Demand Forecasting
Predicting future sales volume from historical data and trends, used to plan purchasing and avoid stockouts or overstock. Better forecasts blend historical sales data with external signals - seasonality, promotions, market trends - rather than relying on a straight-line projection of last year's numbers, which tends to miss sudden shifts in either direction.
In practice
Better forecasts blend historical sales data with external signals - seasonality, promotions, market trends - rather than relying on a straight-line projection of last year's numbers, which tends to miss sudden shifts in either direction.
A retailer forecasts a spike in demand for patio furniture starting in March based on the past three years of sales data, and orders inventory to arrive ahead of that curve.
Why it matters
A forecast that's even modestly more accurate than guesswork translates directly into fewer stockouts, less dead stock, and less cash sitting idle in inventory.
Worth knowing
- Combines historical sales data with external signals like seasonality, promotions, and trends.
- More sophisticated forecasting also accounts for lead time variability, beyond expected demand.
- Forecast accuracy should be reviewed regularly against actual sales to catch systematic bias early.
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Let's talk it through
Tell us where things stand with putting demand forecasting into practice and we'll respond with next steps, no forms, no waiting in a queue.
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