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   To define a forecast rule:

1. Navigate to the Forecast Rules window.

2. Enter a unique name for the rule.

3. Indicate whether the bucket type is days, weeks, or periods.

4. Determine the transaction types to use as demand sources. The quantities associated with each source are used when calculating historical usage:

Sales Order Shipments: Includes sales order issue quantities.

Issues to WIP: Includes WIP issue quantities.

Miscellaneous Issues: Includes quantities issued with user-defined transaction sources, account numbers, and account aliases.

Inter-Org Transfers: Includes quantities issued to other organizations.

5. Indicate the forecast method to use:

Focus: Uses focus forecasting algorithms to forecast demand for the item. This procedure tests the selected items against a number of forecasting techniques and chooses the best one, based on history, as the technique to forecast future demand.

Statistical: Uses exponential smoothing, trend, and seasonality algorithms to forecast demand for the item

   If you choose statistical forecasting, continue with the following steps:

6. Enter the maximum number of past periods upon which to base the forecast

7. Enter the factor by which to smooth demand for each successive period in the forecast. This levels demand throughout the forecast, reducing dramatic upward or downward fluctuations.

You can enter values from 0 to 1. Values closer to 0 give more weight to past demand; values closer to 1 give more weight to current demand.

8. Indicate whether to base the forecast on a trend model.

Turning this option on performs smoothing on the upward or downward trend in demand.

9. Enter the factor by which to smooth the trend change in demand from period to period. This produces a more linear rise or fall in demand from period to period over the course of the forecast.

You can enter values from 0 to 1. Values closer to 1 give more weight to recent changes and trends. Values closer to 0 give more weight to historical trend.

10. Indicate whether to base the forecast on a seasonality model.

Turning this option on bases the forecast on a seasonal adjustments you define for the forecast rule.

11. Enter the factor by which to smooth the seasonality indices you define by period for this forecast rule. This produces a more even pattern of seasonal demand from period to period over the course of the forecast.

You can enter values from 0 to 1. Values closer to 0 give more weight to past seasonal indices; values closer to 1 give more weight to current seasonal indices.

12. Enter an index that describes the seasonal influence on the period. For example, 2 indicates that you expect the forecast to double in that period because of seasonal factors.

13. Save your work.

   To delete a forecast rule:

• You can delete a forecast rule if there are no references to it.