Methods 4-6

These processing options specify which forecast types that the system uses when calculating the best fit. You can also specify whether the system creates detail forecasts for the selected forecast method.

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 processing option to project the next period. This method uses the periods best fit from the processing option plus the number of periods of sales order history from the processing option. 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: Calculates the best fit forecast.

04: Uses the Moving Average formula to create detail forecasts.

2. Number of Periods

Specify the number of periods to include in the average. Enter a number to use or select a number 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 indicated in the processing options and projects this trend to the forecast. You should have the system recalculate the trend monthly to detect changes in trends. This method requires periods best fit plus the number of periods that you indicate in the 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: Calculates the best fit forecast.

05: Uses the Linear Approximation formula to create detail forecasts.

4. Number of Periods

Specify the number of periods to include in the linear approximation ratio. Enter the number to use or select a number 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 (LSR) 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 data. This method requires sales data history for the period represented by the number of periods best fit plus the number of historical data periods specified in the processing options. The minimum requirement is two historical data points. Values are:

Blank: Does not use this method.

1: Calculates the best fit forecast.

06: Uses the Least Squares Regression formula to create detail forecasts.

6. Number of Periods

Specify the number of periods to include in the regression. Enter the number to use or select a number from the Calculator.