7/27/2019 SAP R3 Forecasting Feb 23 2004
1/34
SAP R/3 Forecast Module
Vincent A. Mabert
Indiana University
February 2004
7/27/2019 SAP R3 Forecasting Feb 23 2004
2/34
2
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
3/34
3
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
4/34
4
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
5/34
5
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
6/34
6
Glow Bright (40-100C) Example
7/27/2019 SAP R3 Forecasting Feb 23 2004
7/34
7
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
8/34
8
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
9/34
9
Move to R/3 Forecasting Module
Highlight
Forecasting
option
Click to transfer toforecasting tab
7/27/2019 SAP R3 Forecasting Feb 23 2004
10/34
10
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
11/34
11
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
12/34
12
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
13/34
13
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
14/34
14
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
15/34
15
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
16/34
16
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
17/34
17
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
18/34
18
USA Example: Employ trend model with user
selected parameters - Forecast Execution
Forecast Origin
Choice:
ClickCheckto accept
Echo Selected
Basic Choices:
ClickForecasting
to accept
Echo SmoothingChoices:
ClickForecasting
to accept
7/27/2019 SAP R3 Forecasting Feb 23 2004
19/34
19
USA Example: Employ trend model with user
selected parameters - Forecast Output
Forecast Summary Table
- Basic Metrics
- Periods estimates
Forecast Process
Performance Graph
Select Checkto return
7/27/2019 SAP R3 Forecasting Feb 23 2004
20/34
20
System Selection Approach (SSA)
7/27/2019 SAP R3 Forecasting Feb 23 2004
21/34
21
System Selection
Approach
7/27/2019 SAP R3 Forecasting Feb 23 2004
22/34
22
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
23/34
23
SSA Example: Employ trend model with system determined
parameters -Forecast Output
Forecast Summary Table- Basic Metrics
- Periods estimates
Forecast Process
Performance Graph
Select Checkto see
system determinedsmoothing values
7/27/2019 SAP R3 Forecasting Feb 23 2004
24/34
24
SSA Example: Employ trend model with system determined
parameters - System Estimated Smoothing Factors
Smoothing Factor Values
Select save icon if all
work is to be retained
7/27/2019 SAP R3 Forecasting Feb 23 2004
25/34
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
26/34
26
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 !
7/27/2019 SAP R3 Forecasting Feb 23 2004
27/34
27
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
28/34
28
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
29/34
29
Creating New Part Numbers in SAP
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
30/34
30
Creating New Part Numbers in SAP
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
31/34
31
Creating New Part Numbers in SAP
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
32/34
32
Creating New Part Numbers in SAP
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.
7/27/2019 SAP R3 Forecasting Feb 23 2004
33/34
33
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
7/27/2019 SAP R3 Forecasting Feb 23 2004
34/34
34
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 .