SAP R3 Forecasting Feb 23 2004

7/27/2019 SAP R3 Forecasting Feb 23 2004

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SAP R/3 Forecast Module

Vincent A. Mabert

Indiana University

February 2004


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Outline

Introducing a student to forecasting R/3 Forecast Module Background

Navigating the Forecast Module Applying Forecast Module with Glow-

Bright Data

Participant Hands-on Experience

Educational Objectives


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Student Introduction to Forecasting

Focus upon time series models Start with moving average and simple exponential

smoothing models

Use Excel as primary analysis tool to understand parameter

estimation and starting conditions Employ a short case (Northwestern Parts) to introduce

trend and seasonal issues, with Excel. Then move to SAPR3 system with the same data set.

Finally, assign a more complex case (Glow Bright) forstudents to complete, normally in teams.


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SAP General Approach A number of time series forecast models are available in SAP

R/3 for forecasting, such as constant, trend, and seasonal

models, as well as models for moving average values andweighted moving average values. The user can assign a

forecast model manually, or have the system determine one.

The user controls the forecast in the logistics data of thematerial master by maintaining parameters such as the number

of historical periods and factors for exponential smoothing.

By making the appropriate settings for the material master,user can estimate consumption figures for material that are

smoothed, removing random elements from the data. Also,

there are some user options to adjust data because of outliers.


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Forecast Module Features

Multiple time series analysis modelingoptions

User or system determined components for

trend and/or season inclusion

User or system determined parameter values

System evaluation and tracking messages Consumption and forecast data graphing


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Glow Bright (40-100C) Example


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Glow-Bright Exercise

Some Observations

Highly variable

Recent strong trend

Some seasonal swings

No apparent outliers

Approach Navigate around the forecast module & see features

Employ trend model with user selected parameters

Employ trend model with automated systemselected parameter

Participants explore other options within the SAPR/3 forecast module.


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Navigation to Forecast ModuleLogistics ProductionMaster DataMaterial MasterMaterial ChangeImmediately (click)

Type a part number (e.g. 40-100c) and Enter

Clickforecasting in Selected View

Type a plant number (i.e. 3200) and Enter


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Move to R/3 Forecasting Module

Highlight

Forecasting

option

Click to transfer toforecasting tab


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Open Material Master Record for Part Number

To load data, Click on

Consumption Vals button

Note there are no Consumption

history data for item. It will need

to be added.


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Adding Data History:Determine Data Timeline and Origin

Glow Bright data in sequence of oldest to

most recent. However, R/3 system wants

data in most recent to oldest sequence.

Need to know

current time point

for forecast originthat is established

by SAP R/3

system clock , e. g.

August 2003, and

eliminate theremaining data

from use


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Adding Data History: Sequence Data

Determine mostrecent data point that

matches R/3 clock,

say August 2003

Sequence data history

from most recent time

to oldest

Original Glow Bright data sequence, oldest to most recent


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Adding Data History: Data Copy

Highlight data segment in Excel to

be transferred and click COPY

button. Note: Eleven data cells will

be the maximum set size per transfer

Open R/3 Consumption Values

screen and highlight the start cell for

data transfer


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Adding Data History: Data Paste

To transfer data, use the Ctrl

and V keys. This will paste

the copied data set of 11 or less

points into the R/3 Material

Master Consumption Values

history.

Repeat the sequence of

highlight-COPY of the

spreadsheet data and then Crtl-

V in R/3 until all data are

transferred.

Then save the data by clicking

on the save icon in upper left

corner


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Forecasting Within R/3With data loaded within the material master

forecasting record for an item, an individual now

has two forecasting options to select.

User Selection Approach (USA) all forecast

parameters and models are manually entered byuser.

System Selection Approach (SSA) a set of

specific forecast parameters and models (e.g.,smoothing constant values, simple versus enhanced

smoothing, etc.) are determined by R/3 system.


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User Selection Approach (USA) to Forecasting

Many modeling

options within R/3

system

Go to the FORECATING tab within materialmaster record to start forecasting process.


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USA Example: Employ trend model with user

selected parameters - SETUP

Decisions to be made:

Model choice: T

History Periods: 60Initialization Periods: 12

Forecast Horizon: 12

Season cycle (if appropriate): 12

Auto Initialize: X

Smoothing Factors

- Forecast: = .10 & = .20

- Error (MAD): = .10

Save choices

SelectExecute Forecast


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selected parameters - Forecast Execution

Forecast Origin

Choice:

ClickCheckto accept

Echo Selected

Basic Choices:

ClickForecasting

to accept

Echo SmoothingChoices:

ClickForecasting

to accept


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selected parameters - Forecast Output

Forecast Summary Table

- Basic Metrics

- Periods estimates

Forecast Process

Performance Graph

Select Checkto return


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System Selection Approach (SSA)


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System Selection

Approach


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SSA Example: Employ trend model with system determined

parameters -SETUP

Decisions to be made:

Model choice: T

History Periods: 60

Initialization Periods: 12

Forecast Horizon: 12

Season cycle (if appropriate): 12

Auto Initialize: X

Smoothing Factors

- System determined

- Error (MAD): = .10

Save choices

SelectExecute Forecast


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parameters -Forecast Output

Forecast Summary Table- Basic Metrics

- Periods estimates

Forecast Process

Performance Graph

Select Checkto see

system determinedsmoothing values


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parameters - System Estimated Smoothing Factors

Smoothing Factor Values

Select save icon if all

work is to be retained


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Online Help Steps1. Click on ? button to activate on

line help at top of screen.

2. You will see local

help screen for current

cursor location. Click on

book icon with question

mark.

3. At web online page

type in forecasting and

execute search.

The search engine will

list all pertinent

documents.


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Questions?Participant Exercise

Using the assigned material number (e.g., 40-100C),explore the following features of the forecast module

Configure a trend and seasonal model with user

provided parameters Configure a trend and seasonal model with system

determined parameters

Use a different number of consumption history

Try a different number of initialization periods

Have fun !


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Available Material Numbers forParticipant Use within SAP

40-100R40-100F

40-100Y

60-100C

60-100R60-100F

60-100Y

80-100C

80-100R

80-100F80-100Y

40-200R

40-200Y

80-200Y

60-200C

60-201C

60-200F60-200R

60-200Y

80-200C

80-201C80-200F

80-200R


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Creating New Part Numbers in SAP

1. Navigation to Create New Part:

Logistics ProductionMaster DataMaterial MasterMaterial Create(General) Immediately (click)

While there are useable part numbers

in the R/3 database, it is often useful to

have unique part numbers so that data

sets can be assigned to different users

for assignments. The following

illustrates the required steps.


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2. Create Material Initial

Entry:

Insert unique part number inmaterial entry window (e.g.,

mabert-201)

Click drop down button for

Industry Sector and then

Material Type to select

appropriate entries (e.g.,

Plant Engin/Constn and

Additionals).

Hit enter key or click checkmark in upper left of screen.


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3. Next will appear the

Selected Views screen.

PickForecastingoption and click check

mark.

4. The Organization

Level will be presented,

requiring a plant number(e.g., 3200). Enter value

and click check mark.


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5. At the Forecasting Tab

screen three data entries are

minimum required entries:a. Enter a descriptive name

b. Using drop downforBase unit of

measure, select

appropriate option

c. Using drop down forForecastmodel, select appropriate option

d. Click check mark


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6. Once the three minimum

data entries have been

complete, the record shouldbe retained by clicking the

save icon (Disk) in upper left

corner.

Note: If desired, other screenentry options can be input at

this point and saved.


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Educational Objectives:

Introductory Exercise Introduce student to moving average and smoothing models

Plot consumption data and determine patterns present. Talk about

data management and correcting for outliers

Have students select and use different smoothing models, looking at

MAD error to measure performance

Possible evaluation options for student investigation:

Which smoothing model (simple or enhanced) is best?

What is appropriate smoothing factor value? What should be the initialization period length?

How much demand history should be used?

Other depending upon instructor creativity


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Educational Objectives:

Advanced ExercisePlay the role of a consulting team and advise the client (e.g., Glow-

Bright0 on the best approach to implement and use the SAP

Forecast Module.

Issues to address:

1. What guidelines for implementation and use would you provide the

clients staff to lead an individual through the forecast process?

2. What SAP model selection features should the client use within theforecast module? Does the recommended approach depend upon

the type of patterns in the data like trend, seasonal, noise, etc.? Are

there other important factors that need to be considered?

3. What observations would you provide the client concerning the

Forecast Modules strengths and weaknesses? Remember your roleas a consultant.

The written team report should be typed, double spaced, and no longer

than 10 pages excluding exhibits. All team member names shouldbe on the cover page. Due .

SAP R3 Forecasting Feb 23 2004

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