DP Configuration

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Demand Planning Configuration



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Demand Planning Configuration

Planning Area

Planning Book

Data View

Characteristics Key Figures

CharacteristicsProduct

Region

Sales Area

Customer

Key FiguresActual Sales

Promotions

Demand Plan

Production Quantity

Version000 Active Version

001 Simulation

• DP and SNP data is divided into planning

areas, and subdivided into versions.

• Planning Area contains characteristics, and

key figures for planning, and must be

initialized before planning.



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Master Planning Object Structure

• A master planning object structure (MPOS)

contains plannable characteristics for one or

more planning areas.

• Characteristics determine the levels on which

you can plan and save data. E.g. If planning is

done at say Product level then the MPOS will

contain those Characteristics.

• The MPOS forms part of the definition of a

planning area. The existence of an MPOS is

therefore a prerequisite for being able to create

a planning area.

• Generation of CVC is done in MPOS.

• “Aggregates” are also defined here.

Master Planning Object Structure

DP Characteristics, SNP Characteristics

Aggregates

Planning Area

Characteristics Key Figures Version

Characteristic Value Combinations

CVC



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• A master planning object structure contains plannable characteristics for one or more planning areas. • In Demand Planning, the characteristics can be either standard characteristics and/or ones that you have created yourself in the Administrator Workbench.

Master Planning Object structure



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Characteristic Value Combinations

Characteristic Value Combinations (CVC) define the relationship between characteristic values and form the basis for

aggregation / disaggregation of key figure values

• CVC are created in MPOS because it is the MPOS that

contains all the characteristic to decide at which level

planning will be done.

• CVC are either created manually or automatically

generated.

• It is against these CVC that values in Key Figure are

stored.



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Planning Area

Planning Areas are the central data structures for Demand Planning and Supply Network Planning.

Planning Area

(Characteristics and Key Figures)

General Parameters

• Base Unit of Measure

• Base Currency

• Exchange Rate Type

Storage Buckets Profile

(Defines the time bucket in which data is stored)

• Day

• Week

• Calendar year/month

• Quarter

• Year

• Posting Period

Storage Bucket Profile is a pre-requisite to have a Planning Area.



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Storage Bucket Profile

There are two kinds of time bucket profiles: one is used for storing data (the storage buckets profile), and the other for planning the data (the planning buckets profile). A storage buckets profile defines the time bucketsin which data based on a given planning area is saved in Demand Planning or Supply Network Planning.

In a storage buckets profile, you specify:

· One or more periodicities in which you wish the data to be saved

· The horizon during which the profile is valid.



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Planning Bucket Profile

Information that is incorporated into the definition of the past or future time horizon of demand planning.

The planning buckets profile defines the following:

Which time buckets are used for planning

How many periods of the individual time units are used

The sequence in which the time periods with the various time units appear in the planning table



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Planning Area

Planning areas are the central data structures for Demand Planning and Supply Network Planning. The planning area is created as part of the Demand Planning/Supply Network Planning setup. A planning book is based on a planning area. The end user is aware of the planning book, not the planning area. The liveCache objects in which data is saved are based on the planning area, not the planning book.



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Planning Area Version

APO Master Data (Model Independent)

Planning Area

Characteristics Key Figures

Active Version 000 Planning Version n

Active Model Simulation Model

• Versions store data for a planning area

• To store data for a planning version, time series must be created for that version.

• The time series is created in the Live Cache for each CVC, and each key figure.



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Consistent Planning ModelAg

greg

atio

n

Disaggregation

Planning

Proportional Values Generated

At Lowest Level

Pro-Rata or Proportional Factors

Planning Level• Demand Planning’s method of aggregation /

disaggregation is referred to as a “consistent”

planning model.

• It utilizes the lowest level of actual data to generate

proportional values throughout its planning network

• Changing a value anywhere within this planning

network automatically triggers a proportional re-

propagation throughout the network.

Pro Rata Factors

The data is disaggregated according to the

distribution ratio that the system derives dynamically

from the existing planned data.

Proportional Factors

The proportional factors are percentage-based and

held in the key figure APODPDANT.



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Pro Rata Disaggregation

DC Customer Sales New Sales

LA 350 700

NE Kmart 100 100

LA Kmart 150 300

NE Wal-Mart 50 50

LA Wal-Mart 200 400

Summary

Detail Aggregation

Dis-aggregation

• The Key Figure (E.g. Sales) data is disaggregated according to the distribution ratio that the system derives

dynamically from the existing planned data. The proportional factors are not used in this disaggregation type.

• In this example, the data is distributed to Kmart and Wal-Mart in DC LA based on the ratio 3:4 (150:200)



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Proportional Disaggregation

DC Customer Sales New Sales Prop. Factors

LA 350 700 1.0

LA Kmart 100 100 0.25

NE Kmart 150 350 0.25

LA Wal-Mart 50 50 0.25

NE Wal-Mart 200 350 0.25

Summary

Detail AggregationDis-aggregation

• The Key Figure (E.g. Sales) data is disaggregated as per the disaggregation type Based on Proportional Factor

• Here, Kmart and Wal-Mart in DC-LA have the same proportional factors (0.25), so the data is distributed equally

• The proportional factors are percentage-based and held in the key figure APODPDANT which has to be included

when we create the Planning Area.



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Thank You

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Documents

DP Configuration