forecasting calc

8/10/2019 Forecasting Calc

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SAP AG 2002, Title of Presentation, Speaker Name / 3

Overview of Forecasting in APO

3

33

1

11

Univariate Forecast Basics

2

22 History

4

44 Univariate Models in APOModels in APOModels in APOModels in APO

5

55 Forecast Error Analysis

6

66

7

77

Master Forecast Profile Setup

Forecast Execution

Overview of the Forecasting Workshop


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333 Univariate Forecast Basics



What we will talk about today


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Elements of the Univariate Forecast

Actual Value

Basic Value

Trend Value

Seasonal Index

Length of Season

Initialization

Ex-post Forecast

Forecast

Parameters

Exceptions are models 13, 14, 35, 70, 80, 94


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Actual Value

DataValues

Past Horizon

Historical values used as the basis of theforecast


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Basic Value

DataValues

Forecast Horizon

Basic Value - Controls Vertical Placement

1

22000

1000


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Trend Value

DataValues

Forecast Horizon

Trend - Slope of line

1

2.75

-.75


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Seasonal Index

DataValues

Forecast Horizon

Seasonal index - Divergence from Basic Value

1.00

2.50

.25


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Length of Season

DataValues

Number of Periods in the SeasonTypically = one year

Season


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Initialization

Basic value, Trend value, Seasonal indices, and the MeanAbsolute Deviation are initialized.


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Ex-post Forecast

An Ex post Forecast is a forecast run in the past for comparisonAn Ex post Forecast is a forecast run in the past for comparisonAn Ex post Forecast is a forecast run in the past for comparisonAn Ex post Forecast is a forecast run in the past for comparisonagainst historical values. The exagainst historical values. The exagainst historical values. The exagainst historical values. The ex----post forecast is used to measurepost forecast is used to measurepost forecast is used to measurepost forecast is used to measure

model fit.model fit.model fit.model fit.

Data

Values

Future

Forecast

History

Initialization Ex-post Forecast

History Values

ForecastValues

- Initialization length is dependent on the forecast model- Ex-post forecast length is the history horizon less the

initialization horizon


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Ex-post Forecast

The following Models do not generate an ExThe following Models do not generate an ExThe following Models do not generate an ExThe following Models do not generate an Ex----post forecast:post forecast:post forecast:post forecast:

Manual forecastManual forecastManual forecastManual forecast

Historical data adoptedHistorical data adoptedHistorical data adoptedHistorical data adopted

Weighted moving averageWeighted moving averageWeighted moving averageWeighted moving average

Moving averageMoving averageMoving averageMoving average

70707070

60606060

14141414

13131313


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General Calculation

Basic(t), trend(t), seasonal(t)

=

Function(actual(t), basic(t-1), trend(t-1), seasonal(t-1s))

Forecast = Function(basic(t), trend(t), seasonal(t))


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Period 1 2 3 4 5 6 7 1 2 3 4

History 80 95 100 115 125 110 110

G - Basic Value 102 113

T - Trend 10.0 10.3

EX Ex-post 112 123

F Forecast

History Horizon Forecast Horizon

Trend Model Example Calculate New Basic and Trend

Trend Value T(t) = [ G(t) - G(t-1) ] + (1-)T(t-1)

10.3 = .3 [ 113 102 ] + (.7) 10.0

Basic Value G(t) = V(t) + (1-) [ G(t-1) + T(t-1) ]

113 = .3 (115) + (.7) [ 102 + 10.0 ]

Ex-Post Forecast Ex(t +i) = G(t) + i * T(t)Where i = 1

123 = 113 + 10.3


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Period 1 2 3 4 5 6 7 1 2 3 4

History 80 95 100 115 125 110 110

G - Basic Value 102 113

T - Trend 10.0 10.3

EX Ex-post 112 123

F Forecast


Trend Model Example Calculation Repeated


124 127

10.5 8.3

123 134 135

124

10.5

123 134

124 127 128

10.5 8.3 6.1

123 134 135

134

Forecast P(t+i) = G(t) + i * T(t)




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Period 1 2 3 4 5 6 7 1 2 3 4

History 80 95 100 115 125 110 110

G - Basic Value 102 113 124 127 128 128

T - Trend 10.0 10.3 10.5 8.3 6.1 6.1

EX Ex-post 112 123 134 135

F Forecast 134 140


Trend Model Example Calculation Repeated


Forecast P(t+i) = G(t) + i * T(t)

110

128 128 128

6.1 6.1 6.1

135

134 140 146

1

110

128 128 128 128

6.1 6.1 6.1 6.1

135

134 140 146 152

2




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Model parameters

The model parameters, Alpha, Beta, and Gamma, control theweighting of each historical values.

Alpha is used in the basic value calculation

Beta is used in trend value calculation

Gamma is used in the Seasonal index calculation

The value for the parameters range from 0 to 1. A higher value will

place more emphasis on recent history.

The parameters also control how reactive the forecast is to changes inhistorical patterns.


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Univariate Forecasting Models

Constant ModelsConstant ModelsConstant ModelsConstant Models

Moving Average ModelsMoving Average ModelsMoving Average ModelsMoving Average Models

Trend ModelsTrend ModelsTrend ModelsTrend Models

Seasonal ModelsSeasonal ModelsSeasonal ModelsSeasonal Models

Seasonal Trend ModelsSeasonal Trend ModelsSeasonal Trend ModelsSeasonal Trend Models

Automatic Model Selection I and IIAutomatic Model Selection I and IIAutomatic Model Selection I and IIAutomatic Model Selection I and II

Sporadic DemandSporadic DemandSporadic DemandSporadic Demand

Historical Data ModelHistorical Data ModelHistorical Data ModelHistorical Data Model

Linear RegressionLinear RegressionLinear RegressionLinear Regression

User ExitUser ExitUser ExitUser Exit

C M d l


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Constant Models

3 models within the constant model group:3 models within the constant model group:3 models within the constant model group:3 models within the constant model group:

Constant Model (10)Constant Model (10)Constant Model (10)Constant Model (10)

1111stststst Order Exponential Smoothing (11)Order Exponential Smoothing (11)Order Exponential Smoothing (11)Order Exponential Smoothing (11)

Constant Model with automatic Alpha adaptation (12)Constant Model with automatic Alpha adaptation (12)Constant Model with automatic Alpha adaptation (12)Constant Model with automatic Alpha adaptation (12)

Constant Models follow the pattern:Constant Models follow the pattern:Constant Models follow the pattern:Constant Models follow the pattern:

Same Formula !10 = 11

C t t M d l 1st d E ti l S thi (10 11)


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Constant Model 1st order Exponential Smoothing (10,11)

Use this model when there are no seasonal variations or trendUse this model when there are no seasonal variations or trendUse this model when there are no seasonal variations or trendUse this model when there are no seasonal variations or trendpatterns. Alpha parameter controls the weighting of history inpatterns. Alpha parameter controls the weighting of history inpatterns. Alpha parameter controls the weighting of history inpatterns. Alpha parameter controls the weighting of history in

determining the basic value.determining the basic value.determining the basic value.determining the basic value.

Basic value and exBasic value and exBasic value and exBasic value and ex----post forecast are equalpost forecast are equalpost forecast are equalpost forecast are equal

Higher alpha puts more weight on recent valuesHigher alpha puts more weight on recent valuesHigher alpha puts more weight on recent valuesHigher alpha puts more weight on recent values

Higher alpha makes the forecast more reactive to recent changesHigher alpha makes the forecast more reactive to recent changesHigher alpha makes the forecast more reactive to recent changesHigher alpha makes the forecast more reactive to recent changes inininin

historyhistoryhistoryhistory

E l f C t t M d l


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Example of a Constant Model

Constant Model Automatic Adaptation of the Alpha factor


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Co sta t ode uto at c daptat o o t e p a acto(12)

The system automatically adjusts the alpha factor based on the iThe system automatically adjusts the alpha factor based on the iThe system automatically adjusts the alpha factor based on the iThe system automatically adjusts the alpha factor based on the initialnitialnitialnitialalpha and the smallest tracking signal.alpha and the smallest tracking signal.alpha and the smallest tracking signal.alpha and the smallest tracking signal.


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Moving Average Formulas


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Moving Average-Formulas

Moving Average-13 Weighted Moving Average-14

No ex-post forecast calculated

Trend Models


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Trend Models

There are four models for data with trend:There are four models for data with trend:There are four models for data with trend:There are four models for data with trend:

Forecast with Trend Model (20)Forecast with Trend Model (20)Forecast with Trend Model (20)Forecast with Trend Model (20)

HoltHoltHoltHolts Method (21)s Method (21)s Method (21)s Method (21)

Second Order Exponential Smoothing (22)Second Order Exponential Smoothing (22)Second Order Exponential Smoothing (22)Second Order Exponential Smoothing (22)

Trend model with Automatic Alpha Adaptation (2nd order) (23)Trend model with Automatic Alpha Adaptation (2nd order) (23)Trend model with Automatic Alpha Adaptation (2nd order) (23)Trend model with Automatic Alpha Adaptation (2nd order) (23)


Holts Method (20 21)


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Holt s Method (20, 21)

Basic ValueBasic ValueBasic ValueBasic Value G(t) = V(t) + (1-) [ G(t-1) + T(t-1) ]

Trend ValueTrend ValueTrend ValueTrend Value T(t) = [(G(t) - G(t-1) ] + (1-)T(t-1)

Forecast value for period (t +1)Forecast value for period (t +1)Forecast value for period (t +1)Forecast value for period (t +1) P(t+i) = G(t) + i * T(t)

Formulas

Second Order Exponential Smoothing (22)


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Second Order Exponential Smoothing (22)

Encompasses a double smoothing method where the basic value ofEncompasses a double smoothing method where the basic value ofEncompasses a double smoothing method where the basic value ofEncompasses a double smoothing method where the basic value ofthe first calculation is used as the input for the second. Thethe first calculation is used as the input for the second. Thethe first calculation is used as the input for the second. Thethe first calculation is used as the input for the second. The result is aresult is aresult is aresult is a

basic value that is more reactive to changes in demand whilebasic value that is more reactive to changes in demand whilebasic value that is more reactive to changes in demand whilebasic value that is more reactive to changes in demand while

minimizing the trend effect.minimizing the trend effect.minimizing the trend effect.minimizing the trend effect.

Trend with Automatic Alpha Adaptation (2nd order) (23)


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Trend with Automatic Alpha Adaptation (2nd order) (23)

This is the same as Second Order Exponential Smoothing except thThis is the same as Second Order Exponential Smoothing except thThis is the same as Second Order Exponential Smoothing except thThis is the same as Second Order Exponential Smoothing except theeeesystems determines the Alpha value. The minimum Alpha is 0.05 ansystems determines the Alpha value. The minimum Alpha is 0.05 ansystems determines the Alpha value. The minimum Alpha is 0.05 ansystems determines the Alpha value. The minimum Alpha is 0.05 andddd

the maximum is 0.90. The system determines the Alpha factor thrthe maximum is 0.90. The system determines the Alpha factor thrthe maximum is 0.90. The system determines the Alpha factor thrthe maximum is 0.90. The system determines the Alpha factor throughoughoughough

an iterative processes of comparing the MAD to the Error Total (an iterative processes of comparing the MAD to the Error Total (an iterative processes of comparing the MAD to the Error Total (an iterative processes of comparing the MAD to the Error Total (ET).ET).ET).ET).

(Same Method as Constant with Automatic Alpha Adaptation).(Same Method as Constant with Automatic Alpha Adaptation).(Same Method as Constant with Automatic Alpha Adaptation).(Same Method as Constant with Automatic Alpha Adaptation).

Trend Dampening Profile


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Trend Dampening Profile

As the historical values do not show a decrease in trend as theAs the historical values do not show a decrease in trend as theAs the historical values do not show a decrease in trend as theAs the historical values do not show a decrease in trend as the marketmarketmarketmarketmatures, a Trend Dampening profile can be used to smooth out thematures, a Trend Dampening profile can be used to smooth out thematures, a Trend Dampening profile can be used to smooth out thematures, a Trend Dampening profile can be used to smooth out the

trend.trend.trend.trend.

Seasonal Models


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Seasonal Models

Seasonal Models are used to depict a Seasonal forecast or a recuSeasonal Models are used to depict a Seasonal forecast or a recuSeasonal Models are used to depict a Seasonal forecast or a recuSeasonal Models are used to depict a Seasonal forecast or a recurringrringrringrringpattern, without any upwards or downwards slope. The pattern ocpattern, without any upwards or downwards slope. The pattern ocpattern, without any upwards or downwards slope. The pattern ocpattern, without any upwards or downwards slope. The pattern occurscurscurscurs

across its season or a number of Seasonal Periods (Usuallyacross its season or a number of Seasonal Periods (Usuallyacross its season or a number of Seasonal Periods (Usuallyacross its season or a number of Seasonal Periods (Usually

recommended to be 12 Months). The trend value and Beta in thisrecommended to be 12 Months). The trend value and Beta in thisrecommended to be 12 Months). The trend value and Beta in thisrecommended to be 12 Months). The trend value and Beta in this

case are zero, signifying no upward or downward slope.case are zero, signifying no upward or downward slope.case are zero, signifying no upward or downward slope.case are zero, signifying no upward or downward slope.

Seasonal Models


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There are three Models associated with the seasonal patterns.There are three Models associated with the seasonal patterns.There are three Models associated with the seasonal patterns.There are three Models associated with the seasonal patterns.

Forecast with the Seasonal Model (30)Forecast with the Seasonal Model (30)Forecast with the Seasonal Model (30)Forecast with the Seasonal Model (30)

Seasonal Trend based on the WinterSeasonal Trend based on the WinterSeasonal Trend based on the WinterSeasonal Trend based on the Winters Method (31)s Method (31)s Method (31)s Method (31)

Season + Linear Regression (35)Season + Linear Regression (35)Season + Linear Regression (35)Season + Linear Regression (35)


Forecast with the Seasonal Model (30)


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( )

This model uses first order exponential smoothing where both AlpThis model uses first order exponential smoothing where both AlpThis model uses first order exponential smoothing where both AlpThis model uses first order exponential smoothing where both Alphahahahaand Gamma are assigned.and Gamma are assigned.and Gamma are assigned.and Gamma are assigned.

Season + Linear Regression (35)


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Seasonal indices calculated

Seasonality removed from data

Forecast created using linear regression

Seasonality added back to forecast

Seasonal Trend Models


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There are two Models for the Seasonal Trend method:There are two Models for the Seasonal Trend method:There are two Models for the Seasonal Trend method:There are two Models for the Seasonal Trend method:

Forecast with Seasonal Trend Model (40)Forecast with Seasonal Trend Model (40)Forecast with Seasonal Trend Model (40)Forecast with Seasonal Trend Model (40)

First Order Exponential Smoothing (41)First Order Exponential Smoothing (41)First Order Exponential Smoothing (41)First Order Exponential Smoothing (41)


Also know as: Holt-Winters Method

And Triple Exponential Smoothing

First Order Exponential Smoothing (40, 41)


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This Model encompasses the same Formula as 20,21,30,31 and usesThis Model encompasses the same Formula as 20,21,30,31 and usesThis Model encompasses the same Formula as 20,21,30,31 and usesThis Model encompasses the same Formula as 20,21,30,31 and usesassigned values for the Alpha, Beta and Gamma Coefficients.assigned values for the Alpha, Beta and Gamma Coefficients.assigned values for the Alpha, Beta and Gamma Coefficients.assigned values for the Alpha, Beta and Gamma Coefficients.

Auto Selection Models


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Auto Selection 1 (50)Auto Selection 1 (50)Auto Selection 1 (50)Auto Selection 1 (50)

Test for Trend and Season (53)Test for Trend and Season (53)Test for Trend and Season (53)Test for Trend and Season (53)

Test for Trend (51)Test for Trend (51)Test for Trend (51)Test for Trend (51)

Test for Season (52)Test for Season (52)Test for Season (52)Test for Season (52)

Seasonal model plus test for Trend (54)Seasonal model plus test for Trend (54)Seasonal model plus test for Trend (54)Seasonal model plus test for Trend (54)

Trend Model plus test for Seasonal Pattern (55)Trend Model plus test for Seasonal Pattern (55)Trend Model plus test for Seasonal Pattern (55)Trend Model plus test for Seasonal Pattern (55)

Auto Model Selection procedure 2 (56)Auto Model Selection procedure 2 (56)Auto Model Selection procedure 2 (56)Auto Model Selection procedure 2 (56)


Auto Selection 1 (50, 53)


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Tests for Both Seasonal and Trend Patterns.Tests for Both Seasonal and Trend Patterns.Tests for Both Seasonal and Trend Patterns.Tests for Both Seasonal and Trend Patterns.Seasonal Test: The Auto Correlation Coefficient is Calculated, tSeasonal Test: The Auto Correlation Coefficient is Calculated, tSeasonal Test: The Auto Correlation Coefficient is Calculated, tSeasonal Test: The Auto Correlation Coefficient is Calculated, thehehehe

Pattern is determined to be Seasonal if the Auto CorrelationPattern is determined to be Seasonal if the Auto CorrelationPattern is determined to be Seasonal if the Auto CorrelationPattern is determined to be Seasonal if the Auto Correlation

Coefficient is Greater than 0.3.Coefficient is Greater than 0.3.Coefficient is Greater than 0.3.Coefficient is Greater than 0.3.

Auto Selection 1 (50, 53)


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A Trend Significance test is carried outA Trend Significance test is carried outA Trend Significance test is carried outA Trend Significance test is carried out

If both are determined to be significant, a Seasonal Trend ModelIf both are determined to be significant, a Seasonal Trend ModelIf both are determined to be significant, a Seasonal Trend ModelIf both are determined to be significant, a Seasonal Trend Model isisisis

used.used.used.used.

If neither tests is determined to be Significant, then the ConstIf neither tests is determined to be Significant, then the ConstIf neither tests is determined to be Significant, then the ConstIf neither tests is determined to be Significant, then the Constantantantant

Model is chosen as a defaultModel is chosen as a defaultModel is chosen as a defaultModel is chosen as a default

Test for Trend (51)


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Used to Test only for Trend.Used to Test only for Trend.Used to Test only for Trend.Used to Test only for Trend.

This follows the Trend Significance test previously defined. IfThis follows the Trend Significance test previously defined. IfThis follows the Trend Significance test previously defined. IfThis follows the Trend Significance test previously defined. If nononono

Significance is found then a Constant Model is Chosen.Significance is found then a Constant Model is Chosen.Significance is found then a Constant Model is Chosen.Significance is found then a Constant Model is Chosen.

Test for Season (52)


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Used to Test only for Seasonal Pattern.Used to Test only for Seasonal Pattern.Used to Test only for Seasonal Pattern.Used to Test only for Seasonal Pattern.

This follows theThis follows theThis follows theThis follows the AutoCorrelationAutoCorrelationAutoCorrelationAutoCorrelation Coefficient Test andCoefficient Test andCoefficient Test andCoefficient Test and choseschoseschoseschoses thethethethe

Seasonal Model if the value is above 0.3. If not a Constant ModSeasonal Model if the value is above 0.3. If not a Constant ModSeasonal Model if the value is above 0.3. If not a Constant ModSeasonal Model if the value is above 0.3. If not a Constant Model isel isel isel is

chosen.chosen.chosen.chosen.

Seasonal model plus test for Trend (54)


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Assumes Seasonal pattern exists and tests for Trend pattern. IfAssumes Seasonal pattern exists and tests for Trend pattern. IfAssumes Seasonal pattern exists and tests for Trend pattern. IfAssumes Seasonal pattern exists and tests for Trend pattern. If aaaa

trend significance is found, then a Seasonal Trend Model is usedtrend significance is found, then a Seasonal Trend Model is usedtrend significance is found, then a Seasonal Trend Model is usedtrend significance is found, then a Seasonal Trend Model is used. If. If. If. If

no significance is found than a Seasonal Model is used.no significance is found than a Seasonal Model is used.no significance is found than a Seasonal Model is used.no significance is found than a Seasonal Model is used.


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Summary of the Automodel 1 selection process


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This shows the Model chosen under the following Conditions:This shows the Model chosen under the following Conditions:This shows the Model chosen under the following Conditions:This shows the Model chosen under the following Conditions:

Automodel If:Trend If: Season Then:Uses Model

40 - Seasonal Trend

20 - Trend

Yes

No

Yes byDefault

55 - Trend Model plus testfor Season

30 - Season

40 - Seasonal Trend

Yes by

Default

No

Yes

54 - Seasonal Model plus

test for Trend

30 - Season

10 - Constant

Yes

No

N/A52 - Test for Season

30 - Trend

10 - Constant

N/AYes

No

51 - Test for Trend

10 - Constant

20 - Trend

30 - Season

40 - Seasonal Trend

No

No

Yes

Yes

No

Yes

No

Yes

50 - Auto Selection 1

or

53 - Test for Trend andSeason

Auto Model Selection procedure 2 (56)


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Tests for Constant, Seasonal and/or Trend models while attemptinTests for Constant, Seasonal and/or Trend models while attemptinTests for Constant, Seasonal and/or Trend models while attemptinTests for Constant, Seasonal and/or Trend models while attempting tog tog tog to

minimize Error by adjusting Alpha, Beta and Gamma..minimize Error by adjusting Alpha, Beta and Gamma..minimize Error by adjusting Alpha, Beta and Gamma..minimize Error by adjusting Alpha, Beta and Gamma..

This model adjusts Alpha, Beta and Gamma between the values of 0This model adjusts Alpha, Beta and Gamma between the values of 0This model adjusts Alpha, Beta and Gamma between the values of 0This model adjusts Alpha, Beta and Gamma between the values of 0.1.1.1.1

to 0.5 in 0.1 increments while calculating the Mean Absolute Devto 0.5 in 0.1 increments while calculating the Mean Absolute Devto 0.5 in 0.1 increments while calculating the Mean Absolute Devto 0.5 in 0.1 increments while calculating the Mean Absolute Deviationiationiationiation

(MAD) for each Iteration. The iteration with the Lowest MAD is(MAD) for each Iteration. The iteration with the Lowest MAD is(MAD) for each Iteration. The iteration with the Lowest MAD is(MAD) for each Iteration. The iteration with the Lowest MAD is thethethetheModel chosen with the appropriate smoothing factors.Model chosen with the appropriate smoothing factors.Model chosen with the appropriate smoothing factors.Model chosen with the appropriate smoothing factors.

Comparision between 50 and 56 (Auto model 1 & 2)


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Auto Selection Model 1 Auto Selection Model 2Auto Selection Model 1 Auto Selection Model 2Auto Selection Model 1 Auto Selection Model 2Auto Selection Model 1 Auto Selection Model 2

Chooses Best Fit of Constant, Trend,Chooses Best Fit of Constant, Trend,Chooses Best Fit of Constant, Trend,Chooses Best Fit of Constant, Trend,

Seasonal, Seasonal Trend.Seasonal, Seasonal Trend.Seasonal, Seasonal Trend.Seasonal, Seasonal Trend.

If no Pattern Detected defaults ConstantIf no Pattern Detected defaults ConstantIf no Pattern Detected defaults ConstantIf no Pattern Detected defaults Constant

ModelModelModelModel

Resource Intensive (NotResource Intensive (NotResource Intensive (NotResource Intensive (Not

recommended for Production use)recommended for Production use)recommended for Production use)recommended for Production use)

Not as precise as Auto Model 2Not as precise as Auto Model 2Not as precise as Auto Model 2Not as precise as Auto Model 2

Determines Best Fit through throughDetermines Best Fit through throughDetermines Best Fit through throughDetermines Best Fit through through

Iterative approach of adjustingIterative approach of adjustingIterative approach of adjustingIterative approach of adjusting

smoothing factors (Alpha, Beta,smoothing factors (Alpha, Beta,smoothing factors (Alpha, Beta,smoothing factors (Alpha, Beta,

Gamma) and determining SmallestGamma) and determining SmallestGamma) and determining SmallestGamma) and determining SmallestMean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)

Determines Seasonal and TrendDetermines Seasonal and TrendDetermines Seasonal and TrendDetermines Seasonal and Trend

Significance through Formulas forSignificance through Formulas forSignificance through Formulas forSignificance through Formulas for

Auto Correlation (SeasonalAuto Correlation (SeasonalAuto Correlation (SeasonalAuto Correlation (Seasonal

Significance) and TrendSignificance) and TrendSignificance) and TrendSignificance) and TrendSignificance TestsSignificance TestsSignificance TestsSignificance Tests

The Croston Model (80)


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The Croston Model (80) is used when demand is sporadic orThe Croston Model (80) is used when demand is sporadic orThe Croston Model (80) is used when demand is sporadic orThe Croston Model (80) is used when demand is sporadic or

intermittent. The return is a Constant model to meet the possibintermittent. The return is a Constant model to meet the possibintermittent. The return is a Constant model to meet the possibintermittent. The return is a Constant model to meet the possiblelelele

sporadic demand.sporadic demand.sporadic demand.sporadic demand.


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Manual Forecast (70)


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The Manual Forecast (70) allows the planner to set their own valThe Manual Forecast (70) allows the planner to set their own valThe Manual Forecast (70) allows the planner to set their own valThe Manual Forecast (70) allows the planner to set their own values toues toues toues to

generate a forecast. The values chosen are all the values thatgenerate a forecast. The values chosen are all the values thatgenerate a forecast. The values chosen are all the values thatgenerate a forecast. The values chosen are all the values that makemakemakemake

up the forecast these are the Smoothing coefficients Alpha, Betup the forecast these are the Smoothing coefficients Alpha, Betup the forecast these are the Smoothing coefficients Alpha, Betup the forecast these are the Smoothing coefficients Alpha, Beta,a,a,a,

Gamma (from the Profile) and the Basic, Trend value and SeasonGamma (from the Profile) and the Basic, Trend value and SeasonGamma (from the Profile) and the Basic, Trend value and SeasonGamma (from the Profile) and the Basic, Trend value and Seasonalalalal

Index. This model should not be used for Background Processing.Index. This model should not be used for Background Processing.Index. This model should not be used for Background Processing.Index. This model should not be used for Background Processing.

No Forecast (98)


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No forecast is conductedNo forecast is conductedNo forecast is conductedNo forecast is conducted

External Forecast (99)


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It is also possible to define your own Forecasting model if theIt is also possible to define your own Forecasting model if theIt is also possible to define your own Forecasting model if theIt is also possible to define your own Forecasting model if the modelmodelmodelmodel

you commonly use is not provided. The external Forecasting featyou commonly use is not provided. The external Forecasting featyou commonly use is not provided. The external Forecasting featyou commonly use is not provided. The external Forecasting featureureureure

allows you to use ABAP to code your own forecasting modelallows you to use ABAP to code your own forecasting modelallows you to use ABAP to code your own forecasting modelallows you to use ABAP to code your own forecasting model.

Trend, Seasonal, Seasonal Trend overview: Formula


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Explanation of the Variables


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Forecast Accuracy Measurements


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Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)

Error Total (ET)Error Total (ET)Error Total (ET)Error Total (ET)

Mean Absolute Percentage Error (MAPE)Mean Absolute Percentage Error (MAPE)Mean Absolute Percentage Error (MAPE)Mean Absolute Percentage Error (MAPE)

Mean Square Error (MSE)Mean Square Error (MSE)Mean Square Error (MSE)Mean Square Error (MSE)

Square Root of the Mean Squared Error (RMSE)Square Root of the Mean Squared Error (RMSE)Square Root of the Mean Squared Error (RMSE)Square Root of the Mean Squared Error (RMSE)

Mean Percentage Error (MPE)Mean Percentage Error (MPE)Mean Percentage Error (MPE)Mean Percentage Error (MPE)

Mean Absolute Deviation (MAD)


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The Mean Absolute Deviation is calculated by the absolute differThe Mean Absolute Deviation is calculated by the absolute differThe Mean Absolute Deviation is calculated by the absolute differThe Mean Absolute Deviation is calculated by the absolute differenceenceenceence

between the exbetween the exbetween the exbetween the ex----post forecast and the actual value.post forecast and the actual value.post forecast and the actual value.post forecast and the actual value.

The Mean Absolute Deviation is the error calculation used toThe Mean Absolute Deviation is the error calculation used toThe Mean Absolute Deviation is the error calculation used toThe Mean Absolute Deviation is the error calculation used to

determine the best fit in the Auto Model 2 Forecasting Model.determine the best fit in the Auto Model 2 Forecasting Model.determine the best fit in the Auto Model 2 Forecasting Model.determine the best fit in the Auto Model 2 Forecasting Model.

Error Total (ET)


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The Error Total is the sum of the difference between the ActualThe Error Total is the sum of the difference between the ActualThe Error Total is the sum of the difference between the ActualThe Error Total is the sum of the difference between the Actual andandandand

the Exthe Exthe Exthe Ex----post forecast.post forecast.post forecast.post forecast.

Mean Square Error (MSE)


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The Mean Square Error is the Average of the sum of the square ofThe Mean Square Error is the Average of the sum of the square ofThe Mean Square Error is the Average of the sum of the square ofThe Mean Square Error is the Average of the sum of the square of

each difference between the exeach difference between the exeach difference between the exeach difference between the ex----post forecast and the Actual value.post forecast and the Actual value.post forecast and the Actual value.post forecast and the Actual value.

Mean Absolute Percentage Error (MAPE)


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The Mean Absolute Percentage Error is the Average Absolute PerceThe Mean Absolute Percentage Error is the Average Absolute PerceThe Mean Absolute Percentage Error is the Average Absolute PerceThe Mean Absolute Percentage Error is the Average Absolute Percentntntnt

Error between the Actual Values and their corresponding ExError between the Actual Values and their corresponding ExError between the Actual Values and their corresponding ExError between the Actual Values and their corresponding Ex----postpostpostpost

Forecast Values.Forecast Values.Forecast Values.Forecast Values.

Root mean Square Error (RMSE)


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The Root Mean Square Error is the Square root of the average ofThe Root Mean Square Error is the Square root of the average ofThe Root Mean Square Error is the Square root of the average ofThe Root Mean Square Error is the Square root of the average of thethethethe

sum of the squared difference between the Actual and Exsum of the squared difference between the Actual and Exsum of the squared difference between the Actual and Exsum of the squared difference between the Actual and Ex----postpostpostpost

Forecast value.Forecast value.Forecast value.Forecast value.

Mean Percentage Error (MPE)Mean Percentage Error (MPE)Mean Percentage Error (MPE)Mean Percentage Error (MPE)


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The Mean Percentage Error is the average percentage error of theThe Mean Percentage Error is the average percentage error of theThe Mean Percentage Error is the average percentage error of theThe Mean Percentage Error is the average percentage error of the

difference between the Exdifference between the Exdifference between the Exdifference between the Ex----post Forecast and the Actual values.post Forecast and the Actual values.post Forecast and the Actual values.post Forecast and the Actual values.


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