Demand Forecasting
Σύντομη απάντηση
Estimating future demand per product based on sales history, seasonality and external factors.
Βασικά συμπεράσματα
- Moving averages and exponential smoothing (baseline)
- Seasonal models (Holt-Winters)
- ML time-series models + external factors
What demand forecasting is
Demand forecasting estimates how many units will sell per SKU in a future period, using history, seasonality, promotions, trends and events. It is the basis of stock and replenishment planning.
Why it matters to the board
An accurate forecast simultaneously reduces dead stock and stockouts - the two opposing costs of inventory. Every point of accuracy gained frees up working capital.
Methods
- Moving averages and exponential smoothing (baseline)
- Seasonal models (Holt-Winters)
- ML time-series models + external factors
How CRMconnect helps
CRMconnect consolidates real sales from ERP, marketplaces and the B2B portal and generates SKU-level forecasts that directly feed the reorder point and order proposals.
Πραγματικό παράδειγμα
A beverage distributor with strong seasonality cut dead stock from 9% to 5.8% of revenue after replacing simple-average forecasting with seasonal forecasting consolidated in CRMconnect.
Ενημέρωση:
Συχνές ερωτήσεις
What accuracy is realistic?+
It depends on the XYZ class. For stable items (X), 85-95% is realistic; for chaotic ones (Z), the forecast matters less than a correct buffer stock.
Do I need AI for forecasting?+
Not necessarily. Statistical models cover most cases; AI adds value at high volumes and complex patterns (see AI demand forecasting).
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Σχετικοί όροι
Reorder Point (ROP)
The stock level at which a new order must be placed to avoid a stockout before goods arrive.
LogisticsSafety stock
Extra buffer inventory held to prevent stockouts caused by demand or supply variability.
ΛειτουργίεςXYZ Analysis
Classifying products by demand variability (predictability): X = stable, Y = variable, Z = erratic.
ΤεχνολογίαAI Demand Forecasting
Demand forecasting with machine-learning models that learn patterns from history and external factors, at SKU level.