Demand Sensing
“What will demand be at each node in the next 7 days?”
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A real-world example
What will demand be at each node in the next 7 days?
Traditional demand planning uses monthly or weekly forecasts that miss short-term demand shifts caused by promotions, weather, competitor actions, and upstream disruptions. These misses cascade through the supply chain: stockouts at high-demand nodes, overstock at low-demand nodes. For a retailer with 500 locations and $5B in inventory, a 15% improvement in 7-day demand accuracy reduces carrying costs by $75M and stockout losses by $120M annually.
How KumoRFM solves this
Graph-powered intelligence for supply chains
Kumo connects warehouses, products, orders, shipments, and external signals (weather, events, promotions) into a supply chain graph. The GNN learns how demand propagates across nodes: when a distribution center sees a surge, which downstream stores will feel it 2-3 days later. PQL predicts demand at each node for the next 7 days, incorporating real-time signals that monthly forecasts ignore.
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
WAREHOUSES
| warehouse_id | region | type | capacity |
|---|---|---|---|
| WH001 | US-West | Distribution Center | 50,000 units |
| WH002 | US-East | Regional Hub | 25,000 units |
| WH003 | US-Central | Fulfillment | 15,000 units |
PRODUCTS
| product_id | category | avg_daily_demand | lead_time_days |
|---|---|---|---|
| SKU101 | Electronics | 450 | 3 |
| SKU102 | Apparel | 1,200 | 5 |
| SKU103 | Grocery | 3,500 | 1 |
ORDERS
| order_id | warehouse_id | product_id | qty | timestamp |
|---|---|---|---|---|
| ORD5001 | WH001 | SKU101 | 85 | 2025-03-01 |
| ORD5002 | WH002 | SKU102 | 320 | 2025-03-01 |
| ORD5003 | WH003 | SKU103 | 1,450 | 2025-03-01 |
SHIPMENTS
| shipment_id | from_warehouse | to_warehouse | status | eta |
|---|---|---|---|---|
| SHP201 | WH001 | WH003 | In Transit | 2025-03-03 |
| SHP202 | WH002 | WH003 | Delivered | 2025-03-01 |
EXTERNAL_SIGNALS
| signal_id | region | type | value | date |
|---|---|---|---|---|
| SIG01 | US-West | Weather | Heat wave | 2025-03-05 |
| SIG02 | US-East | Promotion | Flash sale | 2025-03-04 |
| SIG03 | US-Central | Event | Sports final | 2025-03-06 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(ORDERS.qty, 0, 7, days) FOR EACH WAREHOUSES.warehouse_id, PRODUCTS.product_id
Prediction output
Every entity gets a score, updated continuously
| WAREHOUSE_ID | PRODUCT_ID | PREDICTED_DEMAND_7D | VS_BASELINE |
|---|---|---|---|
| WH001 | SKU101 | 680 | +51% |
| WH002 | SKU102 | 2,850 | +18% |
| WH003 | SKU103 | 28,400 | +16% |
Understand why
Every prediction includes feature attributions — no black boxes
WH001 x SKU101 (Electronics at US-West DC)
Predicted: 680 units in 7 days (+51% vs baseline)
Top contributing features
Upcoming heat wave driving electronics demand
Heat wave Mar 5
30% attribution
Upstream order velocity increase
+38% WoW
25% attribution
Historical seasonal pattern
Spring uptick
19% attribution
In-transit shipment to downstream nodes
SHP201 in transit
15% attribution
Competitor stockout signal
Detected
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 retailer with 500 locations and $5B in inventory saves $195M annually by improving 7-day demand sensing by 15%. Kumo's supply chain graph propagates demand signals across nodes, catching short-term shifts that monthly forecasts miss entirely.
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.
