What is demand forecasting?
Demand forecasting estimates future customer demand for products or services using past data, known events and market information. Forecasts drive purchasing, production, staffing and cash planning.
Common forecasting methods
- Naive / last period — simple baseline to beat.
- Moving averages — smooth out noise.
- Seasonal methods — capture weekly, monthly or festival patterns.
- Regression / causal — link demand to drivers like price, weather or promotions.
- Machine learning — combine many signals for complex patterns.
- Qualitative — sales team and expert input for new products or events.
Tips for better forecasts
- Measure accuracy (e.g. MAPE) and track it over time.
- Forecast at the level you decide (item × location × week).
- Include events: holidays, festivals, promotions.
- Review big misses — they teach the most.
How DOOfy helps
Forecasts improve with clean, connected history. AIMOS One brings sales, stock, calendar and event data together and uses them to project demand, so purchasing and staffing can be planned ahead rather than reacting.
Frequently asked questions
What is MAPE?
Mean Absolute Percentage Error — the average size of forecast errors as a percentage of actual demand. Lower is better.
How far ahead should a business forecast?
It depends on the decision: days or weeks for staffing and perishables, months for purchasing and capacity, a year or more for budgets.