866.P4D.INFO | Plan4Demand.com | Info@plan4demand.com If you are still using manual processes to support your demand planning cycles outside of APO, this Leadership Exchange is for you and your team. Join us to learn how to remove the burden of magnitude and get back on the track to leveraging your SAP APO DP to the fullest beginning with Statistical Forecast Optimization. The session will focus on common issues and methods to maximize your implementation in order to really turbo-charge your Demand Planning. To do this, we’ll touch upon ways to simplify the process, which statistical models to use and when, and how to prioritize and manage by exception effectively for the long haul to evolve with your business. A few key takeaways from this session include: How to unclutter the process Which Statistical Model to use & When Tips for holistic optimization Future design considerations Check out this webinar on-demand at http://plan4demand.com/Video-SAP-APO-DP-Statistical-Forecast-Optimization

1. DEMAND PLANNING LEADERSHIP EXCHANGE
PRESENTS:
The web event will begin momentarily
with your hosts:
July 25th, 2012 plan4demand

2.  Goals for the Session
 Uncluttering the Process
 Managing by Exception
 Case Study: Statistical Optimization
 Statistical Forecasting Tips & Tricks
 Design Considerations
 The Bottom Line
 Q&A/Closing

3.  Goal
 Focus on common business challenges seen when
evaluating companies who have implemented APO DP
 Objectives:
 Talk through three key business challenges with
recommendations for improvement
 Design considerations when implementing APO DP
 Key Takeaways

4.  Take a “Building Block” Approach
 Build Data Views which Align to DP Processes
 Pointed Data View for key DP Processes
- History Management
- Statistical Forecast Management
- Sales and Marketing
- Final Consensus Forecast Management

5. 5
 History Management
 Creating a data view that shows current and prior year historical demand
(shipments, orders, etc.) as well as historical promotional information is key to
enabling clean base history process
 The end result is the creation of a historical base that is the input for
generating a statistical forecast

6. 6
 Statistical Forecast Management
 Build a Data View that reflects only base history and statistical base forecast in
order to support the modify and adjust statistical forecast process
 Note that in this example two reference statistical forecasts are present because this
client generated statistical forecasts at various levels to support the forecast process

7. 7
 Sales & Marketing
Management
 Adjustments can be
managed via two data
views if not using an
integrated technology
approach (e.g. CRM)
 Non-Promotional Adjustments
such as distribution changes
 Sales Promotions using APO’s
promotion planning
 Recall that the promotional
and non-promotional
volumes become historical
reference points for the
clean history process over
time

8. 8
 Final Consensus Forecast
 Typically most cluttered data view as components from the “Day in the Life” activities are
combined to get a holistic view
 Review is typically completed at a higher level (e.g. product family / key account or product
across accounts)

9.  Exception Based Management
Typical Business Challenges
 Too many business rules
generating too many alerts.
- Alerts set too rigid for the
process’s maturity stage
 Lack of education on using alert
monitoring and alert profiles to
better manage exceptions (e.g.
Using thresholds for forecast
alerts)

10. 10
How many CVCs per Planner are being
managed on a weekly basis?
Make your selection on the right side of your screen
A. 500 -1,000
B. 1,000- 10,000
C. 10,000-25,000
D. 25,000 +

11. 11
 SDP Alerts (e.g. Macro-Dependent Alerts)
 SDP Alerts are based on the planning book
structure and are calculated using macros
Some typical business rules used to
define macro alerts are:
 Checking for new or discontinuations Potential risk of generating too many alerts:
relative to product shipping from a new
location  Good information but risk of chasing noise in
the supply chain (e.g. rogue shipment)
 Customer has not placed an order in
the past x months
 Forecast exceeding prior year sales by  If forecasting in weekly buckets could
x% generate a lot of alerts!
 Large differences between statistical  Does this make sense right after an
and consensus forecast implementation?

12. 12
 Forecast Alerts
 The system generates
forecast alerts if the
historical data upon which
the forecast is based
cannot be correctly
described by the selected
forecast model
 APO DP uses forecast
error limits
 You can control the
magnitude of alerts
generated and alert
priority by setting up a
forecast alert profile and
using threshold values

13.  Statistical Forecasting Not Being Holistically Optimized
Typical Business Challenges
 Typical demand planner skill is solid from business/product
knowledge
 But lacks statistical forecasting skills
 Implementers create a standard set of forecast profiles,
trend dampening profiles, etc.
 No post go-live checkpoints to assess if the standard approach is
working
 Demand planners not comfortable using statistical
forecasting process available in APO

14. 14
Let’s take a deeper dive into the statistical forecasting pain
points and how to be comfortable using the tool…
What Types of models are you familiar with
within APO below?
Select ALL that apply on the right hand side of your screen
 Manual Forecasting
 Linear Regression
 Season + Linear Regression
 Median Method
 Croston’s Method

15. 15
 Statistical Models / Techniques to select:
 5 – Constant  Copy History
 4 – Trend  Manual Forecasting
 Linear Regression
 2 – Seasonal  Season + Linear Regression
 2 – Seasonal trend  Median Method

 6 – Automatic model selection 1  Croston’s Method
 1 – Automatic model selection 2  External Forecast / No Forecast
Model(s) are assigned to
Profiles
Profiles
Assigned to
SSelections

16.  Based on business discussions and analysis of current
environment, client possessed components of both “Aware” and
“Functional”
 Largest area of opportunity was statistical forecasting and
exception based management both available in APO

17.  Forecast Review / Buy-In Approach
 Worked with demand team to define representative set of products
and customers to use for deriving proposed modeling approach for
POS and Shipment demand
 Reviewed historical demand patterns in APO DP to get a sense on which
statistical forecast models / strategies to use
 Reviewed statistical forecast results and conducted further model
parameter tuning to get a reasonable result but not bias the forecast
 Compared APO generated statistical forecast to Final POS Forecast
(what is supplied by the client demand team)
 Documented and shared findings with team by conducting several
working sessions
 Developed a Roadmap for Forecast Optimization

18. 18
Understand History
• Completeness and accuracy of
data available
• Group products based on
similar demand patterns
• This leads you to a Forecasting
Model Type Pick Forecast Strategy
(e.g. Seasonal Models) within Model Type
• Profiles aligned to Forecast Strategy
• Constant Models
• Seasonal Models
• Trend Models
• Seasonal Trend Models
• Holt-Winter’s (Strategies 40 &41) Build Selections ID
• Seasonal Linear Regression (45) of products based
• Models attached to Profiles on Model Type
 When fitting models to data, it is often useful to analyze how well
the model fits the data and how well the fitting meets the
assumptions of the business

19. Q3 - 2012 Q4 - 2012 Q1 - 2013 Est. # WorkDays
Phase 2 JUN JUL AUG SEP OCT NOV Client Client P4D
DP Roadmap Items Description 28 4 11 18 25 2 9 16 23 30 6 13 20 27 3 10 17 24 1 8 15 22 29 5 12 19 IT Resource DP Resource Resource
Make Copy of APO_DP1 Planning Area in QA IT 3 0 0
Add Fiscal Month to Storage Bucket Prfl 5 0 3
Fcst Key Figures: Prop. Factors for Time Disagg Bi-Weekly Conference Call Touchpoint 1 0 1
Conduct
Incorporate use of Proportional Factors 0 1 3
Conference Room ~ 3 Wks
Macros: Unconsumed Demand & Proj. Inv. 0 1 5
Pilot
Consumption Data View Update 0 1 1
Define Promote to Production Strategy 1 0 1
Define Process for Populating Store Counts 1 1 1
Confirm Business Blueprint 0 1 1
Develop Business
Define Business Scenarios to be Tested 0 3 4
Scenarios 2 Wks
Prepare detailed project plan 0 0 2
Map POS History to Loc 8255 3 0 1
APO DP Structure Promote Prototype to Production 2 Wks 4 0 2
Store Counts for H&G populated in APO DP 3 1 1
Statistical Statistical Forecasting / Outlier Correction 0 5 5
Forecasting Stat. Forecast Alerts / Exception Based Mgmt ~ 3 Wks 0 3 3
Training / Working Determine if macro alerts sufficient; Create new? 0 2 4
Sessions Lifecyle Management (e.g. New / Disc Items) 0 2 3
Incorporate Capturing Promotional & Other Adj. 1 3 3
Promotional Using Promotion Planning Functionality 2 Wks 0 2 4
Adjustments Use of Promotional Attribute Types 0 2 3
Note: Bi-Weekly Conference Calls will be for 1 hour with demand team
 The activities in the roadmap are cumulative in nature – they build upon each other
 Fills the gaps identified in Phase I Assessment and Opportunities
 Different from our original Phase II Plan (inclusion of Conference Room Pilot) in order to:
 Maintain change management momentum
 Recognizes Demand Planning calendars
 Should aid in preparing IT and other divisions – road show approach

20. 20
 Creating a default user setting parameter for saving
statistical model changes as a unique forecast profile:
 In many cases a planner is interactively changing the statistical forecast for
a product but does not wish to override the default forecast profile that is
being used during batch processing for a product family / grouping
 To guard against this happening the planner can go to User Settings  Own Data
and then select the Parameter tab
 Then in the “Parameter ID” type “/SAPAPO/FCST_GUIDS” and for the “Parameter
value” type “X”
 This results in the system saving any forecast profile changes in SDP94 as a unique
forecast profile
Note: Be knowledgeable about the level of aggregation at which the statistical forecast batch
is executed and the aggregation level at which you are managing models, interactively

21. 21
 Efficient Use of Forecast Alert Profiles:
 Below is the high level flow for managing forecast alert profiles (this demonstrates using
interactive forecasting)
1. Forecast Profile Setup
(tcode:/sapapo/mc96b) 2. Alert Profile Setup
• Select forecast error metrics for (tcode:/sapapo/amon_setting)
APO to calculate in Univariate • This is where you create, copy or 3. Interactive Demand Planning
Profile tab delete a forecast alert profile (tcode:/sapapo/sdp94)
• Within diagnostic group can define • Define which forecast alerts to display • Assign alert profile
upper limits for error measurements. and establish threshold limits that • Execute the statistical forecast
specify the alert priority (e.g. Info, • Forecast alerts selected in forecast
Warning, Error) profile will be calculated
• Can review on Forecast Errors tab and
fine tune the model
• Display alerts in SDP94

22. 22
 Efficient Use of Forecast Alert Profiles:
 To review the forecast alerts generated by the system you can set up a
forecast alert profile and specify the error metrics to be used as well as
threshold values that define alert priorities (i.e. information, warning, error)
 Many people use MAPE or MAD to measure relative variability
(how much did I miss the forecast by)
BUT ….
 They also need to measure bias by selecting MPE or Error Total
 Furthermore, if ABCD classification for the product is available you can further
segment alerts based on that attribute so when reviewing MAPE and MPE
based alerts using % thresholds you are doing so based on volume
contribution

23.  Build a Prototype and get the business engaged immediately…don’t
wait until a task on a project plan
 Align Data Views and Key Figures to Demand Processes
(e.g. Clean History / Develop Statistical Forecast / Promotion
Planning / Review Final Forecast)
 Create Macro Alerts (i.e. business rules) that result in a manageable
amount of exceptions
 Focus on Critical Training like statistical forecast and CVC realignment
 Segment Product Portfolio using a combination of ABC classification
(volume) with demand pattern analysis (variability)
- A convenient statistical measure to use is this coefficient of
variation because it considers the variability relative to the
mean

24.  APO DP is a flexible & robust technology solution
 With robustness comes the need to remove the burden of
magnitude
 By focusing on the smaller population (i.e. 80/20 rule)
your organization will be able to use the statistical
forecasting and alert monitoring capabilities in APO DP
and do so in a manner which alleviates the need for a
customized, high maintenance alternative

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