scm apo dp overview

SAPSCM/APO European Initiative

APO Overview Internal Training

Demand Planning Overview

March 2003

- 2 - ©Accenture 2003

Training Agenda

Advanced Planner & Optimizer Overview

Demand Planning Overview

Supply Network Planning Overview

Production Planning & Detailed Scheduling Overview

Global Available-to-Promise Overview

APO Integration & CIF Overview

APO Implementation Considerations


ObjectivesMain Goals of This Section

To understand Demand Planning as an accurate forecasting tool in the APO context. To know Demand Planning main features, in concrete:

Its architecture, data storage and representation attributes Its main tools (Planning Toolbox, Planning Environment, Accuracy Analysis…) The different forecasting methods available

To visualize how DP applies to a real case (Sara Lee). To be aware of main considerations and complexity factors when implementing Demand

Planning. To get familiar with the look of DP and its basic functions through a demo and practising with

simple exercises.


Contents

1. Demand Planning Features & Capabilities

2. Case Study: Sara Lee

3. Key Aspects to Consider When Implementing DP

4. DP Demo: Accelerated Supply Chain Integration APO Template

5. DP Exercises


Demand Planning – Accurate Forecasting A toolkit of statistical forecasting techniques Tightly linked to the R/3 System and the SAP BW SAP (data can be automatically transferred) Tree selection and drill-down capabilities facilitates navigation through multidimensional data

structures Uses the Alert Monitor to report exceptions

Demand Planning Features and Capabilities

Planner’s KnowledgeTask-specific planning tools Flexible views Graphics Promotional planning Life cycle management Cannibalization Accuracy reporting

Statistical MethodsMulti-model approach Average models Exponential smoothing Causal factors Trend dampening Model combination Pick best

Demand Planning Data MartAnticipation ofFuture Demand

Information Collaborative

forecasts

Order & shipmentactuals & history

Cost POS data Nielsen / IRI data ...



C

U

S

T

O

M

E

R

YEARS

MONTHS

WEEKS

DAYS

HOURS

QUARTERS

SELLHOLDMOVEMAKEDESIGN

B2B Exchanges Contract Manufacturers 3PLs / 4PLs

Channel Partners

B2B Exchanges

BUY

S

U

P

P

L

I

E

R

ProductionActivityControl

Order Management

Procurement Manufacturing Execution

System

LoadPlanning

Transport Planning

DistributionRequirements

Planning

MaterialsPlanning

ProductionPlanning Supply Demand

Matching ProductAllocation

Sales Forecasting

Inventory Target Setting

Supply Contract

Negotiations

Network

SourcingCustomer Service

Territory Planning

Available toPromise

In-transit &

On-hand Inventory Tracking

MaterialInventoryTracking

New Product Development Logistics Network Design

DetailedProductionScheduling

Material Requirements

Planning

APO Demand Planning within Supply Chain Planning

Demand Demand PlanningPlanningAPO – DPAPO – DP


SAP APO Demand Planning Architecture Demand Planning is composed of three layers:

Graphical user interface Planning and analysis engine Data mart

Planning ViewsGUI

OLAPProcessor

BusinessPlanningLibrary

StatisticalForecasting

Toolbox

Planning &AnalysisEngine

Planning AreaTime

SeriesCatalog

NotesData Mart



SAP APO Demand Planning Architecture (continued) Performance is of vital importance in any demand planning solution if users are to fully benefit from

available information DP architecture includes several features to ensure high performance:

Dedicated server Multidimensional data mart based on the star schema that supports efficient use of storage

space and of CPU cycles, minimizing query response time Batch forecasting so do not impede online performance

The size of the information treated depends on: Number of characteristics: many characteristics will let the user more flexibility to define the

planning level and to review the information but it makes the system works slower Number of key figures: many key figures will give the user a lot of information related to

forecast but it makes the system works slower Number of characteristic combinations: the time consuming for any calculation (e.g. macros)

depends directly on the number of characteristic combinations Number of planning versions: two planning versions needs double capacity than one Type and number of temporal periods



Demand Planning Features and CapabilitiesData Storage and Representation

Multidimensional Data Storage in the data mart allows to: View data and plan from many different perspectives Drill down from one level to the next

Info Cubes: A multidimensional data structure The primary container of data used in planning, analysis and reporting Contains two types of data, key figures and characteristics (or dimensions):

- Key figures are quantifiable values (e.g. sales in units, orders, shipments, POS…)

- Characteristics or dimensions determine the organizational levels at which you do aggregation and reporting (e.g. products and customers)

Info Cubes also share master data and descriptive text, which are stored in different tables The Online Analytical Processing processor:

Models the business rules considering the aggregational behavior of key figures (e.g. sales summed by product and time)

Guarantees that all business rules are met and the computed views present valid results


Data Storage and Representation (continued) Hierarchies are modeled as combinations of characteristic values (e.g. product are grouped into

product family hierarchies) using proportional and temporal factors, in order to be used as the basis for aggregation, disaggregation and drilling down.

The DP planning level is based on the characteristics definition. In order to be more integrated with R/3 data, the dimensions and characteristics are usually based on R/3 hierarchies:

Product dimension and characteristics are usually based on R/3 product hierarchy Customer dimension and characteristics are usually based on a R/3 customer hierarchy Geographic dimension and characteristics are usually based on the supply network


Dimensions

Hierarchies

Attributes

Facts

Time Series Management

Notes

Promotion Forecast

‘99

‘01

‘00

‘02

Life Cycle

time sequenceAug. Sept.W32 W33 W34 W35 W36 W37 W38 W39 W40 W41

124203

Material

Cus

tom

e r

Period

Product Groups

Regions


Data Storage and Representation (continued) Time Series Management:

Based on catalogs: time series data with related attributes (e.g. promotional patterns and life cycles)

SAP DP allows to reuse time series saving time and ensuring consistency (e.g. reuse a past promotional pattern to estimate the impact of a similar future promotion)

Notes Management maintains all notes entered by planners to create an audit trail of all demand planning activities, which is specially helpful when multiple sources and people are involved (such as in consensus forecasting)


Dimensions

Hierarchies

Attributes

Facts

Time Series Management

Notes

Promotion Forecast

‘99

‘01

‘00

‘02

Life Cycle

time sequenceAug. Sept.W32 W33 W34 W35 W36 W37 W38 W39 W40 W41

124203

Material

Cus

tom

e r

Period

Product Groups

Regions


Planning Environment DP’s rich planning and forecasting functions are based on the Statistical Forecasting Toolbox

and the Business Planning Library. These functions include: Aggregate functions (sum, weighted sum, average) Disaggregate functions (quotas, proportional and equal distribution) Comparison functions (difference, ratio, percent, percent difference, share and correlation) Financial functions (conversion from units into revenue, currency conversion and business

period conversion) Time-series functions (time-phased, average, and weighted average of time series)

A Planning Book is an easy-to-use tree control for selecting data and a frame with a grid and a graphical data display:

Preconfigured planning books for promotional planning, causal analysis, statistical forecasting, life cycle management, etc

These can be used as guides for customized planning books



Planning Environment (continued) You can use Advanced Macros to:

Calculate deviations Make automatic corrections Calculate sales budgets Define your own exceptional situations Launch status queries

Advanced Macros models the calculations based on the individual business tasks to perform principally:

Build a macro consisting of one or more steps Control how macro steps are processed and how results are calculated Use a wide range of functions and operations Define offsets so that the result in one period is determined by a value in the previous period Restrict the execution of a macro to a specific period or periods Write macro results to a row, a column or a cell Create context-specific and user-specific planning views Trigger an alert in the Alert Monitor to inform of particular business situations

Integration with Microsoft Excel



Statistical Forecasting Toolbox A Toolbox of all practical, proven forecasting methods Time Series Models:

Uses past sales to identify level, trend, and seasonal patterns as a basis for creating future projections

Naïve models, moving average, simple linear regression, Brown’s exponential smoothing, Holt-Winters, Box-Jenkins

Stochastic Models: Accurate forecast with sporadic demand pattern Croston model uses exponential smoothing to estimate:

- The size of demand during periods in which demand occur

- The demand frequency Final forecast are determined by distributing the size of demand according to the demand

frequency


Forecasting


Statistical Forecasting Toolbox (continued) Multiple Linear Regression:

Technique for estimating the relationship between past sales and other causal factors Variety of options to model linear and non-linear trends:

- Seasonal patterns

- Life cycle patterns

- Dummy variables and time lags Correlation analysis corrects variables

Pick-the-Best, applies the best method among: All of the available forecasting methods, or The planner-specified forecasting methods

S-Shaped Curves supports complete lifecycle forecasting (introduction/growth and end-of-life phases) Logistic and exponential functions First estimation based on similar products Adjusted over time when sales history is available



Causal Analysis Includes all significant causal factors (price, number of displays, number of stores, temperature,

working days…) in the models and determine how they affect customers’ behavior Simulate sales development according to the mix of causal factors (what-if analysis, marketing mix

planning) Multiple linear regression to model the impact of causal factors

50°F 65° 75° 85° 60°

Uni

t Sal

es

Feb. Mar. April May June July Aug. Sept.



Multi-Tier Forecasting Integrates sell-in data (like POS data) into the process of forecasting sell-through data (like

shipments) Causal model based on significant causal factors to forecast POS Second causal model is used to forecast shipments:

Uses past POS data and the POS forecast as the main causal factor Takes the time lag between POS and shipments into account Considers other causal factors (forward buys, trade promotions…)

POSDataManufacturing Retailer

SalesHistory Consumer

Pro

mot

ion

Adv

ertis

emen

t

timeCom

petit

or P

rom

otio

nsalesSell ThroughSell In

Consumer demand

+ Replenishment lead time

+ Forward buying

= Retailer Demand



Data Analysis Identifies missing values and outliers in the data to improve the quality of the statistical forecast.

Through the outlier, an automatic correction of historical data is done taking into consideration out-of-range data that may disturb the identification of historical pattern

Identifies structural changes in “established” patterns: Level, trend, and amplitude changes Change from unstable to stable behavior Automatic detection via tracking signals

Automatic outlier detection & correction Manual intervention



Promotion Planning Impact of promotions must be projected separately from standard forecast components that are

based on historical sales data Takes prices into account when doing profitability analysis for promotional calendars Reporting capabilities allow to track promotional activities and related costs Archives a promotion pattern in a promotion catalog, so it can be reused Several techniques for estimating the effect of a promotion

Promotion

Planner

Promotion patterns

‘97

‘99

‘98

‘00

Price

Quantity

-10%

Forecast simulation

Profit

Sales



Life Cycle-Management A Demand Planning and Supply Network Planning both components’ function

Planning strategies for a product depend on the stage of its life cycle: Should the product be introduced, and when? How should a product be promoted during the different stages? Should the product be deleted, and when? Should a successor product be introduced? Should a re-launch be started for a product, and when? What is the cannibalization effect of a new product with existing products? Etc.

DP can represent the launch, growth and discontinuation phases by using phase-in, phase-out and like modeling profiles (or combining them):

A phase-in profile reduces demand history by ever increasing percentages during a specific period or periods (simulating upward sales curve – launch and growth phases)

A phase-out profile reduces demand forecast of a product by ever decreasing percentages (simulating downward sales curve – discontinuation phase)

Like modeling creates a forecast using the historical data on a product with a similar demand behavior (new products and products with short life cycles)

Product Launch

Aggregate

End of Life



Consensus-Based Forecasting SAP DP supports consensus-based Sales & Operations Planning (S&OP) Multidimensional data structure of the InfoCubes enables to create multiple plans:

Product levels for Marketing Sales areas and account/channel for Sales Distribution centers and plants for Operations Business units for Finance

Synchronizes multiple plans into one Consensus Plan that drives business Composite Forecasting reconciles and combines different plans on same level and multi-levels


Forecast1

n

... Combine & Reconcile

Sales Forecast

Marketing Forecast


Forecast Accuracy Analysis & Alert Monitor Forecast accuracy reporting:

Helps to assess the accuracy of past forecasts Integrates this knowledge into projections for the future

Stores a series of forecasts for a particular period and compares each deviation of this series to the actual values for the same period (mean absolute deviation, error total, mean percentage error, …)

Reports shoe forecast errors at any level and dimension: Actual versus forecast Actual versus time-lagged forecast Actual versus different planning versions Actual versus budget

Alert Monitor informs in real time via e-mail or exception message if an exception occurs Exception conditions can be defined based on thresholds for special statistics and tracking signals Reports can be sorted:

By forecast error Restrict them to products with a forecast error greater than a specified threshold



Advantages of SAP APO Demand Planning Global server with a BW infrastructure Integrated exception handling, creation of user defined alerts Integration with Production Planning (S&OP scenario) Main memory based planning Flexible navigation in the planning table, variable drill down Extensive forecasting technique Promotion planning and evaluation Collaborative planning via the internet Supports Sales Bills of Material (BOMs)



Contents





5. DP Exercises


Case Study: Sara LeeIntroduction

Main objectives of Demand Planning for Sara Lee: S&OP purposes: Provide the essential input for S&OP monthly cycle (forecast) and create

consensus within the OpCo. Demand Forecast should contain the required detail in order to compare with Business/Sales targets

Supply Planning purposes: Provide updated forecast from different OpCo’s (in weekly buckets) to Supply Planning in order to base Supply Planning on consolidated forecast from each OpCo

Benefits of Demand Planning for Sara Lee: Improve the communication and transparency from all OpCo’s to CoE Provide to Supply Planning short and long term volume estimation for capacity planning Create consensus in the OpCo (together with S&OP) Understanding the demand of each OpCo through deep analysis (KPIs, market intelligence,…) Move from ”Reaction on” toward ”Plan Activities” Improved customer service level Lower obsolete and safety stocks


Case Study: Sara LeeProject Approach

A template has been developed in order to align, cover and support all the processes performed in the Sara Lee Opcos in Europe. In different phases, the Opcos will start to use the new template, changing their actual procedures and/or systems (local roll-outs).

There will be a central team responsible of maintaining the basic and common applications. In every roll-out a local team will be assigned to check that the requirements of the Opco are

covered, to conduct the trainings, etc. Communication between local and central teams:

Either in the central and in the local teams, there will be a member responsible of the communication between them. The communication link will be one-to-one.

CUSTOMIZING: The local team will ask the central for customizing new structures. Every local roll-out will have a different copy of the “Implementation Guidelines”. GAPS: The local team will detect functionality not covered by the template, then, these gaps

must be written down in a document called “EuRoPe fit”. Both teams will have a meeting to determine how each issue in the “EuRoPe fit” must be

solved.


Initial training(central to local)

EuRoPe fit sessions(local)

EuRoPe fit analysis(central & local)

GAP estimation(central)

GAPs approval(project management)

Central GAPs design- Template development -

(central)

Local GAPs design(local)

Case Study: Sara LeeProject Approach (continued)

Procedure for the “EuRoPe fit” Analysis and Development:


Case Study: Sara LeeDemand Planning Processes

Demand Planning Processes are divided into three cycles: AOP/Outlook generation: Provide volumes taken from APO DP as a starting point for the

AOP/Outlook generation Monthly cycle: Update Demand Forecast for the following 24 fiscal periods and provide it to

the Sales and Operations Planning monthly cycle (to create a consensus and run Supply Planning).

Weekly cycle: Review current month forecast to identify supply risks, advise Sales and Marketing of these risks and change the forecast which applies to a period outside of the Supply Planning frozen period.

Tactical Operational

Monthly Cycle Weekly Cycle

Strategic

AOP generation


Case Study: Sara LeeDemand Planning Processes (continued)

AOP/Outlook generation: APO Forecast volume can be used as a

starting point for AOP generation. Volumes are sent to R/3 where it is

converted into value. Volume/value adjustments are done in R/3 AOP volume is sent back to APO for

Supply Planning purposes and KPI analysis

CO-PA(R/3)

APO

CO-PA(R/3)

APO Volumes from APO DP

Convert volume to value

Adjust Volume

Run SNP with Adjusted volume

Volume adjusted after SNP

Convert volume to value

Adjust Volume

Final AOP volume sent to APOInterface SAP - APO

Send Adjusted Volume to APO

Demand Planning

Finance

Supply Planning

Finance

ResponsibleProcess



Monthly Cycle: Demand Planning can be considered as a sub-process of the Sales and Operations Planning

LocalLocal

EuroEuro

Euro & Local

Production Plan

Local S&OP Review

Customer Service Review


UpdatedDemand Forecast

DemandMeeting

DemandMeeting

Local S&OPMeeting

Local S&OPMeeting

OpCo Demand

Plan

Business GroupReview

EuroS&OP

Meeting

EuroS&OP

Meeting

Aggregate OpCo

Demand Plans

Update Supply PlanUpdate Supply Plan

Euro Demand

Balancing

Euro Supply Review

Review Supply Chain

Capacity Check and Inventory

Aggregate Supply

Plan

APO

S&OP high Level flowS&OP high Level flow

S&OP scope

DP scope

S&OP scope with impact

on DP


on DP

LocalLocal

EuroEuro

Euro & Local

Production Plan

Local S&OP Review




DemandMeeting

DemandMeeting

Local S&OPMeeting

Local S&OPMeeting

OpCo Demand

Plan

Business GroupReview

EuroS&OP

Meeting

EuroS&OP

Meeting

Aggregate OpCo

Demand Plans

Update Supply PlanUpdate Supply Plan

Euro Demand

Balancing

Euro Supply Review

Review Supply Chain

Capacity Check and Inventory

Aggregate Supply

Plan

APO

S&OP high Level flowS&OP high Level flow

S&OP scope

DP scope


on DP


on DP



Monthly Cycle: Rolling forecast for month M to M+24 is prepared by Demand Planners: In second last week of month M-1, Based on the history accumulated until month M-2

(*) Caldendar

M.1. Generate KPIs

M.2. KPIsanalysis

Demand Forecast M.5. Create

M.3. Updated Promotional

PlanningM.4. New Product Launches

(*) Calendar is depending on the S&OP requirements


S&OP activities that impact DP

Local S&OP

meeting

M.6. Update Demand Forecast

Last week of previous month (*)Second last week of previous month (*)

Define Supply Plan at Euro

Level

Demand Meeting

Demand meeting

Customer servicereview

Business Group Review



Monthly Cycle: Demand planners will provide every month a rolling forecast for the following 24 fiscal periods. There will be some differences between the first 6 months and the remaining 12 months:

- First 6 months:

• Presented in weeks if needed (in APO DP not much extra work is needed)

• Forecast based on clean history + promotions

- Last 18 months (S&OP requirement for long term capacity checking):

• Presented in months

• Forecast as extrapolation of

• non-cleaned history Monthly Cycle

Horizon

6 months in weeks

18 months in months

Monthly Cycle

HorizonHorizon

6 months in weeks

18 months in months

Non-clean history (no

promotions)Forecast Baseline + Promotions



Weekly Process: Process model overview: The process consists of reviewing the consumption of the forecast within the current month,

facilitating decision making on critical exceptions (e.g. potential stock storage) This will be made by exception based on the following sources:

- Consumption of forecast after the weekly upload (Monday-Tuesday)

- Daily stock-out report coming from R/3

- Order to Cash (CDP) will develop ATP based on: Physical stock + Incoming stock – Promised (reserved) stock

Review current month forecast consumption

Communicate risks to Sales &

Marketing

Provide changes in promotional

planning

Update demand forecast in APO

Update demand forecast with promotional

activities

Communicate changes to Supply

Planning

Monday-Friday

Risks identified?

Yes

No Changes in Promotional Planning?


Case Study: Sara LeeData Structure

SCP data structure are based on CDP hierarchies: Hierarchies defined taking into account the global EuRoPe solution Easy to integrate with CDP

CDP is responsible for defining the content of each of the level of the hierarchies Planning levels are grouped in dimensions. Dimensions do not have any functional impact, and it is

only a way of organising the information in the system. In the SCP EuRoPe Solution it is planned to use 3 dimensions:

Product Customer and Demand organization Geography

APO is based on a Data Warehouse and therefore the information is always consistent at all levels


Case Study: Sara LeeData Structure (continued)

The DP planning level is based on the characteristics definition. In order to be more integrated with R/3 data, the dimensions and characteristics are usually based on R/3 hierarchies:

Product dimension and characteristics are usually based on R/3 product hierarchy

BusinessBusinesslineline

SKUSKU

CategoryCategory

ProductProduct

SegmentSegment

FormatFormat

SubSubSegmentSegment

ProductProduct FamilyFamily

BrandBrand

All views are defined on SKU level

PromoPromo-tional -tional TypeType

SubSubBrandBrand

ConceptConcept Kit Kit Process Process TypeType

Type 1Type 1 VarietyVariety

Example of Planning level


TeaCoffeeHouseholdBody CareLevel

Business Line • Body Care

Category • Bath&Shower

SegmentSub-segment • Bath

Product Family • Herbs

Brand • Radox

Sub-brand • Original

Product • Radox Herbal Bath Original

Process Type • -

Variety • -

Packaging Type • bottle

Product Type • standard

Format • 500 ml

SKU • Radox Herbal bath Original 500 ml

• Bath

• Household

• Airfresheners

• Home

• Starter

• Ambi-Pur

• Perfum d’interieur• Ambi-Pur Perfum

d’interieur Anti-tabacco

• -

• Anti-tabacco

• electrical

• standard

• -• Ambi-Pur Perfum

d’interieu Anti-tabacco starter electrical

• Airfresheners

• Coffee&Tea

• Coffee

• Roasted Coffee regular

• Standard

• Douwe Egberts

• Desert• Douwe Egberts

dessert• Ground

• caffeinated

• Brick pack

• 10% free

• 250 gr

• Douwe Egberts dessert 250 gr 10% free

• Roasted Coffee

• Coffee & Tea

• Tea

• Black

• Regular

• Pickwick

• -

• Pickwick English

• -

• English

• box

• standard

• 80 * 2

• Pickwick English 80*2

• Hot Tea

Concept • - • - • - • -

Case Study: Sara LeeData Structure (continued):

Product Hierarchy in Sara Lee:


Case Study: Sara LeeData Structure (continued)

Customer dimension and characteristics are usually based on a R/3 customer hierarchy

Geographic dimension and characteristics are usually based on the supply network

Customer group

Sales Director

KAM

Customer

Sales Area

Lowest Point of Delivery

APO Supply Network

Clients

DC

Factories


Case Study: Sara LeeForecasting Detailed Process

The following are the detailed activities that Demand Planners will be doing every month with APO This cycle summarizes the scenarios that have been tested in the prototype phase:

Send forecast to Supply Planning

Clean History and Evaluate Forecast Baseline

Add Events

Phase in/out

Create consensus (local S&OP)

Close the Period and KPI generation

Evaluate Forecast Performance

Update Forecast

Select Forecast Levels

Choose statistical algorithm

Monthly cycle

Disaggregation


Case Study: Sara LeeForecasting Detailed Process: Select Forecast Levels

Level of detail of the forecast The lowest level of detail of the forecast (due to Supply Planning or S&OP

requirements) is:

- SKU/customer/lowest point of delivery

- weekly buckets Alternatives in APO

APO gives the opportunity to forecast at any level of aggregation (product, cost, geography) using later Disaggregation and Reconciliation capabilities to allocate demand to the desired level



Add Events

Phase in/out




Update Forecast



Monthly cycleMonthly cycle

Disaggregation

SubBrand Level

Forecast Level

SKU/Customer

DE Dessert

DE Dessert 250g

DE Dessert 500g

Data consistency along

the hierarchy

80.000

6.00072.000

32.000 17.00023.000 4.000 2.000

12.000 5.000 7002.0001.30015.000

5.70017.00013.300MakroAuchan Intermarché

Format Level

SKU Level

Customer Level

SKU/Customer



Advantages of Forecasting at different levels Reduction in Forecast errors: Forecasting aggregated levels of products typically

results in lower forecast errors (principle of compensating errors) Importance to the Business: Analyse your products according to their strategic or

economic value to the company (ABC analysis). “Spend effort when it is required” Customer concentration: Depending on the customer segmentation we can have

the opportunity to focus on customer level forecast (Holland 3 customer 70% of market share, while in Spain only 39% - Nielsen)

Customer collaboration: Requires that you forecast at customer level which you then can share with the customer

Basic rules for selecting a forecast level Select a level aggregated enough:

- To be representative (show continuous trend over periods)

- Not to be too time consuming The level contains set of products homogeneous with similar demand pattern Ensure that is feasible to disaggregate to lower levels (without jeopardising

accuracy)



Add Events

Phase in/out




Update Forecast




Disaggregation



How to define the level at which to forecast: Identify the characteristics of the “group of products” that make them homogeneous

and are therefore suitable for aggregating

Demand VolatilityMarket

IntelligencePromotional

activity

Characteristics

Customer concentratio

n

Range of products

Lifecycle

Customer Collaboratio

n

Strategic Value

• Capacity to predict future demand

• Identify seasonality, trend,...

• Number, frequency and types of promotions

Description

• Number of customers buying the product

• A large number of SKU makes forecast complex

• New product, growing, mature or declining

• Collaboration with customer

• Special agreements

• Importance of the SKU/group of SKU for the company

• Demand is stable and homogeneous for the products of this group• This is a seasonal product. Higher sales from October to March

• No customer promotions

Example: Kiwi Large Shoe Polish 50 ml (H&BC UK)

• High concentration (6 customers – 80% of the market)

• 10 SKUs in this group

• Mature group

• No customer collaboration• Solus listings - Safeway and

Sainsburys

• Shoecare is a core line and therefore high strategic to the company

Homoge-neous

Homoge-neous

Homoge-neous

Customer view

Easy to disaggreg

.

Homoge-neous

Impact for

selecting level

Customer view

Product A



Add Events

Phase in/out




Update Forecast




Disaggregation


Case Study: Sara LeeForecasting Detailed Process: Choose Statistical Algorithm

What is available in APO? Although in APO it is possible to use univariate and multivariable/causal forecast models,

only univariate models are in scope APO provides a wide range of different statistical models and strategies to use for

forecasting (up to 35):

- Linear regression with seasonality

- Trend model

- Trend and seasonal model

- Automatic model selection,... How to select the best model?

First step: Prior to selecting the forecasting statistical model, Demand Planners will have to gather market intelligence to understand with pattern to find.

Second step: Test which model better fits the expected demand pattern. Take into consideration some recommendation:

- Linear regression with seasonality model is very easy to understand by users and require very little maintenance (preferred model by Demand Planning implementation team)

- Most of the other models require some statistical skills by users and time consuming maintenance by DP when reusing the same profile for next rolling forecasts








Add Events

Phase in/out


Update Forecast

Disaggregation


Case Study: Sara LeeForecasting Detailed Process: Clean History & Evaluate Forecast Baseline

Why is necessary to clean history? To enable forecasting to be based on a model created using history that is “free” of impact of

events (non repetitive history) Any outliers (promotions) will give errors in estimating seasonal demand To understand underlying demand e.g. seasonality and ensure that it is reflected in the forecast Ensure that the planned contribution of events is not duplicated in the forecast To provide a baseline forecast to account management so that they can focus on promotions

Should you always clean history? No. Balance the amount of effort required versus the result:

- Focus on major events which significantly effect your baseline.

- Focus on ‘A’ category products.

- If events are repetitive in type and in time then the user could decide not to clean history.

Sales history

source: Information Resources, Inc.

Baseline








Add Events

Phase in/out


Update Forecast

Disaggregation


Case Study: Sara LeeForecasting Detailed Process: Clean History & Evaluate Forecast Baseline

How to clean history in APO? APO gives the opportunity to clean history using different methods:

- outlier correction:

• adjusts automatically historical values lying outside of the tolerance lane towards forecast baseline

• Easy to use and very little work

- Mark events: Outlier correction that only applies to the selected periods

- Manual correction: Manually add an event that subtracts/adds the desired volume to the history

- Promotional planning: Automatically subtract the impact of the promotions defined in promotional planning








Add Events

Phase in/out


Update Forecast

Disaggregation

Total history

Total history - updatedupdated promo = baseline


Forecasting Detailed Process: Clean History & Evaluate Forecast Baseline

Recommendations Use outlier correction…

- ... for the first history upload. Manual clean history for the last two year could be very time consuming,

- ... for B and C products, as requires very little maintenance,

- ... for non-promoted products (to smooth the deviations) Use mark events…

- … when it is clear the period that Demand Planner wants to clean, and the correct impact of the event is unknown

Use manual correction…

- … also when the event o period that the user wants to clean is identified and the impact of the event is known

Use promotional planning…

- … for A products, because although requires significant maintenance (change the impact of events in Promotional Planning if the real impact is different than what was estimated), the forecast baseline can become more significant

Case Study: Sara Lee








Add Events

Phase in/out


Update Forecast

Disaggregation


Forecasting Detailed Process: Disaggregation of the Forecast

What is disaggregation: Disaggregation consists of splitting the forecast made at a higher level in the

hierarchies (i.e, from product format to SKU) and time (from month to week), using APO features

APO gives the opportunity to choose between different alternatives: Historical splitting factors calculated by the system (proportional factors) Factors from previous forecast (pro-rata) Factors calculated outside APO

Depending on each product group the approach may be different, the recommendation is to:

Use proportional factors (only standard SKU) for the first forecast For following forecasts consider changes in the splitting factors done in previous

rolling forecast. Therefore use pro-rata For time disaggregation (e.g., from fiscal periods to weeks) use factors calculated

outside APO as a feasible alternative to what the system provides








Add Events

Phase in/out


Update Forecast

Disaggregation



SKU Level

1000

400250 350

Product Family LevelForecast Level

25% 35% 40%Proportional factors calculated from history (only STD SKUs)

First disaggregation (create) is based on proportional factors

1100

400350 350

Product Family LevelForecast updated at Brand level due to changes at SKU level

25% 35% 40%No changes on the proportional factors

First SKU forecast is updated from 250 to 350 SKU Level

32% 32% 36%New proportion established due to the changes at SKU level. These values do not overwrite the proportional factors

Case Study: Sara LeeForecasting Detailed Process: Disaggregation of the Forecast

Example: Step 1: Forecast data creation: no data exists in advance. Proportional factors have been

calculated

Step 2: Forecast is updated at SKU level


1200

400384 384

Product Family LevelForecast is overwritten based on the new history data

25% 35% 40%Disaggregation is performed based on the existing data

at lower level not in proportional factors anymore

Forecast at SKU level is updated to new values but keeping the proportion of the previous data SKU Level

32% 32% 36%

1200

400350 350

Brand Level25% 35% 40%

SKU Level

32% 32% 36%

April’01 May’01

time1400

560350 490

25% 35% 40%

Forecasting Detailed Process: Disaggregation of the Forecast Step 3: New forecast is run (rolling forecast). Same forecast profile but historical horizon has

been updated with one more period

Note: Changes in the disaggregation factors only affect to the periods which Demand Planners updated in previous rolling forecasts



Forecasting Detailed Process: Disaggregation of the Forecast Disaggregation in time (from fiscal periods to weeks)

Every month Demand Planners will update the forecast for the following 24 months The forecast will be made in fiscal period buckets The disaggregation factor from fiscal periods to weeks will be calculated outside APO

- Every OpCo will calculate if there is a weekly “seasonality” in the fiscal period (e.g., first weeks have lower sales that last weeks). Otherwise fiscal forecast will be equally distributed to weeks.

- This factors will only affect the baseline sales. Promotions will be directly assigned to the weeks when the promotion is executed (Promotional Planning)

P1 P2 P3

Forecast Baseline

W1 W2 W3 W410% 20% 30% 40%

200

20 40 60 80- - 20 20

20 40 80 100W1 W2 W3 W4

Identify weekly profile in fiscal periods

Weekly profileBaseline

PromotionsFinal Forecast

•Forecast baseline for the Period 1: 200•Promotion on week 3 and week 4









Add Events

Phase in/out


Update Forecast

Disaggregation


Forecasting Detailed Process: Add Events What is an event:

An event can be defined as any activity, internal or external, that have a significant impact on demand.

Types of event: There can be many different types of events:

- Sales events (Logistics, Financial, Change in customer listings)

- Marketing events (TV campaign, Change in price policy, …)

- External events (competitors actions, market tendencies,…) Why events are needed in DP:

Demand planners work with APO to get a representative Forecast Baseline (free of impact of events)

Events can represent a very significant volume. It is therefore essential to have a good estimation of the expected impact of sales and to apply them in APO

The information has to be provided to Demand Planners by the people that are closer to the market and customers (Sales and Marketing)

Sales

t

Updated

Forecast

Forecast

BaselinePromotions

Obsolete Stock

New Product

Introductions

Sales

t

Cannibalisation effects








Add Events

Phase in/out


Update Forecast

Disaggregation



Forecasting Detailed Process: Add Events Information required for Sales events

Demand Planner will include the impact of promotions, with a significant impact on sales, to the APO forecast

Every month Sales should provide to the Demand Planner the updated promotional plan for the following 6 months, which ideally will contain:

- Description and customer target of the promotion

- Volume estimation for the SKU (or group of SKUs) on promotion

- Weekly detail of the impact (when possible)

- Estimation of the expected cannibalization (when possible)








Add Events

Phase in/out


Update Forecast

Disaggregation


Week 1 Week 2SKUs promoted

700

+ 15%

500

+20%

• Sanex Shampoo 500 ml

• SKU of Douwe Egberts dessert

Promotion A

Week 3

500

+5%

Week n

-600

-20%

Description of promotion: 10% discount Start date (shipping): 15 February End date (shipping): 28 February

Customer (s) target: Carrefour

Expected promotional volume

Week 1 Week 2SKUs cannibalised

100

-5%

100

- 5%

• Sanex Shampoo 750 ml

• SKUs of JM dessert

Week n

25

0%

Expected cannibalization


Case Study: Sara LeeForecasting Detailed Process: Add Events

Information required for Marketing events For price policy changes:

- Planned dates for price policy changes per account,

- Dates when the changes will be communicated to customers,

- Estimation on change of volume (at aggregated product level) for the periods:

• From communication date to price change date

• From price change date until sales are stabilised

For other Marketing events (TV campaigns )

- Volume estimation for the SKU (or group of SKUs) on promotion

- Estimation of the expected cannibalization (when available)








Add Events

Phase in/out


Update Forecast

Disaggregation


Case Study: Sara LeeForecasting Detailed Process: Add Events

Information required for External events In some occasions an OpCo can be aware of external factors that may have

significant impact on future sales An example of this can be the Coffee demand that can have a strong relationship

with:

- Price

- Price distance

- Market Share

- Market Growth To include these events in APO forecast it is necessary to:

- Make an interpretation of external data (e.g., Nielsen)

- Estimate the impact on future demand that this situation may have

- Communicate the impacts to Demand Planners, who will include it as an event in the promotional calendar








Add Events

Phase in/out


Update Forecast

Disaggregation


Case Study: Sara LeeForecasting Detailed Process: Product Lifecycle Management

What is it? The Product Lifecycle usually has different phases: launch, growth, maturity and

decline Demand Planners need to take product lifecycle into account when forecasting,

especially in the launch and decline phases

Alternatives in APO APO DP provides different alternatives to deal with lifecycle management:

- Like modelling - Choose a product similar in behaviour to the new product introduction and use it’s launch profile to base your forecast on (or % of this profile)

- Phase-in profile - Select a product which has a similar mature sales history and apply a profile for launch. This profile can be based on market intelligence

- Phase-out profile - Apply a time series phase-out profile to simulate the discontinuation of a product.

- Manual profile – A profile for the product is manually entered into the system Demand Planners will choose the best option considering the specific

characteristics of each case








Add Events

Phase in/out


Update Forecast

Disaggregation


Case Study: Sara LeeForecasting Detailed Process: Product Lifecycle Management

Phase In - Phase Out: Product Lifecycle management The information required for each of these activities will be:

For new product introduction (phase-in):

- Introduction strategy for different accounts (new launch calendar),

- Phase-in profile:

• expected volume for the coming periods at SKU level, or…

• … referent SKU (or group of SKU) from which to be used as a Like Profile

- Cannibalisation expected on other SKU’s already listed in customers (estimation of total volume at an aggregated level e.g. format).

For phase-out of existing SKUs:

- Phase-out strategy for different accounts (phase-out calendar),

- Phase-out profile (expected volume for the coming weeks),

- Positive cannibalisation with other products








Add Events

Phase in/out


Update Forecast

Disaggregation


Case Study: Sara LeeForecasting Detailed Process: Update Forecast

What is a job? APO gives the opportunity to run any process in batch jobs (i.e, run a

forecast, include impact of promotions,…) and forecast by exception, making use of alerts to identify potential forecast errors

Advantages of jobs: Reduction in Demand Planner work effort Alerts identify potential errors

Disadvantages of jobs: Demand Planners do not see “on-line” the work they are doing (e.g., adding a

new promotion or disaggregate the forecast) Maintenance of the jobs is required

Recommendation: Make an ABC analysis taking into consideration volume, value and strategic

value of each set of products. For A group of products: On-line forecasting: “Spend effort when is

necessary” For B and C: Forecast by exception

20%

80%

20%

80%

Nº of SKUs Value

ABC analysis








Add Events

Phase in/out


Update Forecast

Disaggregation


Case Study: Sara LeeForecasting Detailed Process: Update Forecast

What is an alert? Alerts in APO are customizing Warnings that a Demand Planner can use to

track results from any activity he/she performs in the system

Forecast (from last period) 100 120 150 170

Forecast (from actual period) 110 90 150 220

Periods P1 P2 P3 P4

Difference in Percentage is Greater than 20% !!!!Watch Out!!!

Alert








Add Events

Phase in/out


Update Forecast

Disaggregation


Contents





5. DP Exercises


Lessons LearnedR/3 Integration:

Data structure between R/3 and APO should be aligned. Otherwise, interface may become very complex. The involvement of Demand Planner in the definition of product/client hierarchy in R/3 is key

When a transactional system is already in place, using of existing data hierarchy is recommended in order to avoid double maintenance. You should evaluate whether to keep current data structure or design a new one

Relationship with other APO modules: Demand is the input to SNP. When distribution network is complex, it may oblige the Demand

Planner to plan demand for many locations. During the Detailed Design phase, consider how the relationship with SNP will be:

If a valid desaggregation strategy for forecasting and dimensioning locations already exists in DP, then it can be reused for SNP

If customer uses percentages to assign locations, then maintenance will be done from SNP In general, final decision depends on Demand Planner management style, being closer to

commercial (DP preference) or production (SNP preference) point of view

Key Aspects to Consider When Implementing DP

forecast

forecast

forecast

forecastsupply

supply

supply

supply


Lessons Learned (continued)Modelling:

Understand the process during the Detailed Design phase is key. Later modifications in APO (characteristics, key figures, …) imply to activate and inactivate planning area, so all data is lost (including forecast). Redoing all the data and managing several environments becomes inconvenient and time consuming. It is recommendable to be specially aware of:

The use of attributes, characteristics and characteristics combinations Ratios definition (the impact is less, given that extra ratios can be created) Desaggregation strategy

Define the security model as soon as possible (in the Detailed Design phase). By doing so, we will be able to estimate customization and planning areas creation effort properly, taking into consideration that:

Authorization managing does not allow to restrict access by data, managing access is only possible at the planning area level

The later may imply to multiply effort as many times as user groups are to be defined Define desaggregation strategy as soon as possible (in Detailed Design phase):

A low level desaggregation may imply that forecast demand by client may not be much accurate. Keep the criteria that customer suggests us to define the optimal desaggregation level

When possible, use temporal desaggregation criteria that APO provides



Lessons Learned (continued)Modelling (cont.):

Providing that customer is not always capable to use complex methods, use more simple forecast methods such as linear regression and seasonability if possible. Other methods can result in a black-box for the customer so that they become more difficult to understand and use effectively

Knowledge transfer: Emphasize knowledge transfer during the project life. Planners need to spend a significant amount of

time to learn and make full use of the tool (e.g. different options for desaggregation). By not considering it, they may feel that Excel can do more than DP. Two approaches are suggested to overcome this risk:

Provide the user a prototype so he/she may experience the tool Create a super-user role: a competent and fully-dedicated user that will support us in training

and support tasks



Lessons Learned (continued)About DP functionality:

The following is a list of functional issues that complicate a DP implementation. Take special care when explaining functionality to the customer as they may differ from SAP official version:

New product cycles: not integrated with R/3, so that product characteristics creation is manual and tedious

New product versions: realignment and phase in – phase out functionalities to manage product versions do not work always properly

Promotions: not agile for the user, many options that are not used Cannibalization: poor functionality, it does not fill customer requirements Kits and Displays: does not cover dependent demand functionality properly (e.g. parent product

characteristics are transferred to dependent products while this is not always true)



Complexity Factors: Process – Business Scenarios


It indicates the complexity of the data structure (product/ client/ location) and the alignment with the transactional system.

DataMart Strategy

• Number of dimensions. (product/client/location).

• Stability of R/3 environment.

• Data structure alignment.

Sales History • Data in SAP standard system.

• Data structure alignment between APO and the other system.

• N. of sources of data.

It indicates the complexity in the integration of sales history.

Planning Cycle • Planning frequency.It indicates the frequency in which planning is performed.

Planning Horizon • Number of months.It indicates the number of months to forecast.

Factor Description Criteria


Complexity Factors: Process – Business Scenarios (continued)


Lifecycle Management

• Number of new products.

• Number of new versions per product.

It indicates the degree of new products introduction.

New Customers Introduction

• Market segmentation.It indicates the degree of new costumers introduction.

Kits and Displays Management

• Frequency of development of kits and displays.

• Variety of kits and displays.It indicates the number and variety of aggregation of final products that are done.


Promotional Planning

• Promotional activity.It indicates the degree of promotional activity.

Aggregation Strategies


• Desaggregation criteria complexity.

• Desaggregation profile volatility.

It indicates the complexity that exists in the data desaggregation .




It defines the demand model.Statistical Forecasting-Time Series

• Demand trend and seasonality.

Statistical Forecasting-Causal

• Number of factors that need to be considered.

This option must be input if causal forecasting is considered.

Upload and Evaluation of an External Forecast

• Number of sources for data obtaining.

• Alignment of data format (SAP standard).

• Data structure alignment with APO.It indicates the complexity of the process of uploading and evaluating external forecasts.

Consensus Forecasting

• Number of forecasts.It indicates the number of forecasts that are to be made among all the departments involved and the degree of consensus reached.





It indicates the complexity that exists in the forecast accuracy tracking process.

Forecast Accuracy Tracking

• Complexity of the KPI calculation.


Demand Forecast Valuation

• Type of system in which valuation is calculated.

• Calculation complexity.

It indicates the complexity that exists in the demand forecast valuation process.

Review and Adjust Forecasts at Various Aggregation Levels



• Desaggregation profile volatility.It indicates the complexity that exists in the review and adjustment of forecasts.

Generate New Forecast (Batch Run)

• Number of SKUs.

• Number of inputs used to perform the forecast.


It indicates the complexity that exists in the generation of forecasts.





It indicates the complexity that exists in the transfer of the forecast to SNP (or other system).

Release of Forecast

• Type of system to which the forecast is to be transferred.

• Number of characteristics that are subject to modification during the transfer.

Alert Monitor – Work by Exception

• Number of alerts to be managed.

• Complexity of the KPI calculation for alert treatment.

It indicates the complexity that exists in the alert management.

Returns Scenario • Frequency of returns.

• Volume of products that are returned.It indicates the number of returns that exist.

Reporting Scenario • Number of reports.

• Complexity of KPI’s calculation.

• Number of filters (conditions for report data selection).

• Complexity of filters operations.

It indicates the complexity that exists in the reporting.



Complexity Factors: Technology – Set Up


Infocubes • Number of characteristics.

• Number of dimensions (product / client / location).

• Number of key figures.

Planning Area • Number of characteristics.


• Number of key figures.

• Number of planning books.

• Temporal buckets.

Data Warehouse Interfaces

• Complexity on the calculation of KPIs.

• KPI calculation frequency

• Level of machine resources load.

Factor Criteria


Complexity Factors: Technology – Set Up (continued)


Master Data Interface

• Standard SAP vs. non-standard SAP system.

• Number of characteristics to maintain.


• Data structures alignment.

R / 3 Interface• Standard SAP vs. non-standard SAP system.

• Data structures alignment.

• Transfer frequency.

Factor CriteriaRoles and Authorizations

• Number of roles.


Complexity Factors: Technology – Conversion


Products • Number of products.

Sales History • Utilization of business warehouse.

• Volume of data.

Factor CriteriaLocations • Number of locations per product.

PPM – DP• PPM loaded from SAP R/3 vs. a non SAP system.

• Number of PPMs.

• Average number of components per PPM.


Contents





5. DP Exercises


DP Demo: Accelerated Supply Chain Integration APO Template

This scenario shows an example of how the customer dimension has to be considered in the Supply Chain Planning Process

To demonstrate that the Supply Chain process has to be customer oriented in order to ensure the required service level at minimum cost.

Improvement of forecast accuracy as a consequence of considering events and sales history at the customer level.

Build up the basics to launch downstream collaborative initiatives (CPFR or CRP/VMI).

GOALS

EXPECTED CUSTOMER BENEFITS

INDEX

Business Description Previous Steps SAP APO Demand Planning


In environments with a great customer concentration, the introduction of customer dimension in the forecast hierarchy is necessary to improve forecast accuracy.

The actual sales information at the customer level is critical to quickly react to deviations in the promotional behavior.

Category ANationwide

All customer

Product ANationwide

All customers

Product BNationwide

All customers

Product BNationwide

customer BProduct B

Nationwidecustomer A

Product BNationwide

customer CPromotional activities with the most significant impact on sales are planned and executed at the customer

level.

The DFU is a combination of product, geographical area and demand group (customer). A DFU level has to be

defined at which forecasts are created and agreed on with all the people involved in the process.


Business Description Definition of DFU’s (Demand Forecasting Units)


There is a compromise between workload and improvement of forecast accuracy in the selection of customers to be considered in the demand forecast.

Only 15 to 20 customers are usually significant to the forecasting process.

The criteria to determine what are these significant customers are: Sales volume: these customers must account for at least 50% of total sales. Promotional activity with significant impact on sales: customers with promotional

activity that eventually could impact more than 10% on total sales must be considered in the forecast process.

Customers involved in collaboration initiatives (CRP/VMI, CPFR, etc.): customers with special agreements.

Customers with very high sales volume in some products: customers which account for over 50% of sales of one product with strategic relevance.


Business Description Criteria to Determine Significant Customers


The market is changing every day with continuous mergers, demergers, acquisitions, etc. that must be reflected in the forecast to better match future demand.

Generally speaking, it is not advisable to change historical data to match changes in the customer composition. It is preferable to introduce events in order to increase or decrease the base sales forecast.

In this case, the forecast is adjusted by introducing an event in the future with the impact in sales of the increased or decreased number of points of sales.

The merge of two customers can be handled by creation of:

• a new data selection including the two “old” customers and the “new” one.

• a database realignment process.

The introduction of a new customer does not entail any change in the

standard methodology.

customer A customer AOR

customer A customer B

New customer


Business Description Procedure to Reflect Changes in Market in SAP APO


Before performing this scenario in SAP APO, it is necessary to delete all the previous transactional data from former users that could affect the scenario results. For that matter, the next steps must be executed:

1. Go to transaction SE38.

2. Fill the program: /SAPAPO/RLCDELETE

3. Press the button to execute it.


Previous Steps


4. Get a variant:

5. Select the variant TEMPLATE_003

DO NOT SELECT ANY VARIANT EXCEPT THIS ONE !!


Previous Steps


6. Execute…

6. … and confirm all messages the system issues.


Previous Steps


Review of Historical Sales Review historical sales of one product and disaggregate the information in the client dimension.

Generate Forecast Choose statistic model. Errors review and model re-selection if needed.

Event Introduction Introduce a promotional event at the client level. Review existing promotions for this client / product.

Forecast Review Review the forecast at the client level and the impact of cannibalization between standard and

promotional products. Alerts due to significant Sales Deviation

Analyze the alerts generated by the system due to significant sales deviation in one client during the promotional period.


SAP APO Demand Planning

MAIN FUNCTIONALITIES


1. Menu path: Demand Planning / Planning / Interactive Planning

2. In the shuffler, press the button and select Data Views.

3. Open the Planning Book PB_003_02 and select (double click) in the data view DV_003_01 Press the button and select Info Objects

4. Select the data selection and Object Selection screen will appear. Select the product P_003_217.

5. Press the button to load the historical product sales.


SAP APO DP Review of Historical Sales


6. To perform a drill down in the client dimension, click on Header button and, in the Total - Account tab strip, select the option Details.

7. To obtain the percentages by client over total sales, press the button


SAP APO DP Review of Historical Sales


1. To generate forecast select in the menu: Settings Forecast profile.

2. Select your Master Forecast Profile: MP_P_003_217 press the button and then press button to obtain the Forecast.

3. To display the graphical forecast press the button and then press the button

4. To choose the other statistic model press the button and select the new model (if you consider it necessary).Press the button


SAP APO DP Generate Forecast


5. To review the errors press the button and obtain the forecast errors for this statistic model.


SAP APO DP Generate Forecast



2. Press the button and the promotional screen will appear.

3. Press the button to create a new event.

4. Fill in the fields as described below: Short Text (Promotion ID): P_217_PROMO* Description: Promotion 217* Cannibalization group: CAN_217 Period: enter “W” Number of periods: enter “3” Begin date: 10.03.03** End date: 30.03.03** Promotional key figure: RPROMEV Planning key figure: RHIST


SAP APO DP Event Introduction

*These fields are filled in with names as example. The user must put their own names or dates

**The begin and end dates must be always in the future


5. Press the button Save in the command bar

6. The screen for the promotional data introduction will appear:



CAN_5246


8. In the shuffler, select the product P_003_217

9. Press the drill down button in the shuffler and select Account (client).

10. Select the client 100890 (which is affected by the promotion).

11. Press the button Assign Objects in the command bar.

12. In the shuffler, press the button and come back to the product selection.

13. Select the product P_003_217 and press the button.

14. In the promotional planning screen, enter the quantities for the first period (e.g. 1.000), for the second periods (e.g. 1.500) and for the third period(e.g. 2000)

14. In the command bar, press the button Change status and then, select the option Planned, in the Future.

15. Save the promotion by pressing the button




16.Return to the interactive planning screen by clicking the button

17.Review the impact on the forecast of the planned promotion(Load the product P_003_217 and select the graphic view )




19.Menu path: Favorites/Promotion Reporting

20.Select the fields planning area PA_003_3, version PV_003_01 and promotion key figure RPROMEV.

21.Press the button




1. At Interactive Planning screen, select the data selection product P_003_5246 and the promotion button to load it into the table.

2. Go to the header, and in the Product tab strip select Details.

3. Review in the promotional and net forecast key figures the impact of cannibalization between the two products (optional).

4. Go to the header, and in the Account tab strip select Details.

5. Review, in the promotional and net forecast key figures, the impact of cannibalization between the two products at the client level. When a product is in promotion, others products may be affected


SAP APO DP Forecast Review



SAP APO DP Alerts

The alert monitor always provides a picture of current structure and supports the management of daily planning activity.

1. Menu Path: Supply Chain Monitoring / Alert Monitor.2. Select in Favorites the option Setting Alert Profile Template 03 (DP.

Select Products or Resources.

Select your preferred alert type.

Once you have selected Products, select the object you want to control.


3. The interactive planning screen appears and the user only has to load the data selection into the table to review what is happening.

4. To do a drill down in the client dimension, go to the header and, in the Total Account (client) tabstrip, select the option Details.


SAP APO DP Alerts


Contents





5. DP Exercises


DP ExercisesDP EXERCISE

SAP APO installation guide and getting started: Before you do the exercises we are going to install the SAP GUI and the access to the Barcelona APO Solution Center.

1. Access the address: https://software.accenture.com/2. In the Search Tool bar click “SAP” and then click “GO”3. Click on the link SAP Frontend-Version 6.104. Follow the wizard instructions

This will leave SAP GUI installed on your hard disk.

5. Click on the SAPlogon icon in your desktop6. Click on the “NEW” button7. Input the following data:

Name: Barcelona APO Solution CenterApplication Server: 170.251.70.208System number: 00

8. Click on the OK button and double click on the access

https://software.accenture.com/


DP ExercisesDP EXERCISEObjective:

To consolidate the DP concepts learnt along the course. At the conclusion of this unit, you will be able to:

Evaluate historical data and create sales forecast. Analyze the differences among the forecasts you get when the statistical models have been

changed. Analyze the variables that APO provides you to asses forecast accuracy based on past data. Create promotional events in APO. Check the promotion impact on the forecast and cannibalization produced between products.

Exercise: The first step is to delete all the previous transactional data from former users that could affect our

results. Select the product P_003_3004 and review sales at client level,both in absolute values and in

percentages. Create a forecast, check the variables and change the statistical model for the forecast. Check the

differences between the models and check the MAD and MAPE statistical parameters.


DP ExercisesDP EXERCISE

Introduce an event: Short Text (Promotion ID): Description: Promotion 3004 (user x) Cannibalization group: CAN_3004 Period: enter “W” Number of periods: enter “3” Begin date: (user) End date: (user) Promotional key figure: RPROMEV Planning key figure: RHIST

Introduce some values for your promotion. Check the promotion impact on the forecast. Check the cannibalisation between products both at product level and at client level.


Before performing this scenario in SAP APO, it is necessary to delete all the previous transactional data from former users that could affect the scenario results. For that matter, the next steps must be executed:

1. Go to transaction SE38.

2. Fill the program: /SAPAPO/RLCDELETE

3. Press the button to execute it.

DP ExercisesDP EXERCISE SOLUTION


4. Get a variant:

5. Select the variant TEMPLATE_003

DO NOT SELECT ANY VARIANT EXCEPT THIS ONE !!



6. Execute…

7. …and confirm all messages the system issues.

All the categories from orders (forecast, purchase requisitions, production orders..) that could affect the scenario results are included in the variant.

All the products and Locations defined in our template are included in the variant.



Review of Historical Sales:


2. In the shuffler, press the button and select Data Views.

3. Open the Planning Book PB_003_02 and select (double click) in the data view DV_003_01 Press the button and select Info Objects

4. Select the data selection and Object Selection screen will appear. Select the product P_003_3004.

5. Press the button to load the historical product sales.


- 100 - ©Accenture 2003

6. To perform a drill down in the client dimension, click on Header button and, in the Total - Account tab strip, select the option Details.

7. To obtain the percentages by client over total sales, press the button


- 101 - ©Accenture 2003

Generate Forecast:1. To generate forecast select in the menu: Settings Forecast profile.

2. Select your Master Forecast Profile: MP_P_003_3004 press the button and then press button to obtain the Forecast.

3. To display the graphical forecast press the button and then press the button


- 102 - ©Accenture 2003

4. To choose the other statistic model press the button and select the new model (if you consider necessary).Press the button

5. To review the errors press the button and obtain the errors parameters for this statistic model:

Mean absolute deviation (MAD): mean absolute deviation gives the mean average difference between the forecasted value and the historical value in the ex-post forecast.

Mean absolute percent error (MAPE): mean absolute percentage error

MAPE MAD


- 103 - ©Accenture 2003

6. You can choose other models and review the new results. Exit without saving pressing

7. To come back to the interactive planning press the button


- 104 - ©Accenture 2003

Next steps:For next steps follow the instructions from the Demo Scenario. Be careful with planning versions and products, so each assistant should use their products.

Use the information given at the exercise to introduce the promotional event as well.


scm apo dp overview

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