July 25th, 2012 plan4demand

DEMAND PLANNING LEADERSHIP EXCHANGE PRESENTS:

The web event will begin momentarily with your hosts:

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

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

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

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

5

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

6

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

7

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)

8

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)

How many CVCs per Planner are being

managed on a weekly basis?

Make your selection on the right side of your screen

10

A. 500 -1,000

B. 1,000- 10,000

C. 10,000-25,000

D. 25,000 +

SDP Alerts (e.g. Macro-Dependent Alerts)

SDP Alerts are based on the planning book

structure and are calculated using macros

11

Potential risk of generating too many alerts:

Good information but risk of chasing noise in

the supply chain (e.g. rogue shipment)

If forecasting in weekly buckets could

generate a lot of alerts!

Does this make sense right after an

implementation?

Some typical business rules used to

define macro alerts are:

Checking for new or discontinuations

relative to product shipping from a new

location

Customer has not placed an order in

the past x months

Forecast exceeding prior year sales by

x %

Large differences between statistical

and consensus forecast

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

12

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

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

14

Statistical Models / Techniques to select:

15

5 – Constant

4 – Trend

2 – Seasonal

2 – Seasonal trend

Copy History

Manual Forecasting

Linear Regression

Season + Linear Regression

Median Method

Croston’s Method

External Forecast / No Forecast

6 – Automatic model selection 1

1 – Automatic model selection 2

Profiles Model(s) are assigned to

Profiles S Selections

Assigned to

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

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

Understand History • Completeness and accuracy of

data available

• Group products based on

similar demand patterns

• This leads you to a Forecasting

Model Type

(e.g. Seasonal Models)

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

18

Pick Forecast Strategy

within Model Type • Profiles aligned to Forecast Strategy

• Constant Models

• Seasonal Models

• Trend Models

• Seasonal Trend Models

• Holt-Winter’s (Strategies 40 &41)

• Seasonal Linear Regression (45)

• Models attached to Profiles

Build Selections ID

of products based

on Model Type

Phase 2 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 1 0 1

Incorporate use of Proportional Factors 0 1 3

Macros: Unconsumed Demand & Proj. Inv. 0 1 5

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

Define Business Scenarios to be Tested 0 3 4

Prepare detailed project plan 0 0 2

Map POS History to Loc 8255 3 0 1

Promote Prototype to Production 4 0 2

Store Counts for H&G populated in APO DP 3 1 1

Statistical Forecasting / Outlier Correction 0 5 5

Stat. Forecast Alerts / Exception Based Mgmt 0 3 3

Determine if macro alerts sufficient; Create new? 0 2 4

Lifecyle Management (e.g. New / Disc Items) 0 2 3

Capturing Promotional & Other Adj. 1 3 3

Using Promotion Planning Functionality 0 2 4

Use of Promotional Attribute Types 0 2 3

Est. # WorkDays

Incorporate

Promotional

Adjustments

Statistical

Forecasting

Training / Working

Sessions

SEP

Q1 - 2013Q4 - 2012

OCT NOVJUL AUG

Conduct

Conference Room

Pilot

Q3 - 2012

JUN

APO DP Structure

Develop Business

Scenarios

~ 3 Wks

2 Wks

2 Wks

2 Wks

~ 3 Wks

Bi-Weekly Conference Call Touchpoint

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

Note: Bi-Weekly Conference Calls will be for 1 hour with demand team

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

20

Efficient Use of Forecast Alert Profiles:

Below is the high level flow for managing forecast alert profiles (this demonstrates using

interactive forecasting)

21

1. Forecast Profile Setup (tcode:/sapapo/mc96b)

• Select forecast error metrics for

APO to calculate in Univariate

Profile tab

• Within diagnostic group can define

upper limits for error measurements.

2. Alert Profile Setup (tcode:/sapapo/amon_setting)

• This is where you create, copy or

delete a forecast alert profile

• Define which forecast alerts to display

and establish threshold limits that

specify the alert priority (e.g. Info,

Warning, Error)

3. Interactive Demand Planning (tcode:/sapapo/sdp94)

• Assign alert profile

• Execute the statistical forecast

• Forecast alerts selected in forecast

profile will be calculated

• Can review on Forecast Errors tab and

fine tune the model

• Display alerts in SDP94

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

22

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

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

August 2nd

Supply Planning Leadership Exchange:

SAP APO SNP Solver Selection Evolution

August 22nd Demand Planning Leadership Exchange:

Developing a Demand Classification Matrix

Join us on LinkedIn: Demand Planning Leadership Exchange Follow us on Twitter: @Plan4Demand

Complete our survey & receive a $5 Starbucks Gift Card

Upcoming Leadership Exchanges Save the Date Or Click to Register Now!