Inventory Planning & Stock Optimization
“How many units of each SKU should we stock at each warehouse over the next 30 days?”
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A real-world example
How many units of each SKU should we stock at each warehouse over the next 30 days?
Inventory carrying costs consume 20–30% of product value annually. Overstocking ties up working capital; understocking means lost sales and expedited shipping at 3–5x standard cost. With thousands of SKUs across multiple warehouses, getting the right quantity at the right location requires understanding cross-product demand dependencies, regional consumption patterns, and supply chain lead times that spreadsheets and simple rules cannot capture.
How KumoRFM solves this
Relational intelligence for every forecast
Kumo connects products to orders, warehouses, suppliers, and seasonal patterns in a single relational graph. Instead of forecasting each SKU-warehouse pair independently, Kumo learns that Product P-100 and P-200 share a supplier with a 21-day lead time, that Warehouse WH-East sees 40% higher volume in Q4, and that recent order velocity for the Electronics category is accelerating. These cross-table signals produce stock-level predictions that account for the full supply chain context.
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
PRODUCTS
| product_id | product_name | category | lead_time_days |
|---|---|---|---|
| P-100 | Wireless Mouse | Electronics | 21 |
| P-200 | USB-C Hub | Electronics | 14 |
| P-300 | Desk Lamp | Home Office | 7 |
ORDER_LINES
| order_id | product_id | warehouse_id | quantity | timestamp |
|---|---|---|---|---|
| ORD-5001 | P-100 | WH-East | 120 | 2025-09-10 |
| ORD-5002 | P-200 | WH-East | 45 | 2025-09-11 |
| ORD-5003 | P-300 | WH-West | 310 | 2025-09-12 |
WAREHOUSES
| warehouse_id | warehouse_name | region | capacity |
|---|---|---|---|
| WH-East | Newark Distribution | Northeast | 500,000 |
| WH-West | Reno Fulfillment | West | 350,000 |
| WH-Central | Dallas Hub | South | 420,000 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(ORDER_LINES.QUANTITY, 0, 30, days) FOR EACH PRODUCTS.PRODUCT_ID
Prediction output
Every entity gets a score, updated continuously
| PRODUCT_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| P-100 | 2025-10-01 | 4,820 |
| P-200 | 2025-10-01 | 1,250 |
| P-300 | 2025-10-01 | 8,340 |
Understand why
Every prediction includes feature attributions — no black boxes
Product P-100 (Wireless Mouse)
Predicted: 4,820 units needed in next 30 days
Top contributing features
Order velocity (14d trend)
+22%
29% attribution
Seasonal pattern (Q4 ramp)
Strong
25% attribution
Warehouse region demand
Northeast peak
20% attribution
Lead time buffer required
21 days
15% attribution
Category growth rate
+18% YoY
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: Cut carrying costs by 20% while reducing stockouts — right-size every SKU at every warehouse with zero manual forecasting.
Related use cases
Explore more forecasting 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.
