Constant

Forecast with constant model

10

Uses first-order exponential smoothing. This strategy is identical to strategy 11. See also Constant Model w. First-Order Exponential Smoothing.

Constant

First-order exponential smoothing

11

Uses first-order exponential smoothing. This strategy is identical to strategy 10. See also Constant Model w. First-Order Exponential Smoothing.

Constant

Constant model with automatic alpha adaptation (first-order)

12

Uses first-order exponential smoothing and adapts the alpha factor. See also Constant Model w. First-Order Exponential Smoothing and Automatic Adaptation of the Alpha Factor.

Constant

Moving average

13

The system calculates the average of the values in the historical time horizon as defined in the master forecast profile. This average for n periods of history is the forecast result for each period in the forecast horizon; that is, the forecast is the same in every period. See also Moving Average Model.

Constant

Weighted moving average

14

The system weights each time series value with a weighting factor. For example, you can define the factors so that recent data is weighted more heavily than older data. You define the weighting factor in a diagnosis group. See also Weighted Moving Average Model.

Trend

Forecast with trend model

20

Uses first-order exponential smoothing. This strategy is identical to strategy 21. See also Trend/Seasonal Models w. First-Order Exp. Smoothing.

Trend

First-order exponential smoothing

21

Uses first-order exponential smoothing. This strategy is identical to strategy 21. See also Trend/Seasonal Models w. First-Order Exp. Smoothing.

Trend

Second-order exponential smoothing

22

Uses second-order exponential smoothing. See also Models with Second Order Exponential Smoothing.

Trend

Trend model with automatic alpha adaptation (second-order)

23

Uses second-order exponential smoothing and adapts the alpha factor. See also Models with Second-Order Exponential Smoothing and Automatic Adaptation of the Alpha Factor.

Seasonal

Forecast with seasonal model

30

Uses first-order exponential smoothing. This strategy is identical to strategy 31. See also Trend/Seasonal Models w. First-Order Exp. Smoothing.

Seasonal

Seasonal model based on Winters' method

31

Uses first-order exponential smoothing. This strategy is identical to strategy 30. See also Trend/Seasonal Models w. First-Order Exp. Smoothing.

Seasonal

Seasonal linear regression

35

Calculates seasonal indexes, removes the seasonal influence from the data, performs linear regression, and reapplies the seasonal influence to the calculated linear regression line. See also Seasonal Linear Regression.

Seasonal trend

Forecast with seasonal trend model

40

Uses first-order exponential smoothing. This strategy is identical to strategy 41. See also Trend/Seasonal Models w. First-Order Exp. Smoothing.

Seasonal trend

First-order exponential smoothing

41

Uses first-order exponential smoothing. This strategy is identical to strategy 40. See also Trend/Seasonal Models w. First-Order Exp. Smoothing.

Automatic model selection

Forecast with automatic model selection

Test for constant, trend, seasonal, and seasonal trend (model selection procedure 1)

50

Choose this strategy if you have no knowledge of the patterns in your historical data.

The system tests the historical data for constant, trend, seasonal, and seasonal trend patterns. The system applies the model that corresponds most closely to the pattern detected. If no regular pattern is detected, the system runs the forecast as if the data revealed a constant pattern.

In this process, the alpha, beta, and gamma factors are determined as follows:

Automatic model selection

Test for trend (model selection procedure 1)

51

Choose this strategy if you think that there is a trend pattern in your historical data, and if you know that there is no other pattern.

The system subjects the historical values to a regression analysis and checks to see whether there is a significant trend pattern. If not, the system runs the forecast as if the data revealed a constant pattern.

The alpha and beta factor is also determined as described under strategy 50 for determining the alpha, beta, and gamma factors.

Automatic model selection

Test for season (model selection procedure 1)

52

Choose this strategy if you think that there is a seasonal pattern in your historical data, and if you know that there is no other pattern.

The system clears the historical values of any possible trends and carries out an autocorrelation test. If no seasonal pattern is detected, the system runs the forecast as if the data revealed a constant pattern.

The alpha and beta factor is also determined as described under strategy 50 for determining the alpha, beta, and gamma factors.

Automatic model selection

Test for trend and season (model selection procedure 1)

53

Choose this strategy if you think that there is a seasonal and/or a trend pattern in your historical data.

The system subjects the historical values to a regression analysis and checks to see whether there is a significant trend pattern. It also clears the historical values of any possible trends and carries out an autocorrelation test to see whether there is a significant seasonal pattern. If a seasonal and/or trend pattern is detected, a trend model, seasonal model, or seasonal trend model is used. If no regular pattern is detected, the system runs the forecast as if the data revealed a constant pattern.

The alpha, beta, and gamma factor is also determined as described under strategy 50.

Manual model selection with test for an additional pattern

Seasonal model and test for trend (model selection procedure 1)

54

Choose this strategy if you think that there is a trend pattern in your historical data, and if you know that there is a seasonal pattern.

The system subjects the historical values to a regression analysis and checks to see whether there is a significant trend pattern. If there is, a seasonal trend model is used. Otherwise, a seasonal model is used.

The alpha, beta, and gamma factor is also determined as described under strategy 50.

Manual model selection with test for an additional pattern

Trend model and test for seasonal pattern (model selection procedure 1)

55

Choose this strategy if you think that there is a seasonal pattern in your historical data, and if you know that there is a trend pattern.

The system clears the historical values of any possible trends and carries out an autocorrelation test. If the test is positive, a seasonal trend model is used. Otherwise, a trend model is used.

The alpha, beta, and gamma factor is also determined as described under strategy 50.

Automatic model selection

Model selection procedure 2

56

Choose this strategy if you wish highly detailed tests of the historical data to be carried out.

The system tests for constant, trend, seasonal, and seasonal trend patterns, using all possible combinations for the alpha, beta, and gamma smoothing factors, where the factors are varied between 0.1 and 0.5 in intervals of 0.1. The system then chooses the model with the lowest mean absolute deviation (MAD). Procedure 2 is more precise than procedure 1, but takes longer.

Copy history

Historical data used as forecast data

60

Choose this strategy if demand does not change at all and you want to opt for the least performance- or work-intensive strategy.

No forecast is calculated. Instead, the historical data from the previous year is copied.

Manual forecast

Manual forecast

70

Choose this strategy if you wish to set the basic value, trend value, and/or seasonal indexes yourself. See also Manual Forecast.

Croston

Croston Method

80

Choose this method if demand is sporadic. SeeCroston Method.

Linear regression

Simple linear regression

94

The system calculates a line of best fit for the equation y = a + bx, where a and b are constants. The ordinary least squares method is used.