scm220 demand planning

SCM220 AbsatzplanungSCM220

Release 401 02.09.2005

SCM220 Demand Planning............................................................................................................................................0-1

Copyright....................................................................................................................................................................0-2

Course Prerequisites...............................................................................................................................................0-3

Target Group..........................................................................................................................................................0-4

Company Profile: Precision Pump Company.........................................................................................................0-5

Plants and Distribution Centers..............................................................................................................................0-6

Customers...............................................................................................................................................................0-7

Course Overview........................................................................................................................................................1-1

Course Objectives...................................................................................................................................................1-2

Course Content.......................................................................................................................................................1-3

Course Overview Diagram.....................................................................................................................................1-4

Course Overview: Business Scenario.....................................................................................................................1-5

Supply Chain Planning at a Glance........................................................................................................................1-6

Benefits of Demand Planning With SAP APO......................................................................................................1-7

Overview of SAP APO Demand Planning.............................................................................................................1-8

The Demand Planning Lever Effect.......................................................................................................................1-9

Factors That Influence Demand Planning............................................................................................................1-10

Demand Planning Concept...................................................................................................................................1-11

APO Application Architecture.............................................................................................................................1-12

Integration Between APO and the Business Information Warehouse..................................................................1-13

Data Structure: InfoCubes....................................................................................................................................1-14

What is a Planning Area?.....................................................................................................................................1-15

The Planning Table...............................................................................................................................................1-16

Planning and Reporting........................................................................................................................................1-17

Forecasting Techniques........................................................................................................................................1-18

Lifecycle Management and Like Modeling.........................................................................................................1-19

Promotion Planning..............................................................................................................................................1-20

Releasing the Demand Plan as Planned Independent Requirements....................................................................1-21

Reporting in APO.................................................................................................................................................1-22

How the Alert Monitor Is Integrated....................................................................................................................1-23

Course Overview: Unit Summary........................................................................................................................1-24

InfoCubes...................................................................................................................................................................2-1

InfoCubes: Unit Objectives....................................................................................................................................2-2

InfoCubes: Overview Diagram..............................................................................................................................2-3

InfoCubes: Business Scenario................................................................................................................................2-4

Demand Planning...................................................................................................................................................2-5

Extracting Transaction Data...................................................................................................................................2-6

Administrator Workbench......................................................................................................................................2-7

InfoObjects and InfoObject Catalogs.....................................................................................................................2-8

InfoArea..................................................................................................................................................................2-9

Data Structure: InfoCubes....................................................................................................................................2-10

InfoCube...............................................................................................................................................................2-11

Fact Table.............................................................................................................................................................2-12

Dimensions...........................................................................................................................................................2-13

Star Schema..........................................................................................................................................................2-14

Source System......................................................................................................................................................2-15

Data Extractors: LIS Example..............................................................................................................................2-16

Updating Information Structures..........................................................................................................................2-17

Configuring the LIS Environment........................................................................................................................2-18

Process: First Delta Update..................................................................................................................................2-19

InfoSource............................................................................................................................................................2-20

Update Rules........................................................................................................................................................2-21

InfoPackage..........................................................................................................................................................2-22

InfoCubes: Unit Summary....................................................................................................................................2-23

InfoCubes - Exercises...........................................................................................................................................2-24

InfoCubes - Solutions...........................................................................................................................................2-26

Demand Planning Configuration................................................................................................................................3-1

Demand Planning Configuration: Unit Objectives.................................................................................................3-2

Demand Planning Configuration: Overview Diagram...........................................................................................3-3

Demand Planning Configuration: Business Scenario.............................................................................................3-4

Demand Planning Master Data...............................................................................................................................3-5

The Master Planning Object Structure...................................................................................................................3-6

Creating Characteristic Value Combinations.........................................................................................................3-7

Configuration at a Glance.......................................................................................................................................3-8

What is a Planning Area?.......................................................................................................................................3-9

Basic Parameters for the Planning Area...............................................................................................................3-10

Assigning Key Figures to a Planning Area..........................................................................................................3-11

Actual Data and Planning Data............................................................................................................................3-12

Defining Key Figures in the Planning Area.........................................................................................................3-13

Creating Proportional Factors...............................................................................................................................3-14

Disaggregation Methods.......................................................................................................................................3-15

Pro Rata Disaggregation.......................................................................................................................................3-16

Disaggregation by Proportional Factors...............................................................................................................3-17

Disaggregation by P and S...................................................................................................................................3-18

Initializing the Planning Area for the Version.....................................................................................................3-19

Parameters for Initializing the Version.................................................................................................................3-20

Configuration at a Glance.....................................................................................................................................3-21

Version Management...........................................................................................................................................3-22

New Characteristic Combinations – Realignment...............................................................................................3-23

Releasing the Demand Plan..................................................................................................................................3-24

Location Split.......................................................................................................................................................3-25

Releasing with Descriptive Characteristics..........................................................................................................3-26

Demand Planning Configuration: Unit Summary................................................................................................3-27

Configuration - Exercises.....................................................................................................................................3-28

Configuration - Solutions.....................................................................................................................................3-34

Planning Books and Macros.......................................................................................................................................4-1

Planning Books and Macros: Unit Objectives........................................................................................................4-2

Planning Books and Macros: Overview Diagram..................................................................................................4-3

Planning Books and Macros: Business Scenario...................................................................................................4-4

Planning Books and Data Views............................................................................................................................4-5

Planning Books.......................................................................................................................................................4-6

Creating a User-Defined Planning View................................................................................................................4-7

Time Bucket Profiles in Supply and Demand Planning.........................................................................................4-8

Variable Time Buckets...........................................................................................................................................4-9

Data Selection.......................................................................................................................................................4-10

Macros..................................................................................................................................................................4-11

Macro Functions...................................................................................................................................................4-12

Creating a Macro..................................................................................................................................................4-13

Creating a Macro..................................................................................................................................................4-14

Macro Example....................................................................................................................................................4-15

Automatic Macro Execution.................................................................................................................................4-16

Using Macros to Generate Alerts.........................................................................................................................4-17

Planning Books and Macros: Unit Summary.......................................................................................................4-18

Planning Books and Macros - Exercises..............................................................................................................4-19

Planning Books and Macros - Solutions..............................................................................................................4-24

Interactive Planning....................................................................................................................................................5-1

Interactive Planning: Unit Objectives....................................................................................................................5-2

Interactive Planning: Overview Diagram...............................................................................................................5-3

Interactive Planning: Business Scenario................................................................................................................5-4

The Planning Table Selection Area........................................................................................................................5-5

The Planning Table Work Area..............................................................................................................................5-6

Aggregation and Disaggregation............................................................................................................................5-7

Drilldown in Interactive Planning..........................................................................................................................5-8

Navigation Within the Work Area.........................................................................................................................5-9

Changing Proportional Factors Interactively........................................................................................................5-10

Basic Functions of the Planning Table.................................................................................................................5-11

Notes in Demand Planning...................................................................................................................................5-12

Value Fixing.........................................................................................................................................................5-13

Collaborative Planning.........................................................................................................................................5-14

Interactive Planning: Unit Summary....................................................................................................................5-15

Interactive Planning - Exercises...........................................................................................................................5-16

Interactive Planning - Solutions...........................................................................................................................5-21

Forecasting.................................................................................................................................................................6-1

Forecasting: Unit Objectives..................................................................................................................................6-2

Forecasting: Overview Diagram.............................................................................................................................6-3

Forecasting: Business Scenario..............................................................................................................................6-4

Automatic Aggregation of Historical Data.............................................................................................................6-5

Assigning Products to Forecast Profiles.................................................................................................................6-6

Master Forecast Profile..........................................................................................................................................6-7

Statistical Forecasting Tools...................................................................................................................................6-8

The Forecasting Process Flow................................................................................................................................6-9

Adjusting Actual Data..........................................................................................................................................6-10


Univariate Forecasting Models.............................................................................................................................6-12

Automatic Model Selection..................................................................................................................................6-13

Example of a Univariate Forecasting Model........................................................................................................6-14

Forecasting Using Exponential Smoothing..........................................................................................................6-15

Exponential Smoothing........................................................................................................................................6-16

Automatic Outlier Correction...............................................................................................................................6-17

Workday Adjustment...........................................................................................................................................6-18

Univariate Forecast Errors....................................................................................................................................6-19

Forecast Comparison............................................................................................................................................6-20

Summary of the Univariate Forecast Profile........................................................................................................6-21

Multiple Linear Regression (MLR)......................................................................................................................6-22

Multiple Linear Regression..................................................................................................................................6-23

Causal Analysis: Advertising Budget...................................................................................................................6-24

Causal Analysis....................................................................................................................................................6-25

Causal Analysis: Frequently Asked Questions....................................................................................................6-26

Causal Analysis Requirements.............................................................................................................................6-27

MLR Profile.........................................................................................................................................................6-28

Measures of Fit: Causal Analysis.........................................................................................................................6-29

Composite Forecasting.........................................................................................................................................6-30

Composite Forecast Profile..................................................................................................................................6-31

Consensus-Based Forecasting..............................................................................................................................6-32

Forecasting: Unit Summary..................................................................................................................................6-33

Forecasting - Exercises.........................................................................................................................................6-34

Forecasting - Solutions.........................................................................................................................................6-36

Promotions and Lifecycle Planning............................................................................................................................7-1

Promotions and Lifecycle Planning: Unit Objectives............................................................................................7-2

Promotions and Lifecycle Planning: Overview Diagram.......................................................................................7-3

Promotions and Lifecycle Planning: Business Scenario........................................................................................7-4

Promotion Planning................................................................................................................................................7-5

Promotions..............................................................................................................................................................7-6

Defining a Promotion.............................................................................................................................................7-7

Assigning Characteristic Values to the Promotion.................................................................................................7-8

Promotion Statuses and Types...............................................................................................................................7-9

Cannibalization.....................................................................................................................................................7-10

Impact of Promotions on the History and Forecast..............................................................................................7-11


Lifecycle Management and Like Modeling.........................................................................................................7-13

Like Modeling......................................................................................................................................................7-14

Lifecycle Management.........................................................................................................................................7-15

Promotions and Lifecycle Planning: Unit Summary............................................................................................7-16

Promotions and Lifecycle Planning - Exercises...................................................................................................7-17

Promotions and Lifecycle Planning - Solutions...................................................................................................7-20

Mass Processing.........................................................................................................................................................8-1

Mass Processing: Unit Objectives..........................................................................................................................8-2

Mass Processing: Overview Diagram....................................................................................................................8-3

Mass Processing: Business Scenario......................................................................................................................8-4

Mass Processing Functions....................................................................................................................................8-5

Steps in Mass Processing (1)..................................................................................................................................8-6

Steps in Mass Processing (2)..................................................................................................................................8-7

Releasing the Demand Plan....................................................................................................................................8-8

Release Profile........................................................................................................................................................8-9

Transfer Profile.....................................................................................................................................................8-10

Running a Job.......................................................................................................................................................8-11

Mass Processing: Unit Summary.........................................................................................................................8-12

Mass Processing - Exercises.................................................................................................................................8-13

Mass Processing - Solutions.................................................................................................................................8-15

Conclusion..................................................................................................................................................................9-1

Course Overview Diagram: Conclusion.................................................................................................................9-2

Course Objectives...................................................................................................................................................9-3

APO Application Architecture...............................................................................................................................9-4

What is a Planning Area?.......................................................................................................................................9-5

Planning and Reporting..........................................................................................................................................9-6

Statistical Toolbox..................................................................................................................................................9-7

Promotion Planning................................................................................................................................................9-8

Reporting in APO...................................................................................................................................................9-9

Recommended Follow-up Activities....................................................................................................................9-10

0

SAP AG 2003

SCM220 Demand PlanningSCM230 Supply Network Planning

Title

FS310 Inkasso/Exkasso

THE BEST-RUN BUSINESSES RUN SAP

© SAP AG 2003

SCM220Demand Planning

System: R/3, Release: 3.1 (SAP APO) 2003/Q3 Material number: 50062959

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SAP AG 2003

Copyright 2003 SAP AG. All rights reserved.

No part of this publication may be reproducedor transmitted in any form or for any purpose without the express permission of SAP AG. The information contained herein may be changed without prior notice.

All rights reserved.

Copyright

Trademarks: Some software products marketed by SAP AG and its distributors contain proprietary software

components of other software vendors. Microsoft®, WINDOWS®, NT®, EXCEL®, Word®, PowerPoint® and SQL Server® are registered

trademarks of Microsoft Corporation. IBM®, DB2®, DB2 Universal Database, OS/2®, Parallel Sysplex®, MVS/ESA, AIX®, S/390®,

AS/400®, OS/390®, OS/400®, iSeries, pSeries, xSeries, zSeries, z/OS, AFP, Intelligent Miner, WebSphere®, Netfinity®, Tivoli®, Informix and Informix® Dynamic ServerTM are trademarks of IBM Corporation in USA and/or other countries.

ORACLE® is a registered trademark of ORACLE Corporation. UNIX®, X/Open®, OSF/1®, and Motif® are registered trademarks of the Open Group. Citrix®, the Citrix logo, ICA®, Program Neighborhood®, MetaFrame®, WinFrame®, VideoFrame®,

MultiWin® and other Citrix product names referenced herein are trademarks of Citrix Systems, Inc. HTML, DHTML, XML, XHTML are trademarks or registered trademarks of W3C®, World Wide Web

Consortium, Massachusetts Institute of Technology. JAVA® is a registered trademark of Sun Microsystems, Inc. JAVASCRIPT® is a registered trademark of Sun Microsystems, Inc., used under license for technology

invented and implemented by Netscape. MarketSet and Enterprise Buyer are jointly owned trademarks of SAP AG and Commerce One.

SAP, SAP Logo, R/2, R/3, mySAP, mySAP.com, and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and in several other countries all over the world. All other product and service names mentioned are the trademarks of their respective companies.

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SAP AG 2003

Course Prerequisites

RecommendedLO935 Flexible Planning

BW310 Business Information Warehouse –Data Warehousing

PrerequisitesSCM200 Supply Chain Planning Overview

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SAP AG 2003

Target Group

??

Target groupProject team members responsible for configuring Demand Planning and creating demand plans

Duration3 days

Notes for the user: The training material is not suitable for a self-teach program. It complements the course

instructor's explanations. Your material includes space for noting down additional information. There may not be enough time to do all the exercises during the course. The exercises are intended to

be additional examples. Participants can also use these exercises after the course, to consolidate what they have learned.

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SAP AG 2003

Company Profile: Precision Pump Company

??

The Precision Pump CompanyThe Precision Pump company was launched in 1971 and is the market leader, selling a large variety of high-tech standard pumps. It has been listed in the New York stock index NASDAQ 100 since 1999.

ProductsIts product catalog contains turbomolecular, centrifugal, rotation, and membrane pumps (used for manufacturing ultrahigh vacuums, for example).

CustomersInclude the electronics industry, semiconductor industry, chemical, pharmaceutical, and process technology industries, through vehicle manufacturers and universities.

The Precision Pump company was launched in 1971 and is the market leader, selling a large variety of high-tech standard pumps. Its product catalog contains turbomolecular, centrifugal, rotation, and membrane pumps. The Precision Pump company has customers from the electronics industry, the semiconductor industry, the chemical, pharmaceutical, and process technology industries, as well as car manufacturers and universities. The company recently became ISO-certified and has been indexed on the New York stock index NASDAQ 100 since 1999. The company has shown a significant increase in returns over the past fiscal year, especially in the

rapidly growing business area of turbomolecular pumps: Many semiconductor industry processes, from wafer production to the finished chip, only function under high vacuum conditions. This area requires final pressures of < 10-10 mbar, which are in the ultrahigh vacuum range.

In the current fiscal year, the Precision Pump company plans to enter the booming DVD growth market. DVDs are rewritable optical storage mediums that have much more storage capacity than a CD. Coating equipment needed to create these rewritable DVDs also uses vacuum technology.

The Precision Pump company has optimized a range of products to meet these special requirements through intensive co-engineering with manufacturers.

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SAP AG 2002

Plants and Distribution Centers

2400 DC Milan2400 DC Milan

2500 DC Rotterdam2500 DC Rotterdam1000 Hamburg (main plant and company headquarters)1000 Hamburg (main plant and company headquarters)

2300 Barcelona2300 Barcelona3800 Denver3800 Denver

3000 New York3000 New York

Plant DC

This slide shows the main part of the Precision Pump company's supply chain. All the locations shown are plants in the connected OLTP (R/3) system:Three production plants: 1000 – Hamburg 2300 – Barcelona 3000 – New YorkThree distribution centers (DCs): 2400 – Milan 2500 – Rotterdam 3800 – Denver

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SAP AG 2002

Customers

Customers of sales organization 1000 (Germany), distribution channel 10, division 00:

1000 Becker, Berlin (transportation zone DE-D000010000) 1032 Institute of environmental research, Munich (transportation zone DE-

D000080000) 1320 Becker, Cologne (transportation zone DE-D000050000) 1031 Global Trade AG, Frankfurt (transportation zone DE-D000060000) 1030 DELA , Energy Trading Company mbH, Darmstadt

(transportation zone DE-D000060000) 1410 Pilar on the Neckar, Heidelberg (transportation zone DE-D000060000) 1321 Becker, Stuttgart (transportation zone DE-D000070000)

Customers of sales organization 2400 (Italy): 2401 Naples Export, Naples (Italy) 2402 Jashanmal International Trading Co., Dubai (United Arab Emirates)

Customers of sales organization 2500 (The Netherlands): 2502 Miller & Son Trading Ltd., London (GB) 2503 Norwegian Import & Export Group, Oslo (Norway)

Customers of sales organization 3000 (USA) 3140 Rainbow Chemical, Boston

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SAP AG 2002

Course Overview

Contents:

Architecture and integration

InfoCubes

Demand Planning configuration

Interactive planning

Forecasting techniques

Promotion planning

Releasing the demand plan

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SAP AG 2002

Course Objectives

At the conclusion of this course, you will be able to:Configure Demand Planning in SAP APO

Create planning books and macros

Create demand plans using univariate forecasting, causal analysis, and composite forecasting

Use marketing and sales tools, such as promotion planning, lifecycle planning, and "like" modeling

Release demand plans to the SAP APO liveCache (for Supply Network Planning and Production Planning/Detailed Scheduling).

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SAP AG 2002

Course Content

Preface

Unit 6 Forecasting

Unit 7 Promotions and Lifecycle Planning

Unit 8 Mass Processing

Unit 9 Conclusion

Unit 1 Course Overview

Unit 2 InfoCubes

Unit 3 Configuration

Unit 4 Planning Books and Macros

Unit 5 Interactive Planning

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SAP AG 2002

Course Overview Diagram

44

33

InfoCubes2211 Course Overview

55

66

77

8899

Configuration

Planning Books and Macros

Interactive Planning

Forecasting

Promotions and Lifecycle Planning

Mass Processing

Conclusion

(C) SAP AG SCM220 4

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SAP AG 2003

Course Overview: Business Scenario

The Precision Pump company is implementing SAP APO Demand Planning to forecast finished products on a monthly basis.

Since it is a consistent planning method, forecast data can be entered on different planning levels and automatically consolidated for the master forecast.

You use the Demand Planning component of SAP Advanced Planning and Optimization (SAP APO) to forecast market demand for your company's products. The result of APO Demand Planning is the demand plan.

Demand Planning is a complex, powerful, and flexible tool that supports your company's demand planning process. It provides user-specific planning layouts and interactive planning books that allow you to include both different departments and even different companies in the forecast creation process. APO Demand Planning has a range of statistical forecasting tools and advanced macro techniques that you can use to create forecasts from past sales based on many different causal factors, test predefined and user-defined forecasting models and results, and consolidate the demand plans from different departments using a consensus-based approach. You can use forecast overrides and promotions to add marketing intelligence and management adjustments. The seamless integration with APO Supply Network Planning supports efficient Sales and Operations Planning (SOP).

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SAP AG 2002

Supply Chain Planning at a Glance

R/3 APO

Capacity Requirements

Planning (CRP)

Material Requirements

Planning (MRP)

Demand Management

Demand Planning (DP)

Supply Network Planning (SNP)

Production Planning/Detailed

Scheduling (PP/DS)

Flexible PlanningStandard SOP

Sales

PurchasingProduction

BWLIS

The process of supply chain planning can be divided into many steps, some of which can be executed by components in SAP R/3 and others that can be executed in SAP APO. It is possible and advisable to integrate these two systems and use both together when planning. The APO Core Interface (CIF) is used for this system integration.

Demand planning, where past sales figures can be used to derive a future program of production, can be executed both within flexible planning in SAP R/3 (using standard Sales & Operations Planning (SOP)), or within Demand Planning (DP) in SAP APO.

Planned independent requirements can be created from SAP R/3 Demand Management or SAP APO Demand Planning. It is also possible to use DP mass processing to set the sales quantities from APO DP as planned independent requirements in R/3 Demand Management.

Sales orders are created in the SAP R/3 system. A global available-to-promise check (Global ATP) for a sales order can be made in APO (integration with PP/DS is also possible).

Supply Network Planning in SAP APO is used for cross-plant planning. Material Requirements Planning can be executed in either R/3 or APO. However, in R/3, capacity

requirements planning must be executed in a second separate step, whereas in the Production Planning and Detailed Scheduling (PP/DS) component of APO, quantities and capacities can be planned simultaneously.

Production execution, which is the processing of manufacturing orders (production or process orders), takes place in R/3.

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SAP AG 2003

Benefits of Demand Planning With SAP APO

Global server with a BW infrastructure

Exception handling is integrated and you can define your own alerts

It is integrated with Production Planning (the SOP scenario)

Planning is based on a main memory

Flexible navigation in the planning table, variable drilldown

Wide range of forecasting techniques

Promotion planning and evaluation, 'like' modeling

Enables collaborative planning over the Internet

DP bills of material (BOMs)

This slide shows the benefits of Demand Planning in SAP APO as opposed to Flexible Planning in R/3: The Business Information Warehouse (BW) infrastructure has user-friendly features for extracting

data from execution systems and running reports for this data in the SAP BW Business Explorer. Macros can be used to perform complex calculations and to define conditions and exception messages

(alerts). e-mails can be sent automatically and statuses can be queried. In the SOP scenario, the feasible production plan from SNP or PP/DS is compared with the original

demand plan. Deviations are identified automatically and reported to the planner. DP provides the following statistical forecasting models: Constant model, trend model, seasonal

model, trend and seasonal model, Croston method with exponential smoothing, linear regression, and causal models with multiple linear regression. External forecasting procedures can also be used here.

Like modeling refers to the forecasting of new products using historical sales data for old products. You can also define product life cycles as part of like modeling.

You can make each planning book accessible to customers or suppliers over the Internet to exchange data quickly and easily.

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SAP AG 2002

Overview of SAP APO Demand Planning

Past Planning data/forecast

Planned independent

requirements for R/3, SNP, and

PP/DSBW

R/3ExcelNon-SAP system

Historical data

APO-BW

Historical data can be extracted from R/3 systems and imported from BW, Excel, and legacy systems. Demand Planning takes past sales data, such as invoiced sales quantities or sales revenue and uses

forecasts to update it for the future. To do this, it can use statistical forecasting techniques, such as the constant, trend, and seasonal models with exponential smoothing or linear regression.

The demand plan is created as a result of the forecast. The demand plan can be released to generate product requirements (planned independent requirements) at specific locations for specific time periods in R/3 or APO. The requirements that are determined can then be fulfilled by externally procuring or producing the product.

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SAP AG 2002

The Demand Planning Lever Effect

Demand PlanningDemand Planning

Supply NetworkSupply NetworkPlanningPlanning

ProductionProductionPlanningPlanning

Small changes made during Demand Planning cause large changes to be made during Production Planning. Therefore, the goal of Demand Planning is to create sales quantity forecasts that are as accurate as possible.

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SAP AG 2003

Factors That Influence Demand Planning

ManufacturerDistributor

Customer

Sales

Price

Advertisement

Season

Weather

Promotions

Price

Sales

Managing all the main factors that influence demand

Managing product life cycles

Collaborative planning

Competitors

Buying habits

The complexity and competitive nature of today's business environment requires organizations to consider many variables when developing a sales and operations plan: Multiple sources of demand plan data; for example, the manufacturer's forecast is based on a

distributor's past sales, and/or point of sales direct from the retailer. Factors influencing demand; for example, the size of the sales force, R&D expenditures, advertising

expenditures, price, promotions, seasonality. Demand plan data can be exchanged with sales organizations, customers, and suppliers over the

Internet (collaborative Demand Planning). Collaborative planning involves comparing your own forecast results with those of your customers.

Composite forecasting involves combining several forecasting techniques to provide the forecast results.

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SAP AG 2002

Demand Planning Concept

Incoming order Quantities Values

Invoice Quantities Values

…

LocationAggregated actual data

Future demand forecast

Statistical forecastsCollaborative forecastsPromotions

Product hierarchy

Sold-to party

Sales organization

Region

BW


In APO Demand Planning, you can choose to plan on any planning level and define any hierarchies you desire. You can plan at both an aggregated level and at detailed level. The automatic aggregation and disaggregation functionality means that your data is always consistent at all levels of detail.

Usually, the operational R/3 system provides the historical sales data on which you base your forecasts. A special extraction structure is used to transfer the historical data (such as invoiced sales quantity, incoming order quantity, and sales revenue) from R/3 to BW. In BW, the data is stored in InfoCubes from where it is then read by the APO planning area. The actual planning then takes place here in APO. The planning results are stored in liveCache in key figures that are specifically created in the planning area.

These forecasts can be enhanced through causal analyses, collaborative planning, and forecasts from other sources. You use causal analysis to model connections between several variables and historical data, and then update this for the future.

Marketing intelligence and management adjustments can be added using forecast overrides and promotions.


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SAP AG 2002

APOSupply Chain Cockpit

OLTP (R/3)

GlobalGlobalATPATP

InformationWarehouse(SAP BW)


Salesorders

Shop floorcontrol

Inventorymanagement

SupplySupplyNetworkNetworkPlanningPlanning

DeploymentDeployment

ProductionProductionPlanning andPlanning and

DetailedDetailedSchedulingScheduling

APO Application Architecture

Transportation PlanningTransportation PlanningTransportationprocessing

LIS, CO-PAHR, FI

Keyperformanceindicators(KPIs)

Planned ind. requirements

Historicaldata

Aggregated actual data can be transferred to APO from OLTP, BW (Business Information Warehouse), Excel, and Legacy systems, and stored in InfoCubes. This data is the basis for forecasting. The demand plan is created as a result of the forecast.

You release the demand plan to Production Planning, which creates planned independent requirements for Supply Network Planning (SNP) and PP/DS. You can also transfer the demand plan to the operating system (OLTP) as planned independent requirements.

The seamless integration with Supply Network Planning (SNP) and PP/DS supports efficient Sales & Operations Planning (SOP).


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SAP AG 2003

APO

Integration Between APO and the Business Information Warehouse

with an internal Business Information Warehouse

External SAP Business Information Warehouse

Source systems:R/3ExcelNon-SAP systems

Forecasting results

Demand history

POS dataCost informationOrder and shipping dataDemand history

Demand history

Central data store for

reporting and analyzing

InfoCube

InfoCube

APOAPODemand PlanningDemand Planning

SAP's Business Information Warehouse (SAP BW) is contained within and completely integrated with the standard SAP APO delivered system.

However, if you intend to execute extensive reporting, it is advisable to implement an independent BW server, and only transfer planning-relevant data to APO.

Since the data structures in BW and APO are identical, you can also use the BW frontend to run reports for APO data.


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InfoCubes are used for storingInfoCubes are used for storingactual data from OLTP systems in APOactual data from OLTP systems in APO

Product hierarchy

Time seriesAug. Sept.

W32 W33 W34 W35 W36 W37 W38 W39 W40 W41

Regions

Reg

ions

Period

Data Structure: InfoCubes

Product

An InfoCube consists of a number of relational tables that are arranged according to the star schema: A large fact table in the center, surrounded by several dimension tables. Dimension tables are independent of one another. The fact table connects the dimensions with the key figures.

InfoCubes are used in BW and Demand Planning as central data containers They consist of key figures, attributes, and time characteristics.

A key figure is a numerical value that can be either a quantity or other value; for example, projected sales value in dollars or projected sales quantity in pallets.

Characteristics are the objects by which you aggregate, disaggregate, and evaluate business data. Time characteristics define the periods over which you display, plan, and store data. The multidimensional nature of the InfoCubes allows for powerful data analysis capabilities using the

selection, drill-up, and drilldown functions of Demand Planning.


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SAP AG 2002

Time seriesTime series

liveliveCacheCache

OrdersOrders

liveliveCacheCache

What is a Planning Area?

Planning areas

Planning book I Planning book II

Collaboration Interactive planning

CoreInterface

Actual dataextraction

BusinessExplorer

A planning area is the central data structure of Demand Planning and Supply Network Planning. It groups together the parameters that define the scope of the planning activities. It also determines where and how the planning results are to be saved.

In Demand Planning and Supply Network Planning, data is divided into planning areas and subdivided into versions. As a result, the data that you save in planning version 1, planning area 1 does not overwrite the data in planning version 1, planning area 2.

The planning area contains characteristics and key figures for planning, and must be initialized for every planning version.


Characteristics are the objects by which you aggregate, disaggregate, and evaluate business data. Key figure data can be read from different InfoCubes or time series objects. Key figure planning data is stored in time series objects in liveCache.


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The Planning Table

Title viewKey figure 1

Key figure 3Key figure 2


Object 1Object 2Object 3Object 4

GraphDesign Capacity Leveling

APO - Product TotalTotal APO - Location

W 24 W 25 W 26 W 28W 27ID Object Text

Text 1Text 2Text 3Text 4

Selection profileUser

Selection ID

Planning bookData views

Macros

Selection

Selected objects

Standard selections

Header information

Right mouse button:Additional settings

The APO Demand Planning and Supply Network Planning modules have a uniform user interface: The planning table. This planning table has two main components: The selection area and the work area.

The selection area (shuffler) is the window you use to choose the InfoObjects to be planned. You can save selections that you frequently use in the shuffler and load existing selections into it. To

open the shuffler, you choose the selection window icon. The selection profile displays all the selection IDs that are assigned for the planner. The planner can use

the selection IDs to access frequently used selections. In the data view area, you choose your planning books and planning views. You can define a filter for

the available planning books and planning views.


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Time sequence

Aug. Sept.

W32 W33 W34 W35 W36 W37 W38 W39 W40 W41

124

203

Material

Cust

omer

Period

Product groups

Regions

Planning and Reporting

Consistent planning (top down, middle out, bottom up)

Slice & dice

Drilldowns and drill-ups

Multiple demand plans used for simulation purposes

Forecast accuracy analysis

Consistent planning is used to keep planning data consistent at all planning levels. Data is aggregated and disaggregated automatically.

Consistent planning throughout the entire enterprise allows detailed plans to be automatically consolidated.

Top-down planning: Proportional factors are used to automatically distribute an aggregated plan down to detail levels (product, customer, sales area, and so on).

Middle-out planning: Mid-level planning data is aggregated up to the overall plan and distributed down to detailed level.

Bottom-up planning: Detailed data is automatically aggregated up to the overall plan. Consistent planning can be used to simulate several different planning scenarios.


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Forecasting Techniques

Univariate forecasting Moving average

Constant models, trend models, seasonal models

Exponential smoothing

Seasonal linear regression

The Holt-Winter's method

Croston's method (for sporadic demand)

Causal analysis Multiple linear regression

Composite forecasting Weighted average of multiple models

The product spectrum of a company includes a variety of products in different stages of their life cycle with different demand types.

APO Demand Planning offers a toolbox of proven forecasting methods from which you can choose the most suitable method for a specific demand type.

Composite forecasting goes beyond the idea of pick-the-best and combines two or more methods. Croston's method allows you to model sporadic demand. The statistical forecasting toolbox provides all the features you require to create accurate forecasts,

including everything from data analysis using time series models through multiple linear regression.


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Actual data of the old product

Lifecycle Management and Like Modeling

Forecast for the new product

Life cycle

Time

Sales

LikeLike

You use lifecycle planning and like modeling to forecast the launch (phase-in) and discontinuation (phase-out) of a product.

A product's life cycle consists of different phases: Launch (phase-in), growth, maturity, and discontinuation (phase-out). You use this process to model the launch, growth, and discontinuation phases.

For all characteristic value combinations, you can use either a like profile, a phase-in profile, or a phase-out profile, or any combination of these.

If the time period of the phase-out profile falls within the history horizon of the master forecast profile, the system adjusts history input values, displays the adjusted values in the original history and corrected history key figures, and writes the adjusted values to the corrected history.

If the time period of the phase-in profile falls within the future horizon specified in the master forecast profile, the system adjusts the baseline (original) forecasts, and writes the adjusted values to the corrected forecast key figure.


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Forecast simulation

Price

Quantity

-10%

Promotion patterns

’99

’01

’00

‚02Planner

Promotion Planning

Quantity

Time

Promotions can have a major impact on consumer behavior. In APO Demand Planning, you can plan promotions or other special events independently of your actual

forecast. You can use promotion planning to model either one-time events, such as the millennium, or repeated

events, such as quarterly advertising campaigns. Additional examples of promotions would be trade fairs, coupons, free-standing inserts, competitors' activities, and market intelligence. Events that impact consumer behavior include upward or downward economic trends and acts of nature.

Promotional uplifts can be modeled using common promotion patterns based on absolute or percentage values. The effect of a past promotion can either be determined automatically from the demand history or be estimated by the planner. A promotion pattern can be archived in a promotion catalog, which means it can be reused if a promotion of the same type is repeated. A copy function in the promotion catalog also supports like modeling of "like products," "like regions," and so on. Several techniques are available for estimating the impact of a past promotion such as multiple linear regression with or without trend or seasonality.


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Releasing the Demand Plan as Planned Independent Requirements

Production PlanningDemand Planning

Key figure: Sales quantity Planned independent requirement

SNPPP/DS

Production quantitiesKey figure: Feasiblesales quantity

MacrosAlerts

Once the various stakeholders in the forecast have reached an agreement, you release the demand plan as planned independent requirements.

Either the demand planner or supply network planner can release the demand plan from Demand Planning.

This release causes planned independent requirements to be created in the order liveCache. These demands (that are not order-based) form the basis of SNP or PP/DS during which bills of material are exploded, capacities are planned, and sourcing is carried out for the entire supply network.

After the planned sales quantities are checked for feasibility in SNP or PP/DS, the results can be transferred back to Demand Planning. Macros are then used to analyze the deviations between the demand plan and the quantities that can feasibly be produced, and alerts are generated if these deviations are too large.


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liveCacheliveCache

You can use the BW Business Explorer to run reports for: Order data from liveCache

Aggregated data in InfoCubes

Reporting in APO

Planningarea

Extractionstructure

RemoteCube

DP InfoCubeBusiness Explorer

You can also use the BW frontend to run reports for APO data. In addition to running reports for the aggregated actual data from InfoCubes, reports are run for all the

order and time series objects from liveCache. You need the following to be able to run live reports for orders and time series: A planning area in APO,

an extraction structure for the planning area, an InfoSource, and an SAP RemoteCube that reflects the liveCache data.


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APOAlertRepo-sitory

SNPSNP

DPDP

PP/DSPP/DS

OthersOthers

Cockpit

Alert Monitor

Example:

Production Planning and

Detailed Scheduling

Supply NetworkPlanning

Problem solving

How the Alert Monitor Is Integrated

Exception messagesfrom the forecasting

technique and user-definedmacro alerts

Exception messages in APO are generally referred to as alerts. In Demand Planning, you can use macros to define your own alerts. The Alert Monitor can be accessed from either the DP planning table or the Supply Chain Cockpit

(SCC). The detected exceptions are collected automatically in the alert repository and reported in the Alert

Monitor. To display information in the Supply Chain Cockpit (SCC), you must first specify all the objects for

which you want to receive alerts in the Alert Monitor profile. The Alert Monitor profile is a form of filter for viewing specific sets of alerts. You must create a

separate profile for each alert selection. You must also enter the name of the Alert Monitor profile you want to use in the Supply Chain Cockpit user profile (see the APO documentation about the Supply Chain Cockpit). If no Alert Monitor profile name is entered in the SCC profile and no work area is assigned to the alert selection, no alerts are displayed in the monitor.


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Course Overview: Unit Summary

You are now able to:

Define the basic concepts of Demand Planning and its integration with other SAP APO components

Describe the basic architecture of Demand Planning within SAP APO


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InfoCubes

Contents:

Administrator Workbench

What is an InfoCube?

Creating InfoCubes

Settings for loading data into InfoCubes

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InfoCubes: Unit Objectives

At the conclusion of this unit, you will be able to:Describe the role and functions of the Administrator Workbench

Describe how InfoCubes, characteristics, and key figures are structured

Describe the methods used to populate InfoCubeswith data from R/3, Excel, and BW

(C) SAP AG SCM220 2

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InfoCubes: Overview Diagram

44

33


55

66

77

8899

Configuration



Forecasting


Mass Processing

Conclusion

(C) SAP AG SCM220 3

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InfoCubes: Business Scenario

In preparation for using Demand Planning, the Precision Pump company wants to create the data structures for storing historical data

The data structures will then be populated with actual data

(C) SAP AG SCM220 4

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SAP AG 2002

Demand Planning

Incoming order Quantities Values

POS dataNielsen/IRI data

LocationAggregated actual data

Future demand forecast

Statistical forecastsCollaborative forecastsPromotions

Product hierarchy

Sold-to party

Sales organization

Region

BW


Administrator Workbench is the tool you use to create InfoObjects and InfoCubes, and to load data from a source system into the InfoCubes. SAP APO 3.0 contains the entire Business Information Warehouse (BW) 2.0.

You use InfoCubes to store actual data and archive planning data. If you have an external data warehouse, such as the SAP Business Information Warehouse (BW), you transfer data relevant for planning to the Demand Planning InfoCubes (the DP data mart).

Aggregated actual data can be extracted from the R/3 system and imported from BW, Excel, and legacy systems.

POS (point of sales) data is sales data that comes direct from the consumer. This can be procured from firms such as Nielsen or IRI (Information Resources Inc.).

(C) SAP AG SCM220 5

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SAP AG 2002

Extracting Transaction Data

OLTP system 1OLTP system 1

InfoCubes

Communication structure

OLTP system 2OLTP system 2

Transfer structure

Extraction source structure

Transfer structure


Transfer structure

Transfer structureTransfer structure


Transfer structure

DataSource

InfoSource

Transaction data Transaction data

Update rules

Transfer rules Transfer rules

R/3

APO

A DataSource is an infrastructure that enables information to be transported between OLTP systems and the APO data mart. This infrastructure consists of various DDIC structures and the transformation rules that apply between them. DataSources regulate the flow of data from the extraction source structure in the source system to the communication structure in SAP BW that then provides the data. DataSources can provide transaction data (that is stored in InfoCubes) and master data (attributes, texts, and hierarchies) that is stored in separate transparent tables. DataSources for transaction data and master data have almost identical structures.

DataSources describe how many pieces of information are available concerning a particular business transaction or transaction type (such as Cost Center Accounting). Therefore, in an operational SAP BW environment, there are many DataSources that describe individual activities within the applications to be analyzed.

When a DataSource is generated, the transfer structure and communication structure are generated in the APO data mart system. Transfer structures always exist as a pair in both a source system and the associated APO data mart system. The transfer structure is used to transfer data from a source system to an APO data mart in its original application format. From there, transformation rules are used to transfer the data to the InfoSource's communication structure.The communication structure is not dependent on the source system and contains all the InfoSource fields that it represents in the APO data mart.

(C) SAP AG SCM220 6

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The Administrator Workbench is the central tool for maintaining:

Source systems of aggregated order data (R/3, Excel, BW, non-SAP systems)

InfoAreas

InfoObjects (characteristics and key figures)

InfoSources

InfoCubes

It schedules and monitors the data to be loaded It is used by APO and BW

The Administrator Workbench is the tool you use for maintaining InfoCubes, InfoObjects, and all system extraction tools for retrieving external data.

The APO data mart uses the same extraction tools as SAP BW, which means it is also able to connect R/3 and non-R/3 systems, other data warehouses, and application data files, and also use third-party extraction tools.

(C) SAP AG SCM220 7

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SAP AG 2002

InfoObjects and InfoObject Catalogs

Characteristics Texts and attributes Hierarchies Navigation attributes Specific characteristic types:

Time characteristics, such as fiscal year Unit characteristics, such as currencies

or units of measure, such as local currencyKey figures: Numeric fields in amounts and quantities that are constantly updated (such as revenue and sales quantity)

Values/quantities

Currency/units of measure

InfoObject catalogs Organize characteristics and key figures

InfoObject is the generic term for the key figures and characteristics of the APO data mart. It comes with standard BW InfoObjects and APO InfoObjects (with the prefix '9A'). When you create your own InfoObjects, you can decide whether to create an APO InfoObject or a BW InfoObject. For characteristics, it is irrelevant whether you create APO characteristics or BW characteristics. For key figures, you should create APO key figures if you intend fixing values or quantities of this key figure at a later stage.

InfoCatalogs can be user-defined and are used to organize characteristics and key figures. Navigation attributes are used for grouping and selecting actual and planning data. Typical navigation

attributes include MRP controller or customer group, which do not represent a separate planning level but are used for grouping.

(C) SAP AG SCM220 8

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SAP AG 2002

InfoArea

Characteristiccatalog

Key figurecatalog

InfoArea

InfoCubes InfoObjects

InfoAreas are user-defined work areas that are used for maintaining, monitoring, and organizing:

InfoObjects

InfoCubes

Update rules

InfoCube content queries

InfoAreas are used to group objects within the Business Information Warehouse: Each InfoCube is assigned to an InfoArea. You can also use InfoObject catalogs to assign InfoObjects to different InfoAreas.

(C) SAP AG SCM220 9

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Data Structure: InfoCubes

Product hierarchy

Time seriesAug. Sept.

W32 W33 W34 W35 W36 W37 W38 W39 W40 W41

RegionsProduct

Reg

ions

Period

InfoCubes are the data repository for Demand Planning. The architecture of an InfoCube is based on a star schema that consists of fact tables and dimension

tables. The multidimensional nature of an InfoCube allows the user to slice and dice the data in many different

ways.


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SAP AG 2002

Central data store for reports, evaluations, and data used in forecasting

Contains two types of InfoObject Key figures

Characteristics (grouped in dimensions)

1 fact table with multiple dimension tables 3 dimensions have been predefined by SAP

Time

Unit

Data package

InfoCube

Key figures include currency, quantity, or number fields, such as sales revenue or sales quantity. A characteristic is an object for which you aggregate and report data. Characteristics include region,

product, customer (unit characteristics) and month, week, day (time characteristics), for example. When deciding which characteristics to include in your InfoCube for Demand Planning, consider that:

Characteristics define the levels at which you can aggregate data Characteristics define the levels at which you can maintain data

In addition to the three dimensions that are generated automatically (time, unit, data package), your InfoCube contains user-defined dimensions. In Demand Planning, one of these user-defined dimensions is Version.

Only create BW InfoCubes because APO InfoCubes are only used internally.


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Fact table

P C T Order qty Revenue Forecast Promotions

250 $ 500.000 500 20

50 $ 100.000 100 5

… … … ...

Fact Table

A fact is a measurable amount, such as quantity, revenue, discount, or sales overhead. It is a statistic used in reports that are generated by the Data Warehouse.

The fact table below shows the table design used in the data mart. Fact tables usually contain numeric data.

The fact table contains the key figure data for each unique combination of characteristic values. An artificial dimension key (the DIM ID) is used for referencing within the fact table. Since artificial keys are formed for connecting the dimension table with the fact table, changes can be made to the master data table without much difficulty and means the 'natural' key does not have to be regenerated every time. During reporting, a resulting quantity is first formed by the selections in the dimension tables. This quantity is then selected directly by the artificial key from the fact table.


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C Customer # Region …

13970522 West …

Time dimensionProduct service dimensionT Time Fiscal year …

10 1998 …

P Product # Product group …

2101004 Display …

Customer dimension

Dimensions

Dimensions group characteristics logically

A dimension table contains a primary key, a dimension number, and characteristics

Dimensions are a way of structuring the characteristics of an InfoCube. From a technical point of view, the characteristics of the dimension table form the "edges" of the data

cube that is stored as an InfoCube in the data mart. The dimensions are connected to the fact table using DIMs. The data in the fact table is accessed by selecting characteristics and their characteristic values from the dimension tables and by generating a corresponding SQL statement that is used to access the fact table.


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Time dimensionProduct service dimension

Fact table

Customer dimension

C Customer # Region …

13970522 West …

P C T Order qty Revenue Forecasts Promotions

250 $ 500.000 500 20

50 $ 100.000 100 5

… … … ...

P Product # Product group …

2101004 Display …

T Time Fiscal year …

10 1998 …

Star Schema

Fact and dimension table combinations

InfoCubes generate a multidimensional data model on the APO data mart's database server. The facts are collected in separate fact tables and the dimensions grouped into separate dimension tables. Both table types are connected with one another in a relational way. Individual dimension characteristic values can, in turn, be subdivided into master data tables. Master data tables, classification tables, and hierarchy data tables are then grouped in a star-like formation around one central fact table. During the analysis, the data from the surrounding smaller tables is read first to reduce the time accessing the large fact table.

This star form of database schema guarantees high reporting efficiency and provides flexible solutions that can be easily adjusted to fluctuating business requirements.

When creating an InfoCube, you concentrate on the key figures and characteristics you need for planning. You must then group your characteristics into dimensions (time and quantity dimensions). The system automatically generates a star schema in the database, based on your entries.


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Source System

A source system is any system that provides data for the data mart.

Source systems include: SAP R/2 systems

SAP R/3 systems (as of 3.0D)

Other BW systems

Non-SAP systems (third-party tools or files)

Each source system represents a logical system. In a test system, you can use the same source system several times to upload data; you do not need to

create a new source system each time you upload. Each source system represents a physical source of data.


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Data Extractors: LIS Example

Enhancements

Application-specific

Types of extractor

BW Contentextractors

Not application-specific

Genericextractors

Genericextractors

Transparenttable

Transparenttable

LIS Database table/view

SAP Query

FICO

HR...

LO Cockpit

Data source

CO PA

FI SL

Transaction data that is transferred to InfoCubes through extractors can originate from a number of different modules. Due to past developmental processes, a wide variety of extraction mechanisms are necessary to extract the data from these different modules. The next few slides provide an example of transaction data extraction from LIS.


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Individual steps

Updating Information Structures

InfoCubes

Transfer rules

Update rules


Transfer structure

Transfer structure

OLTP 1: LIS information structure


Set up LIS environment11

Generate DataSource22

Replicate DataSource33

Create InfoSource44

Assign DataSource55

Create InfoCube66

Create update77

Create InfoPackage88

S5nn S5nnBIW1 S5nnBIW2

DataSource

InfoSource

R/3

APO

The above slide shows the data flow from the source system into the Demand Planning data mart. Tools in the Administrator Workbench are used to transfer this data.

The individual steps show how actual data is loaded into the APO system from an R/3 information structure. This is just one of the ways the data mart can be configured for Demand Planning. For more information about the APO Administrator Workbench, see the Administrator Workbench section of the SAP Business Information Warehouse documentation.

This procedure is applicable as of R/3 release 3.1H.


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SAP AG 2002

Configuring the LIS Environment

BW connection for LIS information structuresBW connection for LIS information structures

Information structure

Display settings

Set up LIS environment

Generate DataSource

Delete environment/DataSource

Delta update in LIS

Activate/deactivate

Generate update

S5nn

11+2+2

If you want to use LIS information structures as source tables, you must make several preliminary settings.

You first have to configure the LIS environment for the associated information structures. To do this, use the following path: Administrator Workbench => Source Systems => (choose the

appropriate source system and click the right-hand mouse button) Customizing for the Extractors... => Generated DataSources => Logistics => Logistics Information System => Connect Information Structures.

Alternatively, you can call up transaction 'SBIW' in the R/3 system or start report RMCSBIWC.


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SAP AG 2002

Process: First Delta Update

OLTP system

InfoSource2LIS_01_S5nn

11+2+2

S5nnBIW2

S5nn

LISupdateprocess

Control tableBIW status '2'

TMCBIW

S5nnBIW1

R/3 APO

1. Delta update

When you start the delta update from OLTP into APO, tables S5nnnBIW1 and S5nnBIW2 are used. The info structure is active in OLTP. Data from S5nnBIW1 is loaded into BW for the first delta update. During the first delta update in APO, the LIS update process in the OLTP system updates both the LIS

info structure itself and the table S5nnBIW2 that is then used for the second delta update request in BW. At the start of every delta update request, table S5nnBIW1 is automatically exchanged with table

S5nnBIW2 to avoid data inconsistencies during the upload. (During a delta update in the BW, the "BIW status" indicator in table "TMCBIW" is automatically switched from 1 (for table S5nnBIW1) to 2 (for table S5nnBIW2).)

The data from a delta table remains in the table until the next delta upload, so a reload is possible up to that point.


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InfoSource

The InfoSource supplies the data mart with data (from a source system) that belongs logically together from a business point of view

Types of InfoSources: Transaction data

Master data, texts, hierarchies

Transfer structures and communication structures are generated from the InfoSource

44InfoCubes

Transfer rules

Update rules


Transfer structure

InfoSource

APO

An extract structure is used to transfer the data from the source system to the InfoSource in the DP data mart. For example, data from an R/3 application can be prepared for the InfoSource in the extract structure.

An InfoSource is a structure in the Administrator Workbench. It contains data that logically belongs together from a business point of view. The InfoSource metadata defines which InfoObjects are contained in the InfoSource as well as the descriptions and technical information of the InfoObjects. You assign the InfoSource to an application component in the Administrator Workbench.

The system generates the transfer structure and communication structure from the InfoSource metadata. The data is passed through these two structures into the InfoArea's InfoCube.

The InfoSource enables the APO system to: Transfer the extracted data from the SAP R/3 OLTP extraction structure Transfer the data to the APO transfer structure using the OLTP transfer structure Convert the data using the APO transfer rules Transfer the data to the APO communication structure. The InfoSource prepares the data to be stored permanently in the InfoCube.

In addition to the LIS info structure, two more tables are needed as extractors in the OLTP system for the delta upload.


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Update Rules

Update rules link the InfoSource to the InfoCube

They assign the characteristics and key figures to the InfoSourceand the InfoCube

There are two update rule methods: Based on key figures from the communication structure (standard)

Using a routine

77InfoCubes

Transfer rules

Update rules


Transfer structure

InfoSource

APO

A number of operations can be performed within the update rules. InfoObjects can be transferred from the communication structure into an InfoCube. Constants can be used for the actual data from the InfoSource. More complex calculations can be made using ABAP/4 routines. The ABAP editor is directly

embedded in the update rule maintenance - a user exit does not need to be defined. Routines can be used to access customer-specific tables and APO-generated tables (master data tables,

hierarchy tables, and so on). Because of this, update rules do not need to be used for fast information changes; all you need to do is maintain the table contents.

Currency conversion for key figures


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InfoPackage Control parameters for loading data

Controlling the data target

Controlling the InfoCubes to be updated

Update parameters

Scheduling and monitoring of jobs

InfoSource

InfoPackage

88

An InfoPackage is created for the InfoSource. The InfoPackage contains all the control parameters for the load. In the InfoPackage, you control which InfoCubes are populated with data during each load. In the InfoPackage, you schedule and monitor the loading process.


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InfoCubes: Unit Summary


Describe the role and functions of the Administrator Workbench

Describe how InfoCubes, characteristics, and key figures are structured

Describe the methods used to populate InfoCubes with data from R/3, Excel, and BW


2.24InfoCubes - Exercises

Unit: InfoCubesTopic: Administrator Workbench

At the conclusion of this exercise, you will be able to:

Identify the dimensions that are defined for an InfoCube

Identify the characteristics that are defined for an InfoCube

Identify the key figures and data types that are defined for an InfoCube

Create new key figures in the Administrator Workbench.

Make BW queries

The course scenario is as follows:

Sales data from sales orders and invoices has been updated to R/3 information structure S628. These data records have been transferred to the APO SALES InfoCube and are to be used as the actual data for Demand Planning.

Your instructor will assign the group number ## that you need for all exercises in this course.

1-1 Explore the structure of the SALES InfoCube in the Administrator Workbench.

1-2 Which dimensions have been defined in the SALES InfoCube and which characteristics are assigned to each individual dimension?

In the following table, note down the dimensions and characteristics from the SALES InfoCube:

Dimension Characteristics


1-3 Which key figures are defined for the SALES InfoCube? What data type do they have?

Key figure Long description Key figure type Data type

2.24.2

1-4 Display the contents of the SALES InfoCube and analyze the characteristic combinations and entries for the key figures.

1-5 In this exercise, you will create an additional key figure in the Administrator Workbench. You will use this new key figure for storing planning data in a subsequent exercise. In the Administrator Workbench, go to the SALES InfoArea and, in the InfoObject catalog for sales key figures, create a new APO key figure for quantities called EXTRA## with the description INTERNET CORRECTION.

If you enter an additional key figure in the fixed key figure field, you will be able to fix values in your key figure at a later point in time.

1-6 Access the BW Business Explorer Analyzer to evaluate the data that is saved in the SALES InfoCube. Log on to the APO system and open the Salesdata query for the SALES InfoCube.Exit the query and Business Explorer Analyzer without saving.


2.25InfoCubes - Solutions

Unit: InfoCubesTopic: Administrator Workbench

Your instructor will assign the group number ## that you need for all exercises in this course.

1-1 Explore the structure of the SALES InfoCube in the Administrator Workbench.

Demand Planning Environment Current Settings Administrator Workbench

You are now in the Modeling screen for Data Targets

Expand the SALES InfoArea by choosing the arrow in front of the InfoArea

Right-click on the SALES InfoCube: Display data model...

Stay in the Administrator Workbench for steps 1-2 to 1-4.

1-2 Which dimensions have been defined in the SALES InfoCube and which characteristics are assigned to each individual dimension?

Dimensions are represented by the three triangle icon.

When you expand a dimension, you see the characteristics assigned to it

In the following table, note down the dimensions and characteristics from the SALES InfoCube:

Dimension Characteristics

Version APO - Planning version

Sales Sales organization, division, sold-to party

Product Product hierarchy, APO – Product, APO - Location

Time Calendar year/month

Data packet Change run ID, record type, request ID(C) SAP AG SCM220 26

Unit Unit of measure, currency key

1-3 Which key figures are defined for the SALES InfoCube? What data type do they have?

Right-click on the SALES InfoCube: Change

Choose the Key figures tab page.

Choose Detail view.

Key figure Long description Key figure type Data typeINORDQTY 2.25.2Incoming order qty Quantity QUAN

INORDVAL Incoming order value Amount CURR

INVQTY Invoiced sales qty Quantity QUAN

INVVAL Sales revenue Amount CURR

1-4 Display the contents of the SALES InfoCube and analyze the characteristic combinations and entries for the key figures

Right-click on the SALES InfoCube: Manage

Select the SALES InfoCube at the top of the screen

Choose the Display Contents icon or F6

Choose Field selct. for output

Select the characteristics that you want to analyze

Do not select SID characteristics that contain the dimension table internal key

Choose Execute

Choose Execute

Review the data in your InfoCube.

Stay in the Administrator Workbench


If you create an additional key figure and enter it in the fixed key figure field, you will be able to fix values in your key figure.

1-5 In this exercise, you will create an additional key figure in the Administrator Workbench. You will use this new key figure in a subsequent exercise for storing planning data. In the Administrator Workbench, go to the SALES InfoArea and, in the InfoObject catalog for sales key figures, create a new APO key figure for quantities called EXTRA## with the description INTERNET CORRECTION.

You are in the Administrator Workbench Modeling screen for Data Targets.

Go to the InfoObjects tab page and expand the SALES InfoArea and the InfoObject catalog for Sales key figures.

Right-click on the InfoObject catalog for Sales key figures: Create InfoObject...

Enter the name EXTRA## and the description INTERNET CORRECTION for the new key figure. Continue.

A dialog box is then displayed that asks to which BW application the object should belong. Choose APO to create a new APO key figure.

On the Type/unit tab page, choose Quantity.

For the Unit/currency, choose 0UNIT.

Activate the key figure and review the InfoObject catalog.

Exit the Administrator Workbench

If you enter an additional key figure in the fixed key figure field, you will be able to fix values in your key figure at a later point in time.

1-6 Access the BW Business Explorer Analyzer to evaluate the data that is saved in the SALES InfoCube. Log on to the APO system and open the Salesdata query for the SALES InfoCube.

To access the BW Business Explorer Analyzer: Start Programs Business Explorer Analyzer


Enable the macros and choose the “open” icon in the blue Business Explorer tasks bar.

Select the “Queries” pushbutton.

Log on to your APO system with your APO user.

Expand the SALES InfoArea and select the Salesdata query for the SALES InfoCube. Choose OK.

You are given an aggregated view of the invoiced sales quantity and invoiced sales value of the three sales organizations. By clicking the right mouse button over key figures or characteristics you can display data in terms of products, sold-to parties, or months.

Exit the query without saving and log out of the SAP Business Explorer Analyzer.


3

SAP AG 2002

Demand Planning Configuration

Contents:

Master data

Planning areas

Consistent planning

Aggregation and disaggregation

Proportional factors


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3.2

SAP AG 2003

Demand Planning Configuration: Unit Objectives

At the conclusion of this unit, you will be able to:Create master data and planning networks

Configure planning area settings

Explain consistent planning

Describe alternative disaggregation methods

Maintain proportional factors

Release demand plan data

(C) SAP AG SCM220 2

3.3

SAP AG 2002

Demand Planning Configuration: Overview Diagram

44

33


55

66

77

8899

Configuration



Forecasting


Mass Processing

Conclusion

(C) SAP AG SCM220 3

3.4

SAP AG 2002

Demand Planning Configuration: Business Scenario

Now the Precision Pump company has created its InfoCubes for saving actual data, it is necessary to define the planning areas that make the characteristics and key figures available for planning.

(C) SAP AG SCM220 4

3.5

SAP AG 2002

Demand Planning Master Data

CharacteristicsCharacteristics Characteristic Characteristic valuesvalues

Characteristic valueCharacteristic valuecombinationscombinations

((CVCsCVCs))

Product

Location

Sold-to party

P-102

P-103

P-104

1000 2400

2500

1000

1001

1002

P-102, 1000, 1000 P-102, 2400, 1000 P-102, 2500, 1000 P-102, 1000, 1001 P-102, 2400, 1001 P-102, 2500, 1002

Planningobject structure

APO

...

Demand Planning characteristics determine the levels at which demand plans are created, changed, aggregated, and disaggregated. For example, your master data could include all the products, product families, regions, and customers that your company is going to plan using APO Demand Planning, plus all corresponding combinations of these (for example, which customers buy which products in which regions). You model these levels in a non-hierarchical selection tree. From a technical point of view, you specify which characteristic values can be planned for the characteristics in your planning area. Demand Planning or Supply Network Planning master data includes all the permissible values of a characteristic. These are called characteristic values. Characteristic values are specific names. For example, the characteristic Location can have the values London, Delhi, or New York.

Characteristic value combinations are the master data of Demand Planning. Characteristic value combinations can be generated on the basis of actual data from an InfoCube, for example. The APO master data transferred using the Core Interface is not checked (product, location, and so on). Therefore, you can only plan products, sold-to parties, and locations for which you have saved valid characteristic value combinations in your planning object structure. Characteristic values and characteristic value combinations are planning objects in Demand Planning.

When you create an InfoCube for actual data in the Administrator Workbench, you always need to choose a BW cube. A planning object structure is always an APO cube. The internal BW in APO stores both APO cubes and BW cubes.

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3.6

SAP AG 2003

The Master Planning Object Structure

Planning area

Product

Division

Region

Location

Sold-to party

Aggregate

Product

Sold-to party

DP characteristics

Master planning object structure

Characteristic valuecombinations

Planning area

You define characteristics for one or more planning areas in the master planning object structure. You must be very careful during this definition stage, since you cannot change characteristics once the system is operational. A planning object structure is an APO InfoCube that is stored in the database of the Administrator Workbench. Demand Planning can use standard characteristics and/or characteristics that you create yourself in the Administrator Workbench. Characteristics determine the levels at which you can plan data and the options you can select. You need specific characteristics for characteristics-based forecasting and when planning DP production process models. You can use checkboxes to incorporate these characteristics into the master planning object structure, if required. To access the characteristic combinations quickly, you can group characteristics in dimensions, in the same way as you do for BW InfoCubes.

You can also create characteristics as navigation attributes in the APO Administrator Workbench. These should be used for selecting and navigation but not as planning levels. You then assign these attributes to their corresponding characteristics. For example, when defining the Customer InfoObject, you can assign the attributes Sales employee and Priority to the Customer characteristic.

Aggregates in APO are also defined for the planning objects structure. An APO aggregate contains a subset of the characteristics included within the master planning object structure. If there are no aggregates, data is only saved at the most detailed level. If aggregates do exist, the system saves the planning data both at the defined aggregate level and at the most detailed level. Data is saved twice but consistently, meaning that the sum of details is equal to the aggregate value. The aggregates here are not

(C) SAP AG SCM220 6

identical to those in the Business Information Warehouse (BW) but they do have the same purpose: They enable fast access to data and thus improve performance. We recommend that you use aggregates for Demand Planning.

(C) SAP AG SCM220 7

3.7

SAP AG 2002

Facttable:

Dimension: Customer

Dimension: Product

Creating Characteristic Value Combinations

Create single characteristic value combinations

Delete single characteristic value combinations

Generate characteristic value combinations

Analyze characteristic value combinations

Realignment

Characteristic value combinations define the valid relationships between characteristic values and form the basis for aggregation and disaggregation of key figure values. There are two ways of creating characteristic value combinations: Individually, by defining a complete set of characteristic values (this creates one data record) Automatically, by generating characteristic value combinations based on existing data from a BW

InfoCube. Here, the system generates all the combinations it finds for a given time period. To keep the characteristic value combinations up to date, you periodically schedule a background job that generates the new characteristic value combinations. As soon as you have updated data into a BW InfoCube (such as updating sales order data with a new customer), the background job generates new characteristic combinations for it. This automatic generation never deletes old characteristic value combinations, it just creates new ones.

If you want to create multiple characteristic value combinations (for new products, for example), it is often a good idea to maintain the characteristic value combinations in Excel, then load them into an InfoCube, and start generation of the characteristic value combinations for this InfoCube.

This creation process results in a network of planning objects.

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3.8

SAP AG 2003

Time seriesTime seriesTime series

liveCache

Configuration at a Glance

Actual data Planning objectstructure

Planning booksData views

Base unit of measureBase unit of measureBase currencyBase currency

Storage buckets profileStorage buckets profileWeek, month, and so onWeek, month, and so on

Version000

BWBW APOAPOCharacteristic combinations

Planning areaPlanning area

A planning area is the central data structure of Demand Planning and Supply Network Planning. It groups the parameters that define the scope of the planning activities. It also determines where and how the planning results are to be stored.

In Demand Planning, data is divided into planning areas and subdivided into versions. As a result, the data that you save in planning version 1, planning area 1 does not overwrite the data in planning version 1, planning area 2.


(C) SAP AG SCM220 9

3.9

SAP AG 2003

Planning book

Planning table


Characteristics Key figures Version

Characteristics Key figures


liveCache


APOAPO

The planning area contains the planning characteristics and key figures and must be initialized for each planning version.



To save planning data in an InfoCube, create an extraction structure for the planning area and connect it to the InfoCube.


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SAP AG 2002

Basic Parameters for the Planning Area

General parameters

Base unit of measure Base currency Exchange rate type

Storage buckets profile

Daily Weekly Monthly Quarterly Yearly Posting period

Planning area

You have to define a base unit of measure for the planning area. Therefore, if you want to plan products in meters, liters, and pieces and have not defined a unit conversion in the product master, the base unit of measure of the product will be subjected to a 1:1 conversion into the base unit of measure of the planning area. You can also define alternative conversion factors for the unit of measure definition in the product master. You need the unit of measure conversion from the product master and planning area to generate planned independent requirements.

Planning data is stored in the base unit of measure and the base currency. If you want to save data in different currencies, you have to work with different planning areas.

The storage buckets profile defines the periods (buckets) in which data is saved in the planning area. You need to have a storage buckets profile before you can create a planning area.

To define storage bucket profiles, go to Current Settings in the Demand Planning or Supply Network Planning menu path and choose Periodicities for Planning Area. You select periodicities in which you wish to save data from the storage buckets profile and specify a default horizon for which you want to create time series. You set the actual horizon when you initialize the planning area for the version.• You can also specify a factory calendar for conversion; to convert monthly data to weekly data, for

example (this is optional). The time stream must be as long as or longer than the horizon. It is not permitted to be shorter. For example, you can store your actual data in the InfoCube on a monthly basis and enter the planning data in the planning area in a more detailed time buckets profile; on a weekly basis, for instance.


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SAP AG 2003

Assigning Key Figures to a Planning Area


Time series objects

Time series Time series objectsobjects

liveCache


APOAPOBWBW

You use the planning area to define whether key figure data is to be read from InfoCubes or time series objects.

You can only plan key figures for time series objects in liveCache.


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SAP AG 2003


Actual Data and Planning Data

Planning key figuresActual key figures

Incoming order value Quantities Values

Planned ind. reqmntsfor SNP and PP/DSR/3

Excel

Forecast

Planned ind. reqmntsR/3 Demand Management

Order objects

liveCache

Time series

objectsliveCache


BWBW

Key figure data can be read from InfoCubes or time series objects.

If you read a key figure from an InfoCube, the data is read from the InfoCube after selection but cannot be changed. (It is not possible to write to an InfoCube directly from interactive planning.)

Therefore, you save planning data in time series objects from liveCache.

You can generate planned independent requirements from the planning data. You can create planned independent requirements in APO interactively or using mass processing. Planned independent requirements in R/3 Demand Management are only generated using mass processing.

To store planning data in the database (in an InfoCube), you create an extraction structure for the planning area. This extraction structure appears as a DataSource for the APO source system in the Administrator Workbench. You use an InfoSource to link this DataSource to the InfoCube. For more information, see notes 373756 and 374534.


3.13

SAP AG 2002

Defining Key Figures in the Planning Area

Key figures

Key figure InfoCube Key figure semantics Category group Category UOMActual sales SalesForecast

InfoCube: The key figure is read from an InfoCube after selection and cannot be altered

Key figure semantics: Define how the key figure is used in SNP

Category group: In SNP, order quantities are grouped into categories from the category group and displayed in the key figure

Category: In SNP, this defines the category with which orders are to be created, if quantities are entered in the key figure

UOM: The unit of measure is read from the product master

This slide shows a detailed view of the Key figures tab page in the planning area. For Demand Planning, you define here whether the key figures are to be read from an InfoCube or

created as time series. If you enter an InfoCube in the InfoCube column, the key figure is read from the InfoCube after selection and cannot be planned. (It is not possible to write to an InfoCube directly from interactive planning.)

If you do not make any entries in the columns for a key figure, it is created as a time series key figure. Categories differentiate between the various stock, receipts, demands, and forecast orders in the APO

system. The system has a set of standard categories that represent the R/3 MRP elements. Additional categories can be created for non-SAP systems.

UOM: If you set this indicator, products are displayed in their base unit of measure in interactive planning, which is not the unit of measure of the planning area. This indicator is set by default. Key figures continue to be stored in the planning area's base unit of measure.


3.14

SAP AG 2002

Product family

Product A Product B

Plant A Plant B

70 % 30 %

50 %50 %

Creating Proportional Factors

Version-dependent

Period-specific (optional)

Stored in key figure: APODPDANT

Maintained in planning table

Constant proportional factors can be used for disaggregation. They are derived from historical data or occasionally past planning data. Disaggregation can be executed in different ways for different key figures.

The proportional factors determine the percentages used to disaggregate aggregated data down to the members.

To automatically calculate proportional factors, you enter the version, the key figure, and the horizon being used. The system first calculates the entire quantity (value) for the key figure in the specified horizon. The quantities (values) of the individual members are then determined and the percentage is calculated.

You then specify the version and horizon for which you want to save these proportional factors. If you choose "Calculate detailed proportions...," you can set the proportions you want to save from

specific past periods for specific future periods; for example, you can base the proportions of June 99 on those for June 98.

You can also maintain the proportional factors interactively in the planning table.


3.15

SAP AG 2002

Disaggregation Methods

S Pro rata

P By proportional factors

I A mixture of P and S

According to the planning data ratio for members

Disaggregation by specified proportional factors

Disaggregation according to the member ratio

Key fig. aggregatn

Key figure Calculation type Disaggregation key figure Time-based disaggregation

Actual sales S, P, I, and so on APODPDANT, for example P, E, N, and so on

You can set separate structural and time-based disaggregation calculation types for each key figure. Structural aggregation and disaggregation:

S: Data is disaggregated according to the planning data ratio for members. If there is no member data, the data is distributed equally between the members.

P: If you change the data at aggregated level, it is distributed between the members according to proportions calculated in the disaggregation key figure (APODPDANT, for example)

I: This is a mixture of the S and P calculation types. It uses member data for S, not P. A: An average aggregation of data is performed and, during disaggregation, the aggregated value is

written to every member; for example, for prices or times. N: You can choose not to have aggregation or disaggregation if key figure data is only required at one

planning level. Time-based aggregation and disaggregation defines how data is disaggregated by time. The buckets for

storing data originate from the storage buckets profile. For example, if you select months and weeks in the storage buckets profile, data is stored proportionately in weeks.

P - Proportional distribution: Data is distributed by time so that each key figure value in the smallest storage bucket corresponds to the time-based proportion of the value in the aggregated period.

E - Equal distribution: Data is distributed equally over the individual storage buckets. N - No time-based distribution: The value in the planning bucket is copied into the storage bucket; for

prices, for example.


3.16

SAP AG 2003

Pro Rata Disaggregation

S Pro RataBy planning data ratio; if this is initial, then equal

1Initial

Region 12 4003 + 100 = 500

Initial 300

Customer A Customer B Customer C1 11100 100 100

2 + 100 = 200

3 3 3125 250 125

2 50 % 22 25 %25 %

This diagram illustrates how disaggregation is performed if you set the Pro rata disaggregation type for the Sales key figure.

The data is disaggregated according to the distribution ratio that the system derives dynamically from the existing planned data. Proportional factors are not used with this disaggregation type.


3.17

SAP AG 2003

Disaggregation by Proportional Factors

P

Based on a different key figure

APODPDANT1 50 % 30 %20 %

Region 12 4003 + 100 = 500

Initial 300

Customer A Customer B Customer C1 1160 150 90

2 + 100 = 250

3 3 3100 250 150

The above slide illustrates how disaggregation is performed if you set disaggregation type P (based on another key figure) for the Sales key figure and enter key figure APODPDANT.

The data is disaggregated according to the proportional factors. Proportional factors are percentage-based and stored in key figure APODPDANT.

Rounding in Demand Planning: In the planning area, if you use a base unit of measure that has no decimal places defined, quantities are only stored in integer values. If you enter quantities at aggregated level, these quantities are first disaggregated by time (from months down to weeks, for example) and then by characteristic combinations. This can lead to deviations from the calculated proportional factors during disaggregation, especially for small quantities (due to rounding errors). You will have fewer rounding errors if you only work with one time buckets profile or one base unit of measure with decimal places.


3.18

SAP AG 2003

Disaggregation by P and S

Region

Customer A Customer B

1

Customer C

I

1 1160 150 90

2 + 100 = 250

2 4003 + 100 = 500

3 3 3

2 62.5 % 22 22.5 %15 %

75 312.5 112.5

Pro Rata; if initial, thenbased on adifferent key figure

Initial 300

APODPDANT1 50 % 30 %20 %

This diagram illustrates how disaggregation is performed for a key figure if disaggregation type I (pro rata disaggregation; if initial on the basis of another key figure) has been set.

If the key figure is initial, the data is disaggregated by proportional factors, according to key figure APODPANT for instance. If planning data already exists, it is disaggregated according to the distribution ratio. The system derives this ratio dynamically from the existing planning data.


3.19

SAP AG 2002

APO master data (model-independent)

Model 1

Planning version 1Version-dependent master data and transaction data

Planning version nVersion-dependent master data and transaction data

Active model

PPMTransportation

lanes

Locations

Products

Active version 000Version-dependent master data and transaction data

Simulationmodel

Initializing the Planning Area for the Version

Planning areaCharacteristics Key figures

To maintain versions, you follow this SAP standard menu path: Master Data -> Planning Version Management -> Model and Version Management. A version is always uniquely assigned to one model. For order planning, transaction data integration with the execution system can only be carried out from the active version 000. In Demand Planning, versions enable you to plan and compare alternative demand plan simulation scenarios in parallel. You can transfer planned independent requirements between all versions, meaning that version 000 does not play a special role in Demand Planning.

Each planning version (or DP version) is a separate set of data. You can display only one version at a time in interactive planning. Different versions of the plan can be saved for simulation, archiving, or measurement purposes.

You can use the BW Business Explorer to compare versions and run reports for APO data. The planning area has to be initialized for each version, both for order key figures and time series key

figures. When time series key figures are initialized, time series are created for characteristic combinations.


3.20

SAP AG 2003

Parameters for Initializing the Version

Memory space in liveCache

PeriodFor example, week or month

Planning version

Horizon

Create time series objects

01.01.2000

31.12.2004

000

Planning area SALES

Start date

End date

50 30

90

PC

EURPlanning data time series

Time seriesTime Time seriesseries

liveCache

Memory space in liveCache

Planning horizonPlanning horizon

Key figuresKey figures



VersionsVersions

Periodicity/ storage buckets profile

Periodicity/ storage buckets profile

You initialize the planning area by right-clicking on the planning area and choosing Create time series objects or by running report /SAPAPO/TS_PAREA_INITIALIZE.

New time series have to be created for new master data. You are not permitted to delete old time series objects when you extend the planning horizon because it would lose planning data. Therefore, you merely move the start and end date for the planning version. You can reinitialize the planning area periodically on a rolling basis. This deletes obsolete time buckets and adds new buckets for the future.

You can use report /SAPAPO/TS_PAREA_INITIALIZE and a dynamic variant to periodically move the initialization horizon of your planning areas in the background. Initializing larger horizons uses up more memory space.

We recommend that you estimate the amount of required memory space early on in the project, since this influences the hardware and operating system requirements. For more information, use the alias Quicksizer in SAPNet. You then specify the number of your characteristic value combinations, key figures, versions, and planning periodicities. If your storage buckets profile contains only months for instance, the memory space required is five times less than if it contained both weeks and months.


3.21

SAP AG 2002

Configuration at a Glance

Planning area


liveCache

Version000

Actual data

Characteristic combinations

Planning objectstructure

Planning booksData views

Base unit of measureBase unit of measureBase currencyBase currency

Storage buckets profile:Storage buckets profile:Week, month, and so onWeek, month, and so on

APOBW

A planning area is the central data structure of Demand Planning and Supply Network Planning. It groups the parameters that define the scope of the planning activities. It also determines where and how the planning results are to be stored.

In Demand Planning, data is divided into planning areas and subdivided into versions. As a result, the data that you save in planning version 1, planning area 1 does not overwrite the data in planning version 1, planning area 2.



3.22

SAP AG 2002

Version Management

Planning area 2

Version 2

Planning area 1

Key figuresassignment

Characteristicassignment

Version 1

Copy versions

Delete versions

You can also use version management to copy Demand Planning versions. A version number first has to be created. After the version number is created, you can use the copy function to copy the data.

You can use the BW Business Explorer to compare versions and run reports for APO data. Both the version data and the version number are deleted when you delete a version using version

management. Demand Planning versions are 10 characters long and can contain alphanumeric characters. Different version management tools are provided for the versions you use in SNP and PP/DS.


3.23

SAP AG 2002

New Characteristic Combinations – Realignment

CharacteristicsCharacteristics Characteristic Characteristic valuesvalues

Characteristic valueCharacteristic valuecombinationscombinations

((CVCsCVCs))

Product

Location

Sold-to party

P-102

P-103

P-104

1000 2400

2500

1000

1001

1002

P-102, 1000, 1000 P-102, 2400, 1000 P-102, 2500, 1000 P-102, 1000, 1001 P-102, 2400, 1001 P-102, 2500, 1002 ...

New, 1000, 1000 New, 2400, 1000 New, 2500, 1000 New, 1000, 1001 New, 2400, 1001 New, 2500, 1002

Realignment:Reorganizationand creation ofnew CVCs

Characteristic value combinations are the master data of Demand Planning. This slide shows that you can copy characteristic value combinations of a predecessor product (P-102) for planning a new product (NEW), for example. You can use realignment to do this. With realignment, you can copy existing characteristic values into new characteristic values. You can decide whether you want to delete (Move) or copy (Copy) the predecessor's data. You can also decide whether you want to overwrite the target data or add it onto existing data. Planning data is always adopted. You can realign planning areas and InfoCubes.

For more information on realignment, read SAP consultation note 360935 and access report /SAPAPO/TS_REALIGNMENT from transaction SE38. You can first generate an input table using this report, where you store the old characteristic values (FROM) and the new characteristic values (TO). You then use this input table to run realignment.


3.24

SAP AG 2002

Releasing the Demand Plan

Time bucket profiles are used to create planned independent requirements

The location shipping calendar is used to determine workdays

Location split

Product split

Daily buckets profile when the DP storage buckets profile does not contain days

Category (FA)Key figure

Demand Planning Production Planning

Order objects

liveCache

Time series

objectsliveCache

Once the various stakeholders in the forecast have reached an agreement, you release the demand plan as planned independent requirements.

To do this, follow this path: Demand Planning -> Planning -> Release to Supply Network Planning The "Add Data" indicator means that the released amounts can be added to planned independent

requirements that might already exist. It is a good idea to use this setting if you want to release from multiple planning areas.

If you defined weekly and monthly buckets in the storage buckets profile of your DP planning area, weekly demands are created interactively. In mass processing, you can use the data view to control whether weekly or monthly requirements are to be created. The period split field in the SNP 2 tab page of the location product master record has an option for an additional split when releasing in the middle of a period.

If the storage buckets profile from the DP planning area does not contain days, you can still split the sales quantities over days using the daily buckets profile. How this split is made depends on the settings in the SNP demand profile screen area in the SNP 2 tab page of the product master.

The demands (that are not order-based) form the basis of SNP or PP/DS during which bills of material are exploded, capacities are planned, and sourcing is carried out for the entire supply network.

After the planned sales quantities are checked for feasibility in SNP or PP/DS, the results can be transferred back to Demand Planning. Macros are then used to analyze the deviations between the


demand plan and the quantities that can feasibly be produced, and alerts are generated if these deviations are too large.


3.25

SAP AG 2002

Define generic quota arrangements or specific quota arrangements for each product

As an alternative to location level disaggregation in the planning area, a percentage distribution can be defined in location distributions.

Location Split

40% 40% 20%

DC 1 DC 2 DC 3

Product demand

If the Location characteristic is contained in the DP planning area, the sales quantities are disaggregated to the locations automatically. When you release the quantities, you specify your product and location characteristics (such as 9AMATNR and 9ALOCNO), and the system releases the exact location product quantities planned.

If you are using location split, you are not permitted to enter the name of the location characteristic. The split from the location split table will be used.

For instance, you can use the product split function to distribute a product group to the members. Product split will always be checked.


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SAP AG 2003

Releasing with Descriptive Characteristics

Production Planning

Demand Planning

Key figure: Sales quantity Planned independent requirement

Product

Location

Sold-to party

Region

Division

Sales organization

ProductProduct

LocationLocation

SoldSold--to partyto party

Usually, planned independent requirements are released for product and location combinations. For example, if you created your demand plan at product level, quantities are disaggregated down to location level and created for all valid locations by releasing planned independent requirements.

However, you can also use descriptive characteristics to create planned independent requirements for sold-to parties, for example. If you release a demand plan with descriptive characteristics, the system creates an independent requirement for every value of the descriptive characteristic. For example, if you plan requirements at customer level and want to plan production for twenty customers, the system creates twenty forecast orders for each period in Demand Planning (and not just one as it would if there were no descriptive characteristics).

Planned orders are linked to the original requirement (pegging). Descriptive characteristics are not assigned to planned orders. However, since planned independent requirements are assigned to the orders, this data is still available.

Therefore, if the incoming order quantities for a customer exceed the planned independent requirement, more is produced for this customer, while the planned independent requirements for the remaining customers do not change. For example, you could also define customer priority as a descriptive characteristic, meaning that in times of limited resources, goods would be produced for higher priority customers first.


To set descriptive characteristics, you use consumption groups in which you assign fields from the ATP field catalog to characteristics from Demand Planning. You use these consumption groups when releasing the demand plan.


3.27

SAP AG 2002

Demand Planning Configuration: Unit Summary


Configure planning area settings

Create planning networks

Explain consistent planning

Describe alternative disaggregation methods

Maintain proportional factors

Release sales data


3.28Configuration - Exercises

Unit: ConfigurationTopic: Creating a planning object structure


Create a planning object structure

Generate characteristic value combinations

Create and initialize a planning area

Calculate proportional factors

Use realignment

The course scenario is as follows:

The actual data is transferred to the APO SALES InfoCube. Each group now creates its own planning object structure and planning area to generate planning data based on shared actual data from the InfoCube.

1-1 Create a planning object structure called POS##. This planning object structure should contain the following characteristics:

9ALOCNO APO - Location

9AMATNR APO - Product

CUST Sold-to party

DIVISION Division

PRODH Product hierarchy

SALESORG Sales organization

Activate your planning object structure.

A dialog box appears: A master plng object structure already exists for this character. comb. Do you want to create an identical master planning object structure? Yes


Unit: ConfigurationTopic: Characteristic value combinations

2-1 Generate characteristic value combinations for your planning object structure POS##. Use historical data from the previous year from the SALES InfoCube, version 000.

You must not select Create time series objects. If planning areas already existed for your planning object structure, the system would create time series for these planning areas and new characteristic combinations.

2-2 Review the characteristic value combinations that have been generated.


Unit: ConfigurationTopic: Creating a planning area

3-1 Create a planning area called PLAN## for your planning object structure POS##. Store the following parameters:

Storage buckets profile MONTH

Statistics curr. EUR

Exchange rate type M

Unit of measure PC

(If you want to plan in weeks and months, you can alternatively also use the PUMP storage buckets profile)This planning area should contain the following key figures:

9APROM1 Promotion

CORR Correction

CORRHIST Corrected history

EXTRA## Internet correction

FINFOR Demand plan

FORECAST Forecast

INVQTY Invoiced sales qty

Make sure that invoice quantity is read from the SALES InfoCube.

When setting the “Forecast” and “Correction” key figures, make sure that the proportional factor from key figure APODPDANT is taken into consideration for disaggregating blank columns.

Data from an InfoCube in a planning area is generally read-only. Writing data in the InfoCube is not usually possible. Time series can be read and written. If you want to save planning data in an InfoCube, you must create an extraction structure for the planning area and update the data in the InfoCube using the Administrator Workbench.


3-2 Create time series for your planning area (PLAN##), for version 000, for 24 months in the past, and 12 months in the future.


Unit: ConfigurationTopic: Calculating proportional factors

4-1 Generate proportional factors for your planning area PLAN## based on the SALES InfoCube. Calculate fixed proportional factors for planning version 000 for the next 6 months.Base the calculation on the mean proportional factor in the “Invoiced sales qty” key figure from version 000 for the past 6 months.

The proportional factor calculation results can be displayed and changed interactively in the planning table. This function is discussed in more detail in the Interactive Planning unit.

For the proportion calculation in this example, the system sums the invoiced sales quantity data of the past 6 months. The system then calculates the mean proportional factor of each characteristic value combination in this horizon and saves it in the APODPDANT key figure.


Unit: ConfigurationTopic: Realignment

Realignment enables you to create new characteristic values and adopt characteristic value combinations and planning data from already existing planning objects for the new characteristic values.

You also have the option of using addition to combine the planning data of several planning objects or overwrite already existing data. Source data can be deleted.

Key figures that are stored in separate InfoCubes can also be realigned.

*5-1 The task for this exercise: Your company wants to launch a new product. In the long-term, this new product is intended to replace product P-104. You need characteristic value combinations to be able to plan this new product. Use realignment to copy the characteristic combinations for product P-104 into the new product, called NEW. Planning will be done in a subsequent exercise. Access transaction SE38 and run program /sapapo/ts_realignment. Generate an input table for your planning area PLAN##. Run the realignment process to copy all characteristic value combinations for pump P-104 into characteristic value combinations for your new product NEW. Do not delete the source characteristic value combinations. Do not realign the InfoCube.

5-2 Review the new characteristic value combinations in the planning object structure.


3.29Configuration - Solutions

Unit: ConfigurationTopic: Creating a planning object structure

1-1 Create a planning object structure called POS##. This planning object structure should contain the following characteristics:

9ALOCNO APO - Location

9AMATNR APO - Product

CUST Sold-to party

DIVISION Division

PRODH Product hierarchy

SALESORG Sales organization

Demand Planning Environment Current Settings Administration of Demand Planning and Supply Network Planning

Choose the “Planning area” button and select Plng object structures.

Right-click on the Plng object structures folder icon: Create master plng object struct.

Enter the name POS## and choose Enter.

For text, enter “Planning object structure for group ##” and select the above characteristics from the right-hand column, and choose the “Add char.” black arrow icon.

Select the “Activate” icon and activate your planning object structure.

A dialog box appears: A master plng object structure already exists for this character. comb. Do you want to create an identical master planning object structure? Yes

Return to S&DP Administration


Unit: ConfigurationTopic: Characteristic value combinations

2-1 Generate characteristic value combinations for your planning object structure POS##. Use historical data from the previous year from the SALES InfoCube, version 000.

In S&DP Administration, right-click on your planning object structure POS##: Maintain char. combinations

Choose the “Generate characteristic combination...” button.

For the InfoCube, enter “SALES” and version number 000.

As your “Start date,” enter a year ago from today.

Give today’s date as your “End date”.

Choose Execute.

Return to the “Maintain characteristic values relevant to planning” screen

You must not select “Create time series objects”. If planning areas already existed for your planning object structure, the system would create time series for these planning areas and new characteristic combinations.

2-2 Review the characteristic value combinations that have been generated.

“Display characteristics combinations.”

Return to S&DP Administration


Unit: ConfigurationTopic: Creating a planning area

3-1 Create a planning area called PLAN## for your planning object structure POS##. Store the following parameters:

Stor. bckts prfl. MONTH

Statistics curr. EUR

Exch. rate type M

Unit of measure PC

(If you want to plan in weeks and months, you can alternatively also use the PUMP storage buckets profile)This planning area should contain the following key figures:

9APROM1 Promotion

CORR Correction

CORRHIST Corrected history

EXTRA## Internet correction

FINFOR Final forecast

FORECAST Forecast

INVQTY Invoiced sales qty

Make sure that invoice quantity is read from the SALES InfoCube.

When setting the “Forecast” and “Correction” key figures, make sure that the proportional factor from key figure APODPDANT is taken into consideration for disaggregating blank columns.

Data from an InfoCube in a planning area is generally read-only. Writing data in the InfoCube is not usually possible. Time series can be read and written. If you want to save planning data in InfoCubes, you must create an extraction structure for the planning area and update the data in the InfoCube using the Administrator Workbench.


In S&DP Administration, choose the “Planning object structures” button, and select “Planning area”

Right-click on the planning areas folder icon: Create planning area.

Enter the name PLAN## as the planning area, your planning object structure POS## as the master planning object structure, and the above parameters. Confirm your entries.

Choose the “Key figs” tab page

Select the key figures you require from the right-hand side and move them to the left-hand column.

Choose the Details: key figure button and enter the InfoCube SALES for the INVQTY key figure.

Choose the Key fig. aggregatn tab page

In the Key fig. column, set FORECAST and CORR to Calculat. type “I”, and in “Disaggreg. key figure” column set them to APODPANT.

Save and return to S&DP Administration

3-2 Create time series for your planning area (PLAN##), for version 000, for 24 months in the past, and 12 months in the future.

Right-click on your planning area: Create time series objects

Enter Plng version 000, a start date of two years ago, and for the end date, enter the date one year from now.

Execute

Exit Administration of Demand Planning and Supply Network Planning


Unit: ConfigurationTopic: Calculating proportional factors

4-1 Generate proportional factors for your planning area PLAN## based on the SALES InfoCube. Calculate fixed proportional factors for planning version 000 for the next 6 months.Base the calculation on the mean proportional factor in the “Invoiced sales qty” key figure from version 000 for the past 6 months.

The proportional factor calculation results can be displayed and changed interactively in the planning table. This function is discussed in more detail in the Interactive Planning unit.

For the proportion calculation in this example, the system sums the invoiced sales quantity data of the past 6 months. The system then calculates the mean proportional factor of each characteristic value combination in this horizon and saves it in the APODPDANT key figure.

Demand Planning Environment Calculate Proportional Factors.

Enter PLAN## as your planning area, and SALES as your InfoCube (in the Basis for proportion calc.).

Select version 000 for which to calculate the proportional factors.

Choose Planning version 000 in Basis for proportion calc. plus the “Invoiced sales qty” key figure, and the period of the last six months.

In Create proportions in horizon, enter the next six months as the horizon.

In Proportion calc. type: choose Calculate fixed proportions in entire horizon.

Select the “Log for proportional calculation”.

Choose Execute.


Unit: ConfigurationTopic: Realignment

Realignment enables you to create new characteristic values and adopt characteristic value combinations and planning data from already existing planning objects for the new characteristic values.

You also have the option of using addition to combine the planning data of several planning objects or overwrite already existing data. Source data can be deleted.

Key figures that are stored in separate InfoCubes can also be realigned.

*5-1 The task for this exercise: Your company wants to launch a new product. In the long-term, this new product is intended to replace product P-104. You need characteristic value combinations to be able to plan this new product. Use realignment to copy the characteristic combinations for product P-104 into the new product, called NEW. Planning will be done in a subsequent exercise. Access transaction SE38 and run program /sapapo/ts_realignment. Generate an input table for your planning area PLAN##. Run the realignment process to copy all characteristic value combinations for pump P-104 into characteristic value combinations for your new product NEW. Do not delete the source characteristic value combinations. Do not realign the InfoCube.

Access the ABAP Editor. To do this, enter transaction /nse38 in the transaction field and choose Enter.

In the Program field, enter program /sapapo/ts_realignment and choose the Execute icon (F8).

Enter your planning area and set the Generate input table indicator.

Choose the Restrict table characteristics button.

Only select characteristic 9AMATNR. Execute.

Choose Execute (F8) to start the program.

In the next screen, choose the Content/edit SE16 (F5) icon.

Then choose the Create entries (F5) icon

In field F 9AMATNR, enter product P-104 and in field T 9AMATNR, enter product NEW.

Save.

Return to the initial screen and set the Execute realignment indicator.

Choose the Execute (F8) icon to run the program.(C) SAP AG SCM220 42

In the subsequent screen, set the No deletion of source data indicator and execute the program.

5-2 Review the new characteristic value combinations in the planning object structure.Master Data Demand Planning Master Data Maintain Characteristic Values

Choose your planning object structure POS##

Choose “Display characteristic value combinations”. Choose the “Selection Condition” button and, for APO - Product, enter product NEW.

Choose Execute (F8) twice.


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Contents:

What is a planning book?

User-defined planning views

Time bucket profiles

Advanced macros

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4.2

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Planning Books and Macros: Unit Objectives

At the conclusion of this unit, you will be able to:Create a planning book

Configure a user-defined planning view

Create macros

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Planning Books and Macros: Overview Diagram

44

33


55

66

77

8899

Configuration



Forecasting


Mass Processing

Conclusion

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Planning Books and Macros: Business Scenario

Now that InfoCubes and planning areas have been defined, planning tables can be configured for the Precision Pump company's demand planners.

Each planner is assigned the planning books including planning views and macros required to perform his or her planning tasks.

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Planning area

Planning Books and Data Views

Planning book

Planning table 2


Key figures Key figures

Planning table 1

Data view 2Data view 1

The planning view also specifies the horizon and time

buckets for planning

The planning book is the most important tool for the demand planner. A planning book is based on the information or subset of information from a planning area. The demand planner does not maintain the planning area.

In the planning book, you select the characteristics and key figures required by the demand planner for his or her tasks.

Each planning book can contain multiple views where you can group key figures for detailed analyses and planning tasks.

In each view, you also define the planning horizon and time buckets profile.

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Multiple books for one planning areaPlanning views specify the planning table layout

Select your own key figures fromthe planning book, such as:Invoiced sales quantityIncoming orderSales revenue

PromotionUnivariate forecastCausal analysisComposite forecast

Planning Books

Manual proportion maintenance

Planning area

User-definedplanning views

Integration of standard views

Standard functions

A planning book contains one or more views of the planning data in the planning area. A planning book has both user-specific views and SAP-defined standard views. To create a planning book, choose Design Planning Books from the Current Settings in Demand

Planning or from Customizing for Demand Planning. An Assistant then helps you through all the necessary steps. Once you have made all your entries on one tab page, choose Continue to go to the next tab page. Once you have completed all the tab pages, you can save the planning book. To do this, choose Complete and confirm all messages that appear. The system saves the planning book and also adds the standard key figures required to execute the chosen functions (see below).

You can now go directly from interactive planning to planning book maintenance by switching from live mode to design mode.

The first time you create a planning book, you can only get to the next view by choosing "Continue" in the wizard. You can only choose the tab pages in change mode.

The first time you access interactive planning, you go to default planning book 9ADP01.

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Planning view:

Creating a User-Defined Planning View

The planning view specifies which key figures are to be displayedin interactive planning

You can create macros to calculate new key figures

Macros can be used to show the previous year's actual data

The planning view specifies the horizon and time buckets for planning

Row attributes define:Planning statusInformation onlyAggregation and disaggregation

You create a view for the planning book. You need at least one view for planning. You enter a view description. The data view description appears in the planning book shuffler, to the

left of the interactive planning table. You can specify the status as Private (1), meaning that the view can only be used by the current user, or

Protected (2), meaning that other users are not able to change the view, but they can use it as a template for creating their own views.

You enter planning bucket profiles to define the planning horizon. The planning bucket profiles must contain either all the periods or a subset of periods from the time buckets profile on which the planning area is based. If you plan past and future dates, you must enter past and future planning bucket profiles. You can specify which part of the past buckets profile you want to be displayed as initial (i) and which part changeable (ii). You enter either a date or an offset to specify the start of the future planning horizon. The offset refers to the number of days between today's date and the start of the future planning horizon. The future planning buckets profile specifies the period to be used (days, weeks, or months, for instance). By setting the "Period" indicator, you can specify that you want to create a column to sum all the future horizon values; in the next release, this column is intended to be used for version comparisons.

If you wish to use two planning tables on one screen (for example, the capacity view in planning book 9ASNP94), select the indicator next to the second table title; you can also enter a text for this table. An

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additional tab page is displayed in the planning book maintenance assistant, where you define the key figures for the second table.

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Time Bucket Profiles in Supply and Demand Planning

The planning buckets profile

Number of periods Basic periodicity Display periodicity

3

2

1

M

W

M

W

D

The planning buckets profile defines the periods (or buckets) inwhich data is displayed and planned in the planning table.

Month 1 Month 2 Month 3

1. 2. 3. Weeks

Days

The planning buckets profile defines: Which periods are used for planning How many periods of each period type are used The display sequence of the periods in the planning table

You can plan according to months, weeks, or (in combination with fiscal year variants) user-defined periods.

When creating a planning buckets profile, only use periodicities or a subset of periodicities that are also defined in the storage buckets profile on which the planning area is based. Do not include any periodicity in a planning buckets profile that is not contained within the storage buckets profile.

Multiple planning buckets profiles and multiple planning horizons can be created for one planning book. The planning buckets profile is assigned to the data view within the planning book. For example, you can create three data views for three different users with each view based on a different planning buckets profile: The marketing department in months, the sales department in months and weeks, and the logistics department in weeks and days. In the example above, the planning horizon is three months. The first two of the three months are

displayed in weeks. The first week is displayed in days. The first row defines the entire length of the time horizon and the following rows define the different

time segments of the horizon. You make entries in the Number and Display periodicity columns. The

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contents of the other columns appear automatically, once you have pressed the Enter key. To see exactly which periods are displayed, choose Period list.


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Details Display DisplayWeek Month in weeks in months

12/99 3/99 12/99 3/99

13/99 3/99 13/99 4/99

13/99 4/99 14/99

14/99 4/99

Variable Time Buckets

Multiple time characteristics can be used

Data can be displayed in any of the time bucketsfrom the storage buckets profile

Data is always stored at detailed level

Data is always stored in the smallest periodicity and is aggregated to the time buckets profile being used. You use the planning bucket profiles you created to define the future planning horizon and past horizon

of a planning book. You enter both of these horizons into the planning book. When displaying the horizons in interactive Demand Planning and interactive Supply Network Planning, the system starts with the smallest period and finishes with the largest period. The future horizon starts with the smallest period (on the start date of the planning horizon) and continues processing from that point onwards, with the largest period at the end. The past horizon starts with the smallest period (on the day before the start of the future horizon) and continues processing from that point backwards, with the largest period at the end.

Time-based aggregation and disaggregation defines how data is disaggregated by time. The buckets for storing data originate from the storage buckets profile. For example, if you select months and weeks in the storage buckets profile, data is stored for June and July 2000 in the following buckets: June 1-4 (Thursday to Sunday) = 4 days; June 5-11 (Monday to Sunday) = 7 days;June 12-18 (Monday to Sunday) = 7 days; June 19-25 (Monday to Sunday) = 7 days; June 26-30 (Monday to Friday) = 5 days.


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Data Selection

Object selection

Show

that meet the following conditions

APO - Product

APO - version 000Sold-to party Becker

User

Selection 1Selection 2

Selection 3

Characteristic values that you want to select

Characteristic values that you want to select

The criteria used to select the

characteristic values

The criteria used to select the

characteristic values

P-100P-101P-101

The shuffler is the window in which you select the InfoObjects you want to plan. You choose characteristics that meet certain conditions from the dropdown boxes in the object selection dialog box (show... that meet the following conditions...).


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Macros

Promotions (absolute and percentage)

Consensus-based forecasting

Overrides

Sales budgets

Deviations

Flexible alerts

Advanced macros are formulas that the user can define and execute within the planning table of a planning book. They are much like spreadsheet formulas, but provide more flexibility and function better than typical formulas in spreadsheets.

The extreme flexibility of the advanced macros enables the planner to model a user-specific planning environment based on an individual's business tasks.


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Macro Functions

Operations based on individual fields, rows, columns, and areas

A macro can have several steps

One step can contain several arguments

Control statements such as, If, Else if

Operators, such as (), <>, sin, sqrt, exp, trunc

Functions, such as, SUM, AVG, MAX, MAD, VAR

Alerts, e-mails

Sales revenueActual sales

Corr. forecastRevenue

Forecast

You can use the advanced macro functions to do the following: Control how macro steps are to be processed (using control statements and conditions). Build a macro consisting of one or more steps. Control how macro results are to be calculated (using control statements and conditions). Select from a wide range of functions and operators. Define offsets that, for example, enable the results of one period to be determined by a value from the

previous period. Restrict the horizon in which the macro is executed to a specific period or periods. Write macro results to either a row, a column, or a cell. Write the results of a macro step to a row, column, cell, or variable, and only use these results in

subsequent iterations, macro steps, or macros. Make ad hoc analyses of forecast or transaction data using specific icons. Create context-specific and user-specific planning views. Trigger an alert in the Alert Monitor to inform the planner of specific company situations.


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Creating a Macro

Macro work area

Clipboard

Automaticexecution

Elements

Corr. forecast

Actual sales

Forecast

M 08/1999 M 09/1999

100 100

150 150

150 130

DefaultLevel changeStartExit

MacrosNew macro

Step 1

Drag&Drop

Depot

MacroBuilderYou can use the planning book or the separate "MacroBuilder" transaction to maintain macros

Macros perform complex mathematical calculations quickly and easily. Macros are executed either directly by the user or automatically at a set point in time. Defining macros is optional.

You create macros either when creating or changing a planning book in Customizing, or in the design mode of interactive planning. You can define a macro either for an entire planning book or for a specific data view. You can also copy macros from existing books into a new book.


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Creating a Macro

Level 1

Macro 1

Macro 2

Level 2Level 2

Step 1Step 1

Step 2Step 2

Level 3Level 3

Result 1Result 1

Result 3Result 3

Level 4Level 4

Key figure A+

Key figure B

Key figure A+

Key figure B

CalculationCalculationStep 1Step 1

Result 2Result 2 Key figure A* 100

Key figure A* 100

Macros are maintained on four levels: The first level contains the different macros.

On the second level, you can subdivide the calculation operations within a macro into different steps. This corresponds to compounding in mathematical formulas.

The third level contains the interim results and final results.On the fourth level, you define the operands and calculation operations.


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"Adjust demand plan" macro

Macro Example

"Check adjustment" macro

1. Step

New demand plan =

Corrected forecast

Manual adjustments

+

1. Step

IF

Adjustment > 500 (condition)

Manual adjustments

> 500

Enter adjustment <= 500 (procedural message)

ENDIF

SAP provides a number of macro examples. To access them, go to the MacroBuilder and select: Macros for: SAP supplied: SAP example and template macros.

All macros can be imported into planning books.


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Event: Savedata

START EXITDEFAULTMacroexecution:

Time

Enter, read, andsave data

Read data

Automatic Macro Execution

Changeplanning levels

LEVEL CHANGE

You can set that a macro is to be executed automatically for a particular planning book. There are three ways to execute a macro automatically: The start macro is run whenever you access the planning table. The default macro is run whenever you regenerate your plan (for example, when you press ENTER)

and when you open or save the plan. This can mean that it is run several times. A level change macro is always run during a drill-up or drilldown. The exit macro is run immediately before data is saved.

To designate a macro for automatic execution, drag it over the Default, Start, or Exit node. If a macro is not set for automatic execution, it can be executed directly in interactive planning or mass

processing. To exclude a start, default, or exit macro from the list of macros that can be executed from the

interactive planning screen, select Cannot be executed directly in the macro's attributes.


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!

Using Macros to Generate Alerts

In Demand Planning, you can use macros to define your own alerts

There are standard alerts for forecasting

Alert types

Macro

Alert type:Condition

Executemacro:

Generatealerts

Alert profile:

Alert type:Priority

Alert Monitor

Displayevaluation

Database alerts

The Alert Monitor informs you of exception situations that occur in your plan. Demand Planning alerts are generated using macros and the forecast.

Dynamic alerts reflect the current planning situation but are not stored in the database. In contrast to the SNP alerts in APO 2.0, alerts are now macro-based and can therefore map data in liveCache. This alert type is NOT suitable for large alert quantities because a large number affects performance.

Database alerts show the planning situation as it was during the planning run or the last time the macro was executed. If you are using large data quantities, you should perform a batch planning run using the database macros. The planning run results show the situation as it was at the time of the planning run. This means that database alerts give a snapshot of the plan during runtime.

You can also use macros to create customer-specific dynamic alert types or database alert types. To generate alerts in the background, create a planning view using the corresponding default macro or use Demand Planning mass processing.

Maintain your alert profile if you want to monitor alerts from Supply and Demand Planning in APO. You use this profile to maintain a user-specific selection of alerts for your area of responsibility.


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Planning Books and Macros: Unit Summary


Create a planning book

Configure a user-defined planning view

Create macros


4.19Planning Books and Macros - Exercises

Unit: Planning Books and MacrosTopic: Planning Books


Create planning books and data views

1-1 Create a planning book called SALES## for your planning area PLAN##. Enter “SALES” as the planning book text, activate manual maintenance of proportional factors, navigation for promotion planning, and the three forecasting views.

Ensure that all the characteristics and key figures from your planning area are added into the planning book.

In the planning book, create a data view called DEMAND PLAN. Set its history horizon to 24 months and its future horizon to 12 months. The historical data for the last 24 months should be visible in interactive planning. Adopt all the key figures from the planning book into the data view. Complete the planning book.

1-2 In the planning book, change the description of the “Invoiced sales quantity” (INVQTY) key figure to ACTUAL SALES. Also create an auxiliary key figure called PREVIOUS YEAR SALES and assign it to the data view.

Change the sequence of the key figures in the data view by dragging PREVIOUS YEAR SALES and dropping it below the Invoiced sales qty key figure.

Auxiliary key figures are only used for displaying in the planning table. The data in this key figure is neither saved, aggregated, nor disaggregated.


1-3 Create a second data view called PROPORTION with the descriptive text MANUAL MAINTENANCE OF PROPORTIONAL FACTORS for your SALES## planning book. This view should allow proportional factors to be maintained in the APODPDANT key figure for the next 12 months.

1-4 Go to interactive Demand Planning, and select your DEMAND PLAN data view from the SALES## planning book.

1-5 In interactive planning, check your SALES## planning book with both data views. Are all the key figures and the data view horizon displayed correctly? Does the “Proportional factor” key figure only exist in the PROPORTION data view?Go to design mode and define the PREVIOUS YEAR SALES and PROMOTION key figures as output key figures.


Unit: Planning Books and MacrosTopic: Planning Books (Part 2)


Create planning books and data views

Define selections

2-1 Selections enable easy access to characteristic combinations that are required on a daily basis. Therefore, in your SALES## planning book in the DEMAND PLAN data view in interactive planning, create a selection called PRODUCT## for version 000 and create products P-102 to P-104. Create a second selection called COLL## for version 000, product P-102, and sold-to party 0000001032. Assign selection PRODUCT## to your selection profile. Load the data for product P-102. Is actual data displayed in the past?

2-2 Create a second planning book called COLL## with the description “Collaborative Demand Planning” for your planning area PLAN##. Assign the INTERNET CORRECTION (EXTRA##) and DEMAND PLAN (FINFOR) key figures and the APO - PRODUCT and SOLD-TO PARTY characteristics to the planning book. Create a data view called COLL## with the description “Collaborative Demand Planning”. This data view should have a planning horizon of three months and contain the key figure of the planning book.

2-3 Go to interactive planning, choose your COLL## planning book with data view COLL## and assign selection COLL## to your selection profile.


Unit: Planning Books and MacrosTopic: Macros

In this exercise, you will create three macros:

1. One macro for mapping sales from the past year

2. One macro for calculating the final demand plan using the sum from forecast and correction

3. One macro for generating alerts if the correction is too large.

3-1 In the DEMAND PLAN data view of the MacroBuilder, create a macro called PREVIOUS YEAR for your SALES## planning book. This macro copies the actual sales from the previous year into the current year’s auxiliary key figure: PREVIOUS YEAR SALES. When you configure the macro, make sure that it is run automatically whenever changes are made in the planning table.

3-2 Write a macro called DEMAND PLAN CALCULATION for your SALES## planning book in the DEMAND PLAN data view that will calculate the final demand plan from the sum of the forecast, promotion, correction, and Internet correction according to the following formula:FORECAST + PROMOTION + CORRECTION + INTERNET CORRECTION = DEMAND PLANConfigure the macro so that it is run automatically whenever a change is made in the planning table.

3-3 In the DEMAND PLAN data view, create a macro called ALERT for your SALES## planning book. This macro should generate an alert when the correction in one column is larger than the forecast.When you configure the macro, make sure that it is run automatically whenever changes are made in the planning table.

3-4 Check your macros in interactive planning for your SALES## planning book with the DEMAND PLAN data view. Double-click on selection PRODUCT##. Load data for product P-102. Also assign the “PUMP” SDP alert profile to your user in interactive planning to display your alerts

3-4-1 In the Forecast key figure, enter 100 PC for the current month. Is the demand plan calculated?

3-4-2 In the correction key figure, enter 200 PC for the current month. Has the demand plan been corrected? Refresh your alerts, is an alert generated?


Do not save. In the MacroBuilder, remove the ALERT macro from the default macros of your SALES## planning book and the DEMAND PLAN data view.


Macro Structure:

Macro:

Level 1: Macro

Level 2: Step

Level 3: Results (key figure)

Level 4: Key figure for calculation (create on next level)

Level 4: Operator/function (append)

Level 4: Another possible key figure (append)

Alert macro:

Level 1: Macro

Level 2: Step

Level 3: Control statement (create on next level)

Level 3: Condition (append)

Level 4: Key figure for the calculation (create on next level)

Level 4: Operator/function (append)

Level 4: Another possible key figure (append)

Level 3: Procedural message/alert (append to condition on level 3)

Level 3: Control statement (for example ENDIF or ELSE) (append to the control statement on level 3)

(Level 3: Delete: Alert)

4.20Planning Books and Macros - Solutions

Unit: Planning Books and MacrosTopic: Planning Books

1-1 Create a planning book called SALES## for your planning area PLAN##. Enter “SALES” as the planning book text, activate manual maintenance of proportional factors, navigation for promotion planning, and the three forecasting views.

Ensure that all the characteristics and key figures from your planning area are added into the planning book.

In the planning book, create a data view called DEMAND PLAN. Set its history horizon to 24 months and its future horizon to 12 months. The historical data for the last 24 months should be visible in interactive planning. Adopt all the key figures from the planning book into the data view. Complete the planning book.

Demand Planning Environment Current Settings Design Planning Books

Enter SALES## as your planning book and choose create

Enter SALES as your Planning book text, and PLAN## as your Planning area. Select “Manual prop. maint.” and set all four of the “Navigate to” views.

Choose “Continue” to go to the next tab page.

Choose the green “+” button (Add all key figures) to copy all the key figures from the planning area into the planning book.


Choose the green “+” button (Add all charact.) to copy all the characteristics from the planning area into the planning book.


Changes can only be made on the “Key fig. attributes” tab page once the planning book has been completed.



On the “Data view” tab page, enter Demand Plan as both the Data view and Data view descr. For the future Time Bucket profile (TB profile ID (future)), enter 12MONTH and for the TB profile ID (history), 24MONTH. Choose ENTER, select “Visible Fr.” and select the date from 24 months ago.


Choose the green “+” button (Add all key figures) to copy all the key figures from the planning area into the data view.

Choose “Complete” to save the planning book and data view.

Stay in the SDP: Interactive Planning - Initial Screen

1-2 In the planning book, change the description of the “Invoiced sales quantity” (INVQTY) key figure to ACTUAL SALES. Also create an auxiliary key figure called PREVIOUS YEAR SALES and assign it to the data view. Change the sequence of the key figures in the data view by dragging PREVIOUS YEAR SALES and dropping it below the Invoiced sales qty key figure.Edit your planning book and go to the “Key fig. attributes” tab page.Choose the Invoiced sales quantity key figure (INVQTY), select “Free txt”, and enter ACTUAL SALES. Save the settings. Enter PREVIOUS YEAR SALES in the key figure by overwriting the existing key figure and also write it in the free text box. Save the settings.Go to the last tab page (“Key figures”) and drag the PREVIOUS YEAR SALES key figure from the right-hand to the left-hand side. Use Drag&Drop to set the sequence of your key figures and choose “Complete”.

Stay in the SDP: Interactive Planning - Initial Screen

Auxiliary key figures are only used for displaying in the planning table. The data in this key figure is neither saved, aggregated, nor disaggregated.


1-3 Create a second data view called PROPORTION with the descriptive text MANUAL MAINTENANCE OF PROPORTIONAL FACTORS for your SALES## planning book. This view should allow proportional factors to be maintained in the APODPDANT key figure for the next 12 months.

Edit your planning book.

Select the Data view radio button, enter PROPORTION; and choose Create.

Go to the “Data view” tab page, and for the Data view description, enter MANUAL MAINTENANCE OF PROPORTIONAL FACTORS with a future Time Buckets profile of 12MONTH. Go to the last “Key figures” tab page and drag the “Proportional factor” key figure from the right-hand to the left-hand side of the book. Choose “Complete.”

Exit the SDP: Interactive Planning - Initial Screen.

1-4 Go to interactive Demand Planning, and select your DEMAND PLAN data view from the SALES## planning book. Demand Planning Planning Interactive Demand Planning

Choose the “Own data views only” icon from above the third selection window down

Expand your SALES## planning book and double-click on your DEMAND PLAN data view.

Stay in interactive planning.

1-5 Check your planning book SALES## with both data views in interactive planning. Are all the key figures and the data view horizon displayed correctly? Does the “Proportional factor” key figure only exist in the PROPORTION data view?Go to design mode and define the PREVIOUS YEAR SALES and PROMOTION key figures as output key figures.

Double-click on each of your data views in turn. Are all of your key figures displayed correctly?

You open Design mode by choosing “Design.” Right click on the LAST YEAR’S SALES, and PROMOTION key figures, and choose Selected Rows Output only. The rows are grayed out.

Choose the “live” button to return to interactive planning and save everything. They are only colored after data selection.

Unit: Planning Books and MacrosTopic: Planning Books (Part 2)


2-1 Selections enable easy access to characteristic combinations that are required on a daily basis. Therefore, in your SALES## planning book in the DEMAND PLAN data view in interactive planning, create a selection called PRODUCT## for version 000 and create products P-102 to P-104. Create a second selection called COLL## for version 000, product P-102, and sold-to party 0000001032. Assign selection PRODUCT## to your selection profile. Load the data for product P-102. Is actual data displayed in the past?

Choose the “Selection window” icon and in “Show”, choose APO - Product. In the lower section you can now store conditions for the products. Version 000 is the default version.

In the next row, again choose APO- Product and use the multiple selection button to enter the three products on the right-hand side.

Choose the “Save selection” icon and write “PRODUCT##” as the Selection description. Save. Adopt.

Choose the “Selection window” icon once again. Only define product P-102 (delete multiple selection) and sold-to party 0000001032 and save the selection as COLL##.

To enable easy access to this selection, assign the selection to the selection profile by choosing the “Selection profile” header bar. A dialog box appears in which you can drag your selection from the right-hand side into your folder. Save.

To load the data, first double-click on selection PRODUCT## and then on product P-102. Go to past dates in the table and make sure that historical data from the InfoCube is displayed in the ACTUAL SALES key figure. Exit interactive planning.

2-2 Create a second planning book called COLL## with the description “Collaborative Demand Planning” for your planning area PLAN##. Assign the INTERNET CORRECTION (EXTRA##) and DEMAND PLAN (FINFOR) key figures and the APO - PRODUCT and SOLD-TO PARTY characteristics to the planning book. Create a data view called COLL## with the description “Collaborative Demand Planning”. This data view should have a planning horizon of three months and contain the key figure of the planning book.Demand Planning Environment Current Settings Design Planning Books

Enter COLL## as your planning book and choose Create

Enter “Collaborative Demand Planning” as your planning book text and PLAN## as your Planning area.


Drag and drop the INTERNET CORRECTION (EXTRA##)and DEMAND PLAN (FINFOR) key figures from the planning area to the planning book.


Drag and drop the APO - PRODUCT and SOLD-TO PARTY characteristics from the planning area to the planning book.



Changes can only be made on the “Key fig. attributes” tab page once the planning book has been completed.


In the “Data view” tab page, enter COLL## as the data view and “Collaborative Demand Planning” as the data view description. Specify 3MONTH as the future time buckets profile.


Choose the green “+” button (Add all key figures) to copy all the key figures from the planning area into the data view.

Choose “Complete” to save the planning book and data view.

2-3 Go to interactive planning, choose your COLL## planning book with data view COLL## and assign selection COLL## to your selection profile.


Unit: Planning Books and MacrosTopic: Macros

In this exercise, you will create three macros:

1. One macro for mapping sales from the past year

2. One macro for calculating the final demand plan using the sum from forecast and correction

3. One macro for generating alerts if the correction is too large.

3-1 In the DEMAND PLAN data view, create a macro called LAST YEAR for your SALES## planning book. This macro copies last year’s actual sales into the current year’s auxiliary key figure PREVIOUS YEAR SALES. When you configure the macro, make sure that it is run automatically whenever changes are made in the planning table.Demand Planning Environment Current Settings MacroBuilder

Enter the Planning book and data view.

Drag the macro from the Elements window in the top left-hand corner and drop it over macros in the central window. In the dialog box, enter LAST YEAR as the name of the macro. Continue.

Drag the step from the Elements window and drop it over your new macro in the central window. In the descriptive text section of the dialog box, enter STEP 1 and reduce the Processing area to the past 12 months. Continue.

Drag a planning table row from the Elements window and drop it over your Step 1 in the central window. In the Attributes of results row section of the dialog box, select PREVIOUS YEAR SALES from the possible entries and define the current month as the column calculation start. Continue.

Drag a planning table row from the Elements window and drop it over your results row in the central window. Choose “Create in next level.” Select ACTUAL SALES from the possible entries as the row in the dialog box. Continue.

Select your macro and first choose Check, and then Generate

Drag the macro from the central window, and drop it over DEFAULT in the Events window, in the top right-hand corner of the screen.

Stay in the MacroBuilder


3-2 Write a macro called DEMAND PLAN CALCULATION for your SALES## planning book in the DEMAND PLAN data view that will calculate the final demand plan from the sum of the forecast, promotion, correction, and Internet correction according to the following formula:DEMAND PLAN = FORECAST + PROMOTION + CORRECTION + INTERNET CORRECTIONConfigure the macro so that it is run automatically whenever a change is made in the planning table.

Drag the macro from the Elements window in the top left-hand corner and drop it onto macros in the central window. In the dialog box, enter DEMAND PLAN CALCULATION as the name of the macro. Continue.

Drag the step from the Elements window and drop it over your new macro in the central window. In the descriptive text section of the dialog box, enter STEP 1 and reduce the Processing area to the next 12 months. Continue.

Drag a planning table row from the Elements window and drop it over your Step 1 in the central window. Select Final forecast from possible entries as the row in the dialog box. Continue.

Drag a planning table row from the Elements window and drop it over your results row in the central window. Choose “Create in next level.” In the dialog box, select Forecast from the possible entries as the row. Continue.

Drag the Operator/function from the Elements window and drop it over your FORECAST row in the central window. Use “Append.” Operator “+” is selected already, continue.

Repeat these steps for the key figures: PROMOTION + CORRECTION + INTERNET CORRECTION.Select your macro and first choose Check, and then Generate


Stay in the MacroBuilder


3-3 In the DEMAND PLAN data view, create a macro called ALERT for your SALES## planning book. This macro should generate an alert when the correction in one column is larger than the forecast.When you configure the macro, make sure that it is run automatically whenever changes are made in the planning table.

Drag the macro from the Elements window in the top left-hand corner and drop it onto macros in the central window. In the Descriptive text section of the dialog box, enter ALERT. Continue.

Drag the step from the Elements window and drop it over your new macro in the central window. In the Descriptive text section of the dialog box, enter STEP 1. Continue.

Drag a Control statement from the Elements window and drop it over your Step 1 in the central window. Choose “Create in next level.” The “IF” condition is selected already, continue.

Drag a Condition from the Elements window and drop it over your Control statement in the central window. Choose “Append.” In the dialog box, enter “Correction > Forecast” as the text. Continue.

Drag a planning table row from the Elements window and drop it over your results row in the central window. Choose “Create in next level.” Select Correction from the possible entries as the row in the dialog box. Continue.

Drag the Operator/function from the Elements window and drop it over your Correction row in the central window. Choose “Append.” Choose Operator “>,” continue.

Drag a planning table row from the Elements window and drop it over your Operator in the central window. Choose “Append.” In the dialog box, select Forecast as the row. Continue.

Drag the Alert/status from the Elements window and drop it over your condition in the central window. Choose “Append.” In the dialog box, enter “Correction > Forecast” as the text. Continue.

Drag a Control statement from the Elements window and drop it over your Alert in the central window. Choose “Append,” and choose “ENDIF” as reference, continue.

Select your macro and first choose Check, and then Generate


Save and leave the MacroBuilder


3-4 Check your macros in interactive planning for your SALES## planning book with the DEMAND PLAN data view. Double-click on selection PRODUCT##. Load data for product P-102. Also assign the “PUMP” SDP alert profile to your user in interactive planning to display your alerts

3-4-1 In the Forecast key figure, enter 100 PC for the current month. Is the demand plan calculated?

3-4-2 In the correction key figure, enter 200 PC for the current month. Has the demand plan been corrected? Refresh your alerts, is an alert generated?

Do not save. In the MacroBuilder, remove the ALERT macro from the default macros of your SALES## planning book and the DEMAND PLAN data view.

Demand Planning Planning Interactive Demand Planning

Double-click on the DEMAND PLAN data view for your SALES## planning book.

To load the data, first double-click on selection PRODUCT## and then on product P-102.

Also assign the “PUMP” SDP alert profile to your user in interactive planning so that your alerts will be displayed:

Settings Assign alert profile

Do not save. Exit interactive planning


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Contents:

Selection

Navigation within the planning table

Interactive maintenance of proportional factors

Value fixing

Collaborative planning

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At the conclusion of this unit, you will be able to:

Interactive Planning: Unit Objectives

Create selection variants

Navigate within the planning table

Maintain planning data at different levels and list the different disaggregation options

Describe how proportional factors are used

Access your planning information from the Internet

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Interactive Planning: Overview Diagram

44

33


55

66

77

8899

Configuration



Forecasting


Mass Processing

Conclusion

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Interactive Planning: Business Scenario

Now the planning table layout has been configured in the planning book, one of the Precision Pump company's demand planners wants to explore the different ways of entering and analyzing data.

Discussions are also taking place about which customers or international subsidiaries would benefit from entering data over the Internet.

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The Planning Table Selection Area


ID Object TextText 1Text 2Text 3Text 4


Selection ID

Planning book/data view

Macros

Navigate between the following areas: InfoObjects, data views, the macro area, and overviewOpen the shuffler. You use the shuffler to make andsave new selections

Show dependent objects; for example, all products in a brand

Select all the characteristic values in the InfoObjects area

Deselect all characteristic values in the InfoObjects area

Return to the previous selection

Redo the last selectionSpecify the format in which you want the characteristic valuesin the InfoObjects area to be displayed

Display current selection

Hide selection area (switch shuffler on/off)

The shuffler is the window in which you select the InfoObjects you want to plan. You choose objects that meet certain conditions from the dropdown boxes in the object selection dialog box (show... that meet the following conditions...).

The selection profile shows the selection IDs being used by the current planner. The demand planner can use this selection profile to quickly access frequently used selections. To add selection IDs to the selection profile, click on Selection profile.

You select your planning books and data views in the Planning book/data view area. If the planner can see all available planning books, he or she has access to planning books used for both Supply Network Planning and Demand Planning.

The Macro (component) area shows macros that are active for all data views in this planning book and for this data view.

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The Planning Table Work Area


Design Capacity LevelingGraph

APO - Product Total APO - Product TotalID Object Text

Text 1Text 2Text 3Text 4


Selection ID

Planning book/data view

Macros

Selected objects Header information

Key figure 1



W 24 W 25 W 26 W 28W 27Load data into the table for the current selectionSwitch between live and design mode for the planning book

Save graphical settings

Access the distribution function

Save the data currently visible in the table to an Excel file

Send the data currently visible in the table to another user

Show/hide/refresh alerts

Open the Alert Monitor and notes management

Maintain/show a note relating to a point in the graphic

Show one key figure, some key figures, or all key figures

The work area is the main display and planning area. It is situated on the right-hand side of the screen and consists of a table and a graphic. Only the table is displayed by default.

To display the data of selected objects in the work area, choose the selected object (by double-clicking on it). If you have selected more than one object, choose Load data.

You use the magnifying glass icon to choose which planning view key figures are to be displayed in the work area.

If the system is connected to a mail server, you have the option of sending plans internally or externally by e-mail. The system automatically creates a Microsoft Excel attachment that contains the plan.

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1. Select your characteristic values and planning version.

2. Load the data for one or more characteristic values.

3. The key figures are aggregated anddisplayed for the characteristicvalues contained in your selection.

4. Create or change your plan.

5. The key figures are disaggregatedautomatically according to the characteristic combinations.

Planning

Aggr

egat

ion

Dis

aggr

egat

ion

Aggregation and Disaggregation

You choose the data you want to plan by selecting characteristic values and the version. Once you have loaded the data, an aggregated view of the selected data is displayed in the interactive

planning screen. When you save the data, it is automatically disaggregated and stored at detailed level. To store selections (characteristic values and versions) that are used on a regular basis, you define

selection variants. These variants can be placed in folders. A delete function is also available for deleting selection variants.

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Current selection:

Customer

Region

Product

Region

Customer

Product

Drilldown in Interactive Planning

Aggregation and disaggregation within Demand Planning enable "consistent" planning. The drill-up and drilldown options in the selection tree enable you to navigate through the characteristic combinations and display the associated key figure values.

You can drill down or drill up through one or multiple levels and in any sequence, the process is not hierarchical.

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Choose key figure

Navigation Within the Work Area

Product Total Location Total

TotalDetails (all)

P-100P-101P-102

TotalDetails (all)

DC 2400DC 2500

Header information

Title view Right mouse button:Switch columns/rowsSynchronize table/graphicUnits of measureGraphicPivot sorting

Shows an overview of all

the details

Shows an overview of all

the details

Shows the details in sequence

Shows the details in sequence

To set header information from the planning table, choose Settings -> Header Information. You can display your characteristic values in sequence using the arrows in the header information. The

total shows you the total of your data in the work area. If you choose Details (all), you are given an overview of all the members in your work area.

You can use Switch columns/rows to arrange the periods vertically. Synchronize table/graphic is used to arrange the same periods below one another.

Pivot sorting allows the sequence of key figures and characteristic values to be set in the all details view.

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Changing Proportional Factors Interactively

Product family

Product A Product B

Plant A Plant B

70 % 30 %

50 %50 %

Version-dependent

Period-specific (optional)

Stored in key figure: APODPDANT

Maintained in planning table

You have to specify the version, key figure, and horizon for automatic calculation. You also have to specify for which version and horizon the proportional factors are to be calculated. If you choose "Calculate detailed proportions," the systems uses the proportions from June 98 to

calculate the proportions for June 99, and so on. You can also maintain the proportional factors in the planning table.


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Switch columns/rowsSynchronize table/graphicChange units of measurePivot sorting

Lock entries

Basic Functions of the Planning Table

Distribute functions

Row totals

Total

OperatorOP Short text

=+-*/

%...

Replace time series values by valueAdd value to time seriesSubtract value from time seriesMultiply time series values by valueDivide time series values by valueValue as a percentage of the time series...

By right-clicking on the cell on the top left of the planning table, you can switch columns with rows, synchronize the table and graphic, change units of measurement, and perform pivot sorting

A wide range of distribution functions is available for fast data entry. When you start to plan, the selected data is locked. Other planners can only use other characteristic

values and versions from the same InfoCube at this time. You can insert a column for row totals in interactive planning or in the planning book.


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Demand plan

Corr. forecast

Actual sales

Forecast

UN M 08.1999 M 09.1999

CAR

CAR

CAR

100 100

150 150

150 1301. Right mouse

button Display note(Forecast/199908)

No note exists

Display administration information

Notes in Demand Planning

Notes management Entering a note

Note navigation

2. Enter your note here

You use notes to explain the reasons for an occurrence, either for your own benefit or for other demand planners; for example, the reasons why a demand forecast is particularly high or low in a certain time period at a certain level.

You use notes navigation to drill down from a higher level to a note at a lower level. You use this option when you are working at a higher level as a demand planner (for example, at regional level) and want to display the explanations for a forecast that was created by another planner at a lower level (for example, at product level).


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Planned total sales: Changedto 30,000 pieces

Planned total sales:20,000 pieces

5,000(25%)

6,000(30%)

3,000(15%)3,000

(15%)

3,000(15%)

9,545(32%)

11,454(38%)

3,000(10%)

3,000(10%)

3,000(10%)

Value Fixing

You can fix the value of a key figure in interactive planning before running the forecast. Once fixed, this value will not change when you change other values of this key figure at other planning levels. In cases where a change to the sum of the detail values conflicts with individual detail values, the individual detail values take precedence.

Prerequisites for fixing:Prerequisite 1: You have created an APO key figure in the Administrator Workbench and assigned it a fixed key figure. Prerequisite 2: If you want to fix values at aggregated levels, you must define an aggregate for this level in your planning object structure.

To fix a key figure value, right-click on the cell of the relevant key figure value. The color changes to red and a padlock icon appears, indicating that this value is now fixed.

To undo the fixing of a key figure value, right-click once again on the cell.


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Internet Transaction Server

Collaborative Planning

Planning area

Own company Customer

Demand plan

Customer overrides

Internet Explorer

Choice of:

Data views

Selections

Macros

APO

Time series

liveCache

Collaborative planning prerequisites: Set up an ITS server for your system and publish the CLP* Services. In table TWPURLSVR in the APO system, maintain the WEB_SERVER for the logical system; for

example, "IGOTO-800.WDF.SAP-AG.DE:1080" and the WEB_PROTCL "HTTP." The following menu path takes you into the Internet and the relevant Internet address: Demand

Planning->Planning->Collaborative Demand Planning. You inform the user of this. Planning prerequisites:

You have assigned a planning book and authorizations to the user You have assigned selection variants to the user You have configured the planning table header information for the user, if drilldowns are required

To enter data over the Internet, choose the change button at the bottom right of the planning table.


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Interactive Planning: Unit Summary


Create selection variants

Navigate within the planning table

Maintain planning data on different levels and list the different disaggregation options

Describe how proportional factors are used

Access your planning information from the Internet


5.16Interactive Planning - Exercises

Unit: Interactive PlanningTopic: Planning, Disaggregation, Header Information

The aim of this exercise is for you to familiarize yourself with interactive planning. The data that you enter here will not be used in any of the later exercises.

This exercise is subdivided into the following sections:

1. Checking disaggregation and how the header information is configured

2. Fixing key figure values

3. Collaborative planning

1-1 You previously calculated the proportional factors based on actual data in the DP Configuration unit. Select your PROPORTION data view and load the data for product P-102. In the header information, select the LOCATION characteristic and drill down to all the location details. Go to the percentages display.

In the third month, change the calculated percentage proportion for location 2400 to 50%. Note down the percentages for the second and third month. Save.

Location Month 2 Month 3

Proportional factor

Total 100 100

1000

2400 50

2500


1-2 Select your DEMAND PLAN data view and load the data for product P-102. Choose only the FORECAST key figure in the planning table. Plan 1000 pieces each month for the next five months using the distribution operator and save. Use the header information to drill down to all the location details. Were your changed proportional factors used for disaggregation in the third month? Why does distribution not exactly reflect the proportional factors?

Rounding in Demand Planning: In the planning area, if you use a base unit of measure that has no decimal places defined, quantities are only stored in integer values. If you enter quantities at aggregated level, these quantities are first disaggregated by time (from months down to weeks, for example) and then by characteristic combinations. This can lead to deviations from the calculated proportional factors during disaggregation, especially for small quantities (due to rounding errors). You will have fewer rounding errors if you only work with one time buckets profile or one base unit of measure with decimal places.

1-3 Reduce the quantity for location 2400 in the third month to 300. Does the total change? Increase the total in the third month back to 1000. Which ratio is used for disaggregation? What do you have to do to disaggregate by the original proportional factors? Drill up.

Which sold-to parties buy product P-102? Drill down to all the sold-to party details.

Which locations supply the sold-to parties? Use pivot sorting to display first the locations and then the sold-to parties.

Drill up twice to return to product level.

Show the row totals for the future columns.

1-4 Use the distribution operator to plan 2000 pieces for product P-103 and 3000 pieces for product P-104 each month for the next five months and save. Load all three products (P-102 to P-104) and drill down by product. Increase the total in the third month back to 4000. The individual product quantities are reduced accordingly.

Unit: Interactive PlanningTopic: Fixing Key Figure Values

2-1 You can only fix APO key figures. In our example, the CORRECTION key figure is an APO key figure. Show the Correction key figure, load product P-102, and drill down by location.Enter 1000 pieces as the total in the third month and fix the detail value for location 2400. Change


the 1000 pieces at total level to 1200 pieces and check that the fixed value has not been changed.

Return to the totals display (drill up) and enter a comment for the corrected value.

In this exercise, the fixing cannot be saved because an aggregate has not been defined for product-location level.


Unit: Interactive PlanningTopic: Collaborative Planning

3-1 Access Collaborative Demand Planning and log on to the system. Choose your planning book COLL##, with data view COLL##, and selection COLL##. Go to change mode. In the INTERNET CORRECTION key figure, enter 10 pieces each month for the next three months for product P-102. Choose Enter and save your entries.

3-2 Go to interactive planning in the Internet, check your entries for your SALES## planning book with the DEMAND PLAN data view. Double-click on selection PRODUCT##. Load the data for product P-102. Drill down by SOLD-TO PARTY. Why do you find planning quantities of 10 pieces each month for the next three months for sold-to party 0000001032?


5.17Interactive Planning - Solutions

Unit: Interactive PlanningTopic: Planning, disaggregation, header information

The aim of this exercise is for you to explore the functions of interactive planning. The data that you enter here will not be used in any of the later exercises.

This exercise is subdivided into the following sections:

1. Checking disaggregation and how the header information is configured

2. Fixing key figure values

3. Collaborative planning

1-1 You previously calculated the proportional factors based on actual data in the DP Configuration unit. Select your PROPORTION data view and load the data for product P-102. In the header information, select the LOCATION characteristic and drill down to all the location details. Go to the percentages display.

In the third month, change the calculated percentage proportion for location 2400 to 50%. Note down the percentages for the second and third month. Save.

Location Month 2 Month 3

Proportional factor

Total 100 100

1000

2400 50

2500


Double-click on the PROPORTION data view for your SALES## planning book. Load the data for product P-102.

Choose the “Header on/off” icon and an icon appears over the planning table for setting header information. Choose the LOCATION characteristic. Adopt. A navigation bar is now displayed for you to choose the individual characteristic values, the total characteristic


values, or an overview (Details (all)) of the characteristic values. Drill down by LOCATION by going to the LOCATION header information and choosing “Details (all)”.

Change the data display from absolute to percentage by choosing the double Sum icon above the planning table.

Change the proportion for location 2400 in the third month to 50%.

Save. (If the Save icon is grayed out, go to the data view, and you will receive a system prompt asking if you want to save)

Stay in interactive planning.

1-2 Select your DEMAND PLAN data view and load the data for product P-102. Choose only the FORECAST key figure in the planning table. Plan 1000 pieces each month for the next five months using the distribution operator and save. Use the header information to drill down to all the location details. Were your changed proportional factors used for disaggregation in the third month? Why does distribution not exactly reflect the proportional factors?

Double-click on the DEMAND PLAN data view for your SALES## planning book.

Load the data for product P-102.

Choose the magnifying glass icon (key figure selection) that is above the planning table and choose the Forecast key figure.

Select the next five periods by holding down the left-hand mouse button and dragging it over the column header. Choose the “Distribute” icon (the calculator icon), check the horizon, enter 1000 pieces for the Forecast key figure and choose Operator “+ or =”. Choose “Distribute” (the green checkmark icon). The changed proportional factors used for disaggregation in the third month were used with rounding errors. Stay in drilldown.

Rounding in Demand Planning: In the planning area, if you use a base unit of measure that has no decimal places defined, quantities are only stored in integer values. If you enter quantities at aggregated level, these quantities are first disaggregated by time (from months down to weeks, for example) and then by characteristic combinations. This can lead to deviations from the calculated proportional factors during disaggregation, especially for small quantities (due to rounding errors). You will have less rounding errors if you only work with one time buckets profile or one base unit of measure with decimal places.

1-3 Reduce the quantity for location 2400 in the third month to 300. Does the total change? Increase the total in the third month back to 1000. Which ratio is used for disaggregation? What do you have to do to disaggregate by the original proportional factors? Drill up.

Which sold-to parties buy product P-102? Drill down to all the sold-to party details.

Which locations supply the sold-to parties? Now drill down by location. Use pivot sorting to display first the locations and then the sold-to parties.


Drill up twice to return to product level.Show the row totals for the future columns.

Reduce the quantity for location 2400 in the third month to 300. The total is also reduced.

Increase the total in the third month back to 1000. The members are disaggregated by the member ratio.

To disaggregate by the original proportional factors, you have to delete the total and reenter them or use rule P for the key figure in the planning area. To drill up, set the LOCATION characteristic in the navigation bar to Total.

To find the sold-to party for product P-102, select the product, choose the “Display dependent objects” icon, and choose Sold-to party or drill down by sold-to party.

Now drill down by location.

Right-click on the title field on the upper left-hand side of the planning table and choose Pivot sorting. In the dialog box, drag Location so that it is above Sold-to party. Continue.

To find the row totals go to Settings Row totals Future

1-4 Use the distribution operator to plan 2000 pieces for product P-103 and 3000 pieces for product P-104 each month for the next five months and save. Load all three products (P-102 to P-104) and drill down by product. Increase the total in the third month back to 4000. The individual product quantities are reduced accordingly.

Load the data for products P-103 and P-104 in turn.

Select the next five periods by holding down the left-hand mouse button and dragging it over the column header. Choose the “Distribute” button, check the horizon, enter the quantity for the Forecast key figure, and choose Operator “+”. Choose “Distribute” (the green checkmark icon).

Select the three products by choosing the “Select all” icon. Now choose the “Load data” icon (the open folder icon).

Stay in interactive planning for the next exercise


Unit: Interactive PlanningTopic: Fixing key figure values

2-1 You can only fix APO key figures. In our example, only the CORRECTION key figure is an APO key figure. Load product P-102, show the Correction key figure, and drill down by location.Enter 1000 pieces as the total in the third month and fix the detail value for location 2400. Change the 1000 pieces at total level to 1200 pieces and check that the fixed value has not been changed.

Return to the totals display (drill up) and enter a comment for the corrected value.

To fix values in the planning table or to enter a note, right-click on the appropriate field.

Use the right-hand mouse button to fix the detailed value

Execute a drill up according to product by choosing “Total” in the product header information.

Use the right-hand mouse button to enter a note for the corrected value.

Save and exit interactive planning

In this exercise, the fixing cannot be saved because an aggregate has not been defined for product-location level.


Unit: Interactive PlanningTopic: Collaborative planning

3-1 Access Collaborative Demand Planning and log on to the system. Choose your planning book COLL##, with data view COLL##, and selection COLL##. Go to change mode. In the INTERNET CORRECTION key figure, enter 10 pieces each month for the next three months for product P-102. Choose Enter and save your entries.

Demand Planning Planning Collaborative Demand Planning

Log on to the system with your APO user.

Choose your planning book COLL##, with data view COLL##, and selection COLL## and select “Choose” on the right-hand side.

Go to change mode by choosing the pencil icon on the far right of the screen.

Exit collaborative planning

3-2 Go to interactive planning in the Internet, check your entries for your SALES## planning book with the DEMAND PLAN data view. Double-click on selection PRODUCT##. Load the data for product P-102. Drill down by SOLD-TO PARTY. Why do you find planning quantities of 10 pieces each month for the next three months for sold-to party 0000001032?


Double-click on the DEMAND PLAN data view for your SALES## planning book. Load the data for product P-102.

The icon for setting header information is located above the planning table. Choose the SOLD-TO PARTY characteristic. Adopt. A navigation bar is now displayed for you to choose the individual characteristic values, the total characteristic values, or an overview (Details all) of the characteristic values. Execute a drill down according to sold-to party. You find planning quantities of 10 pieces each month for the next three months for sold-to party 0000001032 because you only included this sold-to party in the selection for collaborative planning.


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Forecasting

Contents:

Univariate forecasting

Multiple linear regression (MLR) and causal analysis

Composite forecasting

Consensus-based forecasting

Forecast profiles

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Forecasting: Unit Objectives

At the conclusion of this unit, you will be able to:Define forecast profiles with control parameters and forecast errors

Describe the differences between the various forecasting methods and models for univariateforecasting, causal analysis, and composite forecasting

Execute forecasts in the APO system

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Forecasting: Overview Diagram

44

33


55

66

77

8899

Configuration



Forecasting


Mass Processing

Conclusion

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Forecasting: Business Scenario

The Precision Pump company first has to investigate the forecasting techniques and decide which it wants to use.

The demand planner then analyzes the various forecasting models interactively for his characteristic value combinations and configures the forecast profiles for mass processing.

Mass processing is then used to run the actual forecast periodically. Forecast alerts trigger revision of the forecast.

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Past Future

Customer level

Product level

100 pieces 50 pieces

150 pieces

ForecastForecast

180 pieces

120 pieces 60 pieces

Disaggregation by proportional factors

Automatic Aggregation of Historical Data

If you make the forecast at a high level, the historical data is automatically aggregated up to this level. For this reason, the forecast results of a forecast run at a high level (with the results then disaggregated

to detailed level) differ from the results of a forecast run at detailed level. If you run the forecast in the background, you define the aggregation level in the mass processing job. When designing your planning process, you need to consider the aggregation level that would be most

useful for you to forecast; for example, do you want to forecast individual products, product families, or customer-specific demands?

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Assigning Products to Forecast Profiles

Characteristic combinations: Master forecast profile:

A productsA products

B products

C products

Profile A

Profile B

Profile CProfile C

Set interactivelyand use in mass

processing

Set interactivelyand use in mass

processing

Usually, the forecasting methods used for planning important products (A products) are more detailed than those used for less important products (B and C products).

There are various ways of assigning characteristic combinations: You configure your master forecast profile interactively and assign selection IDs to it in mass

processing. You assign the characteristic combinations interactively, store them in the system, and use them in

mass processing. You set it so that the system creates a forecast profile with a unique name when you save the

Demand Planning data for a selection ID. This has the following advantages:The next time you work with this selection ID, the system automatically uses the uniquely named forecast profile to create the forecast (by default).If you make changes to the forecast configuration (for example, by switching from a seasonal model to a seasonal trend model), the system retains the new settings without overwriting the original forecast profile. If you do not set this indicator, the original forecast profile is overwritten by the new settings when you save the interactive planning data.

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Forecast profile

Univariate profile

MLR profile

Composite profile

Master Forecast Profile

Planning area assignment

Definition of the key figure to be forecast

Definition of the past and forecast horizons

Procedural specification for the following forecasting types:

Univariate forecasting

Multiple linear regression


The planner defines a number of different master forecast profiles interactively based on historical data showing constant, trend, or seasonal patterns.

If you also want to forecast different key figures, you need master profiles for each of the key figures. The period indicator defines the time buckets profile for the forecast. This period indicator must be a

time characteristic that has actual data Material forecast: This is relevant for lifecycle planning and/or like modeling. Forecast horizon: The start and end dates of the time period for which you want to create the forecast.

Enter a start date with either an end date or number of periods. If you do not enter a start date, you must enter a number of periods; the system then uses the current date as the start of the planning horizon.

History horizon: The start and end dates of the past period whose actual data is to be used for creating the forecast.

You assign a univariate forecast profile and/or an MLR profile and/or a composite profile to the master forecast profile.

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Forecast

Statistical Forecasting Tools

Univariate forecasting models

Causal analysis


SAP AG 2002

The product spectrum of a company includes a variety of products in different stages of their life cycle, all with different demand types. There is no such thing as a forecasting model that creates 100 percent accurate statistical forecasts for both mature slow-moving products and new products. Approaches that attempt to cover the majority of such demand types are very complex and tend to be a "black box" for the planner. SAP APO Demand Planning offers a "toolbox" of practical and proven forecasting methods. The planner can then choose the best method for a specific demand type.

Univariate forecasting models are models that investigate historical data according to constant, trend, and seasonal patterns, and issue forecast errors accordingly.

Causal analysis is based on causal factors such as prices, budgets, and campaigns. The system uses multiple linear regression (MLR) to calculate the influence of causal factors on past sales, which enables you to analyze the success of specific actions. The calculated connection between causal factors and past sales is then used as a basis for modeling future actions.

Composite forecasting is used to weight multiple forecasts and then gather all the information in one final forecast.

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Correctedhistory

ForecastForecast

Corrected forecast

Use as demand planUse as demand plan

The Forecasting Process Flow

Readactual data

Actual data can be adjusted automatically and manually

Analyzing historical data helps to predict future patterns

Uses proven forecasting models

Models can be selected automatically

Historical data and forecast results can be monitored graphically and corrected

Forecasting is a tool for predicting future activity based on specific criteria. The system reads historical data and calculates corresponding values that it then proposes as future data.

You can create statistical forecasts for any key figure (sales revenue, for example) in any version. The forecast can be calculated using actual data or corrected historical data. The Corrected forecast key figure only contains different data from the Forecast key figure if you use

the workday adjustment function. The corrected forecast can only be used for univariate forecast models. Usually, you make manual corrections to the statistical forecast in your own key figure. If you do not have any historical data for a product (because it is new, for instance), you can base your

forecast on the actual data of a like (similar) product. To do this, you define a like profile in the product master record.

If you want to correct your past and future data, you must define (in Customizing) the key figures that you want to contain the corrected history and corrected forecast. You must also include rows for these key figures in your planning book.

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Adjusting Actual Data

Actual sales key figure

Corrected history key figure

Life cycle

Time

SalesCorrectionCorrection

Delivery problemsSales at a trade fair

To generate more exact forecasts, you must remove the impact of one-time promotions or delivery problems from the actual data. These adjustments are usually made in the Corrected history key figure, rather than the original key figure.

In the forecast profile, you can define whether you want the forecast to be based on original actual data or on corrected actual data.

Automatic adjustment of corrected history has the following uses: If no forecasts have been made for a long time, you can use automatic adjustment of corrected history

to base your forecast on the Corrected history key figure. For periods where there is no corrected history in the database, the system uses the original history. It is July 1 and you have not created any forecasts over the past two months. The corrected history

from two months ago contains manual corrections that you do not want to lose. Therefore, your corrected history does not yet contain values for May or June.

If the system were to base the forecast on these historical values, the forecast results would be inaccurate because of the zero values from May and June.

To prevent such inaccuracies, you set the Automatic adjustment of corrected history key figure. The system then uses the original historical values for May and June.


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1. Phase-in/phase-out profile

2. Workday adjustment

3. Past promotions

4. Outlier correction

5. Manual adjustments


Original actual dataOriginal actual data

Corrected historyCorrected history

You can use various automatic methods to adjust actual data. They can be activated from the forecast profile.

The methods are executed in the sequence given in the above slide. The phase-in and out profiles for lifecycle planning control the phasing in (introduction) of new

products and the phasing out (discontinuation) of old products Workday adjustment ensures that higher values are forecast for periods that have many workdays.

Historical data must be standardized for this With promotion planning, you can extract past actions (special offers, for example) from the actual

data so they are not included in the forecast You can use outlier correction to automatically correct actual data that is outside of the tolerance

range You can also adjust the actual data manually

You can also assign the following elements to a key figure that is to be forecast: A key figure for storing the corrected history A key figure for storing the corrected forecast A key figure for storing ex-post forecasts A key figure for storing ex-post forecasts for the MLR


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Univariate Forecasting Models

Support constant, trend, and seasonal patterns

Available tools:

Manual forecasting

Moving average

Simple linear regression



The Holt-Winters’ method

The Croston method

Forecast

Univariate models are often referred to as time series models. Time series models develop forecasts by assessing the patterns and trends of past sales. Therefore, the

key determinant in the selection of a time series model is the pattern of previous sales data. The general assumption is that future sales will mimic past sales. Past sales patterns are identified and reproduced in the forecast. Once the pattern is identified, the forecaster can select the time series model that is best suited to that particular pattern. For example, if past sales have had seasonal influences (for example, sales are consistently highest in October and April), a forecasting model that compensates for seasonality should be used (which would be Winters' classical decomposition method). If past sales have small fluctuations and no major pattern or trend, then some type of smoothing model (moving average or exponential smoothing) might be best.

The one feature common to all these time series methods is that they are endogenous. This means that a time series model only considers the pattern of previous actual sales (or a 'series' of sales over a certain time period, hence the term time series). If these patterns can be identified and projected into the future, they can be used to create a forecast. Time series models are the most commonly used forecasting methods.


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ConstantMoving averageWeighted moving averageTrendSeasonalTrend and seasonalCopy historyCroston

Test for trendTest for seasonTest for trend and season

Forecasting model

Model selection

Manual

Automatic

Automatic Model Selection

Automatic model selection: You can allow the system to select the most suitable forecast model. To make its selection, the system analyzes historical data. If the system cannot detect any clear time series patterns in the historical data, it automatically selects the constant model.

Procedure 1: If you want the system to select the forecast model, you can choose between various statistical tests and test combinations to determine the model. If you choose procedure 1, you have to set a forecast strategy between 50 and 55 in the univariate forecast profile. The historical data governs the strategy you choose and the test made by the system. In the trend test, the system performs a regression analysis of the historical values and checks if there

is a significant trend pattern. In the seasonal test, the system clears the historical values of any possible trends and then performs an

autocorrelation test. Procedure 2: The system calculates the models to be tested using various combinations of alpha, beta,

and gamma. The smoothing factors are also varied between 0.1 and 0.5 in intervals of 0.1. The system then chooses the model displaying the lowest mean absolute deviation (MAD). Procedure 2 is more precise than procedure 1, but takes much longer. If you want to use procedure 2, you set forecast strategy 56 in the time series forecast profile (univariate profile).


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Forecast results based on constant model

Profile 1: Assuming a constant modelStandard

light bulbs

Profile 2: Assuming a seasonal modelChristmas

lights

Forecast results based on a seasonal model

Christmas

Example of a Univariate Forecasting Model

When configuring Demand Planning, you specify which forecasting models you are going to use for each of your products or product families.

The above example shows the different models a hardware wholesaler would use to forecast two kinds of light bulb.


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B(T) = B(t-1) + (V(t) - B(t-1))

Forecasting Using Exponential Smoothing

History Planning data/forecast

Constant model

Trend model

Seasonal model

Ex-post forecast

The system uses the alpha factor to smooth the basic value, the beta value to smooth the trend value, and the gamma value to smooth the seasonal value. These smoothing factors give a higher weighting to the more recent historical values than to the less recent ones, which means that the more recent values have a larger influence on the forecast.

Constant models determine the basic value of future sales data, trend models determine the basic and trend values, seasonal models determine the basic and seasonal values, and trend and seasonal models determine the basic, trend, and seasonal values.

The formula in the above slide is used for exponential smoothing of the basic value using the alpha factor. It includes: B(t): The basic value for the current period t B(t-1) is the basic value from the previous period t-1 V(t) is the actual requirement (version 000) from period t

The ex-post forecast uses the smoothing factors from historical data to determine the basic, trend, and seasonal values. If the ex-post forecast is accurate in predicting the historical data, the forecast error will be small.


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Parameter

Exponential Smoothing

The smoothing principle: Weighting of the current value: parameters

Weighting of the previous value: (1 – parameter)

Weighting examples (in %):

0.1

0.3

0.5

0.7

Current Current periodperiod

1010

3030

5050

7070

Past period 1

9

21

25

21

Past period 2

8

15

13

6

Past period 3

7

10

6

2

The smoothing factor governs a forecast's reaction time to a change in the pattern. If you choose 0 for the alpha value, the new average equals the old one. In this case, the basic value calculated previously does not change, meaning that the forecast does not react to current data. If you choose 1 for the alpha value, the new average equals the last value in the time series.

The most common values for alpha are between 0.1 and 0.5. Example: An Alpha value of 0.5 weights historical values as follows:

First historical value: 50%, second historical value: 25% Third historical value: 12.5%, fourth historical value: 6.25%

The default alpha factor is 0.3, beta is 0.3, and gamma is 0.3.


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0

1

2

3

4

5

6

7

8

Tolerance range= ep ± *1.25* MAD

ep = Ex-post forecast

Today

Automatic Outlier Correction

You can select Outlier correction in the univariate forecast profile to automatically correct outliers in the historical data on which the forecast is based. The system then uses the sigma factor to calculate a tolerance range for the past time series. Historical data that lies above or below the tolerance range is corrected to correspond to the ex-post value for that point in time.

The sigma factor defines the width of the tolerance range for automatic outlier correction. It defines the permissible number of standard deviations. A smaller sigma factor means a lower tolerance and a larger number of outliers that are detected and corrected. The default sigma factor is 1.25. If you set the sigma factor yourself, SAP recommends that you set it to between 0.6 and 2.

Once the outlier correction has been made, the ex-post forecast is calculated once more using the corrected values.


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PastPast

Specify the average number of workdays (such as 20) in the forecast profile

ForecastForecast

Workday Adjustment

M1 M2 M3

Days 19 21 23

Original act. 1900 2100 2300

Corr. 2000 2000 2000

M4 M5 M6

Days 22 20 18

Uncorr. 2000 2000 2000

Corr. 2200 2000 1800

You can use this function to account for a varying number of workdays in a month The system bases the forecast on an average number of workdays during a forecast period. You specify

these in the Average no. of days field in the univariate forecast profile. In the example in the above slide, the forecast period is "Month" and the assumed number of workdays

in each month is 20. The forecast is run as follows: 1. The system corrects the historical data using this formula:

Corrected history = (original history/actual workdays) * the average number of workdays 2. The system calculates the forecast using the corrected historical data. 3. The system adjusts these initial "uncorrected" forecast results based on this formula:

Corrected forecast = (uncorrected forecast/average workdays) * actual workdays The number of workdays in the period is determined by the factory calendar in the planning area The results of the planning data correction appear in the corrected forecast key figure You can only use the Corrected forecast key figure for univariate forecasting


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Univariate Forecast Errors

MAD (mean absolute deviation)

ET (error total (total forecast error))

MAPE (mean absolute percentage error)

MSE (mean square error)

RMSE (root of the mean square error)

MPE (mean percentage error)

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MAD (mean absolute deviation), ET (error total), MAPE (mean absolute percentage error), MSE (mean square error), RMSE (root of the mean square error), MPE (mean percentage error).

If you run the forecast in interactive planning (from the user-defined or forecast view), the univariate measures of fit are displayed on the Forecast errors tab page.

Absolute errors prevent positive errors (those greater than zero) being canceled out by negative errors (those less than zero).

For more information about univariate forecast error formulas, see the application documentation for APO Demand Planning.


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Version ID MAPE MSE...Fcst ID Alpha Beta...

Planning areaKey figureVersionSelection ID

Forecast errorsParameters

Forecast Comparison

Changes

Forecast version comparison Access from planning table

Creating new forecast profiles

Assigning selections to profiles

Fcst ID Planner Date...

Forecast versions are used to save and compare the parameters from several forecast runs. The system stores forecast errors, the model being used (including parameters), and the users who have already forecast this selection.

Once you have run several forecasts using different models and parameters, you can sort by forecast error to determine the best model and best parameters.


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HistoryHistory

Version and key figure Corrected history

Model parametersModel parameters

Strategy, length of season, smoothing parameters

Control parametersControl parameters

Outlier correction,workday adjustment

Forecast errorsForecast errors

VersionKey figure

Entries:

Forecast strategyPeriods in each season

Parameters: ,

With or without zero consumptionOutlier

Average number of days

MAD, total of errors,MAPE, RMSE,MSE, MPE *

Summary of the Univariate Forecast Profile

You can make changes to the univariate profile configuration in the interactive planning forecast view (settings for forecast model, planning horizons, forecast parameters, and/or forecast). To save the new forecast settings in a profile with a unique name, choose Settings -> Forecast profile -> and set the Create unique forecast profiles when saving plan indicator. A new forecast profile is created for the current selection variant when you save the Demand Planning data. The next time you run the forecast for the same selection variant, the system uses this profile by default. The advantage of working with uniquely named profiles is that if you change the forecast configuration in some way (for example, by switching from a seasonal to a trend model), the system retains the new settings without overwriting the original forecast profile. If you do not set the Create unique forecast profiles when saving plan indicator, the original forecast profile is overwritten with the new settings when you save the interactive planning data. To see which forecast profiles were previously used for which selection variants, choose Goto -> Assignment in the master forecast profile. This name (GUID) is generated automatically and cannot be altered.

The Persmo field (number of periods for seasonal moving average smoothing) is used to smooth seasonal regression. An entry of 0 means linear regression, 1 means seasonal regression with no smoothing, and greater than 1 means smoothing by the number of periods entered.

The promotion key figure is used to create historical value markings for correcting the outlier and using past promotions to adjust the actual data.


You can define upper limits for forecast errors in the diagnosis group. If the calculated forecast errors exceed the threshold values, alerts are generated and the planner can review this characteristic combination once more.


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MLRMLRMultiple Linear Regression (MLR)

MLR is used to determine how a dependent variable (sales, for example) is connected with independent (causal) variables (such as prices, advertising, and seasonal factors).

MLR uses historical data as a basis for calculating the regression coefficients b for causal analysis.

The demand planner has the task of identifying and quantifying the most important independent variables, and of modeling the causal connection.


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Multiple Linear Regression

Forecast

Modeling options: Linear and non-linear trends Seasonal patterns Dummy variables

Model fit analysis R2 Adjusted R2 Durbin-Watson Durbin-h t-test Mean elasticity

Causal analysis is based on causal factors, such as prices, budgets, and campaigns. The system uses multiple linear regression (MLR) to calculate the influence of causal factors on past sales and thus allows you to analyze the success of specific actions. The calculated connection between causal factors and past sales is then used as a basis for modeling future actions.

Multiple linear regression (MLR) is a form of causal analysis. It enables you to analyze the relationship between a single dependent variable and several independent variables. You use the independent variables, the values of which are known to you, to predict the single dependent value (the value you want to forecast). Each predictor variable (Xi) is weighted, the weights (bn) denoting their relative contribution to the overall weighting.

When you create the forecast in Demand Planning using an MLR model, the system calculates a number of statistics to measure the forecast accuracy (see above). If necessary, you can then adjust the model accordingly. For more information about these statistics, see the APO glossary.


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Causal Analysis: Advertising BudgetU

nit s

ales

Feb. Mar. April May June July Aug. Sept.

$ 2000

$ 1000Actual adv.budget:

$ 1500

$ 800Past Future

Plannedbudget:

Calculationof Calculation

of salesusing

In the above example, the Unit sales key figure is the dependent variable. The advertising budget is one of the independent variables. The MLR model calculates the influence of the advertising budget on the unit sales in the past. The model uses the calculated coefficient to incorporate the effect of the planned advertisement in the unit sales forecast.

In multiple linear regression, coefficients (or weightings) of the independent or explanatory variables describe the relative importance of these variables. Coefficients in a causal model indicate how value changes in each of the independent variables (Xs) influence the value of the dependent variables (Y). For example, you can determine the effect that decreasing the advertising budget by $1000 would have on the sales quantity, if all the other variables remain constant.

Elasticity measures how a dependent variable is affected if the explanatory variable is changed by one percent. This is calculated as the percentage change in Y (the dependent variable) divided by the percentage change in X (the explanatory or independent variable). Elasticities tend to differ when measured at different points on the regression line. The mean elasticity is the mean average of the elasticities at these different points.


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Yi = o + 1X1 + 2X2... + n Xn

Demandhistory

Constant Coefficients

Causalfactor

Causal Analysis

Unlimited causal factors (price, temperature, ...)

Multiple linear regression (lags, dummy variables, ...)

What-if analysis/marketing mix planning

In the above formula: Y = Dependent variable 0 = Y intercept or constant n = Coefficients or weights Xi = Independent variables

A practical example of MLR: Consumer demand for product Y = constant + price + advertising + merchandising +distribution +

free market price APO Demand Planning uses the ordinary method of the least squares for MLR. Autocorrelation occurs when the error variables of a regression model are not independent; that is, when

the values of past periods in the forecast model influence the values of current periods. Time series with a strong seasonal or cyclical pattern are often highly correlated.

Autocorrelation is an indication that your independent variables are too closely linked. If you detect a level of autocorrelation that is no longer acceptable, it could mean that you need to adjust the classic MLR model. Durbin-h and Durbin-Watson are autocorrelation measures. For more information, see the APO glossary.


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??Causal Analysis: Frequently Asked Questions

How can we sell X units? What is the most cost-effective way?

How will the market react if we (or our competitors) increase or reduce the price by X%?

How successful were previous promotional events?

To what extent are sales affected by the weather (ice creams and drinks, for instance)?

How will our sales be affected by changes in the economic climate?

What are the factors that determine long-term sales improvement?

Example for point 1: If you have modeled product price and advertising budget as causal factors, you can use causal analysis to determine the most cost-effective way of achieving target sales. Is it more cost effective to reduce the price or increase the advertising budget?


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You need experience in statistics to model

causal connections

Causal Analysis Requirements

Data requirements Actual data for all variables Companies often want to compare with competitors but it is

difficult to obtain the necessary actual data Forecasts for independent variables

Logical challenges Which variables influence sales? How do variables influence sales? Outlier, trend, and seasonal modeling

Statistical problems Correlation, autocorrelation


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MLR Profile

Historical data (demand history, for example) Key figure, version

Diagnosis group Threshold values for forecast errors

Causal factors: Key figure from version (price, for example)

Time series variables (marketing budget, temperature, Easter weeks)

You enter: A name and a description for the MLR profile. A key for the diagnosis group containing the upper and lower limits for MLR errors. If the MLR

errors exceed the threshold values, alerts are generated and the planner can then review this characteristic combination once more.

The key figure on which the forecast is to be based. This is a key figure from the planning area that you specified in the master forecast profile. It does not have to be the key figure to which the forecast results are written. The system uses the historical values from this key figure to calculate the coefficients in the MLR model. In the MLR equation, these are the historical values of Y.

The version of the historical data on which you want the forecast to be based. You can use key figures from either a planning area or time series as causal factors. Transformation: You can set a lag here. For example, if you enter -1, the forecast value is moved one

period into the future; that is, it takes one period for the variable to impact demand.


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AAutocorrelationutocorrelation (past(past periods influence current periods)periods influence current periods) DurbinDurbin--Watson (without lagged variables: The acceptable range is Watson (without lagged variables: The acceptable range is

between 1.5 and 2.5)between 1.5 and 2.5) DurbinDurbin--h (with lagged variables: Below 1.96 is acceptable)h (with lagged variables: Below 1.96 is acceptable)

CCorrelationorrelation of the dependent variable with an independent variableof the dependent variable with an independent variable tt--statistic (no correlation if tstatistic (no correlation if t--test is greater than +/test is greater than +/-- 1.4)1.4)

MMeasureeasure of fitof fit R2R2 (above 0.90 means that the model is good)(above 0.90 means that the model is good) Adjusted R2 (if it is significantly lower than R2, you are probaAdjusted R2 (if it is significantly lower than R2, you are probably bly

missing an explanatory variable)missing an explanatory variable)

IInfluence of an independent variablenfluence of an independent variable Coefficient, elasticityCoefficient, elasticity

Measures of Fit: Causal Analysis

For full definitions of the above measures of fit, see the APO glossary. If you run MLR in interactive planning, you can view the MLR measures of fit by using the Switch

parameters on or off button in the application toolbar (the causal view). If you run MLR with mass processing, you can view the MLR measures of fit by defining alerts to show

when the measures exceed certain limits. These alerts can then be viewed in the Alert Monitor.


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ØØForecast

Univariate

MLR

1

n

...Combine

andreconcile

UnivariateResultMLR

Composite Forecasting

Weighted average of multiple forecasting methods

Simple average

Composite forecasting combines forecasts created independently using different forecasting methods for the same data basis (demand history) of specific brands, individual products, or product families. The underlying objective is to use the strengths of each method and create a single forecast, either by simply averaging the forecasts and giving each equal weight or by weighting each forecast and summing them based on the residual errors of each method.

The business analyst's goal in combining the forecasts is to develop the best forecast possible. The composite forecasts of several mathematical and/or judgmental methods have been proven to outperform individual forecasts of any of the methods.


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Example:

Constant: 30%+ Seasonal: 30%

+ MLR: 40%

= Final forecast

Composite Forecast Profile

Select forecast method

Specify percentage weighting factors

Define time-dependent weighting (optional)

You enter: A name for the composite forecast profile. A description of the composite forecast profile. The name of a univariate profile that you want to include in the composite forecast. You can use the

Univ.Profile pushbutton at the bottom of the dialog box to choose the profile you want. The name of the MLR profile you want to include in the composite forecast. You can use the MLR

profile pushbutton at the bottom of the dialog box to choose the profile you want. A percentage to specify the weighting of this profile in the composite forecast. For example, if you

have three profiles, you might enter 30, 30, and 40 in this column. This weighting is not time-based. The name of a weighting profile. A weighting profile assigns different weightings to different periods.

You can use the Weighting profile pushbutton at the bottom of the dialog box to choose or create a weighting profile. This entry is optional.


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Multiple forecasts can be created for different departments

The sales and marketing department

The production planning department

The accounts department

Multiple forecasts are integrated into one consensus-based demand plan to drive business activities

User-specific view in planning book

40%

30%

30%

MacroMacro

Financial budget

Sales and marketing

Production planning

Actual data (previous year)

Cons.-based forecast

35

44

40

42

40

48

45

30

20

32

37

50

30

27

41

Consensus-Based Forecasting

A consensus-based sales and operations planning process is one in which you view forecasts from various departments with different business goals, such as sales, marketing, logistics, and finance, and integrate them into a single consensus forecast. This then drives the business planning process.

APO Demand Planning supports participants in consensus meetings (for example, Sales & Operations Planning - SOP) by providing information that enables them to compare forecasts and identify, discuss, and close gaps that affect their business decisions. The goal is to make changes that are agreed by all parties. The result is a consensus-based forecast.

Planning books are typically based on the data and planning tools required by a user for their role in the organization.

A planning book for creating consensus-based forecasts would be a good example of a frequently used planning book. This planning book would contain the forecasts from several departments, such as the sales and marketing, and accounts departments.

In the above example, a pre-defined average is calculated using a macro and displayed in a row named consensus-based forecast.

In this case, it would probably be advisable to define the sales and marketing, and accounts rows of the book as read only (data entry not possible) and the consensus-based forecast row as a planning row in which adjustments can be made.


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Forecasting: Unit Summary


Define forecast profiles with control parameters and forecast errors

Describe the differences between the various forecasting methods and models for univariate forecasting, causal analysis, and composite forecasting

Execute forecasts in the APO system


6.34Forecasting - Exercises

Unit: Statistical ToolboxTopic: Statistical forecasting techniques

Interactive forecasting is used mostly for setting and checking parameters in the forecast profile. For example, you can use it to create different forecast profiles for A, B, and C products. Usually, mass processing is used for assigning profiles to characteristic value combinations and for periodic forecasting.

1-1 Define the special functions for the forecast key figures. Assign the FORECAST key figure to the forecast and the CORRHIST key figure to the corrected history.You can use these special functions to base your forecast on the corrected history.

1-2 Create a forecast profile for your planning area.Enter the following parameters:

Planning area PLAN##

Master prfl. Master

Description Profile for product P-102

Forecast key figure FORECAST

Period indicator M

Forecast horizon periods 12

History horizon periods 24


1-3 Create a univariate forecast profile for your planning area.Show the recommendation and enter these parameters:

Profile UNI##

Description Statistical forecast for P-102

Key figure INVQTY

Version 000

Forecast strategy 11

Alpha, Beta, Gamma 0.3

Sigma 1

Persmo 1

Seas. periods 12

Forecast errors Select all

Promotion key figure 9APROM1

1-4 Assign your UNI## univariate forecast profile to your master forecast profile.

1-5 Go to interactive planning, load the data for product P-102 into the planning table, and run a univariate forecast.Switch on the graphic and analyze the historical data.

Execute multiple forecasts using different models and smoothing parameters. Each time, check the forecast errors (especially the mean absolute deviation MAD), and the forecast messages. Also analyze the forecast results in graphical form.Switch on the outlier correction and check whether deviations have occurred between the original history and corrected history. The correction depends on the forecast model.Compare the results from the past 10 forecast runs and save the parameters from the run with the smallest MAPE in the forecast profile.

Where can you automatically assign the selection to the profile?

Save your plan.


6.35Forecasting - Solutions

Unit: Statistical ToolboxTopic: Statistical forecasting techniques

Interactive forecasting is used mostly for setting and checking parameters in the forecast profile. For example, you can use it to create different forecast profiles for A, B, and C products. Usually, mass processing is used for assigning profiles to characteristic value combinations and for periodic forecasting.

1-1 Define the special functions for the forecast key figures. Assign the FORECAST key figure to the forecast and the CORRHIST key figure to the corrected history.You can use these special functions to base your forecast on the corrected history.

Demand Planning Environment Current Settings Administration of Demand Planning and Supply Network Planning

Select the planning area view

Right-click on your Planning area (PLAN##): Forecast settings

Assign the key figures. Adopt.

1-2 Create a forecast profile for your planning area.Enter the following parameters:


Master prfl. Master

Description Profile for product P-102

Forecast key figure FORECAST

Period indicator M

Forecast horizon periods 12

History horizon periods 24

Demand Planning Environment Maintain Forecast Profiles

First create a master forecast profile and enter the above parameters


1-3 Create a univariate forecast profile for your planning area.Show the recommendation and enter these parameters:

Profile UNI##

Description Statistical forecast for P-102

Key figure INVQTY

Version 000

Forecast strategy 11

Alpha, Beta, Gamma 0,3

Sigma 1

Persmo 1

Seas. periods 12

Forecast errors Select all


1-4 Assign your UNI## univariate forecast profile to your master forecast profile.

Enter the above parameters in the Univariate profile tab page. Save the single profile.

Go back to the Master profile tab page and select your univariate forecast profile UNI##. Save.

1-5 Go to interactive planning, load the data for product P-102 into the planning table, and run a univariate forecast.Switch on the graphic and analyze the historical data.

Execute multiple forecasts using different models and smoothing parameters. Each time, check the forecast errors (especially the mean absolute deviation MAD), and the forecast messages. Also analyze the forecast results in graphical form.Switch on the outlier correction and check whether deviations have occurred between the original history and corrected history. The correction depends on the forecast model.Compare the results from the past 10 forecast runs and save the parameters from the run with the smallest MAPE in the forecast profile.

Where can you automatically assign the selection to the profile?

Save your plan.



If necessary double-click on the DEMAND PLAN data view for your SALES## planning book

Start the forecast by choosing the “Stat.” (Univariate forecast) button

The forecast is then executed automatically

In the upper part of the screen, you see the STAT icon for executing the forecast, the Forecast comparison icon, and the Show/hide table icon that switches the graphic on or off.

In the lower section of the screen you can change the forecast parameters.

You can automatically assign the selection to the profile by going to Settings Forecast profile and by selecting: “Save assignment of selection for forecast profile”. By saving in the forecast view, the planning data, the profile, and the assignment are all saved.


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Contents:

Promotion planning

Lifecycle planning

Like modeling

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SAP AG 2003

Promotions and Lifecycle Planning: Unit Objectives

At the conclusion of this unit, you will be able to:Create and assign promotions

Explain how promotions are extracted from historical data

Describe how product lifecycles can be modeled in APO Demand Planning

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Promotions and Lifecycle Planning: Overview Diagram

InfoCubes

Course Overview

Configuration



Forecasting


Mass Processing

Conclusion

44

3322

11

55

66

77

8899

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SAP AG 2002

Promotions and Lifecycle Planning: Business Scenario

The marketing department at the Precision Pump company intends to implement promotion planning to better estimate the effects of planned promotions on the demand plan.

The marketing department also intends to use APO lifecycle planning to plan the launch of new products (phase in) and the discontinuation of old products (phase out).

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SAP AG 2003

Promotion Planning

Price

Quantity

-10%

Promotion patterns

’99

’01

’00

‚02Planner

Forecast simulation

Quantity

Time

In APO Demand Planning, you can plan promotions or other special events independently of the rest of your forecast. You can use promotion planning to model one-time events, such as an earthquake, or repeated events, such as quarterly advertising campaigns. Additional examples of promotions include trade fairs, coupons, free-standing inserts, competitor activities, and market intelligence. Events that impact consumer behavior include upward or downward economic trends and acts of nature.

Planning promotions separately has the advantage that: You can compare forecasts that have promotions with those that do not. You can correct the sales history by subtracting past promotions from it to obtain unpromoted

historical data for the baseline forecast. The interactive planning process can be kept completely separate from that of promotion planning. For

example, the sales force might be responsible for interactive planning while marketing is responsible for promotion planning.

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M07/01

M08/01

M09/01

M10/01

M11/01

Time

Forecast100%

Demand planStart of promotion

Promotions

MacroMacro

Period

Percentage promotion

Promotion key figure

Demand plan

M 1

+10%

10

110

M 2

+20%

20

120

M 3

+10%

10

110

M 4

-5%

-5

95

M 5

100

Unit

%

KG

KG

Corrected forecast 100 100 100 100 100 KG

Promotional uplifts can be modeled in absolute or percentage values by promotion patterns. A promotion pattern that occurred in the past can be automatically detected and recreated for future periods. A promotion pattern can be archived in a promotion catalog, which means it can be reused if a promotion of the same type is repeated.

The above example shows how a promotion that has been defined as a percentage affects the forecast.

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SAP AG 2002

Object assignmentSettings:

Corrected historyPromotion key figureCharacteristic level

Promotion

Planningversions

Start date and periods

Forecast key figure

Defining a Promotion

Percentage/absolute

Time series

19991999

Product 1..

Product 5

Product 5..

Product 20

When you define a promotion, you need to enter the following parameters: Start date Number of periods Version Key figure (forecast) to which the promotion refers

You must activate a promotion in interactive planning for it to be displayed there. You can see which promotions are included in the Promotion key figure in interactive planning. To do

this, right click on the relevant key figure and choose Promotion list. A window showing this information is then displayed. Note that the promotions you see here depend on the products in the selection variant.

You can use transaction /n/sapapo/mp39 to start promotion reports (report /SAPAPO/PROMOTION_REPORTING; note 384550).

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SAP AG 2003

Assigning Characteristic Values to the Promotion

Object selection

Show

that meet the following conditions:

APO - Product

APO - version 000Product P-102

The last drilldown level must correspond to the characteristic value level of the promotion

Once you have created a promotion and entered the planned absolute or percentage changes, you choose the characteristic combinations for which the promotion is to be planned. You select the characteristic combinations in the selector and assign them to the promotion.

You have to decide the characteristic level at which you want the assignment to be made. If you choose the Product characteristic, then product level must be the last drilldown level for assignment. For example, you can first assign sales organizations to the promotion, then sold-to parties, and finally the relevant products.

If you want to assign several characteristics to a promotion, you do not create a selection that refers to all characteristics (for example, display all products for market segment A and B plus products 1, 2, 3, and 4), instead you create a separate selection for each characteristic that is to be assigned to a promotion and assign these in turn.

Example: A promotion is to be used for an entire market. The lowest promotion key figure is 9AMATNR. When defining the selection, you must first select the market and assign it to the promotion, then in a second selection, you must choose the associated products.

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SAP AG 2002

Promotion Statuses and Types

Promotion attribute types for classifying and selecting promotions

For example, "Store reduction" and "Media support"

Collaborativepromotion planning

Stored in liveCache

Status: "Draft"Status: "Draft"

Status: "Planned,Status: "Planned,for future"for future"

Promotion created:

Status: "Offered" (tocustomer)

Status: "Confirmed byStatus: "Confirmed bycustomer"customer"

For promotion planning without customer participation, after you have processed the promotion, you set the status to "Planned, for future" to activate the promotion.

For Internet-based collaborative promotion planning, you first set the status to "Offered" (to customer). The customer can accept (status: Confirmed by customer) or reject (status: Rejected (by customer)) the promotion in the Internet. The next step is to activate the promotions confirmed by the customer. Maintaining a partner at the customer location is a prerequisite of collaborative promotion planning. To create a collaboration partner, follow this menu path: Supply Chain Collaboration -> Environment -> Current Settings -> Collaboration Partner.

The Status field of the Promotion tab page in the promotion planning screen displays the status of a promotion. If you want to change a status, choose the appropriate icon in the promotion planning application toolbar situated at the top right of the screen.

Your company can have a maximum of 10 promotion attribute types. For example, you might have promotion attribute types "Store reduction" and "Media support".

There can be many user-defined attributes for each promotion attribute type. For example, the promotion attribute type "Media support" could have the attributes "TV", "Radio", and "Web".

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The created promotions are first stored on the database. Once a promotion is activated, it is written to liveCache. Therefore, the data is still available even after initializing liveCache. You can use report /SAPAPO/PROMOTION_UPDATE_30 to activate promotions or restore consistency after initializing liveCache or the planning area.


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SAP AG 2002

Cannibalization

Corrected forecastSales of productwith special offer

You use cannibalization groups to model the effect promotions will have on similar products.

M07/99

M08/99

M09/99

M10/99

M11/99

Time

Original forecast100%

Sales of similar product

M07/99

M08/99

M09/99

M10/99

M11/99

Time

Original forecast100%

A single promotion has both a positive and negative influence on sales of products from one cannibalization group.

To use cannibalization groups, select the Check cannibal. group option from the Promotion tab page in the Promotion Planning screen. When you create a promotion for one of the products from the group, the other promotions are created automatically.

Example: You plan a 5% price reduction on liter bottles of "Peach blossom" shampoo, which will cause a 30% increase in sales whereas sales of 250ml bottles will go down by 3%, and sales of 500ml bottles by 5%. You define the cannibalization group like this:

Liter bottle +30250ml bottle -3500ml bottle -5

You create a promotion for the liter bottle. Negative promotions are created automatically for the 250ml and 500ml bottle in the percentage ratio that is predefined by the cannibalization group factors.

You can only execute cannibalization for percentage promotions.


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SAP AG 2002

Impact of Promotions on the History and Forecast

History(including a promotion)History(including a promotion)

Corrected history Forecast

Corrected forecast+ promotionCorrected forecast+ promotion

Past Future

When you work with promotions, you can show and plan the impact of promotions separately. Future promotions are displayed in the Promotion key figure in interactive planning. You can use a

macro to ensure that promotions are included in the Demand plan key figure. You can forecast future demand using the corrected history (minus promotions). To remove past

promotions from the Corrected history key figure, you enter the key figure of the past promotions in the forecast profile and select Change values.

You can also define a post promotion key figure and instruct the system to calculate the actual effect the promotions had on sales (how sales changed). If you want to correct the history using the planned promotion, you do not need a post promotion key figure.

There are several methods for measuring the impact of a promotion in the past and for estimating the impact of a similar promotion in the future; these include multiple linear regression with or without trend or seasonality.


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1. Phase-in/phase-out profile

2. Workday adjustment

3. Past promotions

4. Outlier correction

5. Manual adjustments

Original actual dataOriginal actual data

Corrected historyCorrected history

To generate more exact forecasts, the impact of one-time promotions or delivery problems needs to be removed from the actual data. These adjustments are usually made in the Corrected history key figure, rather than the original key figure.

You can use various automatic methods to adjust actual data. They can be activated from the forecast profile.

The methods are executed in the sequence given in the above slide. The phase-in and phase-out profiles for lifecycle planning control the phasing in (launch) of new

products and the phasing out (discontinuation) of old products Workday adjustment ensures that higher values are forecast for periods that have many workdays.

Historical data must be standardized for this With promotion planning, you can extract past actions (special offers, for example) from the actual

data so they are not included in the forecast You can use outlier correction to automatically correct actual data that is outside of the tolerance

range. You can also adjust the actual data manually

You can also assign the following elements to a key figure that is to be forecasted: A key figure for storing the corrected history A key figure for storing the corrected forecast A key figure for storing ex-post forecasts


A key figure for storing ex-post forecasts for the MLR


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Lifecycle Management and Like Modeling

Actual data of the old product

Forecast for the new product

Life cycle

Time

Sales

LikeLike

A product's life cycle consists of different phases: Launch, growth, maturity, and discontinuation. In APO Demand Planning, you can use phase-in and phase-out profiles to represent the launch, growth, and discontinuation phases.

You use lifecycle planning and like modeling to forecast the phase-in of new products and phase-out of old products.

In the phase-in profile, you enter ever-increasing percentages during a specified period or periods to forecast the new product, thus mimicking the upward sales curve that you expect the product to display during its launch and growth phases. If past corrections fall, the Corrected history key figure can be adjusted automatically.

A phase-out profile reduces the sales forecast for a product by ever-decreasing percentages during a specified period, thus mimicking the downward sales curve that you expect the product to display during its discontinuation phase. If the corrections in the past fall, the Corrected history key figure can be reduced automatically.

You can use a phase-in profile, a phase-out profile, a like profile, or any combination of these profiles for all characteristic value combinations.


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SAP AG 2002

Assigning the like profiles to characteristic values (maintained

in the forecast profile)

x, , ixi

Oldproduct

Newproduct

Substitution

Newproduct

New product launch

?

Like Modeling

To forecast a new product using historical data from old products, you create a like profile

Some products do not have sufficient historical data to provide the basis for a forecast. With a like profile, you can create a forecast using the historical data of a product or products with similar sales behavior. It is advisable to use like profiles for new products and products with short lifecycles.


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Lifecycle Management

Newproduct

You define phase-in and phase-out profiles to model the lifecycle of old and new products

100%

0%Phase out

Oldproduct

Assigning phase-in and phase-out profiles to characteristic values

(maintained in the forecast profile)

Correctedhistory

Correctedhistory ForecastForecast

Phase outPhase out Phase inPhase in Phase outPhase out

If the phase-out profile period is within the history horizon specified in the master forecast profile, the system adjusts the corrected history. If no key figure has been assigned to the corrected history in the planning area, the corrected values are displayed in the statistical forecast view but the corresponding row is not saved.

If the phase-in profile period is within the future horizon specified in the master forecast profile, the system directly adjusts the Forecast key figure.

You must set the Material forecast indicator in the master forecast profile.


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SAP AG 2002

Promotions and Lifecycle Planning: Unit Summary


Create and assign promotions

Explain how promotions are extracted from historical data

Describe how product lifecycles can be modeled in APO Demand Planning


7.17Promotions and Lifecycle Planning - Exercises

Unit: Promotions and Lifecycle PlanningTopic: Promotion planning

1-1 Define the last object assignment planning level for your promotions.In this example, the last assignment for your planning area PLAN## and key figure 9APROM1 should be made at product level.

1-2 In interactive planning, create and save a percentage promotion with the following parameters.

Name Promo##

Description Promotion, group ##

Type %

Number of periods 3

Start of promotion The beginning of the next month

Planning version 000


Plan. key figure FORECAST

1-3 Enter the percentage changes: The promotion should increase sales by 10% in the first month, 20% in the second month, and 10% in the third.Assign product P-102 to the promotion, activate the promotion, and save it.

1-4 Return to interactive planning to see if the promotion data is displayed and if your default macro sums this data for the demand plan.


Unit: Promotion Planning and Lifecycle PlanningTopic: Like modeling

Like modeling can be used to forecast new products with historical data from old products. You can also use it to define the life cycle of the old and new product.

This exercise will be done as follows:

4. You create new characteristic combinations for the product.

5. You specify the product whose historical data is to be read and you also choose the life cycle

6. You run the forecast.

2-1 If you did not do the Configuration unit exercise for realignment, create a new characteristic value combination for the new product NEW for your planning object structure POS##. Enter the following characteristic values and create time series objects for them:

Location 2400Product NEWSold-to party 1000Division 01Product hierarchy 0110Sales organization 2400

2-2 Assign characteristic 9AMATNR to the basic life cycle for your planning area PLAN##. Define the product whose historical data is to be read. Create a like profile called LIKE## and store 100% of the data from product P-102.

2-3 Create a phase-in profile called PHASEIN##. The product launch (phase in) should start in the next month and last for four months. There should be a 20% increase per month. Nothing should be forecast before the product launch.


2-4 Create a phase-out profile called PHASOUT##. The phase out of the product should start six months from now and end nine months from now. There should be reduction of 20% per month. After the product discontinuation, nothing should be forecast.

2-5 Assign your LIKE##, PHASEIN##, and PHASEOUT## profiles to the NEW product. Enter a new master forecast profile called LIKE with the indicator “material forecast”, by overwriting the old MASTER profile.

2-6 Create a selection called NEW## for the new product NEW. Run a forecast in interactive planning and check the results.


7.18Promotions and Lifecycle Planning - Solutions

Unit: Promotions and Lifecycle PlanningTopic: Promotion planning

1-1 Define the last object assignment planning level for your promotions.In this example, the last assignment for your planning area PLAN## and key figure 9APROM1 should be made at product level.Demand Planning Planning Promotion Maintain Promotion Key Figures

Specify PLAN## for the planning area, key figure 9APROM1, and 9AMATNR as the characteristic for promotion level.

1-2 In interactive planning, create and save a percentage promotion with the following parameters.

Short text Promo##

Description Promotion, group ##

Type %

Number of periods 3

Start of promotion The beginning of the next month

Planning version 000


Plan. key figure FORECAST


Choose the “PROMO” button

Choose the “Create promotion” button

Enter the parameters and save.


1-3 Enter the percentage changes: The promotion should increase sales by 10% in the first month, 20% in the second month, and 10% in the third.Assign product P-102 to the promotion, activate the promotion, and save it.After saving the promotion you go to a table where you can enter percentage changes.

10 20 10

Choose product P-102 from the selection window and double-click on it. To assign the product to the promotion, choose the “Assign objects” button. The objects and calculation of the promotion’s effect now appear in the planning table.

To make the promotion effective for future requirements, change promotion status to “Planned, in the future”, using the “Change status” button.

Save the promotion.

1-4 Go back to Interactive Planning and see if the promotion data is displayed and if your default macro sums this data in the demand plan.

Choose the “Interactive Planning” button

The percentage changes are displayed in the promotion key figure.

Your default macro calculates the demand plan from the following sum: Forecast + Correction + Promotion + Internet Correction.


Unit: Promotions and Lifecycle PlanningTopic: Like modeling

Like modeling can be used to forecast new products with historical data from old products. You can also use it to define the life cycle of the old and new product.

This exercise will be done as follows:

1. You create new characteristic combinations for the product.

2. You specify the product whose historical data is to be read and you also choose the life cycle

3. You run the forecast.

2-1 If you did not do the Configuration unit exercise for realignment, create a new characteristic value combination for the new product NEW for your planning object structure POS##. Enter the following characteristic values and create time series objects for them:

Location 2400Product NEWSold-to party 1000Division 01Product hierarchy 0110Sales organization 2400

Master Data Demand Planning Master Data Maintain Characteristic Values

Select your planning object structure POS##

Choose “Create characteristic combination....”

Enter characteristic values and select Adjust time series objects immediately.

Choose Display characteristics combinations to check the new entry.


2-2 Assign characteristic 9AMATNR to the basic life cycle for your planning area PLAN##. Define the product whose historical data is to be read. Create a like profile called LIKE## and store 100% of the data from product P-102.Demand Planning Environment Maintain Forecast Profiles

Enter your planning area PLAN## and the MASTER master forecast profile.

Choose the “Basic lifecycle” button and use characteristic 9AMATNR.

Goto LIKE profiles Define

Enter the following data and save:

“Like” profile LIKE##

Description Like P-102

Ref. products P-102

Action S

Weighting factor (%) 100

Stay in the forecast profile until exercise 2-5

2-3 Create a phase-in profile called PHASEIN##. The product launch (phase in) should start in the next month and last for four months. There should be a 20% increase per month. Nothing should be forecast before the product launch.Goto Phase-in/out profiles Define

Time series ID PHASEIN##

Description Launch

Start date One month from now

End date Five months from now

Period M

Before start date, apply constant factor

Select,

Factor 0

Choose “Proposal” to automatically insert the number of periods

Choose “Edit time series” to enter the percentage values

20 40 60 80


Copy.

2-4 Create a phase-out profile called PHASOUT##. The phase out of the product should start six months from now and end nine months from now. There should be reduction of 25% per month. After the product discontinuation, nothing should be forecast.Goto Phase-in/out profiles Define

Time series ID PHASEOUT##

Description Product phase out

Start date Six months from now

End date Nine months from now

Period M

After end date, apply constant factor:

Select,

Factor 0

Choose “Proposal” to automatically insert the number of periods

Choose “Edit time series” to enter the percentage values

80 60 40 20

Copy.

2-5 Assign your LIKE##, PHASEIN##, and PHASEOUT## profiles to the NEW product. Enter a new master forecast profile called LIKE with the indicator “material forecast”, by overwriting the old MASTER profile.

Use the “Assign life cycle” button to assign your LIKE##, PHASEIN##, and PHASEOUT## profiles to the master profile for your new product NEW.

Overwrite your old master forecast profile MASTER with LIKE.

For description, choose “Material forecast”

Save and exit the forecast profile maintenance.

2-6 Create a selection called NEW## for the new product NEW. Run a forecast in interactive planning and check the results.Demand Planning Planning Interactive Demand Planning


Open the selection window and choose APO - product in “Show”. Version 000 is the default version.

Choose product again in the next row and enter NEW in the right-hand side.

Choose the “Save selection” icon and enter “NEW##” as the selection description. Save.

Double-click on the NEW product in the selection window.

Start the forecast by choosing the “Stat.” (Univariate forecast) button

Select your LIKE master profile

The forecast is then run automatically. Switch on the graphic and check the product phase-in and phase-out stages.


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Mass Processing

Contents:

Macro calculation in the background

Forecasting in the background

Release of Demand Planning data to liveCache

Transfer of Demand Planning data to R/3

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8.2

SAP AG 2003

Mass Processing: Unit Objectives

At the conclusion of this unit, you will be able to:Configure and execute mass processing for macros, forecasts, and demand plan releases

Describe how sales quantities are released to Production Planning and explain how data is distributed between locations.

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Mass Processing: Overview Diagram

44

33


55

66

77

8899

Configuration



Forecasting


Mass Processing

Conclusion

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8.4

SAP AG 2002

Mass Processing: Business Scenario

Now that the forecast profiles have been set interactively for the different products, the periodic background forecast is to be configured.

Mass processing provides the forecast data at the start of the planning cycle. The planners can then analyze and correct the data.

At the end of the planning cycle, the data is then released automatically to Production Planning.

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8.5

SAP AG 2002

R/3 plants

Macro execution

Forecasting

Release to APO Production Planning

40% 40% 20%

Mass Processing Functions

40% 40% 20% Transfer to R/3 Demand Management

Creation ofplanned ind. reqmts

The most convenient way to run planning activities involving large volumes of data is in the background. SAP provides a mass processing function for this purpose.

The above graphic lists the mass processing actions that can be performed in APO Demand Planning.

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Create profile Schedule jobCreate jobDefine activity

Planningbook

Planningbook

ActionAction

ProfileProfile

ActivityActivity

Aggregationlevel

Aggregationlevel

VersionVersion

Selection variant(s)Selection variant(s)

Planningview

Planningview

Steps in Mass Processing (1)

The above graphic depicts the steps you go through for mass processing. "Action" refers to either macro execution, forecasting, or release of data to SNP.

You can perform several actions within one job as long as they have the same activity. For example, you might run a mass processing job that runs several macros at the same time, or a job that makes a statistical forecast and then releases the results to Production Planning.

The sequence in which actions within one activity are processed depends on the sequential numbers you define for them in the activity.

The system performs all actions for the first characteristic value before processing the second characteristic value. However, if you want the system to perform a specific action for all characteristic values first before it starts the next action, you must define separate planning activities.

For each job, you can define the selection of data that should be considered when processing the job as well as the level to which it should be aggregated before the actions are executed.

Planning jobs are reusable.

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Profiles

Activity

Create job and schedule

Release/

transfer

Forecast

Macro

MacroMacro ForecastForecast

Release/transfer

Steps in Mass Processing (2)

40% 40% 20%

You create activities by following this menu path: Demand Planning -> Environment -> Current Settings -> Define Activities for Mass Processing.

Before you can create a planning activity, you need to have either an existing macro, forecast profile, release profile, or transfer profile.

You create jobs by following this menu path: Demand Planning -> Planning -> Demand Planning in the Background -> Create Demand Planning in the Background.

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Releasing the Demand Plan

Time bucket profiles are used to create planned independent requirements

The location shipping calendar is used to determine workdays

Location split

Product split

Daily buckets profile when the DP storage buckets profile does not contain days

Category (FA)Key figure

Demand Planning Production Planning

Order objects

liveCache

Time series

objectsliveCache

If the forecast has been completed in Demand Planning, you release the forecast quantities to liveCache as planned independent requirements.

You trigger the release from this menu path: Demand Planning -> Planning -> Release to Supply Network Planning

The Add data indicator means that the released amounts can be added to planned independent requirements that might already exist. It is a good idea to use this setting if you want to release from multiple planning areas.

If the Location characteristic is contained in the DP planning area, the sales quantities are disaggregated to the locations automatically. If you want to use the allocation in the location split table for products, you do not have to specify the Location characteristic (for example, 9ALOCNO).

For instance, you can use the product split function to distribute a product group to the members. If product split has been maintained for a product, it will always be considered.

If the storage buckets profile from the DP planning area does not contain days, you can still split the sales quantities over days using the daily buckets profile. How this split is made depends on the settings in the SNP demand profile screen area in the SNP 2 tab page of the product master.

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Daily buckets profile

Release Profile

Specify planning area

Define key figure

Specify version

Enter daily buckets profile (optional)

Enter category (optional)

Category (FA)

Order objects

liveCache

Key figure

Time series

objectsliveCache

The key figure you release must be a quantity key figure. If the storage buckets profile from the DP planning area does not contain days, you can still split the

sales quantities over days using the daily buckets profile. How this split is made depends on the settings in the SNP demand profile screen area in the SNP 2 tab page of the product master.

In Customizing, you can define different categories for planned independent requirements (FA, FB, FC…) and use them to represent demand prioritization.

The system creates or updates the orders in liveCache according to their category. The category is determined during the release to SNP as follows: 1. Has a category been entered in the release profile? If so, this category is used. 2. If not, was a category set during definition of the requirements strategy in Customizing? (You

define the strategy for the product in the Proposed strategy field of the Demand tab page in the product master record.) If so, this category is used.

3. If not, the category FA (forecasts) is used.

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Transfer Profile

Specify planning area

Define key figure

Specify version

R/3 requirements type

R/3 Demand Management version

Should version be active?

Planned ind.reqmt

Key figure

Time series

objectsliveCache

R/3

The key figure you release must be a quantity key figure. If you do not enter an R/3 requirements type, it will be taken from the main strategy in the R/3 material

master.


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Jobforecast

Job

Job queue Job overview

Revise

Schedule

Jobcomplete

Job 1

Product 1 ForecastProduct 2 Forecast....Product 100 Forecast

Jobforecast

Spool list

Running a Job

To create jobs, follow this menu path: Demand Planning -> Planning -> Demand Planning in the Background -> Create Demand Planning in the Background

To find the job overview, follow this menu path: Demand Planning -> Planning -> Demand Planning in the Background -> Job Overview of Demand Planning in the BackgroundThe job overview lists your jobs and their system status. For a detailed results list, see the spool list. Errors are indicated by a red traffic light.

During revision, to receive a list of the characteristic combinations with status, go to: Demand Planning-> Planning -> Demand Planning in the Background -> Check Demand Planning in the Background.


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Mass Processing: Unit Summary


Configure and execute mass processing for macros, forecasts, and demand plan releases

Describe how sales quantities are released to Production Planning and explain how data is distributed between locations.


8.13Mass Processing - Exercises

Unit: Mass ProcessingTopic: Forecasting and Release to Production Planning

The aim of this exercise is to set up mass processing so that the following can be run in the background:

4. The forecast for P* products and

5. The macro for calculating the demand plan, and

6. The transfer to R/3 Demand Management

1-1 Create a planning activity called FOR## for your SALES## planning book and your DEMAND PLAN data view. Enter your MASTER master forecast profile and save.

1-2 Create a job called FOR## for mass processing using your SALES## planning book, your DEMAND PLAN data view, and planning version 000.Enter your activity FOR## and the selection PRODUCT##.Set the aggregation level to product.

1-3 Schedule the job and review the results. Make sure that the forecast has been made for all three products P-102, P-103, and P-104.


1-4 Create a profile called REL## for release to R/3 Demand Management. Create a transfer profile for the transfer to an inactive version ## using the following data.


Key figure FINFOR

Version 000

Product characteristic 9AMATNR

Location characteristic 9ALOCNO


R/3 version ##

Active Do not set

1-5 Create a planning activity called ACT## for your SALES## planning book, and your DEMAND PLAN data view. Enter the DEMAND PLAN CALCULATION macro and your release profile REL##, and, after each entry, increase the action counter.

1-6 Create a job called JOB## for mass processing using your SALES## planning book, and your DEMAND PLAN data view, and planning version 000.Enter your activity ACT## and the PRODUCT## selection.Set the aggregation level to product/location.

1-7 Schedule the job and review the results. Make sure that planned independent requirements were generated in R/3, in inactive version ##; for example, for product P-102 and plant 2400.


8.14Mass Processing - Solutions

Unit: Mass ProcessingTopic: Forecasting and Release to Production Planning

The aim of this exercise is to set up mass processing so that the following can be run in the background:

7. The forecast for P* products

8. The macro for calculating the demand plan

9. The transfer to R/3 Demand Management

1-1 Create a planning activity called FOR## for your SALES## planning book and your DEMAND PLAN data view. Enter Master as the master forecast profile and save.

Demand Planning Environment Current Settings Define Activities for Mass Processing

Create a planning activity called FOR## with the description “Forecast.”

Enter your MASTER master forecast profile and choose the “Copy action” icon.

Save the activity.

1-2 Create a job called FOR## for mass processing using your SALES## planning book, your DEMAND PLAN data view, and planning version 000.Enter your activity FOR## and the selection PRODUCT##.Set the aggregation level to product.

Demand Planning Planning Demand Planning in the Background Create Demand Planning in the Background.

Specify FOR## as the job number and, as the job name, enter “Forecast for group ##.”

Enter your planning book SALES##, your data view DEMAND PLAN, and version 000. Execute.

Enter activity FOR## and the selection PRODUCT##.

In Aggregation level, only select Product.

Save your job.

1-3 Schedule the job and review the results. Make sure that the forecast has been made for all three products P-102, P-103, and P-104.


Demand Planning Planning Demand Planning in the Background Schedule Demand Planning in the Background.

Enter FOR##, execute, choose “Immediate,” and save.

Is the job finished?

Demand Planning Planning Demand Planning in the Background Job Overview of Demand Planning in the Background. Execute

Check whether the forecasts have been made.


Open the selection window and get the PRODUCT## selection.

Double-click on the products in the selection window in turn.

Planning data can now be seen in the Forecast key figure.

1-4 Create a profile called REL## for transfer to R/3 Demand Management. Create a transfer profile for the transfer to an inactive version ## using the following data.


Key figure FINFOR

Version 000

Product characteristic 9AMATNR

Location characteristic 9ALOCNO


R/3 version ##

Active Do not set

Demand Planning Environment Current Settings Maintain Transfer Profiles

Create a transfer profile called REL##.

Enter the above data.

1-5 Create a planning activity called ACT## for your SALES## planning book, and your DEMAND PLAN data view. Enter the DEMAND PLAN CALCULATION macro and your release profile REL##, and, after each entry, increase the action counter.

Demand Planning Environment Current Settings Define Activities for Mass Processing

Create a planning activity called ACT## with the description “Macro and release.”


Go to the Macro tab page.

Enter the DEMAND PLAN CALCULATION macro, choose the “Copy action” icon, and increase the action counter. Go to the Transfer prfl R/3 tab page.

Enter your transfer profile REL## and choose the “Copy action” button.

Save the activity.

1-6 Create a job called JOB## for mass processing using your SALES## planning book, and your DEMAND PLAN data view, and planning version 000.Enter your activity ACT## and the PRODUCT## selection.Set the aggregation level to product/location.

Demand Planning Planning Demand Planning in the Background Create Demand Planning in the Background..

Specify JOB## as the job number and, as the Job name, Job for group ##.

Enter your planning book SALES##, your data view DEMAND PLAN, and Version 000. Execute.

Enter activity ACT## and the selection PRODUCT##.

In Aggregation level, only select Location and Product.

Save your job.


1-7 Schedule the job and review the results. Make sure that planned independent requirements were generated in R/3, in inactive version ##; for example, for product P-102 and plant 2400.

Demand Planning Planning Demand Planning in the Background Schedule Demand Planning in the Background..

Enter JOB##, execute, choose “Immediately,” and save.

Is the job finished?

Demand Planning Planning Demand Planning in the Background Job Overview of Demand Planning in the Background. Execute.

Check in the R/3 system to see if planned independent requirements have been generated.

From the R/3 standard menu: Logistics Production Production Planning Demand Management Planned Independent Requirements Display

Set “Selected version,” enter version ##, and display the planned independent requirements for product (Material) P-102 and location (Plant) 2400, for instance.


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Conclusion

Architecture and integration

InfoCubes

Demand Planning configuration


Forecasting techniques

Promotion planning


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Course Overview Diagram: Conclusion

44

33


55

66

77

8899

Configuration



Forecasting


Mass Processing

Conclusion

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Course Objectives

At the conclusion of this course, you will be able to:Configure Demand Planning in SAP APO

Create planning books and macros

Create demand plans using univariate forecasting, causal analysis, and composite forecasting

Use marketing and sales tools, such as promotion planning, lifecycle planning, and like modeling

Release demand plans to the APO liveCache (for Supply Network Planning and Production Planning/Detailed Scheduling).

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APO Application Architecture

APOSupply Chain Cockpit

OLTP (R/3)

GlobalGlobalATPATP

InformationWarehouse(SAP BW)


Salesorders

Shop floorcontrol

Inventorymanagement

SupplySupplyNetworkNetworkPlanningPlanning

DeploymentDeployment

ProductionProductionPlanning andPlanning and

DetailedDetailedSchedulingScheduling

Transportation PlanningTransportation PlanningTransportationprocessing

LIS, CO-PAHR, FI

Key performanceindicators(KPIs)

Planned ind.requirements

Historical data

Aggregated actual data can be transferred to APO from OLTP, BW (Business Information Warehouse), Excel, and Legacy systems, and stored in InfoCubes. This data is the basis for forecasting. The demand plan is created as a result of the forecast.

You release the demand plan to Production Planning, which creates planned independent requirements for Supply Network Planning (SNP) and PP/DS. You can also transfer the demand plan to the operating system (OLTP) as planned independent requirements.

The seamless integration with Supply Network Planning (SNP) and PP/DS supports efficient Sales & Operations Planning (SOP).

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Time seriesTime series

liveliveCacheCache

OrdersOrders

liveliveCacheCache

Planning areas

Planning book I Planning book II

Collaboration Interactive planning

CoreInterface

Actual dataextraction

BusinessExplorer

A planning area is the central data structure of Demand Planning and Supply Network Planning. It groups the parameters that define the scope of the planning activities. It also determines where and how the planning results are to be saved.

In Demand Planning and Supply Network Planning, data is divided into planning areas and subdivided into versions. As a result, the data that you save in planning version 1, planning area 1 does not overwrite the data in planning version 1, planning area 2.

The planning area contains characteristics and key figures for planning and must be initialized for every planning version.



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Planning and Reporting

Time sequence

Aug. Sept.

W32 W33 W34 W35 W36 W37 W38 W39 W40 W41

124

203

Material

Cust

omer

Period

Product groups

Regions

Consistent planning (top down, middle out, bottom up)

Slice & dice

Drilldowns and drill-ups

Multiple demand plans used for simulation purposes

Forecast accuracy analysis

You can use Demand Planning to simulate multiple planning scenarios online, plan consistently throughout your enterprise (top down, middle out, or bottom up), drill up and down, aggregate and disaggregate. It also supports the slice-and-dice method.

Consistent planning is used to keep planning data consistent at all planning levels. Data is aggregated and disaggregated automatically.

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Statistical Toolbox

Univariate forecasting Moving average

Constant models, trend models, seasonal models



The Holt-Winter's method

The Croston method (for sporadic demand)

Causal analysis Multiple linear regression

Composite forecasting Weighted average of multiple models

The product spectrum of a company includes a variety of products at different stages of their life cycle with different demand types.

APO Demand Planning offers a toolbox of proven forecasting methods from which you can choose the most suitable method for a specific demand type.

Composite forecasting goes beyond the idea of pick-the-best and combines two or more methods. The Croston method allows you to model sporadic demand. The statistical forecasting toolbox provides all the features you require to create accurate forecasts,

including everything from data analysis using time series models through multiple linear regression.

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Promotion Planning

Price

Quantity

-10%

Promotion patterns

’99

’01

’00

‚02Planner

Forecast simulation

Quantity

Time

Promotions can have a major impact on consumer behavior. In APO Demand Planning, you can plan promotions or other special events independently of the rest of

your forecast. You can use promotion planning to model either one-time events, such as the millennium, or repeated

events, such as quarterly advertising campaigns. Additional examples of promotions include trade fairs, coupons, free-standing inserts, competitors' activities, and market intelligence. Events that impact consumer behavior include upward or downward economic trends and acts of nature.

Promotional uplifts can be defined in units or percentages by promotion patterns. A promotion pattern that occurred in the past can be automatically detected using sales history or estimated by the planner. A promotion pattern can be archived in a promotion catalog, which means it can be reused if a promotion of the same type is repeated. A copy function in the promotion catalog also supports "like" modeling of "like products," "like regions," and so on. Several techniques are available for estimating the impact of a past promotion such as multiple linear regression with or without trend or seasonality.

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Reporting in APO

liveCacheliveCache

You can use the BW Business Explorer to run reports for: Order data from liveCache

Aggregated data in InfoCubes

Planningarea

Extractionstructure

RemoteCube

DP InfoCubeBusiness Explorer

You can also use the BW frontend to run reports for APO data. In addition to running reports for the aggregated actual data from InfoCubes, reports are run for all the

order and time series objects from liveCache. You need the following to be able to run live reports for orders and time series: A planning area in APO,

an extraction structure for the planning area, an InfoSource, and an SAP RemoteCube that reflects the liveCache data.

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Recommended Follow-up Activities

Go through the exercises for Demand Planning

Read online documentation

Read IMG documentation

Read release notes


scm220 demand planning

Documents