Yield Optimization
“What parameters maximize yield?”
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A real-world example
What parameters maximize yield?
Yield losses in process manufacturing average 3-8% of total output. Design of experiments (DOE) finds local optima but cannot explore the full parameter space across changing material batches and equipment conditions. For a semiconductor fab producing $2B in annual output, a 1% yield improvement is worth $20M. For a chemical plant at $500M output, it is worth $5M.
How KumoRFM solves this
Graph-powered intelligence for manufacturing
Kumo connects recipes, process parameters, materials, equipment, and output quality into a manufacturing graph. The GNN learns the yield surface across the full parameter space, accounting for material-batch-to-batch variation and equipment drift that DOE assumes away. PQL predicts yield for any parameter combination, enabling operators to find the optimal set point for the current material batch and equipment state.
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
RECIPES
| recipe_id | product | target_yield | version |
|---|---|---|---|
| REC01 | Compound-X | 94% | v3.2 |
| REC02 | Alloy-Y | 91% | v2.8 |
| REC03 | Film-Z | 88% | v4.1 |
PARAMETERS
| run_id | recipe_id | temp_c | pressure_bar | time_min |
|---|---|---|---|---|
| RUN701 | REC01 | 245 | 12.5 | 180 |
| RUN702 | REC02 | 1,420 | 0.8 | 45 |
| RUN703 | REC03 | 185 | 3.2 | 22 |
MATERIALS
| material_id | batch | purity_pct | particle_size_um |
|---|---|---|---|
| MAT301 | B-2025-088 | 99.7% | 45 |
| MAT302 | B-2025-091 | 99.2% | 52 |
| MAT303 | B-2025-095 | 99.9% | 38 |
EQUIPMENT
| equipment_id | type | calibration_date | drift_pct |
|---|---|---|---|
| EQ101 | Reactor | 2025-02-15 | 0.3% |
| EQ102 | Furnace | 2025-01-20 | 1.1% |
| EQ103 | Coater | 2025-02-28 | 0.1% |
OUTPUT_QUALITY
| run_id | actual_yield | grade | timestamp |
|---|---|---|---|
| RUN701 | 93.2% | A | 2025-03-01 |
| RUN702 | 88.5% | B+ | 2025-03-01 |
| RUN703 | 89.1% | A- | 2025-03-01 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT AVG(OUTPUT_QUALITY.actual_yield, 0, 1, days) FOR EACH RECIPES.recipe_id, PARAMETERS.run_id
Prediction output
Every entity gets a score, updated continuously
| RECIPE_ID | OPTIMAL_TEMP | OPTIMAL_PRESSURE | PREDICTED_YIELD |
|---|---|---|---|
| REC01 | 248 C | 12.8 bar | 95.4% |
| REC02 | 1,415 C | 0.75 bar | 92.1% |
| REC03 | 182 C | 3.0 bar | 90.8% |
Understand why
Every prediction includes feature attributions — no black boxes
Recipe REC02 -- Alloy-Y on Furnace EQ102
Predicted: Predicted yield: 92.1% (vs current 88.5%, +3.6%)
Top contributing features
Temperature adjustment from 1420 to 1415 C
-5 C
30% attribution
Material purity interaction with pressure
99.2% x 0.75 bar
25% attribution
Furnace calibration drift compensation
1.1% drift
19% attribution
Hold time extension to 48 min
+3 min
15% attribution
Particle size impact on sintering
52 um
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 semiconductor fab producing $2B in annual output gains $20M per 1% yield improvement. Kumo's manufacturing graph finds optimal parameters for each material-batch and equipment-state combination, going beyond the local optima that DOE provides.
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.
