | SUP102 |
| MAT203 | Epoxy Compound | Viscosity: 4000-5000 cP | SUP103 |
INSPECTIONS
| inspection_id | material_id | result | defect_rate | date |
|---|---|---|---|---|
| INS301 | MAT201 | Pass | 0.2% | 2025-02-15 |
| INS302 | MAT202 | Fail | 4.8% | 2025-02-20 |
| INS303 | MAT203 | Pass | 0.1% | 2025-02-22 |
PRODUCTION_RUNS
| run_id | material_id | equipment_id | output_quality |
|---|---|---|---|
| RUN401 | MAT201 | EQ01 | 99.8% |
| RUN402 | MAT202 | EQ02 | 95.2% |
| RUN403 | MAT203 | EQ03 | 99.9% |
SPECS
| material_id | parameter | target | tolerance |
|---|---|---|---|
| MAT201 | Tensile Strength | 580 MPa | +/- 10 MPa |
| MAT202 | Melt Flow Index | 20 g/10min | +/- 2 |
| MAT203 | Viscosity | 4500 cP | +/- 500 cP |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(INSPECTIONS.result = 'Fail', 0, 1, days) FOR EACH MATERIALS.material_id
Prediction output
Every entity gets a score, updated continuously
| MATERIAL_ID | SUPPLIER | FAIL_PROB | RECOMMENDED_ACTION |
|---|---|---|---|
| MAT201 | MetalWorks Inc | 0.05 | Skip-lot OK |
| MAT202 | PlastiForm Co | 0.72 | Full inspection |
| MAT203 | ChemSynth Ltd | 0.02 | Skip-lot OK |
Understand why
Every prediction includes feature attributions — no black boxes
MAT202 -- ABS Resin Batch from PlastiForm Co
Predicted: 72% fail probability (Full inspection recommended)
Top contributing features
Recent inspection failure rate trend
+280% (3 months)
30% attribution
Supplier quality tier deterioration
A to B
24% attribution
MFI spec drift in last 3 batches
Trending high
20% attribution
Production run quality with this material
95.2%
15% attribution
Time since last supplier audit
4 months
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: An automotive manufacturer saves $47M annually ($12M in inspection costs + $35M in prevented warranty claims) by predicting incoming batch quality. Kumo's quality graph detects supplier process drift and material spec trends that skip-lot sampling misses.
Related use cases
Explore more supply chain 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.
