ETA Prediction
“When will this shipment arrive?”
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A real-world example
When will this shipment arrive?
Inaccurate ETAs ripple through the supply chain: warehouses staff for arrivals that don't come, production lines idle waiting for delayed components, and customers receive wrong delivery promises. Carrier-provided ETAs are 40-60% inaccurate beyond 3 days out. For a logistics company managing 100K shipments per month, reducing ETA error by 30% saves $18M annually in wasted dock labor, expediting fees, and customer penalties.
How KumoRFM solves this
Graph-powered intelligence for supply chains
Kumo connects shipments, carriers, routes, weather forecasts, and port congestion into a logistics graph. The GNN learns how delays propagate: when port congestion in Shanghai affects carrier X's transit times on route Y, and how weather patterns at intermediate points compound into final delivery delays. PQL predicts arrival time per shipment, updating continuously as new signals arrive.
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
SHIPMENTS
| shipment_id | carrier_id | origin | destination | ship_date |
|---|---|---|---|---|
| SHP501 | CAR01 | Shanghai | Los Angeles | 2025-02-20 |
| SHP502 | CAR02 | Rotterdam | New York | 2025-02-22 |
| SHP503 | CAR01 | Busan | Seattle | 2025-02-25 |
CARRIERS
| carrier_id | name | on_time_rate | avg_delay_days |
|---|---|---|---|
| CAR01 | OceanLine Express | 72% | 2.4 |
| CAR02 | Atlantic Cargo | 85% | 1.1 |
ROUTES
| route_id | origin | destination | avg_transit_days | stops |
|---|---|---|---|---|
| RT01 | Shanghai | Los Angeles | 14 | 0 |
| RT02 | Rotterdam | New York | 10 | 1 |
| RT03 | Busan | Seattle | 11 | 0 |
WEATHER
| region | date | condition | severity |
|---|---|---|---|
| Pacific | 2025-03-02 | Storm | Moderate |
| Atlantic | 2025-03-01 | Clear | None |
| Pacific | 2025-03-04 | Fog | Light |
PORT_CONGESTION
| port | date | vessels_waiting | avg_wait_days |
|---|---|---|---|
| Los Angeles | 2025-03-01 | 42 | 3.2 |
| New York | 2025-03-01 | 18 | 1.0 |
| Seattle | 2025-03-01 | 12 | 0.5 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT FIRST(SHIPMENTS.actual_arrival, 0, 30, days) FOR EACH SHIPMENTS.shipment_id
Prediction output
Every entity gets a score, updated continuously
| SHIPMENT_ID | CARRIER | ORIGINAL_ETA | PREDICTED_ETA | DELAY_DAYS |
|---|---|---|---|---|
| SHP501 | OceanLine Express | 2025-03-06 | 2025-03-10 | +4 |
| SHP502 | Atlantic Cargo | 2025-03-04 | 2025-03-05 | +1 |
| SHP503 | OceanLine Express | 2025-03-08 | 2025-03-09 | +1 |
Understand why
Every prediction includes feature attributions — no black boxes
Shipment SHP501 -- Shanghai to Los Angeles via OceanLine Express
Predicted: Predicted arrival: March 10 (+4 days delay)
Top contributing features
LA port congestion (42 vessels waiting)
3.2 day avg wait
32% attribution
Pacific storm on route (Mar 2)
Moderate severity
26% attribution
Carrier OceanLine historical delay rate
28% late
18% attribution
Current vessel position (behind schedule)
-1.5 days
14% attribution
Fog advisory at destination (Mar 4)
Light
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 logistics company managing 100K monthly shipments saves $18M per year by reducing ETA error 30%. Kumo's logistics graph captures delay propagation across routes, ports, weather, and carrier patterns that carrier-provided ETAs systematically 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.
