apo dp forecasting

Forecasting

Dr. Stefan TheisSAP

SAP AG 2001, SCM, Dr. Stefan Theis 2

Use of statistical methods

Statistical methods can support the planning process but they cannot solve basic planning problems

E.g.: Periodicity change from months to weeks

Powerful forecasting software can calculate millions of forecasts on the lowest level of detail but this is not always the appropriate planning level

Nobody can control / check millions of forecasts With millions of data sets everything happens (Murphys law) It is sometimes better to plan on higher (controllable) level

and breakdown the results to detail using fixed rules

Demand Planning Forecasting


Content

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Causal Analysis (MLR)

Selection of Forecasting Method

Data Preparation

Composite Forecasts

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Evaluation

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Univariate Forecasting


Data preparation (outlier correction)

Statistical methods can only run on appropriate data

Therefore: This step is the most important one !

Adaptations can be necessary for: Start of real history Level changes Negative / zero values Missing values Special events (e.g. strike, promotions, earth quake) Causal effects

With increasing data amount, the data preparation gains more and more importance but causes tremendous effort


Input for forecasting

The result of the data preparation process should be representative for the ordinary development corrected by

all unnormal influences (for univariate forecasting) all unnormal influences not covered by the explanatory

variables (for MLR)

If data shows no structure (visually identifiable), it will be a challenge to create a forecast (no matter which method will be applied)


Recommendation

The standard algorithm for outlier correction implemented in APO 3.0 seems to be not appropriate in many projects

Therefore a macro is recommended. This has the following advantages:

There is no unique definition for an outlier. A macro can be defined for any customer definition

By working with different key figures, the original history remains unchanged and the corrected quantities can easily be saved

Please note:

If the sum of corrected quantities is not approximate 0, the forecast has to be adapted correspondingly !


Pirelli example

Improvement of forecast accuracy at Pirelli

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Const Trend Seasonal Seasonal L inear

Regression

L inear

Regression

Trend - Season

Forecast Method

Fo

rec

ast

Err

or

Ind

ex

Original history Cleaned history

Results based on 44 SKUs


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Data Preparation

Composite Forecasts

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Evaluation

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Selection of forecasting methods I

It is not appropriate to use the same forecasting method for all items

Basic classifications include: Forecast / Planning - Horizon: short <-> medium <-> long Linear <-> Non-linear development of the trend Univariate forecast <-> Causal analysis (MLR) Product type: new, mature, sporadic (e.g. parts) Different parameter settings


Selection of forecasting methods II

The assignment can be based on logical reasons

product classification (e.g. part, standard) planning purpose / business requirements

pilot study grouping of products assignment of parameters

Ex-Post error measures can be used but should not be the only criteria

The assignment should be checked in regular intervals


Automatic model selection in APO

Model selection 1 (Parameters fixed) Test for trend and season (50 and 53) Test for trend (51) Test for season (52) Seasonal model with test for trend (54) Trend model with test for season (55)

Model selection 2 (Parameter kombination tested) Test for trend and season (56)

Never use model selection for regular updates !


Comments on selection methods implemented in APO

Advantages: Easy to use Checks a multitude of different methods Fast

Disadvantages: No real optimization Selection criteria is only MAD The results cannot be used directly, but selections have to be

created manually and assigned to corresponding forecast profiles


Alternative process using macros

1) Define different forecast profiles

2) Define any measurement for accuracy comparison

3) Create activity with the following steps (per characteristic combination)

a) Calculate 1. forecast profile and accuracy measurement (macro)

b) Calculate 2. forecast profile and accuracy measurement (macro)

c) ...

d) Compare accuracy measurements and assign forecast profile with lowest accuracy (macro)

4) Run batch job with above activity for all characteristic combinations

5) Result: Optimal forecast profile is assigned per characteristic combination


Macro: Measurement calculation

Very easy error measure not to be used

in reality


Macro: Compare measurement and assign profile

FORECAST_PROFILE_SET(

‘Profilename‘ ;

‘Model to be used (S = Univariate, M = MLR, C = Composite)‘ ;

‘Version (Often using the command: KEYFS_VERSION( Row:... )‘ ;

‘ Forecast Key Figure (can be omitted) ‘

)


Activity


Batch job


Result: Assignment of forecast profiles


Content

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Data Preparation

Composite Forecasts

4444

Evaluation

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Univariate forecasting methods I

Constant Exponential smoothing 1st order (10/11) Exponential smoothing 1st order withadaptation (12) Moving average (13) Moving weighted average (14) Croston (80)


Exponential smoothing

ParameterCurrent period

First historical

period

Second historical

period

Third historical

period

0.1 10 9 8 7

0.3 30 21 15 10

0.5 50 25 13 6

0.7 70 21 6 2

Basic weighting principle:

Weight of current value: Parameter

Weight of last periods value: (1 – Parameter)

Example of weighting (in %) of historical values


Example: Forecast of constant history

Constant Forecast Methods

950

975

1000

1025

1050

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Month

Hist FS10/11 FS12 FS13 FS14


Example: Forecast of trend-seasonal history

Constant Forecast Methods

300

700

1100

1500

1900

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Month

Hist FS10/11 FS12 FS13 FS14


Univariate forecasting methods II

Trend Exponential smoothing 1st order / Holt (20/21) Exponential smoothing 2nd order (22) Exponential smoothing 2nd order withadaptation (23) Linear Regression (94)

Season (without trend) Exponential smoothing 1st order / Winters (30/31)

Trend - Season Exponential smoothing 1st order (40/41) Seasonal linear regression (35) New in APO 3.0 !


Example: Forecast of trend history

Trend / Seasonal Forecast Methods

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

MonthHist FS20/21 FS22 FS23 FS30/31 FS40/41


Example: Forecast of seasonal history


0

500

1000

1500

2000

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3000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Month

Hist FS20/21 FS22 FS23 FS30/31 FS40/41


Example: Forecast of trend-seasonal history


0

500

1000

1500

2000

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3000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Month

Hist FS20/21 FS22 FS23 FS30/31 FS40/41


Univariate forecasting methods III

Others History (60) Manual Forecasting (70) New in APO 3.0 ! No forecast (98) New in APO 3.0 ! External Forecast / User Exit (99)


Planning equipment (APO 3.0)

Outlier correction

Ignore zero consumption

Promotion planning

Phase in / out profiles

Like profiles

Trend dampening

(Dis-) Aggregation with fixed or detailed proportional factors

Macros

There is only slight statistical methodology behind these (more or less) manual modelling procedures !


Content

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Data Preparation

Composite Forecasts

4444

Evaluation

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Multiple linear regression (MLR)

MLR can assess how the development of one (dependent) variable can be explained by several (independent) variables (and a constant value)

For a causal analysis MLR does the final calculation of the regression coefficients

The input data for the MLR, i.e. the modelling of the causal effects is the key issue


Causal analysis: Typical variables

Trend (any function) Seasonality (dummy or Fourier swingings) Climatic conditions (e.g. temperature, precipitation) Economy (e.g. GDP, inflation, unemployment rate) Product specific (e.g. price/costs, new model/version,

advertising, marketing activities, promotions) Demography (e.g. population in age classes) Others (e.g. life cycle, replacement demand, distribution)


Causal analysis: Typical business questions

What can we do in order to reach sales of X units ? What is the cost optimal solution ?

How will the market react, if we (our competitor) increase the price by Y % ?

How much of our sales are influenced by climatic conditions (e.g. ice cream, beverages) ?

What is the risk of an economic weakness for our sales ? What is the target group for our advertising ? Which variables drive the long term development ?


Causal analysis challenges I

Data challenges Historical data for all variables to be included in the model Comparison to competitors products seems often to be

appropriate but data is not always available Forecasts are needed for all variables

Logical challenges Which variables influence the development ? What are the effects of each variable ?


Causal analysis challenges II

Statistical challenges Autocorrelation Multicollinearity

Modelling challenges Outliers Trend Seasonality Effect of each variable

Time dynamic ElasticityTime dependent weighting

Substancial experience is required for modelling causal effects !


Causal analysis advantages

Full modelling flexibility Explanation of the historical influence of causal factors Enables „what-if“ simulations Risks / opportunities can be estimated The MLR results are easy to understand and can directly be

used to optimize the strategies


Content

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Data Preparation

Composite Forecasts

4444

Evaluation

5555

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Composite forecast

Enables the combination of different forecasts with a constant or time dependent weighting

Can be used for the „one number“ principle in combination with an internal or external collaboration process

Different studies proved that composite forecasts deliver in average a higher forecast accuracy than every single method included in the composite forecast


Content

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Data Preparation

Composite Forecasts

4444

Evaluation

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Evaluation

The planning / forecasting process has to be reviewed permanently / in regular intervals. This can include the analysis of:

KPIs (e.g. service level, out of stock) Financial data (e.g. turnover, profit) Promotions / advertising Special influences (e.g. strike) Causal effects Forecast accuracy (-> model selection)

The results should be documented and archived

The exclusive comparison of the forecast with real sales is not appropriate !

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