Demand Forecasting
Using historical data to predict sales volume. Enables ordering the right amount of inventory at the right time.
In practice
Demand forecasting is the difference between a stockout during a holiday and operational calm.
Common misconception
'Forecasting requires complex BI.' A simple spreadsheet works: sales for month X × 3 years × growth factor ± events.
How to fix it
Not accurate? (1) account for seasonality from the same month last year, (2) one-time events that skewed data, (3) compare forecast vs. actual monthly.
Example
A toy store: flat order quantities caused a stockout in December (340% demand) and excess in January (40% demand). A 3-year analysis saved $18,000/year.
Priority
A basic spreadsheet: month X × 3 years → average → × growth factor. 3 hours of work.
Related terms
Keep learning. Metrics that touch the same area:
- Lead TimeOperationsThe time from ordering with a supplier to receiving inventory. Critical for calculating the reorder point.
- Inventory ManagementOperationsTracking, ordering, and maintaining inventory. Too much means cash tied up. Too little means stockouts.
- Safety StockLogisticsExtra inventory held beyond what average demand requires — protects against demand fluctuations, lead-time delays, and forecasting errors. Safety Stock = Z × √Lead Time × Demand STD.
- SKU – Stock Keeping UnitOperationsA unique identifier for each product variation. The basis for precise inventory management.
- StockoutOperationsA product unavailable for shipping. Immediate lost sales plus damaged trust.
- DropshippingOperationsSelling without holding inventory — a supplier ships directly to the customer. Low margin (5-20%), no quality control.
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