Inventory Optimization
“What is the optimal stock level at each location?”
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A real-world example
What is the optimal stock level at each location?
Static safety stock formulas ignore the relationships between locations, products, and suppliers. They over-stock slow movers and under-stock fast movers, especially during demand transitions. For a distributor with 2,000 SKUs across 50 locations, a 20% reduction in excess inventory frees $80M in working capital while maintaining or improving fill rates.
How KumoRFM solves this
Graph-powered intelligence for supply chains
Kumo connects locations, products, inventory levels, demand history, and lead times into a multi-echelon graph. The GNN learns how demand variability at downstream locations cascades to upstream stocking decisions, how lead time volatility differs by supplier-product-location combination, and which products substitute for each other during stockouts. PQL predicts optimal stock levels that minimize total cost (carrying + stockout) per location-product pair.
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
LOCATIONS
| location_id | type | region | storage_capacity |
|---|---|---|---|
| LOC01 | Warehouse | US-West | 100,000 units |
| LOC02 | Store | US-West | 5,000 units |
| LOC03 | Store | US-East | 8,000 units |
PRODUCTS
| product_id | category | unit_cost | shelf_life_days |
|---|---|---|---|
| SKU201 | Electronics | $120 | N/A |
| SKU202 | Perishable | $4.50 | 14 |
| SKU203 | Apparel | $35 | N/A |
INVENTORY
| location_id | product_id | on_hand | on_order | timestamp |
|---|---|---|---|---|
| LOC01 | SKU201 | 2,400 | 500 | 2025-03-01 |
| LOC02 | SKU202 | 180 | 0 | 2025-03-01 |
| LOC03 | SKU203 | 450 | 200 | 2025-03-01 |
DEMAND_HISTORY
| location_id | product_id | daily_demand_avg | demand_std |
|---|---|---|---|
| LOC01 | SKU201 | 85 | 22 |
| LOC02 | SKU202 | 45 | 18 |
| LOC03 | SKU203 | 28 | 8 |
LEAD_TIMES
| supplier_id | product_id | avg_lead_days | lead_std_days |
|---|---|---|---|
| SUP01 | SKU201 | 5 | 1.2 |
| SUP02 | SKU202 | 2 | 0.5 |
| SUP03 | SKU203 | 8 | 2.1 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT AVG(INVENTORY.optimal_qty, 0, 14, days) FOR EACH LOCATIONS.location_id, PRODUCTS.product_id
Prediction output
Every entity gets a score, updated continuously
| LOCATION_ID | PRODUCT_ID | OPTIMAL_STOCK | CURRENT_STOCK | ACTION |
|---|---|---|---|---|
| LOC01 | SKU201 | 1,800 | 2,400 | Reduce by 600 |
| LOC02 | SKU202 | 280 | 180 | Reorder 100 |
| LOC03 | SKU203 | 380 | 450 | Reduce by 70 |
Understand why
Every prediction includes feature attributions — no black boxes
LOC02 x SKU202 (Perishable at US-West Store)
Predicted: Optimal stock: 280 units (current: 180, reorder 100)
Top contributing features
7-day demand forecast
315 units
31% attribution
Lead time from supplier
2 days avg
23% attribution
Shelf life constraint
14 days
19% attribution
Demand volatility (weekend spike)
+40% Sat-Sun
16% attribution
Substitute product availability
Low
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 distributor with 2,000 SKUs across 50 locations frees $80M in working capital by right-sizing inventory at every node. Kumo's multi-echelon graph optimizes stock levels by learning demand cascade patterns and lead time variability that static safety stock formulas ignore.
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.
