Supplier Risk Scoring
“Which suppliers are likely to miss delivery?”
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A real-world example
Which suppliers are likely to miss delivery?
A single late delivery from a critical supplier can halt a production line, costing $500K-$2M per day in lost output. Traditional supplier scorecards update quarterly and miss leading indicators: financial stress, quality trend deterioration, capacity strain from competing orders. For a manufacturer with 200 tier-1 suppliers, predicting late deliveries 2 weeks ahead saves $25-40M per year in expediting costs and production delays.
How KumoRFM solves this
Graph-powered intelligence for supply chains
Kumo connects suppliers, purchase orders, deliveries, quality records, and financial signals into a temporal graph. The GNN detects supplier stress patterns that scorecards miss: when a supplier's quality metrics slip while their order book grows, when their sub-tier suppliers show delivery volatility, and when financial stress indicators correlate with upcoming late deliveries. PQL predicts delivery risk per PO, giving procurement teams time to activate backup suppliers.
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 | category | tier |
|---|---|---|---|
| SUP001 | PrecisionParts Co | Machined Components | Tier-1 |
| SUP002 | ElectroPower Ltd | Electronic Modules | Tier-1 |
| SUP003 | RawMat Global | Raw Materials | Tier-2 |
PURCHASE_ORDERS
| po_id | supplier_id | product | qty | due_date |
|---|---|---|---|---|
| PO2001 | SUP001 | Gear Assembly | 500 | 2025-03-15 |
| PO2002 | SUP002 | Control Board | 1,200 | 2025-03-12 |
| PO2003 | SUP003 | Steel Alloy | 10 tons | 2025-03-10 |
DELIVERIES
| delivery_id | po_id | actual_date | days_late | qty_short |
|---|---|---|---|---|
| DEL301 | PO1990 | 2025-02-20 | 0 | 0 |
| DEL302 | PO1991 | 2025-02-18 | 3 | 50 |
| DEL303 | PO1992 | 2025-02-25 | 0 | 0 |
QUALITY_RECORDS
| inspection_id | supplier_id | defect_rate | date |
|---|---|---|---|
| QR401 | SUP001 | 0.8% | 2025-02-15 |
| QR402 | SUP002 | 3.2% | 2025-02-20 |
| QR403 | SUP003 | 0.4% | 2025-02-22 |
FINANCIALS
| supplier_id | credit_score | payment_days_trend | news_sentiment |
|---|---|---|---|
| SUP001 | A | Stable | Neutral |
| SUP002 | B- | Increasing | Negative |
| SUP003 | A+ | Stable | Positive |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(DELIVERIES.days_late > 0, 0, 30, days) FOR EACH PURCHASE_ORDERS.po_id
Prediction output
Every entity gets a score, updated continuously
| PO_ID | SUPPLIER | DUE_DATE | LATE_PROB | RISK_TIER |
|---|---|---|---|---|
| PO2001 | PrecisionParts Co | 2025-03-15 | 0.12 | Low |
| PO2002 | ElectroPower Ltd | 2025-03-12 | 0.78 | Critical |
| PO2003 | RawMat Global | 2025-03-10 | 0.06 | Low |
Understand why
Every prediction includes feature attributions — no black boxes
PO2002 -- ElectroPower Ltd, Control Board x 1,200
Predicted: 78% late delivery probability (Critical)
Top contributing features
Recent delivery delay trend
3 days late avg
29% attribution
Defect rate increase (3-month trend)
+180%
24% attribution
Credit score deterioration
B- (was A)
21% attribution
Order book capacity utilization
94%
16% attribution
Negative news sentiment
Restructuring rumor
10% 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 with 200 tier-1 suppliers saves $25-40M per year by predicting late deliveries 2 weeks ahead. Kumo's supplier graph detects the compound stress signals (quality decline + financial strain + capacity overload) that quarterly scorecards miss.
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.
