dp modeling
DESCRIPTION
Dp ModelingTRANSCRIPT
Forecasting Workshop
11/17/2003
SAP AG 2002, Title of Presentation, Speaker Name / 2
Background
What it is: A detailed presentation focusing on the Mathematical methods of prediction that are standard in APO-Demand Planning.
It is based on the Functionality found in release 3.1.
Anyone interested in upgrading it to 4.0 ?
SAP AG 2002, Title of Presentation, Speaker Name / 3
Overview of Forecasting in APO
333
111
Univariate Forecast Basics
222 History
444 Univariate Models in APOModels in APOModels in APOModels in APO
555 Forecast Error Analysis
666
777
Master Forecast Profile Setup
Forecast Execution
Overview of the Forecasting Workshop
SAP AG 2002, Title of Presentation, Speaker Name / 4
333 Univariate Forecast Basics
444 Univariate Models in APOModels in APOModels in APOModels in APO
555 Forecast Error Analysis
What we will talk about today
SAP AG 2002, Title of Presentation, Speaker Name / 5
333 Univariate Forecast Basics
444 Univariate Models in APOModels in APOModels in APOModels in APO
555 Forecast Error Analysis
SAP AG 2002, Title of Presentation, Speaker Name / 6
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
SAP AG 2002, Title of Presentation, Speaker Name / 7
Actual Value
Data
Valu
es
Past Horizon
Historical values used as the basis of theforecast
SAP AG 2002, Title of Presentation, Speaker Name / 8
Basic Value
Data
Valu
es
Forecast Horizon
Basic Value - Controls Vertical Placement
1
22000
1000
SAP AG 2002, Title of Presentation, Speaker Name / 9
Trend Value
Data
Valu
es
Forecast Horizon
Trend - Slope of line
1
2
.75
-.75
SAP AG 2002, Title of Presentation, Speaker Name / 10
Seasonal Index
Data
Valu
es
Forecast Horizon
Seasonal index - Divergence from Basic Value
1.00
2.50
.25
SAP AG 2002, Title of Presentation, Speaker Name / 11
Length of Season
Data
Valu
es
Number of Periods in the Season
Typically = one year
Season
SAP AG 2002, Title of Presentation, Speaker Name / 12
Initialization
Basic value, Trend value, Seasonal indices, and the Mean Absolute Deviation are initialized.
SAP AG 2002, Title of Presentation, Speaker Name / 13
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 measure post forecast is used to measure post forecast is used to measure post forecast is used to measure model fit.model fit.model fit.model fit.
Data
Valu
es
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
SAP AG 2002, Title of Presentation, Speaker Name / 14
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
SAP AG 2002, Title of Presentation, Speaker Name / 15
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))
SAP AG 2002, Title of Presentation, Speaker Name / 16
Trend Model Example – Initialization & Ex-Post
Period 1 2 3 4 5 6 7 1 2 3 4
History 80 95 100 115 125 110 110
G - Basic Value 102
T - Trend 10.0
EX Ex-post
F Forecast
History Horizon Forecast Horizon
Basic Value Initialization
Trend Initialization
Ex-Post Forecast
Ex(t +i) = G(t) + i * T(t)
Where i = 1
SAP AG 2002, Title of Presentation, Speaker Name / 17
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) = ββββ [ G(t) - G(t-1) ] + (1-ββββ)T(t-1)
10.3 = .3 [ 113 – 102 ] + (.7) 10.0
Basic Value G(t) = ααααV(t) + (1- αααα) [ 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
SAP AG 2002, Title of Presentation, Speaker Name / 18
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 – Calculation Repeated
Ex-Post Forecast Ex(t +i) = G(t) + i * T(t)Where i = 1
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)
Basic Value G(t) = ααααV(t) + (1- αααα) [ G(t-1) + T(t-1) ]
Trend Value T(t) = ββββ [ G(t) - G(t-1) ] + (1-ββββ)T(t-1)
SAP AG 2002, Title of Presentation, Speaker Name / 19
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
History Horizon Forecast Horizon
Trend Model Example – Calculation Repeated
Ex-Post Forecast Ex(t +i) = G(t) + i * T(t)Where i = 1
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
Basic Value G(t) = ααααV(t) + (1- αααα) [ G(t-1) + T(t-1) ]
Trend Value T(t) = ββββ [ G(t) - G(t-1) ] + (1-ββββ)T(t-1)
SAP AG 2002, Title of Presentation, Speaker Name / 20
Model parameters
The model parameters, Alpha, Beta, and Gamma, control the weighting 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 in historical patterns.
SAP AG 2002, Title of Presentation, Speaker Name / 21
333 Univariate Forecast Basics
444 Univariate Models in APOModels in APOModels in APOModels in APO
555 Forecast Error Analysis
SAP AG 2002, Title of Presentation, Speaker Name / 22
Univariate Forecasting Models
� Constant ModelsConstant ModelsConstant ModelsConstant Models
� Moving Average Models Moving Average Models Moving Average Models Moving 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
SAP AG 2002, Title of Presentation, Speaker Name / 23
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
SAP AG 2002, Title of Presentation, Speaker Name / 24
Constant Model – 1st order Exponential Smoothing (10,11)
Use this model when there are no seasonal variations or trend Use this model when there are no seasonal variations or trend Use this model when there are no seasonal variations or trend Use this model when there are no seasonal variations or trend patterns. Alpha parameter controls the weighting of history in patterns. Alpha parameter controls the weighting of history in patterns. Alpha parameter controls the weighting of history in patterns. 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 changes Higher alpha makes the forecast more reactive to recent changes Higher alpha makes the forecast more reactive to recent changes Higher alpha makes the forecast more reactive to recent changes in in in in
historyhistoryhistoryhistory
SAP AG 2002, Title of Presentation, Speaker Name / 25
Example of a Constant Model
SAP AG 2002, Title of Presentation, Speaker Name / 26
Constant Model – Automatic Adaptation of the Alpha factor (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 initial nitial nitial nitial alpha and the smallest tracking signal.alpha and the smallest tracking signal.alpha and the smallest tracking signal.alpha and the smallest tracking signal.
SAP AG 2002, Title of Presentation, Speaker Name / 27
Moving Average Models
Two models are within the Moving Average groupTwo models are within the Moving Average groupTwo models are within the Moving Average groupTwo models are within the Moving Average group
Moving Average (13)Moving Average (13)Moving Average (13)Moving Average (13) -------- the forecast equals the average of the the forecast equals the average of the the forecast equals the average of the the forecast equals the average of the historical periodshistorical periodshistorical periodshistorical periods
Weighted Moving Average (14) Weighted Moving Average (14) Weighted Moving Average (14) Weighted Moving Average (14) –––– the forecast equals the weighted the forecast equals the weighted the forecast equals the weighted the forecast equals the weighted average of the historical periods. The weighting is defined viaaverage of the historical periods. The weighting is defined viaaverage of the historical periods. The weighting is defined viaaverage of the historical periods. The weighting is defined via a a a a weighting profile.weighting profile.weighting profile.weighting profile.
Period Weighting
-5 10%
-4 20%
-3 30%
-2 40%
-1 50%
SAP AG 2002, Title of Presentation, Speaker Name / 28
Moving Average-Formulas
Moving Average-13 Weighted Moving Average-14
No ex-post forecast calculated
SAP AG 2002, Title of Presentation, Speaker Name / 29
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)
HoltHoltHoltHolt’’’’s 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)
Same Formula !20 = 21
SAP AG 2002, Title of Presentation, Speaker Name / 30
Holt’s Method (20, 21)
Basic Value Basic Value Basic Value Basic Value G(t) = ααααV(t) + (1-αααα) [ G(t-1) + T(t-1) ]
Trend ValueTrend ValueTrend ValueTrend Value T(t) = ββββ [(G(t) - G(t-1) ] + (1-ββββ)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
SAP AG 2002, Title of Presentation, Speaker Name / 31
Second Order Exponential Smoothing (22)
Encompasses a double smoothing method where the basic value of Encompasses a double smoothing method where the basic value of Encompasses a double smoothing method where the basic value of Encompasses a double smoothing method where the basic value of the 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 a result is a result is a result is a basic value that is more reactive to changes in demand while basic value that is more reactive to changes in demand while basic value that is more reactive to changes in demand while basic 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.
SAP AG 2002, Title of Presentation, Speaker Name / 32
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 the e e e systems 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 and d d d 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 through ough ough ough 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).
SAP AG 2002, Title of Presentation, Speaker Name / 33
Trend Dampening Profile
As the historical values do not show a decrease in trend as the As the historical values do not show a decrease in trend as the As the historical values do not show a decrease in trend as the As the historical values do not show a decrease in trend as the market market market market matures, 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 thetrend. trend. trend. trend.
SAP AG 2002, Title of Presentation, Speaker Name / 34
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 recurring rring rring rring pattern, 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 occurs curs curs curs across its season or a number of Seasonal Periods (Usually across its season or a number of Seasonal Periods (Usually across its season or a number of Seasonal Periods (Usually across its season or a number of Seasonal Periods (Usually recommended to be 12 Months). The trend value and Beta in this recommended to be 12 Months). The trend value and Beta in this recommended to be 12 Months). The trend value and Beta in this recommended 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.
SAP AG 2002, Title of Presentation, Speaker Name / 35
Seasonal Models
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 Winter’’’’s 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)
Same Formula !30 = 31
SAP AG 2002, Title of Presentation, Speaker Name / 36
Forecast with the Seasonal Model (30)
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 Alpha ha ha ha and Gamma are assigned.and Gamma are assigned.and Gamma are assigned.and Gamma are assigned.
SAP AG 2002, Title of Presentation, Speaker Name / 37
Season + Linear Regression (35)
�Seasonal indices calculated
�Seasonality removed from data
�Forecast created using linear regression
�Seasonality added back to forecast
SAP AG 2002, Title of Presentation, Speaker Name / 38
Seasonal Trend Models
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) Same Formula !
40 = 41
Also know as: Holt-Winters Method
And Triple Exponential Smoothing
SAP AG 2002, Title of Presentation, Speaker Name / 39
First Order Exponential Smoothing (40, 41)
This Model encompasses the same Formula as 20,21,30,31 and uses This Model encompasses the same Formula as 20,21,30,31 and uses This Model encompasses the same Formula as 20,21,30,31 and uses This Model encompasses the same Formula as 20,21,30,31 and uses 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.assigned values for the Alpha, Beta and Gamma Coefficients.
SAP AG 2002, Title of Presentation, Speaker Name / 40
Auto Selection Models
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)
Same Formula !50 = 53
SAP AG 2002, Title of Presentation, Speaker Name / 41
Auto Selection 1 (50, 53)
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, the he he he Pattern is determined to be Seasonal if the Auto Correlation Pattern is determined to be Seasonal if the Auto Correlation Pattern is determined to be Seasonal if the Auto Correlation Pattern 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.
SAP AG 2002, Title of Presentation, Speaker Name / 42
Auto Selection 1 (50, 53)
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 is is is is 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 Constant ant ant ant Model is chosen as a defaultModel is chosen as a defaultModel is chosen as a defaultModel is chosen as a default
SAP AG 2002, Title of Presentation, Speaker Name / 43
Test for Trend (51)
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 no no no no 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.
SAP AG 2002, Title of Presentation, Speaker Name / 44
Test for Season (52)
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 the This follows the This follows the This follows the AutoCorrelationAutoCorrelationAutoCorrelationAutoCorrelation Coefficient Test and Coefficient Test and Coefficient Test and Coefficient Test and choseschoseschoseschoses the the the the 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 is el is el is el is chosen.chosen.chosen.chosen.
SAP AG 2002, Title of Presentation, Speaker Name / 45
Seasonal model plus test for Trend (54)
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 a a a a 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.
SAP AG 2002, Title of Presentation, Speaker Name / 46
Trend Model plus test for Seasonal Pattern (55)
Assumes Trend exists and test for a Seasonal Pattern. If the Assumes Trend exists and test for a Seasonal Pattern. If the Assumes Trend exists and test for a Seasonal Pattern. If the Assumes Trend exists and test for a Seasonal Pattern. If the Autocorrelation Coefficient is greater than 0.3 than a Seasonal Autocorrelation Coefficient is greater than 0.3 than a Seasonal Autocorrelation Coefficient is greater than 0.3 than a Seasonal Autocorrelation Coefficient is greater than 0.3 than a Seasonal Trend Trend Trend Trend Model is used, if the Seasonal test is not found to be SignificaModel is used, if the Seasonal test is not found to be SignificaModel is used, if the Seasonal test is not found to be SignificaModel is used, if the Seasonal test is not found to be Significant than a nt than a nt than a nt than a Trend Model is used.Trend Model is used.Trend Model is used.Trend Model is used.
SAP AG 2002, Title of Presentation, Speaker Name / 47
Summary of the Automodel 1 selection process
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 by
Default
55 - Trend Model plus test
for 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 and
Season
SAP AG 2002, Title of Presentation, Speaker Name / 48
Auto Model Selection procedure 2 (56)
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 to g to g to g 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 Deviation iation iation iation (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 the the the the Model 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.
SAP AG 2002, Title of Presentation, Speaker Name / 49
Comparision between 50 and 56 (Auto model 1 & 2)
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 Constant If no Pattern Detected defaults Constant If no Pattern Detected defaults Constant If no Pattern Detected defaults Constant ModelModelModelModel
Resource Intensive (Not Resource Intensive (Not Resource Intensive (Not Resource 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 through Determines Best Fit through through Determines Best Fit through through Determines Best Fit through through Iterative approach of adjusting Iterative approach of adjusting Iterative approach of adjusting Iterative approach of adjusting smoothing factors (Alpha, Beta, smoothing factors (Alpha, Beta, smoothing factors (Alpha, Beta, smoothing factors (Alpha, Beta, Gamma) and determining Smallest Gamma) and determining Smallest Gamma) and determining Smallest Gamma) and determining Smallest Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)
Determines Seasonal and Trend Determines Seasonal and Trend Determines Seasonal and Trend Determines Seasonal and Trend Significance through Formulas for Significance through Formulas for Significance through Formulas for Significance through Formulas for Auto Correlation (Seasonal Auto Correlation (Seasonal Auto Correlation (Seasonal Auto Correlation (Seasonal Significance) and Trend Significance) and Trend Significance) and Trend Significance) and Trend Significance TestsSignificance TestsSignificance TestsSignificance Tests
SAP AG 2002, Title of Presentation, Speaker Name / 50
The Croston Model (80)
The Croston Model (80) is used when demand is sporadic or The Croston Model (80) is used when demand is sporadic or The Croston Model (80) is used when demand is sporadic or The 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 possible le le le sporadic demand.sporadic demand.sporadic demand.sporadic demand.
SAP AG 2002, Title of Presentation, Speaker Name / 51
Historical Data Model (60)
The Historical Data Model (60) allows you to use your HistoricalThe Historical Data Model (60) allows you to use your HistoricalThe Historical Data Model (60) allows you to use your HistoricalThe Historical Data Model (60) allows you to use your Historical values values values values as your forecast. as your forecast. as your forecast. as your forecast.
SAP AG 2002, Title of Presentation, Speaker Name / 52
Manual Forecast (70)
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 to ues to ues to ues to generate a forecast. The values chosen are all the values that generate a forecast. The values chosen are all the values that generate a forecast. The values chosen are all the values that generate a forecast. The values chosen are all the values that make make make make 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 Seasonal al al al 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.
SAP AG 2002, Title of Presentation, Speaker Name / 53
No Forecast (98)
No forecast is conductedNo forecast is conductedNo forecast is conductedNo forecast is conducted
SAP AG 2002, Title of Presentation, Speaker Name / 54
External Forecast (99)
It is also possible to define your own Forecasting model if the It is also possible to define your own Forecasting model if the It is also possible to define your own Forecasting model if the It is also possible to define your own Forecasting model if the model model model model 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 feature ure ure ure 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.
SAP AG 2002, Title of Presentation, Speaker Name / 55
Trend, Seasonal, Seasonal Trend overview: Formula
Explanation of the Variables
SAP AG 2002, Title of Presentation, Speaker Name / 56
333 Univariate Forecast Basics
444 Univariate Models in APOModels in APOModels in APOModels in APO
555 Forecast Error Analysis
SAP AG 2002, Title of Presentation, Speaker Name / 57
Forecast Accuracy Measurements
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)
SAP AG 2002, Title of Presentation, Speaker Name / 58
Mean Absolute Deviation (MAD)
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 difference ence ence ence 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 to The Mean Absolute Deviation is the error calculation used to The Mean Absolute Deviation is the error calculation used to The 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.
SAP AG 2002, Title of Presentation, Speaker Name / 59
Error Total (ET)
The Error Total is the sum of the difference between the Actual The Error Total is the sum of the difference between the Actual The Error Total is the sum of the difference between the Actual The Error Total is the sum of the difference between the Actual and and and and the Exthe Exthe Exthe Ex----post forecast.post forecast.post forecast.post forecast.
SAP AG 2002, Title of Presentation, Speaker Name / 60
Mean Square Error (MSE)
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 ofeach 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.
SAP AG 2002, Title of Presentation, Speaker Name / 61
Mean Absolute Percentage Error (MAPE)
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 Percent nt nt nt 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----post post post post Forecast Values.Forecast Values.Forecast Values.Forecast Values.
SAP AG 2002, Title of Presentation, Speaker Name / 62
Root mean Square Error (RMSE)
The Root Mean Square Error is the Square root of the average of The Root Mean Square Error is the Square root of the average of The Root Mean Square Error is the Square root of the average of The Root Mean Square Error is the Square root of the average of the the the the 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----post post post post Forecast value.Forecast value.Forecast value.Forecast value.
SAP AG 2002, Title of Presentation, Speaker Name / 63
Mean Percentage Error (MPE)Mean Percentage Error (MPE)Mean Percentage Error (MPE)Mean Percentage Error (MPE)
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 thedifference 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.
SAP AG 2002, Title of Presentation, Speaker Name / 64
Thank You