Linear Regression
- Purpose: Predicts future inventory needs based on trends in past demand.
- Key Assumption: Demand will grow or decline at a steady rate over time.
- How It Works:
- NetSuite analyzes historical demand data points and applies a statistical linear growth model.
- Generates a forecast based on the slope of the demand trend.
- Use Case:
- Ideal for items showing consistent growth or decline (e.g., trending products or those phasing out).
- Advantages:
- Effective for projecting demand trends without seasonal influence.
- Easy to understand and apply to steady-growth scenarios.
- Limitations:
- Doesn’t handle seasonal or cyclical demand well.
2. Moving Average
- Purpose: Provides a smoothed forecast by averaging demand over a defined period.
- Key Assumption: Past demand fluctuations will average out in the future.
- How It Works:
- Calculates a rolling average from past periods to smooth out irregularities.
- Projects future demand as the average of these periods.
- Use Case:
- Suitable for products with stable demand and minimal seasonal fluctuations.
- Advantages:
- Reduces noise from outlier data (e.g., spikes or dips).
- Straightforward and reliable for non-seasonal items.
- Limitations:
- Ignores growth trends or seasonality.
3. Seasonal Average
- Purpose: Captures and projects demand patterns based on seasonal trends.
- Key Assumption: Future demand will follow historical seasonal patterns.
- How It Works:
- Analyzes past demand over corresponding seasonal intervals (e.g., monthly).
- Projects future demand based on these patterns.
- Use Case:
- Best for items with strong seasonality (e.g., holiday merchandise, summer/winter products).
- Advantages:
- Tailors forecasts to specific cyclical trends.
- Useful for optimizing stock levels during peak seasons.
- Limitations:
- Requires sufficient historical data to identify accurate patterns.
- Must be set to Monthly intervals in NetSuite.
4. Sales Forecast
- Purpose: Leverages forward-looking sales data from NetSuite’s sales operations to predict inventory demand.
- Key Assumption: Sales forecast data accurately reflects expected customer demand.
- How It Works:
- Uses sales-related transactions (e.g., sales orders, invoices, estimates, opportunities, item fulfillments) to project demand.
- Incorporates both historical and projected sales data for forecasting.
- Use Case:
- Ideal for businesses actively managing sales forecasts in NetSuite.
- Advantages:
- Incorporates business-driven forecasts (not just historical trends).
- Aligns inventory with anticipated sales activities.
- Limitations:
- Accuracy depends on the quality and consistency of sales forecast data.
- Requires active management of sales operations in NetSuite.
Data Source and Transaction Types for Sales Forecast:
When using the Sales Forecast method, the following transaction types are considered:
- Cash Sale
- Invoice
- Estimate
- Opportunity
- Sales Order
- Item Fulfillment
NetSuite aggregates these transactions to build a demand projection based on historical and forward-looking data.