For More Information visit www.plan4demand.com | Call 866-P4D-INFO | or Email info@plan4demand.com Whether you are just implementing SAP's Demand Planning Module or have been "Live" for ages, Part 1 of this 2 Part series will cover SAP DP Forecasting and design tips for all occasions. Watch to learn practical tips and gain real world insights into these specific areas! - Demand Planning Hierarchies - Demand Histories - Statistical Forecasting - Product Lifecycle Management - Total Forecast Fit Presented by Gary D. Griffith and Jerry Sanderson Check out this webinar on-demand at http://www.plan4demand.com/Video-10-Tips-for-SAP--APO-DP-Part-1

1. DEMAND PLANNING LEADERSHIP EXCHANGE
PRESENTS:
The web event will begin momentarily with
your host:
& Guest Commentator
March 27th, 2013 plan4demand

2. Proven SAP Partner “Plan4Demand has consistently put
in extra effort to ensure our Griffin
 More than 500 successful SCP plant consolidation and demand
engagements in the past decade. planning projects were successful.”
-Scott Strickland, VP Information Systems
 We‟re known for driving measurable Black & Decker
results in tools that are adopted across
our client organizations.
 Our experts have an minimum of 10 years
supply chain experience.
 Our SAP team is deep in both technology
and supply chain planning expertise; have
managed multiple implementations; have
a functional specialty.

3. 3
Session 1 explores decisions that effect the
technical design of the your system
Guides you toward design aspects that builds a strong
DP foundation
A phase II redesign for these points will be painful
Session 2 deals has a functional slant that will
allow you to leverage the application
Sets the stage for a successful go-live
Guides you on successful forecast performance and
process management

4. 4
#1 Demand Planning Hierarchy
- Characteristics & Navigational Attributes
#2 Dealing with History - Multiple Demand
Streams
#3: Statistical Forecasting - Demand Pattern
Recognition
#4: Lifecycle Management
#5: Total Forecast Fit for Purpose

5. 5
The Demand Planning Hierarchy definition is the
foundation of the Demand Planning Process.
It is often a challenge for the demand team to
determine what is needed for planning purposes vs.
what is needed for reporting or forecast validation
For APO DP this means understanding when a data
element is a Characteristic vs. a Navigational
Attribute.
Get the Characteristic definitions wrong and it‟s a
costly fix as they are developed/maintained in the
Planning Object Structure (i.e. a building block in
APO)

6. 6
Demand realignments come into play when considering
Characteristics and Navigational Attributes and represent a
pain point for APO implementations
Characteristics in APO DP must exhibit stability in their values
or the use of demand realignments will be needed.
 A change to a Characteristic‟s value invalidates the use of the original
CVCs after a realignment is executed.
 New historical demand will require a new statistical forecast
 Companies manage the complexity of this process by realigning every 1-
3 months based on market conditions such as customer
mergers/acquisitions.
When a Navigational Attributes‟ value changes there is no need
for realignment;
 A Navigational Attribute is linked to a Characteristic and is not part of the
key in the database.
 Example: CPG client required validation of the forecast for 16oz.
Beverage  Size was configured as a Navigational Attribute and was
linked to the Product Characteristic

7. 7
Planning Object Structure is
where you specify the
Characteristics you want to use for
planning purposes.
The more Characteristics you
define the greater the number of
Characteristic Value Combinations
(CVC)
 CVC represents the master data for
APO DP
 Increase in CVCs means more
overhead to manage in APO both
technically and from a planner
perspective
The use of Navigation Attributes as
an alternative to Characteristics is
to help minimize the number of

8. 8
Why Navigational Attributes?
Since no CVC is actually created
for storage, we reduce the overall Navigational
system processing times and then Attributes
improve system performance.
If you decide to add Navigational
Attributes later you do not need to
deactivate the Planning Object
Structure to do so.
The planner will not see the
difference between Characteristics Characteristic
and Navigational Attributes in
Interactive Demand Planning
(tcode: SDP94).

9. 9
Navigational Attribute Pitfalls
Navigational Attributes cannot be
used in promotion planning
(reference SAP OSS Note 413526)
so that must be considered during
the design phase.
Extensive use of Navigation
Attributes leads to a large number
of tables in the „join‟ during
selections in Interactive Demand
Planning and can impact system
performance.
 Note: Use Navigational Attributes to
support the forecasting process (i.e.
validation); general reporting should
be done in the organization‟s BI
environment (e.g. SAP Business

10. 10
#1 Demand Planning Hierarchy
- Characteristics & Navigational Attributes
#2 Dealing with History - Multiple Demand
Streams
#3: Statistical Forecasting - Demand Pattern
Recognition
#4: Lifecycle Management
#5: Total Forecast Fit for Purpose

11. 11
History used in Demand Planning needs to be viewed
as Demand Planning History
History Stream Use Case
 Multiple history streams used to validate forecast assumptions
 Main purpose for history use will be for the generation of a
Statistical Forecast
 Gain better understanding of historical promotion trade
acceptance and sell through

12. 12
What types of Demand History Streams
are used in your forecasting system?
Answer on your screen
Select ALL that apply
A. Sales Order History
B. Shipment History
C. Point of Sale (POS) History
D. Other

13. 13
Define historical demand
streams, their source and
level of aggregation needed
to support the forecasting
process
What data is available and
what most closely maps to
your definition of “true
demand” ?
Is there customer information
available like POS or
warehouse withdrawal data
that better reflects true
demand?
Is there sufficient historical
demand and can this data be
consistently delivered to APO

14. 14
Definition of “True Demand”
 Use of Shipments vs. Sales
Orders
- Best practice says to use
sales orders because
shipments only reflect a
companies ability to meet
unconstrained demand
- If you are using shipments
check to see if you have
access to POS data or other
data points to analyze as well
- Cut & Ship policies in order
management do create
overstated sales orders
 Independent of the demand being
used you need to validate that
there is sufficient history and that
the history can be cleaned for
market intelligence supplied by
Sales & Marketing

15. 15
Advantages of using
POS or warehouse
withdrawal data when
forecasting in weekly
time buckets
 Week-to-week continuity for
POS data which may not
be present when using
sales orders or shipments
at granular levels
 Customer/consumer
focused information which
moves the organization to
being “demand driven” or
“pull driven”

16. 16
Define what Level of Aggregation for inbound
historical demand is required to support the forecast
process
 For example, POS might be by Item x Key Account and sales
orders by Item x Key Account x Location
- Define approach for loading POS if APO DP is defined as
Item x Key Account x Location at the lowest level of detail
Sales Orders
Key 1. Use Sales Orders to
Item Location
Account calculate proportional factors
(Key Figure: APODPDANT)
Key 2. Use APODPDANT to
Item ???
Account disaggregate POS across
Locations
POS

17. 17
Promotional Insights
History captured with defined promotional information
will have many uses
 Allows for easy cleansing to support forecasting future
promotions
 Allows Demand Planners to better understand timing
characteristics for shipments
- Early ship – Late ship, reorders, and cannibalization of
other products

18. 18
#1 Demand Planning Hierarchy
- Characteristics & Navigational Attributes
#2 Dealing with History - Multiple Demand
Streams
#3: Statistical Forecasting - Demand Pattern
Recognition
#4: Lifecycle Management
#5: Total Forecast Fit for Purpose

19. 19
Analyze and understand the historical demand patterns for your
product portfolio to enable the best Statistical Forecast at the
appropriate level of aggregation
Analyze and identify the logical grouping of CVCs that will accurately
reflect seasonality/trend and allow for forecast profiles to be
streamlined and efficiently implemented
 A common problem in using APO for statistical forecasting is that planners
do not know how the system thinks (e.g. ex-post forecast)
 In addition, they become overwhelmed because APO has ≈15 forecasting
techniques that become ≈ 30 variants (e.g. exponential smoothing with or
without alpha optimization)

20. 20
Look for products that are promoted together or
exhibit similar seasonality/trend patterns
 Hint: If the similar demand pattern grouping exhibits
seasonality then pick a model that includes seasonality 
Not necessary to test for seasonality via auto model selection
procedures in APO.
Week 2011 Sales 2012 Sales
Notice the shift in 1 97 101
180
seasonality from 2
3
89
93
98
99
160
one year to the 4 95 85
140
120
next; This data 5
6
147
127
88
110 100
could fail a test for 7 145 125 80
2011 Sales
2012 Sales
8 125 158
seasonality but it 9 76 140
60
is clear 10
11
72
98
76
104
40
seasonality exists 12 85 88
20
0
1 2 3 4 5 6 7 8 9 10 11 12

21. 21
We want to aggregate
to smooth through the
noise and yet forecast Item
the variability in
demand that can be
estimated Estimate
A A
We define noise as g Item x Location l
random or g l
r o
unexplained error e c
 For example, a small g a
retailer reducing their a t
t Item x Customer x Location e
inventory by 5% is e
insignificant (i.e.
noise) but Wal-Mart
doing the same would
be a true event.

22. 22
By aggregating Similar Demand Patterns - Total
demand  10000
8000
generating a 6000
4000
forecasting  2000
0
disaggregating the
forecast we are not Total
only improving
statistical forecast Similar Demand Patterns
2000
accuracy but also 1500
creating fewer alerts 1000
500
for demand planners 0
to manage
Product A Product B Product C Product D

23. 23
Measure Statistical Forecast Error
If using regular turn business as definition of baseline
demand then still need to measure and analyze statistical
forecast error
 Too often clients do not measure regular turn forecast error
because there are no regular turn sales orders in the
transactional system
 This leads to overreacting to what you are seeing (i.e. too
subjective) rather than analyzing and synthesizing data
 Measure the total forecast against the sales orders and then
analyze the forecast components (i.e. statistical, Marketing
input, Sales input) to gain insights on what may be driving the
total forecast error
Base Increment
Total
Sales
Statistical al Orders
Forecast
Forecast Forecast
Derive Base Error Total Error

24. 24
#1 Demand Planning Hierarchy
- Characteristics & Navigational Attributes
#2 Dealing with History - Multiple Demand
Streams
#3: Statistical Forecasting - Demand Pattern
Recognition
#4: Lifecycle Management
#5: Total Forecast Fit for Purpose

25. 25
Where a product is on its life cycle
curve determines the forecasting
approach we use
Introduction/Growth: Use of Like
Modeling & Phase-In Assumptions
in conjunction with more reactive or
aggressive algorithms
Maturity: Fairly stable demand
pattern links well to traditional time
series techniques
Decline: Phasing out of forecasted
demand based on assumptions is
required (e.g. continue to supply but
Highly dependent on industry
not manufacture)
(e.g. Electronics vs. Food
Understanding the forecast process Manufacturer)
based on where the product is in
the lifecycle and what statistical
model is appropriate falls under
demand classification

26. 26
Define profile strategy and approach for using product
lifecycle management in APO DP
Determine how product replacements / transitions are handled by
the business and then define approach in APO that aims to
balance business scenario coverage with maintenance/overhead
of too many profiles
Replacement Launching of a New Product
Old
Product
New
Product ? New
Product
x , å, å a x
i i
Assigning the Like Profile to
characteristic values (maintain in the
Forecast Profile)

27. 27
For example, CPG client
built 12 phase-in/phase-
out profiles per demand
planner based on analysis
of past product
replacements / transitions
 Reduced manual
maintenance effort and
enabled more focus on
improving forecast accuracy
 Built credibility with
Sales/Marketing & Supply
Planning because the
process was a well
managed, stable ramping up
and ramping down of
forecasted volume

28. 28
#1 Demand Planning Hierarchy
- Characteristics & Navigational Attributes
#2 Dealing with History - Multiple Demand
Streams
#3: Statistical Forecasting - Demand Pattern
Recognition
#4: Lifecycle Management
#5: Total Forecast Fit for Purpose

29. 29
Our Demand Planning Process
Includes Collaboration and Input from:
Answer on your screen
A. Supply Chain Only
B. Supply Chain and Finance
C. Supply Chain, Finance, and Commercial
D. Supply
Chain, Finance, Commercial, with inputs
from Customers, Marketing & Consumer
Insights
E. No Collaboration Used

30. 30
Define your Total Forecast as a consolidation of the
wealth of forecast information available within your
organization.
Maintain transparency to the Total Forecast definition so
everyone understands their accountabilities from design to
execution.
Define what will make up the Total Forecast by understanding
what information is available and who will communicate that
information
 Baseline Statistical Forecast: Should it reflect regular turn
business (less promotional activity) or total business?
 Marketing Activity: Will Promotional Activity, FSI and Advertising
volume impacts be communicated by Marketing?
 Trade Promotion Management: How will we integrate trade
promotion information such as products, customers, timing &
volume from Sales?

31. 31
Maintaining Transparency Example
Core project team needs to have Statistical
Base
Lift
transparency with the wider business Estimates
stakeholder team so that during UAT there
are no surprises
 Example of a dysfunctional approach when
stakeholders have not been aligned:
- Demand Planning: Assumed forecasting
regular turn business and promotional
activity delivered by Sales & Marketing in a Trade
timely manner Promotions
Promotional
- Marketing: Assumed Demand Planning Calendar
was forecasting total demand and looking
for exceptions
- Sales: Knew timing and high level volume
estimates in the short term but Demand
Planning wanted the information by Item
- Result: There was no alignment in what
makes up the total forecast requirements

32. 32
Properly Defining Baseline Demand
Define baseline demand as regular turn or total business?
 You first decide how baseline demand and incremental demand will be
managed
- Will estimated lift be communicated by Sales/Marketing?
- Will the lift cover the entire forecast horizon?
- At what level of detail will the lift be communicated?
- Will the communication be in a defined, timely and consistent
manner ?
 Is it on the demand planning calendar?
 If you have a promotionally driven business then baseline demand is
typically defined as regular turn business and you negotiate the lift
accountability with Sales/Marketing
 If you are constrained by your ERP system, how do you derive regular
turn business when historical demand is stated in either total sales
orders or total shipments?
- First you clean the total historical demand to create regular turn
demand history.
- To do this you need help from Sales/Marketing to identify

33. 33
Market Intelligence
Integration of Market Intelligence
 What is Market Intelligence ?
- Competitive Intelligence such as
competitor pricing strategies
- Market Share & Market Trends
- Customer Insights such as brand loyalty
 The challenge is how do we turn this
intelligence into a volume estimate or
% impact to be integrated into the consensus forecast?
- Business Intelligence tools can often support that process
- Forecast drivers are often based on business assumptions.
 Collecting and tracking them is important because the
drivers may shift and vary
 These must always be re-evaluated

34. 34
Market Intelligence Example of Highly Promoted
Products:
Marketing Forecast Inputs
 Communicated promotional volume, timing by product / product
grouping for next 12-18 months in monthly buckets
 Captured & tracked strategic planning
assumptions  became reference point for
use in validating that logic was still on target
Sales Inputs:
 Communicated promotional volume, timing by
account x product for the next 1-3 months in weekly buckets
 Captured and tracked sales execution assumptions  became
reference point for use in validating that logic was still on target
 Demand Planning had to validate if the communicated forecast was
attainable or just reflected sales quotas
- Make sure you are not polluting the forecast with bias

35. 35
The CVCs and Navigational Attributes are the foundation of
APO DP - Design it right and results will be:
 System performance gains through fewer CVCs
 Fewer demand realignments
 CVCs need to be static master data (slowly changing can be accommodated)
 Whereas navigational attributes are used with filtering and validating forecast
information
Finding true demand is a journey best facilitated by considering
multiple demand streams to create the baseline demand definition that
feeds the generation of the statistical forecast.
Use demand pattern recognition to define the appropriate level
of aggregation (e.g. better depict seasonal trends) to generate
the statistical forecast
 Improved prediction and a more manageable number of CVCs
 Fewer forecast alerts, lifecycle profiles, etc.
Take into consideration CVCs required to support the integration of
Market Intelligence at the appropriate review level.

36. 36
Product lifecycle management in APO DP is a high
maintenance business process for the demand team
 Define a robust profile strategy that is repeatable
 Define a stage gate approach to the process for scalability.
Maintain transparency to the Total Forecast definition
so everyone understands their accountabilities from
design to execution
 Minimize go live surprises
 Produces a higher quality Forecast

37. Join us on LinkedIn: Demand Planning Leadership Exchange
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10 Tips for SAP APO DP | Part 2
April 17th | 12:15 PM ET
If you use SAP to Plan… Think

38. For Additional Information or a PDF
Copy
Contact:
Jaime Reints
412.733.5011
jaime.reints@plan4demand.com