Data

Demand & operations forecasting

Forecasting models built on your sales, seasonality, and external signals, delivered inside the planning tools your team already works in.

Book a discovery call How it works

Operations planner reviewing a demand forecast chart in a warehouse office

The problem

Planning still runs on last year’s numbers and gut feel. The result is too much stock in one place, not enough in another, and teams scrambling every peak season.

Sounds familiar?

Planning happens in spreadsheets copied from last year
Stockouts and overstock happen at the same time
Staffing is set weeks before demand is known

What we build

The working parts, wired into your systems.

Signal pipeline
Sales history, promotions, pricing, weather, and calendar effects combined into one modelling dataset.
Forecast models
Models per product, location, or team, with a range of likely outcomes — not a single guess.
Planner workflow
Forecasts land in your ERP or planning tool, where planners can adjust them and record why.
Accuracy tracking
Forecast versus actual is measured every cycle, so the model and the process keep improving.

Connects with

SAP
Oracle
NetSuite
Snowflake
Databricks
Power BI

What changes

TodayWith DataHill

Today: Plans are based on last year plus a percentage
With DataHill: Plans reflect current demand signals
Today: Peaks catch teams off guard
With DataHill: Peaks are planned weeks ahead
Today: Nobody knows how accurate the plan was
With DataHill: Accuracy is measured and improved every cycle

How we deliver it

Weeks 1–2
Discover
Assess data history, choose the first product range or site, and set the accuracy baseline.
Weeks 3–8
Build
Build and back-test models, then run them alongside the current plan before switching over.
Ongoing
Run
Retrain on new data, watch for drift, and extend to more ranges and sites.