Intelligence
ML Forecaster
A forecasting engine trained on your own order history, running local to your data.
C/4 Intelligencev0.1.0NOBLLE / MRP / ML FORECASTER
Demand forecast
Demand Forecast, Next 12 Weeks
Consumption Planning
Apex Precision Industries
PART-7820-14
Global Castings Ltd.
PART-5512-09
Metallix Components
PART-9031-07
What it does
ML Forecaster in the order-to-cash flow
The ML Forecaster learns demand patterns from a factory's own order history, seasonality, repeat cycles per customer, quoting-to-conversion behaviour and material consumption, and projects them forward at the level planners actually buy and schedule.
It is designed to run local to the factory's data rather than shipping operational history to a general-purpose model. Forecasts are explainable: each projection exposes the historical signals behind it so a planner can accept, adjust or override with context.
Forecast output is operational, not decorative. Projected demand feeds material planning for long-lead items, informs capacity conversations before they become crises, and supports inventory targets that reflect actual variability.
Accuracy is tracked over time. Forecast versus actual is retained so the value of the model, and the confidence a planner should place in it, is measurable rather than assumed.
At a glance
Local, factory-specific demand forecasting.
- Mobile-first interface for factory floor use
- Operable through WhatsApp via Sentinel
- Grounded in the causal ontology engine
- Deployed in weeks, not months
Key capabilities
What you get with ML Forecaster
Demand forecasting by customer and part
Projections at the grain planners work with, informed by repeat-order cycles and seasonality.
Material consumption projection
Forecast demand explodes through bills of materials to anticipate long-lead purchasing.
Local-first processing
Built to run against your data locally, keeping commercially sensitive history in your control.
Explainable outputs
Each forecast shows the drivers behind it so planners can trust or challenge the number.
Scenario planning
Model optimistic, expected and conservative demand to test capacity and cash implications.
Accuracy tracking
Forecast versus actual is retained per period so confidence is evidence-based.
Platform fit
How ML Forecaster fits with the rest of Noblle
Noblle is one operational model, not a suite of disconnected apps. Each module writes into the same record of the factory.
Connects to
MRP
Forecast demand extends the planning horizon beyond firm orders.
Connects to
Supply Chain
Long-lead purchasing decisions use projected consumption.
Connects to
CRM
Customer order history is the primary training signal.
Product gallery
ML Forecaster interface
Product screenshots to be inserted.
C/4 Intelligencev0.1.0NOBLLE / MRP / ML FORECASTER
Demand forecast
Demand Forecast, Next 12 Weeks
Consumption Planning
Apex Precision Industries
PART-7820-14
Global Castings Ltd.
PART-5512-09
Metallix Components
PART-9031-07
C/4 Intelligencev0.1.0NOBLLE / MRP / ML FORECASTER
Scenario comparison
Current plan
+1 large enquiry
+320 units, +6 days lead time
Impact at W28 compared to current plan
C/4 Intelligencev0.1.0NOBLLE / MRP / ML FORECASTER
Consumption planning
Forecast consumption is netted against live stock and open purchase orders, so reorder dates move as the floor logs production.
Explore the platform
Other Noblle modules
See ML Forecaster in action
We configure the demo to your production process, not a generic dataset.