SKU-Level Demand Forecasting
“What will SKU-level demand be at each store next week?”
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A real-world example
What will SKU-level demand be at each store next week?
Retailers lose $1.1T globally to overstock and stockouts each year (IHL Group). A grocer with 500 stores and 40,000 SKUs must make 20M replenishment decisions weekly. Traditional time-series models forecast each SKU-store pair independently, missing cross-product cannibalization (when the organic brand goes on sale, the conventional brand drops 30%), weather-driven demand shifts (ice cream sales spike 2x when temperatures exceed 90F), and promotional halo effects (discounting chips lifts salsa sales by 18%). These blind spots cause 8-12% forecast error that compounds into millions in waste and lost sales.
How KumoRFM solves this
Relational intelligence built for retail and e-commerce data
Kumo connects products, transactions, stores, promotions, weather, and supplier data into a single relational graph. The model learns that SKU-4201 (organic milk) at Store S-14 will see 340 units next week because a competitor store nearby closed, a BOGO promotion starts Wednesday, and regional temperatures are forecast to stay cool (higher dairy consumption). Cross-product signals, like the substitution relationship between organic and conventional milk, emerge automatically from the graph without manual feature engineering.
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
| sku_id | product_name | category | brand | unit_cost |
|---|---|---|---|---|
| SKU-4201 | Organic Whole Milk 1gal | Dairy | Valley Fresh | $3.20 |
| SKU-4202 | Conv. Whole Milk 1gal | Dairy | DairyPure | $2.80 |
| SKU-4310 | Tortilla Chips 12oz | Snacks | Casa Crunch | $2.10 |
SALES_HISTORY
| sku_id | store_id | date | units_sold | revenue | promo_active |
|---|---|---|---|---|---|
| SKU-4201 | S-14 | 2025-09-22 | 48 | $287.52 | False |
| SKU-4201 | S-14 | 2025-09-23 | 52 | $311.48 | False |
| SKU-4310 | S-14 | 2025-09-22 | 85 | $339.15 | True |
STORES
| store_id | name | format | region | sqft |
|---|---|---|---|---|
| S-14 | Union Square Market | Urban | West | 42,000 |
| S-22 | Midtown Grocery | Suburban | Northeast | 55,000 |
| S-37 | Lakeside Fresh | Rural | Midwest | 35,000 |
PROMOTIONS
| promo_id | sku_id | store_id | type | start_date | end_date |
|---|---|---|---|---|---|
| PR-801 | SKU-4201 | S-14 | BOGO | 2025-10-01 | 2025-10-07 |
| PR-802 | SKU-4310 | S-14 | 20% Off | 2025-09-20 | 2025-09-26 |
| PR-803 | SKU-4202 | S-22 | None | N/A | N/A |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(SALES_HISTORY.UNITS_SOLD, 0, 7, days) FOR EACH PRODUCTS.SKU_ID, STORES.STORE_ID WHERE STORES.REGION = 'West'
Prediction output
Every entity gets a score, updated continuously
| SKU_ID | STORE_ID | PREDICTED_UNITS | CURRENT_STOCK | REORDER_QTY |
|---|---|---|---|---|
| SKU-4201 | S-14 | 412 | 180 | 232 |
| SKU-4202 | S-14 | 195 | 220 | 0 |
| SKU-4310 | S-14 | 520 | 300 | 220 |
Understand why
Every prediction includes feature attributions — no black boxes
SKU-4201 (Organic Whole Milk) at Store S-14
Predicted: 412 units predicted for next week
Top contributing features
BOGO promotion starting Oct 1
+65% lift
30% attribution
Competitor store closure nearby
+12% traffic
22% attribution
Weekly seasonal trend
Peak dairy week
19% attribution
Cross-product substitution (conv. milk)
-15% conv.
16% attribution
Temperature forecast (cool week)
62F avg
13% 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 stockouts by 40% and overstock by 25%, freeing $2-5M in working capital per quarter for a 500-store grocery chain.
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.
