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 2 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! - Developing a Working Prototype - Defining Roles & Responsibilities - Managing the Forecast Process - Exception Management - Measuring Forecast Performance Presented by Gary D. Griffith and Ed Neville. Check out this webinar on-demand at http://www.plan4demand.com/Video-10-Tips-for-SAP-APO-DP-Part-2

1. DEMAND PLANNING LEADERSHIP EXCHANGE
PRESENTS:
The web event will begin momentarily with
your host:
& Guest Commentator
April 17th, 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 average 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 explored 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 has a functional slant that will allow you to
better leverage the application
Sets the stage for a successful go-live
Guides you on successful forecast performance tracking and
improved process management

4. 4
#6 Developing a Working Prototype
#7 Manage the Forecasting Process
#8 Change Management
– Start with Roles & Responsibilities
#9 Exception Management
#10 Forecast Performance

5. 5
Improve the quality of your decisions and decrease idle time
between decisions by building a “Working Prototype”
Planning solutions are difficult to depict in PowerPoint and often
clients face change management and alignment issues when they
first see the configured solution during testing
While a working prototype expands the initial design phase of
the project it provides a payout throughout the rest of the project.
 Helps mitigate risk and institutionalizes a learning organization better
prepared for system hand-off
 Moves people more quickly from a “Look & Feel” focus to a “Use of
Data” focus
 Provides a better understanding of how
transactions are interrelated and the impact
on the overall system design

6. 6
Client used prototype Client did not use prototype
 Used DP Bill of Material  Interactive demand planning
capabilities which could have graphical capabilities were
resulted in a delayed design expected when most Data Views
decision were built in a tabular format
 Allowed planners opportunity to  Less efficient Data Views existed
see the configured solution and because user navigational needs
assess alignment with business not well understood (e.g. drilling
needs down on selected key figures)
 Built early negative attitudes
 Facilitated Fit/Gap analysis
resulting from a perceived
 Started the user adoption inefficient design
process early and kept team  Delayed Go Live caused by users
engaged advancing the learning curve, high
risk of reverting to old ways

7. 7
The process, by it’s nature of
being part art and part science
is vulnerable to subjectivity.
The process needs to be tightly
defined and managed
Understand the drivers of
uncertainty and apply focus to those critical areas
 Cleaning historical demand for one time events or other anomalies
 Managing statistical forecasting exceptions and tuning models
 Incorporating judgmental input and measure/understand the impacts
such as potential bias
This focus reduces clutter in the forecasting process and leads to
improved forecast results

8. 8
Impacts of maintaining structure in the forecasting process:
Reduces likelihood of bias entering the forecast process
Builds a high level of credibility in the forecast
 Remember since the output of forecasting is an estimate (i.e. “best
guess”) there is a certain amount of “leap of faith” that everyone takes.
Allows for faster development of action plans to close
“forecasting gaps”, such as significant gaps to the Budget /
Quarterly Revisions
 Forecast is better understood and respected
Creates an ecosystem for sustainable change
 Shapes the way people think about and approach the forecasting
process, addressing issues and making trade off decisions
Drives standardized workflow – locally & globally

9. 9
Focused approach achieved by
aligning planning book / data views
with the key steps in the forecasting
process:
 Clean History Planning Book
 Statistical Forecast Planning Book
 Consensus Forecast development,
validation and modifications
Planning Book
Less art & more science is better at
this stage
It’s a building block process that is
standards-driven to ensure quality
results

10. 10
Our Demand Planning Process
Roles & Responsibilities are:
Answer on your screen
A. Not Well Defined
B. Defined For Supply Chain Only
C. Defined For All Demand Planning Process
Key Stakeholders, But Lack Adherence
D. Defined, Well Structured & Adhered To
E. Do Not Know

11. 11
Change Management is an area that needs constant
care and feeding. The most effective ways to ease the
transition to a new or improved DP process are:
Have a solid understanding of the core competencies
required for the Demand Planning Process
Ensure roles and responsibilities are defined or redefined
properly to meet the goals of the new or improved design
Assess your demand planning team’s fit to these roles and
responsibilities
 Identify team’s strengths and improvement opportunities

12. Gain an understanding of the core competencies your
company needs to succeed at Demand Planning
Determine the team’s process and technology Consensus
Based
strengths and identify opportunities for Demand
Planning
improvement. Examples include:
 Collaboration & Generation of the Consensus
APO Analytical
Forecast Demand Skills
Planning
 Improved Statistical Forecasting Capabilities
 Effective use of exception based management
Mainly accomplished through attrition hire demand planners
familiar with DP best practice process knowledge as well as
having prior APO DP experience. This can be a major asset
throughout the implementation.

13. 13
It is not uncommon for projects to shortcut or eliminate efforts
around Roles and Responsibilities
 You see this happening more if the tasks are to redefine the roles and
responsibilities
 For example, redefining existing position roles and responsibilities can be
quite the endeavor as HR will need to be involved and senior
management approval is often required
 The impact of not doing this however is on the ability to measure
demand planner performance and adherence to the new process
Take the time to define or redefine in order to meet the new or
changing goals
 Captures “Tribal Knowledge” and “Know-How”
 Rationalizes common best practices and captures critical knowledge to
pass on to others in a systematic way
 Drives a “change in thinking” within the team from a production culture to
a performance culture through understanding “end game” and “personal
measurements”
 Engages the team to think beyond their specific “silo area”

14. 14
Build transparency in R = Responsible, A = Accountable, C = Consulted, I = Informed
the communication of Activity
Demand
Planner
Sr.
Demand Demand Sales
Planner Manager Finance Mktg SPS CCID
the Roles and Drive highest level of item/location forecast accuracy
Publish regular forecast accuracy updates and forecast
R R R C, I A A C, I
actualization to cross functional partners to drive
Responsibilities consensus on problem areas and messaging
Understand and challenge the assumptions of
R R R C, I A A C, I
assessments or changes to the forecasts based on cross
 Clear Job Descriptions functional inputs
Inquire about forecast changes and request for
R R A C, I C, I C, I C, I
and use of RACI additional investigation if changes do not meet
expectations A A A C,I R R R
Matrix are tools at Identifying the impact of trade spend and any
assumptions of that impact on sales lifts A A C, I A C, I R C, I
your disposal
Provide DSMP volumetric impact by customer A A C, I A C, I R C, I
Provide merchandising calendars and explanation to
specific promotions and sales lifts A A C, I A R C, I C, I
 Do not fear an Provide recommendations for overriding changes in
forecast inputs based on understanding of implications
evolving process,
and assessments of impacts R R A C, I C, I C, I C, I
Gain consensus and alignment of forecast inputs to be
entered into APO DP from cross functional team R R A C, I A A A
allow it to change and Provide input to Demand Review meeting based on
pre-defined templates R R A C, I C, I C, I C, I
become a driver for Facilitate Demand Review meeting to Brand Manager
Make the decision on the forecast volume based on
A A R C, I C, I C, I C, I
enhancing supply facts and consensus from respective cross functional
inputs. R R A C, I C, I C, I C, I
chain performance

15. 15
Any DP roles and
responsibilities assessment
needs to cover the
analytical and statistical
competency of the team to
determine how to best
focus training and / or skill
enhancements
Do not be afraid to bring
in a statistician to help
analyze historical demand
patterns and recommend
model selection and tuning
approaches for go live and
post go live optimization

16. 16
Our Exception Based Management Process is Best
Described As Being:
Answer on your screen
A. Not Well Defined
B. In General More Reactive
C. More Proactive
D. No Exception Based Management Used

17. 17
Become reliant on exception
management to surface future
issues today.
Look for recent trends in alerts
 For example, are we consistently
under or over forecasting for
recent time periods implying
potential forecast bias?
Do not overreact to an
exception but rather look for
repeated patterns (i.e. pattern
recognition)
 Do not be afraid to use Excel
pivot tables to help identify
patterns in alerts by looking at
product and customer groupings

18. 18
Build a strong Management by Exception (MBE) process
Ensure the process is skewed towards proactive MBE which
anticipates exceptions within the forecasts
 Maintain the highest focus in this area
Continue to use reactive MBE or ones which deal with
detection of exceptions that already have occurred
 If proper focus is delivered to proactive MBE the need for the
reactive MBE will lessen over time
Example of
identifying potential
obsolete products

19. 19
Statistical forecast thresholds
are defined via forecast
alert profiles and contain
Information / Warning /
Error status capabilities to
help with prioritization
Define alert threshold values that result in a manageable number of
alerts being reviewed by a demand planner
 One of the major pain points expressed by planners across industry
verticals is “too many alerts”
 For macro driven alerts the macro itself may need to be modified which
involves some configuration
 A working prototype would provide good insight on alert threshold tuning
needs prior to go live
 Use of pattern recognition is critical

20. 20
Build flexibility into your
Forecast Performance
Baseline & Consensus Forecast
Measurement & Tracking
Process 7000
6000
Provide the ability to 5000
4000
measure all inputs that 3000
make up the consensus 2000
forecast number 1000
0
 Statistical forecast
Sep-11
Sep-12
Sep-13
Mar-11
May-11
Mar-12
May-12
Mar-13
May-13
Jul-12
Jan-11
Jul-11
Jan-12
Jan-13
Jul-13
Nov-13
Nov-11
Nov-12
performance to get the
historical / objective Baseline Fcst Consensus Fcst
perspective
 Sales & Marketing
inputs/overrides to
gauge where working
well and where to focus

21. 21
Track performance at the input/influence level of aggregation
Provide drill down capability that will allow you to isolate the
contributors of the error
 The statistical forecast is often measured at multiple levels of the
forecast hierarchy for the supply chain
- Item x Location for Distribution Planning
- Item for Manufacturing & Purchasing
 An example from a Food company for Sales & Marketing inputs:
- Sales: Item x Key Account for next 3 months
- Marketing: Item for months 3 -18

22. 22
FVA is defined as the change in a forecasting performance metric (whatever metric
you happen to be using, such as MAPE, forecast accuracy or bias) that can be attributed
to each particular step and participant in your forecasting process
FVA analysis also compares both the statistical forecast and the analyst forecast to
what’s called a naïve forecast
In FVA analysis, you would compare the analyst’s override to the statistically
generated forecast to determine if the override makes the forecast better
In this case, the naïve model was able to achieve MAPE of 25%
• The statistical forecast added value by reducing MAPE five
percentage points to 20%
• However, the analyst override actually made the forecast worse,
increasing MAPE to 30%
• The override’s FVA was five percentage points less than the naïve
model’s FVA, and was 10 percentage points less than the
statistical forecast’s FVA
Source: Michael Gilliland SAS Chicago APICS 2011

23. 23
Measure lagged forecast
error and lagged For a chemicals company this meant:
forecast bias to - Lag1 (i.e. Forecast for April;
determine what is not Developed in March) for
working and what are the distribution and manufacturing
implications - Lag 6 (i.e. Forecast for September;
 The lags must reflect the
Developed in March) for Purchasing
distribution,
manufacturing and
purchasing lead time
requirements
Enables ongoing
improvement of forecast
performance as we
measure not only the
statistical forecast but
also all of the intelligence
components and at the
levels provided

24. 24
Adults learn in a very different way, the new social media is
training us to communicate in short rapid bursts
 With a prototype that builds out throughout the project we are able to
capture and validate changes and move at a rapid pace
The Forecast Process, by it’s nature of being part art and part
science is vulnerable to subjectivity and an easy process to run-off
course
 Maintain enough control to enable consistency without stifling change
An Exception Management process is very similar to a diet. Hard
to start, keep on track and moving, but pays off in the long run
Forecast performance is key to a successful Demand Planning
process. Solid KPIs will allow you to insure continuous progress.
Follow the trend line not the headline

25. Join us on LinkedIn: Demand Planning Leadership Exchange
Follow us on Twitter: @Plan4Demand
THANK YOU!
Save the Date or Click Below to Register!
S&OP HANA | May 8th
Presented by Andrew McCall, S&OP Solution Leader
If you use SAP to Plan… Think

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