Demand Forecasting
“How many units of each product will sell at each store over the next 3 months?”
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A real-world example
How many units of each product will sell at each store over the next 3 months?
Retailers order inventory based on last year's averages, leading to 25–30% overstock on slow items and stockouts on trending ones. A single stockout event costs $1M+ in lost revenue for large retailers. Accurate SKU-level forecasts at each store would let you order 2,400 Classic Tees instead of 5,000 and 350 Flannels instead of 1,000 — freeing millions in working capital while keeping shelves stocked.
How KumoRFM solves this
Relational intelligence for every forecast
Kumo learns from the full relational graph — products connected to transactions, stores, suppliers, promotions, and seasonal calendars. Traditional time-series models see each SKU-store pair in isolation. Kumo sees that Article A001 shares supplier and seasonal patterns with similar items, amplifying the demand signal even for new or slow-moving products. The graph structure captures cross-product substitution effects, regional preferences, and promotional lifts that flat models miss entirely.
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
ARTICLES
| article_id | article_name | category | supplier_id |
|---|---|---|---|
| A001 | Classic Tee | apparel | SUP-12 |
| A002 | Slim Flannel | apparel | SUP-07 |
| A003 | Cargo Shorts | apparel | SUP-12 |
TRANSACTIONS
| txn_id | article_id | store_id | quantity | revenue | timestamp |
|---|---|---|---|---|---|
| TXN-90001 | A001 | S-14 | 3 | $74.97 | 2025-09-15 |
| TXN-90002 | A002 | S-14 | 1 | $48.00 | 2025-09-15 |
| TXN-90003 | A003 | S-22 | 2 | $69.98 | 2025-09-16 |
STORES
| store_id | store_name | region | format |
|---|---|---|---|
| S-14 | Union Square | West | flagship |
| S-22 | Midtown Mall | Northeast | standard |
| S-37 | Lakeside Plaza | Midwest | outlet |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(TRANSACTIONS.QUANTITY, 0, 3, months) FOR EACH ARTICLES.ARTICLE_ID WHERE ARTICLES.CATEGORY = "apparel"
Prediction output
Every entity gets a score, updated continuously
| ARTICLE_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| A001 | 2025-10-01 | 2,412 |
| A002 | 2025-10-01 | 876 |
| A003 | 2025-10-01 | 341 |
Understand why
Every prediction includes feature attributions — no black boxes
Article A001 (Classic Tee)
Predicted: 2,412 units sold in next 3 months
Top contributing features
Seasonal trend (Q4 uplift)
+34%
31% attribution
Store traffic (flagship locations)
High
24% attribution
Promotion active (fall campaign)
Yes
19% attribution
Supplier lead time
14 days
14% attribution
Price point vs. category avg
-8%
12% 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 overstock by 25% and eliminate stockouts on high-demand SKUs — freeing $2–5M in working capital per quarter.
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.
