Predictive Analytics and Demand Forecasting
Models built on your own order, dispatch and consumption history that project future demand at the level you plan against, shown with the assumptions and error range visible rather than a single confident number.
What This Produces
A forecast of demand, sales or consumption at whatever level you plan against — SKU, location, week — built from your own historical records, and shown alongside the assumptions and error range behind it, not presented as a single certain figure.
What Feeds the Forecast
Historical Order and Dispatch Data
The core input the forecast is trained against.
Seasonality and Festival-Period Patterns
Recurring seasonal shifts specific to your business, not a generic calendar.
Promotions and Price Changes
Past promotional periods factored in so they don't distort the baseline pattern.
External Factors You Choose to Include
Weather, local events or other factors added only where they genuinely affect your demand.
What Affects Forecast Quality
- The length and consistency of your historical data.
- How often your actual demand pattern genuinely shifts.
- The granularity you're forecasting at — a single SKU is harder to forecast well than a category.
Building and Checking It
Historical Data Review
Checking what history is available and how consistent it is.
Model Build and Backtesting
Testing the model against periods you already know the actual outcome for.
Assumption and Error-Range Documentation
Writing down what the model assumes and how wrong it can reasonably be.
Dashboard for Ongoing Use
A dashboard your planning team can check and act on regularly.
What Needs to Exist First
Businesses with less than a season or two of consistent recorded history don't have enough to forecast against yet. Recording that history consistently needs to happen before this is worth building.
Frequently asked questions
How far ahead can this forecast?
It depends on how much history you have and how volatile your demand is — we set expectations for your specific data before any build starts, rather than promising a fixed horizon.
What drives the cost of a forecasting model?
How many SKUs or locations are forecast individually, and how much historical data needs cleaning before use.
What drives how long a forecast model takes to build?
How consistent and complete your historical records already are.
Does this work with our existing ERP or POS data?
Yes, it is built to read from the order, dispatch or point-of-sale system you already record data in.
Who owns the forecast model?
You do — the model, its assumptions and the historical data it was built on stay yours.
What records do we need from you?
Historical order, dispatch or consumption records, ideally covering a full season or more.
Tell us what you need.
Send a short brief and one of our engineers will come back to you — usually the same day.
- No obligation
- We reply the same working day
- Your details stay private