Anticipate demand and assess your planning options.
Typical challenges
Sounds familiar?
Planning is based on averages and spreadsheets
External drivers such as weather or events are not considered
Shortages or overcapacity are noticed too late
What we deliver
Forecast demand using historical data and relevant business drivers
Compare scenarios to support capacity and resource decisions
Highlight changing demand and potential supply gaps
Connect forecasts with existing planning processes and systems
How it works
1. Data check
History, granularity and data quality are checked, and relevant drivers are identified.
2. Feature engineering
External drivers such as weather forecasts, calendar effects and events are added to the historical data.
3. Model training & backtesting
Forecast models are trained and tested against past periods and a simple baseline.
4. Automated forecast pipeline
New data and weather forecasts are processed automatically, for example every day.
5. Thresholds & alerts
Forecasts are compared with defined limits; e-mail alerts warn early about critical demand.
6. Dashboard & integration
Forecasts, trends and alerts in one dashboard and, where needed, in your planning systems.
Technologies & business value
Technologies: Python, machine-learning forecasting models, weather data integration, Microsoft Azure, Power BI, e-mail alerting
Business value: Better planning visibility and earlier action on demand changes.
Related success stories:
Frequently asked questions
How much historical data do we need?
Typically at least one to two years of daily data to capture seasonality. We check feasibility with your data first.
How far ahead can you forecast?
It depends on data and drivers. AquaPredict, for example, forecasts municipal water demand 14 days in advance.