Process

The system applies the selected forecasting methods to past sales order history and compares the forecast simulation to the actual history. When you generate a forecast, the system compares actual sales order histories to forecasts for the months or weeks that you indicate in the processing option, and computes how accurately each of the selected forecasting methods would have predicted sales. Then the system recommends the most accurate forecast as the best fit.

Mean Absolute Deviation (MAD) is the mean of the absolute values of the deviations between actual and forecast data. MAD is a measure of the average magnitude of errors to expect, given a forecasting method and data history. Because absolute values are used in the calculation, positive errors do not cancel out negative errors. When comparing several forecasting methods, the one with the smallest MAD has is the most reliable for that product for that holdout period.

Percent of Accuracy (POA) is a measure of forecast bias. When forecasts are consistently too high, inventories accumulate and inventory costs rise. When forecasts are consistently too low, inventories are consumed and customer service declines. A forecast that is ten units too low, then eight units too high, then two units too high is an unbiased forecast. The positive error of ten is canceled by negative errors of eight and two.

1. Mode

Specify whether the system runs in proof or final mode. Values are:

Blank: Proof mode, creating a simulation report.

1: Final mode, creating forecast records.

2. Large Customers

Specify whether to create forecasts for large customers. Based on the Customer Master table (F0301), if the ABC code is set to A and this option is set to 1 the system creates separate forecasts for large customers. Values are:

Blank: Does not create large customer forecasts.

1: Creates large customer forecasts.

3. Weekly Forecasts

Specify weekly or monthly forecasts. For weekly forecasts, use fiscal date patterns with 54 periods. For monthly forecasts, use fiscal date patterns with 14 periods. Values are:

Blank: Monthly forecasts.

1: Weekly forecasts.

4. Start Date

Specify the date on which the system starts the forecasts. Enter a date to use or select a date from the Calendar. If you leave this field blank, the system uses the system date.

5. Forecast Length

Specify the number of periods to forecast. You must have previously established fiscal date patterns for the forecasted periods. If you leave this field blank, the system uses 3.

6. Actual Data

Specify the number of periods of actual data that the system uses to calculate the best fit forecast. If you leave this field blank, the system uses 3.

The system applies the selected forecasting methods to past sales order history and compares the forecast simulation to the actual history. When you generate a forecast, the system compares actual sales order histories to forecasts for the months or weeks you indicate in the processing option and computes how accurately each of the selected forecasting methods would have predicted sales. Then, the system recommends the most accurate forecast as the best fit.

7. Mean Absolute Deviation

Specify whether the system uses the Mean Absolute Deviation formula or the Percent of Accuracy formula to calculate the best fit forecast. Values are:

Blank: Percent of Accuracy formula.

1: Mean Absolute Deviation formula.

8. Amounts or Quantity

Specify whether the system calculates the best fit forecast using amounts or quantities. If you specify to use amounts, you must also extract sales history using amounts. This also affects forecast pricing. Values are:

Blank: Quantities.

1: Amounts.

9. Fiscal Date Pattern

Specify the fiscal date pattern type to use for the forecast calculations. When generating weekly forecasts, the fiscal date pattern defined here must be set up for 52 periods.

10. Negative Values

Specify whether the system displays negative values. Values are:

Blank: Substitutes a zero value for all negative values.

1: Displays negative values.