DEMAND PLANNING
BASIC PROCESS
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Goal of the Presentation
1
DP Concepts
2
DP Standard Process in Dow
3
DP Lite Process in Dow
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DP Concepts – Topics to be covered
1
Overview of DP
2
Various Methods of Forecasting
3
Aggregation & Disaggregation
4
Integration with BW
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Overview of DP
Supply Chain Strategic Planning
Strategy
2- 5 years
Demand Planning
1 – 18
months
Planning
Supply Network Planning
Production Planning &
Detailed Scheduling
Scheduling
Execution
Source
Source
Plan
Global
ATP
Production
Outbound
Transportation
Inbound
Transportation
Measurement
Monitoring
Deployment &
Transportation
Planning
Monitoring
Source
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1 – 13
weeks
Analysis
Make
Deliver
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Integrated Planning Process
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SAP Planning Process
Customer
Orders
• Historical Data
• Inventory Levels
• Customer
Forecast
• Sales Forecast
• Market Info /
Unconstrained
Consensus
Demand
Forecast
Trends
• Other Inputs
• Inventory
Targets
• Capacity
Constraints
Constrained
Consensus
Supply Plan
Replenishments
补单
Daily
Production
Schedules
• Customer /
Other
Constraints
Reconcile Discrepancies
Planning
Horizon
1-2 Years
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6-18 Months
1-12 Weeks
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Demand Planning Concept
Aggregated actual data
汇总数据
Location
Future demand
forecast
Product hierarchy
Sold-to party
Sales organization
Region
l
l
l
Incoming order
value
n
Quantities
n
Values
Invoice
n
Quantities
n
Values
BW
R/3
l
Statistical forecasting
l
Collaborative forecasting
l
Promotions
Excel
Non-SAP system
…
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Core Competencies
Data Analysis & Adaption
Promotion / Event Planning
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Statistical Forecasting Methods
Lifecycle Management
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Demand Planning – Forecast
The Forecasting Procedure
Reading
Reading
the
the
actuals
actuals
Adjusted
Adjusted
history
history
 Actuals can be adjusted both
automatically and manually
 Analysis of historical data helps
to predict future patterns
Forecast
Forecast
Corrected
Corrected
forecast
forecast
 Usage of proven forecasting
models
 Automatic model selection can
be used
 Graphic monitoring and
adjustment of historical data, and
forecast results
Used
Used as
as demand
demand plan
plan
 SAP AG 2001
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Forecast Adjustments
Actual Data Adjustment
Actual sales key figure
Delivery
problems
Trade fair sales
Sales
Corrected history key figure
Adjustment
Time
 SAP AG 2001
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Types of Forecasting Methods
• One Dimensional (Univariate)
–
Time Series Techniques, using only history alone
时间序列技术，仅使用历史记录
• Multidimensional
–
Causal Techniques, built on a relationship(s) between past sales and some other variable(s)
因果技术，建立在过去销售和其他一些变量之间的关系之上
Causal
Time Series
History
F
o
r
e
c
a
s
t
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Population
Shipments
Income
Level
F
o
r
e
c
a
s
t
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Forecasting Methods
•
Forecasting Methods
– Exhaustive Library of Univariate Techniques 单变量技术的穷举
– Multi-linear Regression, Composite methods for advanced forecasting 多元线性回归
，用于高级预测的复合方法
•
Supports constant, trend, and seasonal patterns 支持恒定，趋势和季节性模式
– Moving Averaging 移动平均
• Simple
• Weighted
– Exponential Smoothing 指数平滑
• One parameter - Constant Model 参数 - 常数模型
• Seasonal/Trend Model 季节/趋势模型
– Simple Regression 简单回归
• Least squares method - one explanatory variable 最小二乘法 - 一个解释变量
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Forecast Measures
•
Directional Measures 定向措施
– Total Error (Total Forecast Error) 总误差（总预测误差）
– MPE (Mean Percentage Error)
•
Absolute Measures
MPE（平均百分比误差）
绝对措施
– MAD (Mean Absolute Deviation)
MAD（平均绝对偏差）
– MAPE (Mean Absolute Percentage Error) MAPE（平均绝对百分比误差）
– MSE (Mean Square Error) MSE（均方误差）
– RMSE (Root of the Mean Square Error) RMSE（均方根误差的根）
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Promotion Planning
 Promotion Planning
 Ability to compare the unpromoted forecast with the promoted forecast.
 Ability to correct sales history by deducting from it past promotions, thus obtaining
unpromoted historical data for the baseline forecast.
Forecast simulation
Sales
Total sales:
Profit
Total profit:
Promotion 1
-10%
Price
Promotion patterns
2010
2011
2012
2013
Planner
Planner
Quantity
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Promotional Planning
Start of promotion
Demand Plan
100 %
Forecast
M
M
07/01 08/01
M
M
09/01 10/01
Time
M
11/01
Period
M1
M2
M3
M4
M5
Unit
Corrected forecast
100
100
100
100
100
KG
Promotion Percentage
Promotion key figure
Demand Plan
+10% +20% +10%
-5%
%
KG
10
20
10
-5
110
120
110
95
100
Macro
KG
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Lifecycle Planning
 The lifecycle planning in APO demand planning can be used to model the various phases of product
lifecycle. The like modelling helps to forecast new characteristic value combinations (CVCs) which do
not have historical data. The phase-in/ phase-out profiles help to model lifecycle stages like launch,
growth, maturity and discontinuation of a product. The lifecycle planning is fully integrated with
product interchangeability functionality and can be leveraged in univariate, multiple linear regression
and composite forecasting.

Use historical sales patterns for a new product introduced in the same family

Ramp down the forecast of products to be discontinued and ramp up the introduction of newer
products
Actuals of the old product
Sales
Forecasting of the new product
Lifecycle
Like
Time
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Causal Forecast Techniques
Multiple Regression
 Causal model - more than one explanatory variable
Constant
Coefficients
Yi = bo + b1X1 + b2X2... + bn Xn
Sales
Causal
Factor
 Unlimited Causal Factors (Price, Temperature, ...)
 Multiple Linear Regression (Lags)
 What-If Analysis / Marketing Mix Planning (ads, promos, …)
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Causal Analysis- Example
Causal Analysis: Advertising Budget
Future
Feb.
Mar.
April
budget:
May
June
$ 1500
budget:
Calculation
of sales
using b
Planned
$ 1000
Actual
advertising
$ 2000
Calculation
of b
July
$ 800
Unit Sales
History
Aug.
Sept.
 SAP AG 2001
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Composite Forecast
 Weighted average of multiple forecast methods
 Simple average
Univariate
1
Forecast
Univariate
...
Combine &
Reconcile
MLR
Result
MLR
n
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Key Demand Planning Elements
•
Aggregation/Dis-aggregation
– Slice and dice data in various ways. Various options in deciding the basis
for dis-aggregation (proportional factors)
•
Macro Builder
– Simple/advanced macros to automate routine activities and generation of
alerts
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Aggregation & Disaggregation
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Disaggregation Methods
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Macros
 Macros are user-defined formulas that can be
executed within the grid of a planning book.
 Operations on single fields, rows, columns and
areas
 Several steps in a macro possible
 One step can contain multiple arguments
 Control statements like if, else if
 Operators like (), <>, sin,sqrt, exp, trunc
Planning view
InfoCube
Invoiced sales
Actual sales
Forecast
Corr. forecast
 Functions like sum, avg, max, mad, var
Revenue
 Alerts
宏是用户定义的公式(规则)，可以在
Planning book 的网格中执行。 单个字段，
行，列和区域的操作 宏可能的几个步骤 一
步可以包含多个参数 控制语句，如if，else
if 运算符如（），<>，sin，sqrt，exp，
trunc 函数如sum，avg，max，mad，var
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 Operations on single fields,
rows, columns and areas
in one
 Several steps possible
Macros
macro
Macro Builder(/SAPAPO/ADVM)
 One step can contain several
arguments
Perform complex calculations quickly and easily.

 Control statements such
asdirectly
if, elsebyif the user in
Executed either
Operators
such as at a
interactive 
planning
or automatically
(), <>, sin, sqrt, exp, trunc
predefined point in time during a background
job

 Functions such as
SUM, AVG, MAX, MAD, VAR
Functions include
Mathematical,
Statistical ,
Alerts,
Mails

Invoiced sales
Actual sales
Forecast
Corr. Forecast
Profit
Logical, Boolean, Date as well as very specific
Planning related

Can be scheduled in Batch Jobs, send
automatic emails, Alert Planners among many
other functions
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Macros
Consensus forecast
35
48
37
44
40
45
30
50
30
Actuals (previous year) 42
20
27
Consensus forecast
32
41
Finance budget
Marketing
Sales
40
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40%
30%
30%
Macro
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Automatic Macro Execution
•
•
•
When user load the selection in planning book system calls default and start macros.
Whenever user press enter or any change in planning data system calls default
macro.
At the time of save data system calls exit macro.
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Macros & Alerts
 Using Macros to Generate Alerts
 Standard as well as customized alerts using macros
 Different types of alerts:
 Database Alert – Generated only when you run the macro created for it.
 Dynamic Alert – Generated when the data is changed in the planning books.
Alerts Priorities
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Character Value Combinations (CVC)
•
T-code: /N/SAPAPO/MC62
•
A characteristic value combination is the combination of characteristic values with
which you want to plan. It is sometimes referred to as a characteristic combination.
•
You can only plan data if you have defined such a combination.
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Data Store objects and Info Cube
•
•
A Data store/Info cube object serve as a storage location for consolidated and cleansed
transaction data or master data on a document (granular) level.
Data Store objects have the option of overwriting records ,where Info cube does not.
Data Stores
Info cubes
T-code: Listcube
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Planning Books and DataViews
•
•
The Planning Books/Data views is made up of rows or Key figures that intersects with time
buckets which are columns .
Many of the Key figures that planners see in Planning book/Data View are actually
calculated values.
Selection window
Key figures
Planning book
Data Views
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