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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
- 3 - ©Accenture 2003
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.
- 4 - ©Accenture 2003
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
- 5 - ©Accenture 2003
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 ...
- 6 - ©Accenture 2003
Demand Planning Features and Capabilities
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
- 7 - ©Accenture 2003
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
Demand Planning Features and Capabilities
- 8 - ©Accenture 2003
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 Capabilities
- 9 - ©Accenture 2003
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
- 10 - ©Accenture 2003
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
Demand Planning Features and Capabilities
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
- 11 - ©Accenture 2003
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)
Demand Planning Features and Capabilities
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
- 12 - ©Accenture 2003
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
Demand Planning Features and Capabilities
- 13 - ©Accenture 2003
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
Demand Planning Features and Capabilities
- 14 - ©Accenture 2003
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
Demand Planning Features and Capabilities
Forecasting
- 15 - ©Accenture 2003
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
Demand Planning Features and Capabilities
- 16 - ©Accenture 2003
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.
Demand Planning Features and Capabilities
- 17 - ©Accenture 2003
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
Demand Planning Features and Capabilities
- 18 - ©Accenture 2003
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
Demand Planning Features and Capabilities
- 19 - ©Accenture 2003
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
Demand Planning Features and Capabilities
- 20 - ©Accenture 2003
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
Demand Planning Features and Capabilities
- 21 - ©Accenture 2003
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
Demand Planning Features and Capabilities
Forecast1
n
... Combine & Reconcile
Sales Forecast
Marketing Forecast
- 22 - ©Accenture 2003
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
Demand Planning Features and Capabilities
- 23 - ©Accenture 2003
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)
Demand Planning Features and Capabilities
- 24 - ©Accenture 2003
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
- 25 - ©Accenture 2003
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
- 26 - ©Accenture 2003
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.
- 27 - ©Accenture 2003
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:
- 28 - ©Accenture 2003
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
- 29 - ©Accenture 2003
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
- 30 - ©Accenture 2003
Case Study: Sara LeeDemand Planning Processes (continued)
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
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
S&OP scope with impact
on DP
LocalLocal
EuroEuro
Euro & Local
Production Plan
Local S&OP Review
Customer Service 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
S&OP scope with impact
on DP
- 31 - ©Accenture 2003
Case Study: Sara LeeDemand Planning Processes (continued)
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
UpdatedDemand Forecast
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
- 32 - ©Accenture 2003
Case Study: Sara LeeDemand Planning Processes (continued)
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
- 33 - ©Accenture 2003
Case Study: Sara LeeDemand Planning Processes (continued)
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?
- 34 - ©Accenture 2003
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
- 35 - ©Accenture 2003
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
- 36 - ©Accenture 2003
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:
- 37 - ©Accenture 2003
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
- 38 - ©Accenture 2003
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
- 39 - ©Accenture 2003
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
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 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
- 40 - ©Accenture 2003
Case Study: Sara LeeForecasting Detailed Process: Select Forecast Levels
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)
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 cycleMonthly cycle
Disaggregation
- 41 - ©Accenture 2003
Case Study: Sara LeeForecasting Detailed Process: Select Forecast Levels
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
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 cycleMonthly cycle
Disaggregation
- 42 - ©Accenture 2003
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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
Total history
Total history - updatedupdated promo = baseline
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
Case Study: Sara Lee
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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
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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
Case Study: Sara Lee
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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
Case Study: Sara Lee
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
Case Study: Sara Lee
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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)
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
Case Study: Sara Lee
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
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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)
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
Send forecast to Supply Planning
Clean History and Evaluate Forecast Baseline
Close the Period and KPI generation
Evaluate Forecast Performance
Select Forecast Levels
Choose statistical algorithm
Monthly cycleMonthly cycle
Add Events
Phase in/out
Create consensus (local S&OP)
Update Forecast
Disaggregation
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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
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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
- 60 - ©Accenture 2003
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
Key Aspects to Consider When Implementing DP
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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
Key Aspects to Consider When Implementing DP
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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)
Key Aspects to Consider When Implementing DP
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Complexity Factors: Process – Business Scenarios
Key Aspects to Consider When Implementing DP
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
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Complexity Factors: Process – Business Scenarios (continued)
Key Aspects to Consider When Implementing DP
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.
Factor Description Criteria
Promotional Planning
• Promotional activity.It indicates the degree of promotional activity.
Aggregation Strategies
• Number of dimensions. (product/client/location).
• Desaggregation criteria complexity.
• Desaggregation profile volatility.
It indicates the complexity that exists in the data desaggregation .
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Complexity Factors: Process – Business Scenarios (continued)
Key Aspects to Consider When Implementing DP
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.
Factor Description Criteria
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Complexity Factors: Process – Business Scenarios (continued)
Key Aspects to Consider When Implementing DP
It indicates the complexity that exists in the forecast accuracy tracking process.
Forecast Accuracy Tracking
• Complexity of the KPI calculation.
• Number of dimensions. (product/client/location).
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
• Number of dimensions. (product/client/location).
• Desaggregation criteria complexity.
• 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.
• Desaggregation criteria complexity.
It indicates the complexity that exists in the generation of forecasts.
Factor Description Criteria
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Complexity Factors: Process – Business Scenarios (continued)
Key Aspects to Consider When Implementing DP
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.
Factor Description Criteria
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Complexity Factors: Technology – Set Up
Key Aspects to Consider When Implementing DP
Infocubes • Number of characteristics.
• Number of dimensions (product / client / location).
• Number of key figures.
Planning Area • Number of characteristics.
• Number of dimensions (product / client / location).
• 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
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Complexity Factors: Technology – Set Up (continued)
Key Aspects to Consider When Implementing DP
Master Data Interface
• Standard SAP vs. non-standard SAP system.
• Number of characteristics to maintain.
• Number of dimensions (product / client / location).
• 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.
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Complexity Factors: Technology – Conversion
Key Aspects to Consider When Implementing DP
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.
- 71 - ©Accenture 2003
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
- 72 - ©Accenture 2003
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
- 73 - ©Accenture 2003
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.
DP Demo: Accelerated Supply Chain Integration APO Template
Business Description Definition of DFU’s (Demand Forecasting Units)
- 74 - ©Accenture 2003
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.
DP Demo: Accelerated Supply Chain Integration APO Template
Business Description Criteria to Determine Significant Customers
- 75 - ©Accenture 2003
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
DP Demo: Accelerated Supply Chain Integration APO Template
Business Description Procedure to Reflect Changes in Market in SAP APO
- 76 - ©Accenture 2003
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 Demo: Accelerated Supply Chain Integration APO Template
Previous Steps
- 77 - ©Accenture 2003
4. Get a variant:
5. Select the variant TEMPLATE_003
DO NOT SELECT ANY VARIANT EXCEPT THIS ONE !!
DP Demo: Accelerated Supply Chain Integration APO Template
Previous Steps
- 78 - ©Accenture 2003
6. Execute…
6. … and confirm all messages the system issues.
DP Demo: Accelerated Supply Chain Integration APO Template
Previous Steps
- 79 - ©Accenture 2003
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.
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO Demand Planning
MAIN FUNCTIONALITIES
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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.
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Review of Historical Sales
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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
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Review of Historical Sales
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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
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Generate Forecast
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5. To review the errors press the button and obtain the forecast errors for this statistic model.
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Generate Forecast
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1. Menu path: Demand Planning / Planning / Interactive Planning
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
DP Demo: Accelerated Supply Chain Integration APO Template
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
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5. Press the button Save in the command bar
6. The screen for the promotional data introduction will appear:
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Event Introduction
CAN_5246
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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
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Event Introduction
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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 )
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Event Introduction
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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
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Event Introduction
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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
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Forecast Review
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DP Demo: Accelerated Supply Chain Integration APO Template
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.
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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.
DP Demo: Accelerated Supply Chain Integration APO Template
SAP APO DP Alerts
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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
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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
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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.
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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.
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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
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4. Get a variant:
5. Select the variant TEMPLATE_003
DO NOT SELECT ANY VARIANT EXCEPT THIS ONE !!
DP ExercisesDP EXERCISE SOLUTION
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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.
DP ExercisesDP EXERCISE SOLUTION
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Review of Historical Sales:
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_3004.
5. Press the button to load the historical product sales.
DP ExercisesDP EXERCISE SOLUTION
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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
DP ExercisesDP EXERCISE SOLUTION
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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
DP ExercisesDP EXERCISE SOLUTION
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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
DP ExercisesDP EXERCISE SOLUTION
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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
DP ExercisesDP EXERCISE SOLUTION
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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.
DP ExercisesDP EXERCISE SOLUTION