Method 4-6

These processing options let you specify which forecast types that the system uses when calculating the best fit forecast for each level in the hierarchy. You can also specify whether the system creates summary forecasts for the selected forecast method.

Enter 1 to use the forecast method when calculating the best fit. If you leave the processing option blank, the system does not use that forecast method when calculating the best fit and does not create summary forecasts for the method.

The system defines a period as a week or month, depending on the pattern that is chosen from the Date Fiscal Patterns table (F0008). For weekly forecasts, verify that you have established 52 period dates.

1. Moving Average

Specify which type of forecast to run. This forecast method uses the Moving Average formula to average the months that you indicate in the Number of Periods processing option to project the next period. This method uses the periods for the best fit from the Actual Data processing option under the Process 1 tab plus the number of periods of sales order history. You should have the system recalculate this forecast monthly or at least quarterly to reflect changing demand level. This method is useful for mature products without a trend. Values are:

Blank: Does not use this method.

1: Uses the Moving Average formula to create summary forecasts.

2. Number of Periods

Specify the number of periods to include in the Moving Average forecast method. Enter a number to use or select it from the Calculator.

3. Linear Approximation

Specify which type of forecast to run. This forecast method uses the Linear Approximation formula to compute a trend from the periods of sales order history and projects this trend to the forecast.

You should have the system recalculate the trend monthly to detect changes in trends. This method uses period's best fit plus the number of periods that you indicate in the Number of Periods processing option of sales order history. This method is useful for new products or products with consistent positive or negative trends that are not due to seasonal fluctuations. Values are:

Blank: Does not use this method.

1: Uses the Linear Approximation formula to create summary forecasts.

4. Number of Periods

Specify the number of periods to include in the Linear Approximation forecast method. Enter the number to use or select it from the Calculator.

5. Least Squares Regression

Specify which type of forecast to run. This forecast method derives an equation describing a straight line relationship between the historical sales data and the passage of time. Least Squares Regression fits a line to the selected range of data such that the sum of the squares of the differences between the actual sales data points and the regression line are minimized. The forecast is a projection of this straight line into the future. This method is useful when there is a linear trend in the sales data. This method uses sales data history for the period represented by the number of periods best fit plus the number of historical data periods specified in the Number of Periods processing option. The system requires a minimum of two historical data points. Values are:

Blank: Does not use this method.

1: Uses the Least Squares Regression formula to create summary forecasts.

6. Number of Periods

Specify the number of periods to include in the Least Squares Regression forecast method. You must enter at least two periods.

Enter the numbers to use or select them from the Calculator.