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Demand Planning with SAP Solutions: A Detailed Guide to Features, Functions, Implementation, and Best Practices

Rick AndersonDeloitte Alistair ThorntonDeloitte Consulting

2

In This Session…

• Demand Planning is usually a critical process in a planning environment

• Understand what SAP Demand Planning can do to support your forecasting process

• Get some best practices for implementing SAP APO in your organization

Supply

Demand

S&OP Cycle

1. Functional Planning Analysis

3. Cost Impact Assess

5. S&OP Meeting

4. Collection &

Aggregation of Inputs

2. Integrated Analysis

3

What We’ll Cover …

• The importance of a good forecast• SAP Demand Planning (DP) – What it can do

Demand Planning overviewCreating a forecast using DP’s Statistical ModelsEnhancing a forecast with key DP tools

• SAP Demand Planning – How to implement itPlanning and designing your DP solutionConfiguring and testing your DP solution

• Wrap-up

4

Modern Enterprises Have Unique Forecasting Challenges

• Satisfy all customer requests (right product, right quantity, right time) without carrying excess inventory

• Create and analyze forecast data at different levelsSales might want to create a forecast for customersOperations might want to create a forecast for a plantMarketing might want to create a forecast for product brands

• Enable communication across business units within an organization

Forecasts should not be produced in isolated “silos”• Incorporate forecast data from supply chain partners into the

demand planning processShare data with customers, suppliers, contractors, etc.

5

But There Are Compelling Reasons to Improve …

• In a recent article, Gartner says that using a robust forecasting process:

Improves forecast accuracy by 54% Increases inventory turns by 33% Reduces inventory safety stock by 25% Increases profit margins by reducing discounts

• In another article, AMR Research comes to similar conclusions:Organizations can expect a 10-25% improvement in forecast accuracy through the implementation of better forecasting tools and processes

6

Customer Service• Fill rates• Percentage of orders

shipped complete • Back orders leading to

customer service issues• On-time shipment

performance• Customer complaints

Inventory Management• Supply not aligned with

demand• Low inventory turns• High obsolescence

Finance• Inventory carrying cost

Manufacturing• Production schedule

disruptions through rush orders

• Increased outsourcing • Increased overtime expense

Warehousing• Excess space/storage cost• Inconsistent shipping/

receiving volumes

Transportation• Inconsistent shipment

volumes and sizes • Heavy use of premium/

expensive space/storage costSales and Marketing• Lost sales opportunities • Tarnished brand image and

questionable customer loyalty

ForecastErrorInput

The Value Proposition – Reduced Forecast Error• Reducing forecast error has a positive financial impact that

propagates throughout the entire supply chain

7

Some Organizations Still Don’t Get It

• Despite this potential benefit, many demand planning processes suffer from these symptoms

Demand Planning is isolated, and not well integrated with other planning and execution processesPlanning and execution processes do not explicitly account for forecast errorsThe demand planning system is reactive, due to limited information and visibility of future demandMultiple systems used for forecasting, planning, and execution and are not integrated

8

Modern Demand Planning Processes …

• Drive all planning and scheduling activities in the downstream supply chain

• Are fully integrated with other forecasting processes, driving a consensus-oriented approach

• Measure and track accuracy/error accurately and frequently to drive improvement

• Are tightly integrated with other forecasting, planning, and execution systems in an operational environment

Demand Planning

Supply Network

Planning

Production

Planning

9





• Wrap-up

10

Demand PlanningForecasting

Simulating

Collaboration

Monitoring

Production Planning/ Detailed SchedulingPlanning

Scheduling

Monitoring

Transportation Planning/ Vehicle Scheduling

Route Optimization

Consolidation

Carrier Selection

Tendering

SAP SCM

Supply Network Planning (SNP)Network Modeling

Master Scheduling

Deployment

Trans. Load Builder

Vendor ManagedInventory (VMI)

Global Available-to-PromiseCapable-to-Promise

Allocation

Substitution

DP SNP PP/DS gATP TP/VS

SAP Demand Planning Is Part of SAP SCM 2007

11

SAP Demand Planning – The 50,000 Foot View

SAP BI

DP

SAP APO

SNP/PP/DS

Historical Data(Shipments, Orders)

Planned Orders(Production, Transport)

Non-SAP

SAP ERP

12

Demand Planning Tools:• Collaborative Forecasting• Consensus-based Forecasting• Promotional Planning• Lifecycle Management• Advanced Macros• Accuracy Reporting

Demand Planning Tools:• Collaborative Forecasting• Consensus-based Forecasting• Promotional Planning• Lifecycle Management• Advanced Macros• Accuracy Reporting

Statistical Methods:Multi-Model Approach• Average Models• Seasonal• Causal Factors• Trend, Seasonal Trend• Composite Models• Pick Best

Demand Planning Data MartAnticipation ofFuture Demand

Input DataCollaborative ForecastsHistory – Orders/ Shipments, etc.Point of Sale(POS) DataNielsen/InformationResources, Inc. (IRI) DataFlat File

SAP Demand Planning – The 5,000 Foot View

13

SAP Demand Planning Requirements and Misconceptions

MISCONCEPTIONS• I need BW in order to use DP• I need SAP ERP in order to use DP• I need to implement the other SAP APO

modules in order to use DP• I can only use DP to create a quantity-

based forecast• I can’t do any reporting out of DP

MISCONCEPTIONS• I need BW in order to use DP• I need SAP ERP in order to use DP• I need to implement the other SAP APO

modules in order to use DP• I can only use DP to create a quantity-

based forecast• I can’t do any reporting out of DP

REQUIREMENTS• Good, clean data• DEV, QA, and PROD servers• Due to use of liveCache, servers must have adequate memory (more on this later)

• If using with SAP ERP or SNP/PP/DS, integration models must be generated in SAP ERP and all needed master data must be transported to SAP APO

• If using with SAP NetWeaver BI, the SAP NetWeaver BI system must be created as a source system in SAP APO (same for SAP ERP)

REQUIREMENTS• Good, clean data• DEV, QA, and PROD servers• Due to use of liveCache, servers must have adequate memory (more on this later)

• If using with SAP ERP or SNP/PP/DS, integration models must be generated in SAP ERP and all needed master data must be transported to SAP APO

• If using with SAP NetWeaver BI, the SAP NetWeaver BI system must be created as a source system in SAP APO (same for SAP ERP)

14

SAP Demand Planning Comparison

Area FunctionalitySAP Demand

Planning SAP ERP

Forecasting

Basic Methods (trend, seasonality, etc.)Advanced Methods (multiple linear regression)Collaborative Forecasting (over Internet)Characteristics-Based Forecasting (CBF)Multiple Levels of Forecasting (Top Down, Bottom Up, Middle Out)Ease of Consensus Forecasting

AccuracyPredefined MetricsEx-post AnalysisOutlier Corrections

Life Cycle PlanningPhase InPhase OutLike Profiles

Promotion PlanningBasic PromotionsCannibalization

ReportingStandard SAP BW ConnectionAd Hoc SAP Business Explorer (SAP BEx) Analyzer

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• Wrap-up

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Creating a Forecast Using DP’s Statistical Models

1 – Univariate/Time Series models2 – Multivariate/Causal analysis3 – Composite models

17

1 – Univariate/Time Series Forecasting Models

• Univariate forecasting is better known as “time series”forecasting

A time series consists of data collected, recorded, or observed over successive increments of time

• Univariate models detect and project the following statistical patterns

ConstantTrendSeasonal

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1 – Univariate/Time Series Forecasting Models (cont.)

• Univariate models use only one independent and one dependent variable

The independent variable is time (day, week, month, etc.)The dependent variable is something like sales or shipments

• Time series are analyzed to discover past patterns of variability that can be used to forecast future values

The assumption is that past patterns will continue in the future

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Good for products that are inherently seasonal

Fair for products that have low or sporadic sales

Poor for products that have long lead times, are promotion driven, are competitor influenced, or are new products

1 – Univariate/Time Series Forecasting Models (cont.)

• The purpose is to model the patterns of past demands in order toproject them into the future

• Good in very short-term horizon• Requires past, internal data to forecast the future• Most cost effective and easily systematized• Forecaster should have an understanding of statistics

20

1 – Univariate/Time Series Models: Types of Approaches

• Types of time series approaches in SAP Demand PlanningConstant

Varies very little from a stable mean value Trend

Falls or rises constantly over a long period of time with only occasional deviations

SeasonalPeriodically recurring peaks and troughs differ significantly from a stable mean value

Seasonal trendPeriodically recurring peaks and troughs, but with a continual increase or decrease in the mean value

IntermittentDemand is sporadic 21

1 – Selecting the Right Univariate/Time Series Model

• General criteria for time series model selection

Croston’s ModelWith Sporadic/Intermittent Demand

Auto Model Selection 1Auto Model Selection 2

No Knowledge of Patterns in Historical Data

Winter’s ModelTrend and SeasonalityHolt’s ModelTrend, No Seasonality

Moving AverageExponential Smoothing

No Trend, No Seasonality

Model to SelectPattern of Data

22

1 – Univariate/Time Series Forecast Strategies

23

1 – Univariate/Time Series Forecasting Models: Advantages

• Well suited to situations where sales forecast is needed for a large number of products

• Work very well for products with fairly stable sales Can smooth out small random fluctuations, however

• Easy to understand and use, and can easily be automated• Good for short-term forecasting• Only requires past observations of demand

24

1 – Univariate/Time Series Forecasting Models: Disadvantages• Require a large amount of historical data• Adjust slowly to changes in sales

This can cause significant forecast error if large fluctuations exist in current data

• Not well suited for forecasts with long-term horizons

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2 – Multivariate/Causal Forecasting Models

• Try to mathematically quantify the relationship between a dependant variable and one or more independent variables (e.g., batteries and hurricanes)

• Data requirementsActuals for all variablesForecast for all independent variables

• Uses statistical technique called Multiple Linear Regression (MLR) and the following equation:

Yi = βo + β1X1 + β2X2... + βn Xn

SalesHistory

Constant Coefficients

CausalFactor

26

2 – Multivariate/Causal Forecasting Models (MLR)

• Make projections of the future by modeling the relationship between demand and other external variables (model causes of demand)

• More time-intensive and less easily systematized• Forecaster should have solid training in statistics

Good for products that are inherently seasonal, have long lead times, are promotion-driven, or are competitor-influenced

Fair for products that have low or sporadic sales, or are new products

N/A

27

2 – Multivariate/Causal Forecasting Models: Example

Uni

t Sal

es

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

$2,0

00

$1,0

00

$1,5

00

$800

History Future

Calculationof β Calculation

of salesusing β

PlannedAdvertisingBudget

ActualAdvertisingBudget

28

2 – Multivariate/Causal Models: Advantages

• Support “what-if” analysis• Can provide accurate short, medium, and long-term forecast given

good leading indicators• It allows you to incorporate seemingly unrelated data into your

Demand Plan

MORE INPUTS = BETTER FORECAST!

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2 – Multivariate/Causal Models: Disadvantages

• Accuracy depends on a consistent relationship between independent and dependent variables

And an accurate estimate of the independent variable• Can be time-consuming to develop and maintain• Require strong understanding of statistics• Require larger data storage than time series models

30

3 – Composite Forecast Models

• Combine forecasts from different statistical modelsTime series, causal, judgmental

• Can combine forecasts by: Averaging the forecasts – giving each one equal or different weightsVarying the weightings assigned to each forecast over time

Constant Model ForecastSeasonal Model ForecastCausal Model Forecast

= 30% Weighting= 30%= 40%

31

3 – Composite Forecast Models (cont.)

• Composite forecasting can be carried out in two ways: Standard

Carry out forecasts with specified profiles and then total the results, taking the weighting factors/profiles into account

Automatic selectionYou can specify that the system automatically selects the best profile based on one measure of error

32

3 – Composite Forecast Profile

33

3 – Composite Forecast Profile (cont.)

34

3 – Creating a Forecast Using DP’s Statistical Models – Tips

• Think carefully about which levels you want to forecaste.g., Local Brand, Product Group, Product Category, CustomerYour decision affects system performance, aggregation/ disaggregation, InfoCube design, etc.

• Causal analysis cannot determine which independent variables are relevant

It will, however, tell you how well an independent variable explains changes in the dependent variable

MLR measures of fit• Make sure that the data you want to use in causal analysis is not

only readily available, but of sound quality

35

3 – Creating a Forecast Using DP’s Statistical Models –Tips (cont.) • You can manually select the error metric you wish to use as the

basis for comparing forecast strategiese.g., MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), etc.Used by the automatic selection models

• Beware of spikes in historical data They can produce inaccurate spikes in the corresponding forecast

Outlier correction

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• Wrap-up

37

Forecasting and Simulating Demand: Four Key Tools

1 – Consensus-based forecasting2 – Collaborative forecasting3 – Promotions planning4 – Life cycle planning

38

1 – Consensus-Based Forecasting

• Allows different organizational units to generate their own demand plans

According to their unique goals, time horizons, and forecasting philosophies

• Each forecast can then intelligently be reconciled into a singleconsensus forecast

• The result is a one-number demand plan that has been agreed to and is owned by all stakeholders

39

1 – Consensus-Based Forecasting (cont.)

1Planning Creates Statistical/CausalForecast for all SKUs using Historical Data

2Marketing Adjusts Statistical Forecastand Plans Promotions

3Sales Adjusts Statistical Forecast 4

Customer Adjusts Statistical Forecastvia Internet

5Forecasts Are Reconciledinto a Single Consensus Forecast

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1 – Consensus-Based Forecasting (cont.)

• TipsWhen reconciling all of your different forecasts, start with a simple technique (like average or weighted average)

You can then progress to more advanced methods, like weighting based on forecast accuracy

Properly designing planning book/data views is criticalKey figures used in the consensus process must be assigned to each planning book/data view involved

41

DemandPlanner

SAP Demand Planning

SAP Demand PlanningInsert forecast

21

4 Alert broadcasting (e.g., email)

6 Forecast adjustment

Create statisticalforecast

CHANNEL PARTNER MANUFACTURER

AccountManager

Internet

5Review

forecasts

Internet

3Create

consensusforecast

2 – Collaborative Forecasting: Typical Process

Source: SAP

42

2 – Collaborative Forecasting

• TipsAgree on the process

Define role of each partnerEstablish confidentiality of shared infoCommit resourcesAgree on exception handling and performance measurement

Create a joint business plan and establish products To be jointly managed, including category role, strategy, and tactics

Jointly agree on control techniques

43

3 – Promotions Planning

• Lets you create, monitor, and include promotional activities into your demand planning process

Promotional activities can be one-time events or repeated events Examples of one-time events include the 2000 MillenniumExamples of repeated events include coupon campaigns, new advertising spots, and price cuts

• Can model promotions at a variety of different levelsFor specific customers or a group of customersFor specific products or a subset of productsFor a specific region of a national sales campaign

44

3 – Promotions Planning (cont.)

• The advantages of planning promotions separately are:You can compare forecasts with promotions to those without promotionsYou can correct the sales history by extracting past promotions from it

Thus obtaining unpromoted historical data for the baseline forecast

The processes of interactive planning and promotion planning can be kept completely separate

e.g., the sales force might be responsible for interactive planning while marketing is responsible for promotions planning

45

3 – Promotions Planning: Cannibalization

• Cannibalization is the negative impact that promoting one material can have on similar materials

• In Demand Planning, we use Cannibalization Groups to model these effects

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3 – Promotions Planning: Collaboration• Collaborative Promotion Planning enables sales department and

its respective customers to jointly develop a promotion planCustomers can review, approve/reject online promotionsSales representatives will monitor promotions and activate them upon customers’ approval

47

4 – Life Cycle Planning

• Like ModelingUse historical sales patterns for new products

• Phase In/OutRamp down for discontinued products Ramp up for newer products

Actuals of the old product

Forecasting of the new product

Life Cycle

Time

Sales

Like

Actuals

Product BProduct A

48

5 – Seasonal Planning

• Prior to SAP APO 5.0, the system defined seasons based on periodicity of a Planning Area

• Starting with SAP APO 5.0, seasons can be defined at a more granular level: CVC or a group of CVCs

49





• Wrap-up

50

Some SAP Demand Planning Facts

• Hundreds and hundreds of companies are using itIt’s very mature, very stable

• SAP Demand Planning has been implemented in nearly all industries

It offers some limited industry-specific functionality• SAP Demand Planning can be used with SAP ERP, with SAP BW,

or bothIt can also be used in a standalone environment with no other SAP applications

• But: You do NOT have to be using SAP NetWeaver BI/SAP BW to use SAP Demand Planning

SAP Demand Planning comes with an embedded SAP NetWeaver BI/SAP BW that can be used to do some basic DP reporting (with the BEx Analyzer/Explorer)

DP projects generally take between 3-6 months, depending on complexity, scope, etc. 51

Common Pitfalls on SAP Demand Planning Projects

• Not enough attention given to the business processFocus should be on improving how you do Demand PlanningSAP Demand Planning is an enabler – it won’t fix the problems by itself

• Project is owned by IT, not the business users• Inadequate resourcing

Not having enough capacityMissed deadlines, overworked resources, poor quality

Not having the right mix of skills

52

Common Pitfalls on SAP Demand Planning Projects (cont.)

• Poor InfoCube designAffects system performance, synchronization with other systems (SAP NetWeaver BI/SAP BW), availability of needed data, format of needed data, etc.

• Inadequate time given to understanding statistics behind SAP Demand Planning

Can lead to using wrong forecast models, coefficients, etc.

53

Planning and Designing Your Demand Planning Solution

• There are many implementation options to considerFunctional scope

Which parts of SAP Demand Planning to implementOrganizational scope

Which parts of the business to implementWhich products to include

TimingWhen each part of the organization will implement it

• So, how do I figure this out?Blueprint/Design phase“Value Assessments”Prototypes

Developing a prototype of your proposed DP solution BEFORE the realization phase of your project is a great way to validate your overall design

54

Planning and Designing Your Demand Planning Solution (cont.)• Another way to compare

implementation options is to assess each one against (four) key factors:

Time-to-Benefit

Project Duration

Time required to achieve full benefit potential; is impacted by technology, processes adherence, data quality, and organization alignment

The elapsed time required to implement the solution throughout the business; key drivers are usually the resource skill sets and mix, use of accelerators, and business acceptance

Cost

Risk

The total cost of implementing the solution (process and technology); key variables are the lapsed time and mix of external versus internal resources

The probability that significant issues delay project progress and/or result in additional cost; key drivers are usually the number of concurrent tasks, interdependencies, and resources

55

Evaluating SAP Demand Planning Implementation Options

Risk Time-to-Benefit DurationCost

3. No Pilot, full DP Low High Short Long Short LongLow High

• Higher external support partially offset by compressed timeline

• Higher business risk• Higher change

management risk

• Shortest time-to-benefit• Steep benefits realization

curve expected

• Shortest duration• Single implementation

phase

• Cost savings from smaller scope off-set by longer timeline

• Higher change management risk

2. DP Pilot, then ProductLine

Low High Short Long Short LongLow High

• Projected longest time to benefit

• Longer duration due to staged implementation

1. DP Pilot, then BU Low High Short Long Short LongLow High

• Least amount of change management risk

• Benefits realization could begin sooner than alternative number two

• Longer duration due to staged implementation

• Cost savings from smaller scope are offset by longer timeline

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• Three most important things to consider (once scoping decisions have been made):

1 – Your team2 – Your methodology and timeline3 – Your process

Planning and Designing Your Demand Planning Solution

57

1 – Your Team

• Ideally, your project team should consist of representatives from management, the business, and IT

• This ensures the proper mix of skills/experiences Management skills

Project sponsorship, project managementBusiness skills

Statistical forecasting methodsProduct/brand specialists

Technical skillsBasisSAP BW, SAP ERPExisting systems

Communication and knowledge transfer are CRITICAL. Small, isolated teams keep internal knowledge and expertise concentrated to a few individuals.

58

2 – Your Methodology and Timeline

• Typical methodologyPhase 1 – Analysis/Blueprint

Requirements gatheringProcess definitionProcess blueprinting

Phase 2 – Pilot DesignSystem buildSystem configuration

Phase 3 – Final Design BuildFinal design buildFinal design configuration

Phase 4 – Testing Unit TestingIntegration TestingTraining Documentation

Phase 5 – Go-Live Prep/Go-LivePhase 6 – Post Go-Live Support

59

2 – Your Methodology and Timeline (cont.)

• A typical project timeline

Phase Task Activities Month 1 Month 2 Month 3 Month 4 Month 5 Month 6Blueprint Definition Confirm business requirements

Application workshopsIntegration workshops

Realization: Prototype Functional Pilot Process and role modelsAPO DP buildIntegration designFunctional unit configuration

Realization: Integration System Pilot Build value-adding scenariosBuild interfacesBuild data conversion

User & System Acceptance Testing Complete unit test scriptsComplete integration test scriptsComplete stress tests

Final Prep & Go-live Integrate and Go-live Data conversion and testing Detailed cutover and go-live prep

Final end-user training Go-live and support

60

3 – Your Process

• Remember – SAP Demand Planning is being implemented to support a business process

• Properly analyzing the “as-is” and designing the “to-be” demand planning process drives how much value you’ll get out of a DP project

InfoCube/Planning AreaDesign/Maintenance


Forecast ProfilesDesign/MaintenanceForecast Profiles

Design/Maintenance

Creation ofStatistical forecast


Creation of promotions


Reconciliation ofDemand Plans


Historical dataupdates


Monitoringforecast accuracy


Planning BookDesign/Maintenance


Manual adjustments

Manual adjustments

Release of Demand PlanRelease of

Demand Plan

Evaluate/UpdatePlanning Area/Planning Books


Create new master data


Generate new CVCs/prop factorsGenerate new

CVCs/prop factors

Load newmaster data to planning area


Data MaintenanceActivities

Business ProcessActivities

61

A Typical Demand Planning Process




Design/Maintenance













Manual adjustments

Manual adjustments

Release of Demand PlanRelease of

Demand Plan





Generate new CVCs/prop factors






62

InfoCube/Planning Area Design/Maintenance

• InfoCubes are multi-dimensional relational database structures that store planning data for use in Demand Planning

• InfoCubes consist of:Characteristics – Planning objects that represent levels at which a forecast might be created

Product Brand, Region, Customer Group

Key Figures – Planning objects that contain data representing a numerical value

Historical Orders, Statistical ForecastTime Buckets – Represent periods of time in the InfoCube

Months, Weeks

DP DataMartDP DataMart

SalesActuals

MarketingForecast

Cube

Field SalesForecast

Cube

63

InfoCube/Planning Area Design/Maintenance (cont.)


SalesActuals

MarketingForecast

Cube

Field SalesForecast

Cube

SAP Demand Planning Planning Object Structure

• Contains plannable characteristics and aggregates for use in liveCache

• Characteristics determine the levels on which you can plan and save data

• Can be either standard characteristics and/or ones that you have created yourself in the Administrator Workbench

64



SalesActuals

MarketingForecast

Cube

Field SalesForecast

Cube

SAP Demand PlanningPlanning Object Structure

“Generate characteristic combinations”

Product Group100, 101, 102

Product FamilyLiquids, Gels

Customer GroupRetail, Internet

RegionSouth, North

Sales Organization3000, 3005

Product Group100, 101, 102

Product FamilyLiquids, Gels

Customer GroupRetail, Internet

RegionSouth, North

Sales Organization3000, 3005

65



SalesActuals

MarketingForecast

Cube

Field SalesForecast

Cube


DP Planning Area(s) [liveCache]

• Central structures for Demand Planning used to store current planning data

• Specifies the following:Key figures to be usedUnit of measure in which data is plannedCurrency in which data is plannedStorage buckets profile that determines buckets in which data is storedAggregate levels on which data can be stored in addition to the lowest level

To enhance performanceSettings for how each key figure is disaggregated, aggregated, and saved

66



SalesActuals

MarketingForecast

Cube

Field SalesForecast

Cube DP Planning Book(s)DP Planning Book(s)• Very configurable tabular and

graphical presentations of forecast data

• Each planning book can have multiple data views

• Data views present planning book data in different ways

Time periodsSpecific key figures



67




SalesActuals

MarketingForecast

Cube

Field SalesForecast

CubeDP Planning Book(s)DP Planning Book(s)

Marketing ViewHistorical Orders

Historical ShipmentsFinancial Forecast

Marketing Final ForecastLE LAG 1LE LAG 2LE LAG 3

Field Sales ViewHistorical Orders

Historical ShipmentsSales Final Forecast

KAF LAG 1KAF LAG 2KAF LAG 3

Product Supply ViewHistorical Orders

Historical Shipments

PS S&OP ViewHistorical Orders

Historical ShipmentsHistorical Returns


68


• Important things to considerAt what levels do I want to forecast?

Product group? Customer?Characteristics vs. navigational attributes

What time buckets do I want to forecast in? Months, weeks, quarters?

What data do I need to support the process? Historical shipments, marketing forecast

Where will this data come from?How large must the server be to support this process?

69

• Fixing key figuresTo be able to “fix” values, key figures must be configured to be fixable when created as InfoObjects

You must also specify another key figure to which these fixed values are stored

• Aggregates help improve performance By fixing/saving data at a specific level of aggregation

The system then reads only the aggregation level Note: It is recommended that any aggregates you create in SAP Demand Planning also be created in any synchronized SAP NetWeaver BI/SAP BW system

• Think hard about how key figures should aggregate and disaggregate data (statistical forecast vs. unit price)

InfoCube/Planning Area Design/Maintenance – Tips

70

InfoObject Configuration

71

Key Figure Configuration

72


• Using Navigational AttributesTry to boost performance by acting as a “substitute”characteristic

You can’t plan at the attribute level, but you can select and navigate with themExample:

For characteristic “Customer” you might assign attributes of “Sales Rep” and “Priority”You then specify master data for these, like “John Smith”or “Bob Jones”

Attributes also help speed up the realignment process

73

Navigation Attribute Configuration

74


• When using navigational attributes, however:BE CAREFUL!

They can actually adversely affect performance during data selection and navigation in interactive planning

Attributes read a large number of tables from the associated InfoObject master dataThis can increase processing time

My advice: Find the right balance• Don’t initialize planning areas that aren’t needed

Once a planning area is initialized, it consumes liveCache memory

75





Design/Maintenance





Reconciliation ofdemand plans

Reconciliation ofdemand plans







Manual Adjustments

Manual Adjustments

Release of demand plan

Release of demand plan











76

Planning Book Design/Maintenance

• Planning books are very configurable interfaces to your DP planning data

They act as the main tool for viewing and modifying plans in SAP Demand PlanningThey are usually designed from scratch for each project

Although there are many “SAP-delivered” ones Can show any key figure from a planning area sliced and diced (using any characteristics in the associated planning object structure)

• SAP APO supports multiple books per planning area, with multiple views per book

Planning Book Design/Maintenance (cont.)

Header

StandardSelections

Macros

Grid

Shuffler

Switch to otherbook/views

78

Planning Book Design/Maintenance (cont.)

• Important things to considerWhich organizational groups need planning books?

Marketing, sales, etc.Do these groups need to share data, or will they generate their own?

Consensus forecasting processAre any special calculations needed to support the forecasting process? (macros)

79

Planning Book Design/Maintenance – Tips

• SAP provides a “wizard” to guide you step-by-step through the process of creating planning books and data views

• A good way to start is to copy a standard SAP planning book and modify it to meet your needs

• You can edit the layout and format of a data view directly from DP Interactive Planning

Click the “Design” icon located on the planning table toolbar• To help improve performance

Keep the number of key figures in each planning book/data view to a minimumTry not to use Default macros – these automatically execute whenever data is changed

80

Planning Book Wizard

81





Design/Maintenance













Manual Adjustments

Manual Adjustments

Release of Demand Plan






Generate new CVCs/Prop factors


Load newmaster data to Planning Area




82

Creation of Statistical Forecast

• Important things to considerHow often do the statistical models need to run?Should they be run interactively or in batch?At what level is the forecast going to be run?

• TipsIf you plan on executing your forecast by using a batch job, be mindful of the sequence you run it in

You’ll probably have multiple jobs that have to be run with it

You can compare the results from different forecast runs directly through the SAP Demand Planning desktop

83





Design/Maintenance













Manual adjustments

Manual adjustments













84


• Important things to considerWhere will the Demand Plan be sent?

SNP, SAP ERP Demand Management, Legacy systemsHow often should forecasts be released?

• TipsTo pass forecasts to SAP ERP, the Core Interface (CIF) must be configured

As all material/location master data objects must exist in SAP APO for the standard release transactions to work

SAP recommends that you use a separate data view for the transfer process

This data view should contain only the key figure containing the forecast data to be transferred, and a forecast horizon only (no history)

85

Release of Demand Plan (cont.)

• Legacy integrationSAP APO Demand Planning is tightly integrated with:

SNP via liveCache (TimeSeries liveCache Order liveCache)SAP ERP via CIF Interface

The SAP APO Demand Planning can also be integrated with a legacy system via BAPI “BAPI_PBSRVAPS_GETDETAIL2”The BAPI enables:

Extraction of data from TimeSeries liveCache to a file/interfaceAggregation on weekly/monthly basis of forecasts results

86


InfoCube/Planning AreaDesign/ Maintenance

InfoCube/Planning AreaDesign/ Maintenance


Design/Maintenance













Manual Adjustments

Manual Adjustments












Business ProcessActivities 87

Historical Data Updates

SAP ERP

SAP BW/BIDP

SAP APO(part of SAP SCM)

SNP/PP/DS

Historical Data(Shipments, Orders)

Planned Orders(Production, Transport)

Non-SAP

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Historical Data Updates (cont.)

• Important things to considerWhat is “Historical Data”?

Actual ship date? Requested ship date? Customer requested date? Order entry date?

At what level will historical data be kept?How often should SAP Demand Planning be updated?How are cross-period events handled?

• TipsCLEARLY define what constitutes historical data, and make sure all groups (especially BI) understand this definition

SAP Demand Planning needs, in most cases, at least two years of historical data for statistical models to produce high quality results!

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Historical Data Updates (cont.)

• Improving Demand ForecastSales Order History (SOH) vs. Point of Sale (POS)

Most DP implementations use classic Sales Order HistoryAs companies delve into CPFR (Collaborative Planning, Forecasting, and Replenishment) implementations, customers/suppliers begin to share genuine demand information – POSPOS eliminates Bullwhip Effect from Sales Order History and accounts for more accurate demand forecastSAP APO DP allows seamless integration of POS via EDI852 transaction (IDoc: PROACT01)

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Design/Maintenance













Manual Adjustments

Manual Adjustments












Business ProcessActivities 91

Monitoring Forecast Accuracy

• You monitor forecast accuracyTo find out whether you are using the right forecast models To identify what adjustments are needed to forecast models To project expected deviation from the planned forecast

• Important things to considerWhat’s your definition of “Forecast Accuracy”?

Example: FA = 1 – ([Actual – Forecast]/Actual)How will you monitor Forecast Accuracy?

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Four DP Methods with Which to Monitor Forecast Accuracy

1 – Univariate forecast error measurements 2 – Multiple Linear Regression model measures of fit 3 – Planned/actual comparison using macros4 – Use an SAP NetWeaver BI/SAP BW front end for forecast

accuracy reporting

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1 – Univariate Forecast Error Measurements

• Univariate measures of error available in SAP Demand Planning

MAD Mean Absolute Deviation

Total Error Total Forecast Error

MAPE Mean Absolute Percentage Error

MSE Mean Square Error

RMSE Root of the Mean Square Error

MPE Mean Percentage Error – Bias

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2 – Multiple Linear Regression Model Measures of Fit

• SAP Demand Planning offers you many ways to measure how well an MLR model fits the available data

R-square Adjusted R-square Durbin-h Durbin-Watson T-test Mean elasticity

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3 – Planned/Actual Comparison Using Macros

• Macro automatically calculates forecast error, then triggers an alert if that error exceeds preset levels

Information alert if 10% or lessWarning alert if between 10% and 20%Error alert if greater than 20%

InformationWarningError

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Monitoring Forecast Accuracy

• Forecast Fit vs. Forecast AccuracyForecast Fit – describes the relative difference between actual historical data and a hypothetical forecast generated by systemForecast Accuracy – describes the relative difference between actual historical data and actual forecast previously generated by the systemSAP APO Demand Planning is pre-built with standard Forecast Fit algorithms. Therefore, a demand planner may select forecast fit errors he may monitor during each statistical forecast generationForecast Accuracy algorithms can be either designed using SAP APO macros or via SAP BI Reporting functionality

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4 – Use an SAP NetWeaver BI/SAP BW Front End for Forecast Accuracy Reporting• You can use the SAP BEx Analyzer/Explorer to extract data

directly from SAP Demand Planning – identical to an SAP NetWeaver BI/SAP BW report

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Monitoring Forecast Accuracy: Some Tips

• Establish and communicate your organization’s definition of “forecast accuracy”

• Set up effective ways of monitoring forecast accuracy This is key to understanding how to adjust/change the forecast process

• Leverage DP data structures They make it easy to analyze forecast accuracy at a variety of different levels:

Specific product brandsSpecific customers

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Monitoring Forecast Accuracy: Some Tips (cont.)

• Dedicate plenty of time/resources to figuring out which statistical models are the most appropriate to use

It is not appropriate to use the same model for all itemsA model should not be arbitrarily selected based on presumptions aloneModel parameters should be tested and continually reviewedData should be analyzed for patterns or the absence thereof

Those closest to the business should be tapped for domain knowledge

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Monitoring Forecast Accuracy: Some Tips (cont.)

• If you use the rolling error analysis format:You must include key figures to which the past period data can be written

• You can report accuracy data directly out of SAP Demand Planning

Prerequisites for ad hoc reporting out of a DP InfoCube:A planning area in SAP APOAn extraction structure for the planning areaAn InfoSourceA standard or remote Cube that reflects liveCache data

• Using the standard error metrics generated by SAP APO is a greatway to judge forecast accuracy

MAPE is generally regarded as the best metric to use

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Error Metrics – MAPE vs. MPE

Forecast Actual Error Absolute ErrorAbsolute

Percent Error

Product 1 3 1 -2 2 200%

Product 2 0 50 50 50 100%

Product 3 25 75 50 50 67%

Product 4 75 74 -1 1 1%

Product 5 100 75 -25 25 33%

TOTAL 203 275 72 128

AVERAGE 40.6 55 14.4 25.6 80%

With Product 1Without

Product 1

Mean Percent Error (MPE)80% 50%

=avg(absolute percentage error)

Mean Absolute Percent Error (MAPE)47% 46%

=sum(absolute errors)/sum(actuals)

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You can enhance the usefulness of MAPE by adding weighting factors (standard cost, list price) to the calculation. This results in higher-value items having a greater influence on the overall error than lower-value ones, and resolves one of the drawbacks of using MAPE – the assumption that the absolute error of each item is equal.

Error Metrics

• ImplicationsMPE is easily skewed by a single entry

Product 1 drives much of the error in the previous exampleMAPE doesn’t suffer from this, and is much more balanced, and therefore offers a more practical metric for monitoring forecasterror/accuracy

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Managing Planning Exceptions with Alerts

• Notify users of “exception” situations occurring in the demand planning process

• An exception can be:Changes in bias, new trends, unexpected demands Orders exceed forecastOrders fall short of forecast, which may lead to excess inventoryForecast accuracy is below a specified threshold

• Alerts can be filtered and prioritized based on severity throughconfiguration

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• Wrap-up

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Realization/Configuration

• This stage is critical for successful knowledge transfer! Standard approach is DEV, QA, PRODUCTION

• Common issuesOverly complex data model

Too many characteristics, key figures, planning books, macros doing complicated calculations, etc.My advice – Keep your initial model simple, because you can always go back later and add stuff

Don’t assume you’re configuring a static modelYour business is dynamic, and so your DP environment must be as well

Realignment

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System Sizing Is CRITICAL!

• Things that affect performanceAmount of historical data

Don’t keep 5+ years of data if you don’t need itNumber of materials, characteristics, key figures, versions, etc.

Forecast for key materials at just a few levelsNumber of macros and what they do

Be VERY careful here; macros are system hogsAvoid “Default” macros

Levels of forecastingForecast at a high level and have system disaggregate

Time bucketsForecast in week/month buckets, not days

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SAP QuickSizer and liveCache

liveCache size = function (combinations, time buckets, versions)This is multiplicative, non-linear, and difficult to predict!

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• Note that figures of 16-64gb are to be expected in production systems

• Consider W2003 64-bit NT or Unix for performance

• Ideally, multi-way CPUs (at least 4-way min.)

SAP QuickSizer and liveCache (cont.)

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Great new feature introduced in SAP SCM 5.0:• Ability to create time series objects not only

for specific key figures in a planning area, but over specific time horizons. This can greatly reduce the overall amount of memory needed for liveCache.



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Testing Your SAP Demand Planning Implementation

• Testing SAP Demand Planning usually consists of:Design Unit test, System test, Data Migration testDisaster Recovery test, Stress testsFinal Integration test, User Acceptance test

• Key: Analyze which statistical methods are appropriateForecast errors

MAD, MAPE, etc.Ex-post forecast

A forecast that is run in past periods for which actual demand history is also available

Don’t wait until the end to test with realistic data volumes!

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SAP ERPDev

SAP APO Dev*

SAP APO QAS*

SAP ERPQAS

SAP ERPPRD

SAP APO PRD

SAP APO liveCache

SAP ERPCopy

* Internal liveCache acceptable – but must be 64-bit machinefor SAP APO 4.1 and later releases (UNIX or Windows 2003 64-bit)

CIFCIFCIF

Development and Stress Test – Considerations

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Go-Live

• Training“Train-the-Trainer” approachInstructor should be from client

• SupportWho can users call for help?

Competency Center modelConsulting support

• Raging go-live party

One of the more common problems in a DP project is user acceptance. These problems usually manifest themselves most prominently during the go-live phase.To help alleviate this, provide users with some temporary means of feeling comfortable with the system (e.g., Override key figure).

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Some DP Project “Horror Stories”

• Historical data – “What’s an Order?”Develop clear definitions early in the process

• User acceptance – “I ain’t usin’ that thing!”Get users involved early Develop a prototype

• Performance“I’ve executed the forecast model – see you on Monday”

Figure out data scale early – how many materials, etc. Use QuickSizer, SAP, Consulting PartnersPerform multiple sizing checks

• Forecast Models – “Just use the Constant model”Educate users on statistics

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• Wrap-up

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Resources

• Web linksInternational Institute of Forecasting

www.forecasters.org SAP SCM documentation

http://help.sap.com SAP Business Suite SAP Supply Chain Management)

The Institute for Forecasting Education www.forecastingeducation.com

• BooksJohn E. Hanke and Arthur G. Reitsch, Business Forecasting(Prentice Hall, 1995).Wolfgang Eddigehausen, The SAP APO Knowledge Book –Supply and Demand Planning (Wolfgang Eddigehausen, 2002).

ISBN 0-9581791-0-7117

Resources (cont.)

• Great SAP APO Discussion Grouphttp://supplychain.ittoolbox.com/related-groups/SAP/technical-functional/sap-apo

• SCM ExpertHayrettin Cil and Mehmet Nizamoglu, “23 Ways to Squeeze Better Performance from APO DP Processing and Reporting”(SCM Expert, August/September, 2004).

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7 Key Points to Take Home

• SAP Demand Planning offers strong functionality in the followingareas:

Sound forecasting methods (Basic and Advanced)Collaborative forecasting methodsLife cycle and promotion planningExcellent way of monitoring accuracyQuick reporting using SAP Business Explorer (SAP BEx Analyzer)Superior integration with other modules of SAP APO

• SAP Demand Planning is extremely flexible – so proper planning is the key to a successful DP environment!

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7 Key Points to Take Home (cont.)

• SAP Demand Planning offers several powerful forecasting toolsUnivariate/time series models where constant, trend, or seasonal patterns existCausal models to investigate relationships between variables Composite models combine forecasts to reduce error

• Causal forecasting can be very effective when used in conjunction with Time Series forecasting

But understand what it actually does, and that it’s more demanding to set up and maintain

DP supports a business process – getting the process right is therefore critical

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7 Key Points to Take Home (cont.)

• Keep your design simple, but be careful Detail may grow exponentially as you go DP SNP PP/DSIf you “build a data monster,” you will have to “maintain the monster”

• Develop clear definitions of your data and mappings of where they’re coming from/going to

“Historical data” to the DP team may mean something different than to the SAP BW team

• Assembling the right team – and the right mix of business and technical skills – can make or break your SAP APO project

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Your Turn!

How to contact me:Alistair Thornton – [email protected]

Rick Anderson – [email protected]

DisclaimerSAP, R/3, mySAP, mySAP.com, xApps, xApp, SAP NetWeaver®, Duet™, PartnerEdge, and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and in several other countries all over the world. All other product and service names mentioned are the trademarks of their respective companies. Wellesley Information Services is neither owned nor controlled by SAP.

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