Inventory Optimization
“How much safety stock does each warehouse need?”
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A real-world example
How much safety stock does each warehouse need?
US retailers hold $680B in inventory at any given time (US Census Bureau), with carrying costs averaging 20-30% of inventory value annually. Setting safety stock too high ties up capital and increases spoilage risk. Setting it too low causes stockouts that cost $1T+ globally each year (IHL Group). Traditional safety-stock formulas use a single standard-deviation calculation that ignores supplier reliability differences, seasonal demand spikes, promotional calendars, and cross-warehouse transfer capabilities. A $10B retailer could free $200-400M in working capital by right-sizing safety stock.
How KumoRFM solves this
Relational intelligence built for retail and e-commerce data
Kumo connects SKUs, warehouses, supplier lead times, historical demand variability, promotion schedules, and cross-warehouse transfer times into a relational graph. The model calculates that Warehouse WH-03 needs 1,200 units of SKU-4201 as safety stock because its primary supplier has a 92% on-time rate (vs 99% for WH-01's supplier), demand variance is 2.3x higher due to promotional activity, and the nearest backup warehouse is 4 days away. These relational signals produce safety-stock recommendations that maintain 98.5% service levels while reducing average inventory by 20%.
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 | name | region | capacity_units | stores_served |
|---|---|---|---|---|
| WH-01 | Northeast DC | Northeast | 2,400,000 | 120 |
| WH-03 | West DC | West | 1,800,000 | 85 |
| WH-05 | Central DC | Midwest | 2,100,000 | 95 |
SKU_DEMAND
| sku_id | warehouse_id | avg_weekly_units | demand_std_dev | seasonality |
|---|---|---|---|---|
| SKU-4201 | WH-01 | 8,400 | 840 | Low |
| SKU-4201 | WH-03 | 6,200 | 1,420 | High |
| SKU-4310 | WH-01 | 12,000 | 1,100 | Medium |
SUPPLIER_PERFORMANCE
| supplier_id | warehouse_id | on_time_rate | avg_lead_days | variability_days |
|---|---|---|---|---|
| SUP-12 | WH-01 | 99.1% | 3.2 | 0.5 |
| SUP-12 | WH-03 | 92.4% | 5.1 | 2.3 |
| SUP-07 | WH-01 | 97.8% | 4.0 | 1.0 |
TRANSFER_NETWORK
| from_warehouse | to_warehouse | transit_days | cost_per_unit |
|---|---|---|---|
| WH-01 | WH-05 | 1.5 | $0.12 |
| WH-05 | WH-03 | 3.0 | $0.22 |
| WH-01 | WH-03 | 4.0 | $0.35 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(SKU_DEMAND.AVG_WEEKLY_UNITS, 0, 4, days) FOR EACH WAREHOUSES.WAREHOUSE_ID, PRODUCTS.SKU_ID WHERE SKU_DEMAND.SEASONALITY IN ('High', 'Medium')
Prediction output
Every entity gets a score, updated continuously
| WAREHOUSE_ID | SKU_ID | SAFETY_STOCK_REC | CURRENT_SAFETY | CHANGE | SERVICE_LEVEL |
|---|---|---|---|---|---|
| WH-01 | SKU-4201 | 1,680 | 2,100 | -420 | 98.7% |
| WH-03 | SKU-4201 | 3,410 | 2,800 | +610 | 98.5% |
| WH-01 | SKU-4310 | 2,420 | 2,750 | -330 | 98.6% |
Understand why
Every prediction includes feature attributions — no black boxes
SKU-4201 at Warehouse WH-03 (West DC)
Predicted: 3,410 units safety stock recommended (+610 vs current)
Top contributing features
Supplier on-time rate (below threshold)
92.4%
28% attribution
High demand variability
1,420 std dev
25% attribution
Upcoming promotional lift
+65% expected
20% attribution
Nearest backup warehouse distance
4.0 transit days
16% attribution
Seasonal demand peak approaching
Q4 ramp
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: Reduce average inventory by 20% while maintaining 98.5% service levels, freeing $200-400M in working capital for a $10B retailer.
Related use cases
Explore more retail & e-commerce 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.
