Demand Planning
“What will order volume be by product line next quarter?”
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A real-world example
What will order volume be by product line next quarter?
Manufacturing demand plans drive capacity allocation, material procurement, and workforce scheduling. A 10% forecast error means either excess capacity ($2-5M wasted) or insufficient capacity (lost orders worth $5-10M). Traditional statistical forecasts miss the demand network: how customer ordering patterns shift based on their end-market conditions, competitor moves, and macroeconomic signals. For a $1B manufacturer, improving quarterly forecast accuracy by 15% saves $8-12M in misallocated capacity.
How KumoRFM solves this
Graph-powered intelligence for manufacturing
Kumo connects customers, orders, products, historical forecasts, and market data into a demand graph. The GNN learns how demand propagates through the customer network: when a major customer's end-market shifts, which products and product lines will see correlated demand changes. PQL forecasts quarterly order volume per product line, incorporating real-time customer signals that traditional time-series models treat as external variables.
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
CUSTOMERS
| customer_id | name | industry | annual_spend |
|---|---|---|---|
| CUST01 | AutoMakers Inc | Automotive | $45M |
| CUST02 | AeroSpace Corp | Aerospace | $28M |
| CUST03 | ConsumerTech Ltd | Electronics | $18M |
ORDERS
| order_id | customer_id | product_line | qty | timestamp |
|---|---|---|---|---|
| ORD7001 | CUST01 | Precision Parts | 12,000 | 2025-02-15 |
| ORD7002 | CUST02 | Specialty Alloys | 3,500 | 2025-02-20 |
| ORD7003 | CUST03 | Micro Components | 45,000 | 2025-02-28 |
PRODUCTS
| product_line | margin_pct | capacity_util | lead_time_weeks |
|---|---|---|---|
| Precision Parts | 32% | 78% | 6 |
| Specialty Alloys | 45% | 85% | 8 |
| Micro Components | 28% | 92% | 4 |
FORECASTS
| product_line | quarter | forecast_qty | actual_qty | error_pct |
|---|---|---|---|---|
| Precision Parts | Q4-2024 | 48,000 | 52,300 | -8.2% |
| Specialty Alloys | Q4-2024 | 14,000 | 12,800 | +9.4% |
| Micro Components | Q4-2024 | 180,000 | 195,000 | -7.7% |
MARKET_DATA
| industry | indicator | trend | confidence |
|---|---|---|---|
| Automotive | EV production ramp | Strong growth | High |
| Aerospace | Defense spend increase | Moderate growth | Medium |
| Electronics | Consumer demand soft | Flat | High |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(ORDERS.qty, 0, 90, days) FOR EACH PRODUCTS.product_line
Prediction output
Every entity gets a score, updated continuously
| PRODUCT_LINE | Q2_2025_FORECAST | VS_Q1_ACTUAL | CONFIDENCE |
|---|---|---|---|
| Precision Parts | 58,200 | +12% | High |
| Specialty Alloys | 14,800 | +8% | Medium |
| Micro Components | 188,000 | -2% | High |
Understand why
Every prediction includes feature attributions — no black boxes
Product line: Precision Parts -- Q2 2025 forecast
Predicted: 58,200 units (+12% vs Q1)
Top contributing features
AutoMakers EV production ramp signal
Strong
30% attribution
Customer order velocity (last 60 days)
+18% trend
25% attribution
Seasonal Q2 uptick pattern
Historically +8-10%
19% attribution
Competitor capacity constraint (supply shift)
Detected
15% attribution
Historical forecast bias correction
-8.2% under-forecast
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 $1B manufacturer saves $8-12M per year by improving quarterly demand accuracy 15%. Kumo's demand graph connects customer end-market signals, order velocity trends, and competitive dynamics that time-series models treat as flat exogenous variables.
Related use cases
Explore more manufacturing 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.
