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How to Improve Forecast Accuracy With SAP APO
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© SAP 2007-2008 / 0
How to Improve Forecast Accuracy with SAP APO Demand Planning
Dr. Gerald HeisigSAP AG
© SAP 2007-2008 / 2
In This Session, You’ll Get An Overview About ...
SAP APO Demand Planning (DP) basic architectureKey DP features
Promotion PlanningLifecycle ManagementSeasonal PlanningEtc.
The broad range of statistical forecasting methods in DP and guidelines for selecting the right method Integration of DP into other SAP solutions and SAP APO modules
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© SAP 2007-2008 / 3
What We’ll Cover …
Introduction to Demand PlanningUnderstanding the basic structures and architecture Tapping into Demand Planning features Looking at different forecasting techniquesDemonstration: Demand PlanningWrap-up
© SAP 2007-2008 / 4
Pain Points in Demand Planning
Differences in planned demand and actual sales
Incorporation of all necessary demand information like promotions, product lifecycles, or other events in your demand plan
Demand visibility and consistency across all your departments and users
Sophisticated statistical forecasting
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Actual Planned
© SAP 2007-2008 / 5
Implications for Your Demand Management
Bad forecast quality
Incomplete and inaccurate demand
High number of stock outs
High inventory levels
Slow response to changing market
Lack of information for right planning decisions
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 2 2 23 24
Actual Planned
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© SAP 2007-2008 / 6
SAP APO Demand Planning (DP)
Calculates future demand as accurate as possible
Comprehensive forecasting toolset Statistical forecasting with causal and time-series methodsAutomatic outlier detection availableHighly configurable planning books with macro functionalitySupporting aggregation/ disaggregation logicLifecycle Planning Plan promotions separately from the rest of your forecastOffline PlanningSeasonal Planning Collaborative Demand Planning
Improved forecast qualityOne tool for power and business userConsolidated demand plan (different regions, countries, departments, … )
Key BenefitsFeatures
© SAP 2007-2008 / 7
DP Interactive Planning
© SAP 2007-2008 / 8
What We’ll Cover …
Introduction to Demand PlanningUnderstanding the basic structures and architectureTapping into Demand Planning features Looking at different forecasting techniquesDemonstration: Demand PlanningWrap-up
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© SAP 2007-2008 / 9
Integration Between SAP SCM and SAP NetWeaver Business Intelligence (SAP NetWeaver BI)
SAP SCMSAP SCM
With internal SAP BI
APO Demand Planning
Demand History
External SAP BI
InfoCube
InfoCubeCentral data store for reporting and analyzing
Source systems include:SAP ERP ExcelNon-SAP systems
POS dataCost informationOrder and shipping data
Demand History
Demand History
Forecasting results
© SAP 2007-2008 / 10
Data in SAP Supply Chain Management (SAP SCM)
DP master Data: CVCs
TransactionalData SAP BI extraction
structures
SAP SCMSAP SCMSAP ERPSAP ERP
MasterData
TransactionalData
MasterData
ATP DP
PP/DS SNP
LC
CIF
SAP BI
LocationProductResourcePPM/PDS
ATP = Available-to-Promise, SAP BI = SAP NetWeaver BI, CVCs = Characteristic Value Combinations, CIF = Core Interface, DP = Demand Planning, LC = liveCache, SNP = Supply Network Planning, PP/DS = Production Planning and Detailed Scheduling, PPM = Production Process Model, PDS = Product Data Structure
© SAP 2007-2008 / 11
Demand Planning Master Data: Characteristic Value Combinations
Characteristics
Product
Location
Customer
Characteristicvalues
Prod01Prod02Prod03
DC01DC02DC03
Cust01Cust02Cust03
Characteristicvalue combinations
(CVCs)
Prod01, DC01, Cust01Prod01, DC01, Cust02Prod01, DC01, Cust03Prod01, DC02, Cust01Prod01, DC03, Cust02Prod01, DC03, Cust03… Planning
ObjectStructure
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© SAP 2007-2008 / 12
Planning Object Structure and Planning Area
The planning object structure is an APO InfoCube saved in the database of the internal SAP NetWeaver® BI systemThe characteristic value combinations can be created automaticallyOptionally, for better performance you can create additional aggregatesThe planning object structure with the relevant characteristic value combinations (DP master data) is assigned to a planning area
© SAP 2007-2008 / 13
Planning Area
A planning area is the central data structure for saving planning data for Demand Planning and Supply Network Planning
Characteristics and key figures and their functions for planning are determined hereIt groups together the central parameters that define the scope of the planning activities
It also determines whether planning results are to be saved as orders or time series
Planning book
Planning area
Interactive Planning
Characteristics Key figures
Planning version
Characteristics Key figures
© SAP 2007-2008 / 14
Planning Book
A planning book is based on information or a subgroup of information from a planning areaIn the planning book, select the characteristics and key figures required for the demand planner’s individual tasksEach planning book can contain several views where you can store key figures for detailed analyses and planning tasksIn each view you must also determine the planning horizon and time buckets profileA planning area can have more than one planning book, but a planning book can be linked to only one planning area
Planning book
Planning area
Sales Rep Data View
Characteristics Key figures Planningversion
Characteristics Key figures
Sales ManagerData View
Key figuresKey figures
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© SAP 2007-2008 / 15
SAP NetWeaver BI and Demand Planning Reporting
Reporting for Demand Planner and Sales RepsReporting for Demand Planner and Sales Reps
Regional ForecastReporting
Planned ActualDeviation
Top 10 DeviationsPlanned/Actual
ForecastAccuracy
SAP APOSAP APO
Demand PlanningDMI
DP delivers planning data through Data Mart Interface (DMI)SAP NetWeaver BI InfoCube for information consumers
Historical
Planned
© SAP 2007-2008 / 16
What We’ll Cover …
Introduction to Demand PlanningUnderstanding the basic structures and architectureTapping into Demand Planning features Looking at different forecasting techniquesDemonstration: Demand PlanningWrap-up
© SAP 2007-2008 / 17
Interactive Planning
FlexibilityFree definition of planning books and data viewsCreation of data groups (selections) and user-specific assignment Multi-level planning with full visibility (drill up/down)Supporting different aggregation/disaggregation logicData representation on different periodicities and horizonsText can be added to any cell (notes management)Copy and paste (within grid and from/to Microsoft Excel)Graphic with data manipulation possibilitiesUser-specific customization
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© SAP 2007-2008 / 18
Interactive Planning (cont.)
Macros Enable any kind of calculationCan be started any time on any level
© SAP 2007-2008 / 19
Interactive Planning – Alerts
Alerts can be customized user specific Alerts are triggered during batch processing or interactive planning for:
Forecast errors exceeding borders defined by the userAny kind of check carried out by a macro
Alerts are communicated to the user by:
Visualization in the alert monitorMailSMS message
© SAP 2007-2008 / 20
Lifecycle Management
Lifecycle Planning simulates the launch, growth, maturity, and discontinuation phases of different productsMimics the sales curve that you expect the product to display during the following phases:
Launch and growth Discontinuation
Actuals for old product
Like Modeling
Forecast for new product
Lifecycle
Phase-in profile Phase-out profile
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© SAP 2007-2008 / 21
Realignment
Generate new characteristic value combinations based on existing combinations The key figures for realignment can be selectedAn empty Excel file can be created with the structure of the required realignment steps to upload and execute realignment
© SAP 2007-2008 / 22
Promotion Planning
Attempts to predict the outcome of the effect of an event, e.g., an annual promotion or advertising campaignCharacteristics of promotion planning include:
Separation of base sales data from changes caused by the eventEvaluating the effect of the promotional spending
Past Future
Corrected forecast Forecast
History(with promotions)
Corrected forecast+ promotions
Promotions can be imported from SAP CRM Marketing Planner
© SAP 2007-2008 / 23
Cannibalization
You use cannibalization groups to model the impact of a promotion on sales of related products
Sales for special offer product
M07/03
M08/03
M09/03
M10/03
Time
Corrected forecast
Original forecast
M07/03
M08/03
M09/03
M10/03
Time
Original forecast
Sales for similar product
Corrected forecast
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© SAP 2007-2008 / 24
Seasonal Planning
Freely definable seasons and planning years are introduced that can be flexibly assigned to characteristic combinations
2003 2004 2005 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
Product A (Apparel) SRING '03 SUMMER '03 FALL '03 HOLIDAY '03 SRING '04 SUMMER '04 FALL '04 HOLIDAY '04
Product B (Apparel) SRING '03 SUMMER '03 FALL '03 HOLIDAY '03 SRING '04 SUMMER '04 FALL '04 HOLIDAY '04
Product 1 (Footwear) SEASON C '02 SEASON A '03 SEASON B '03 SEASON C '03 SEASON A '04 SEASON B '04
Product 2 (Footwear) SEASON C '02 SEASON A '03 SEASON B '03 SEASON C '03 SEASON A '04 SEASON B '04
Apparel Planning Year Footwear Planning Year
© SAP 2007-2008 / 25
ep = ex-post forecast
Today0
1
2
3
4
5
6
7
8
Tolerance range
= ep ± σ *1.25* MAD
Automatic Outlier Correction
© SAP 2007-2008 / 26
Batch Processing/Process Chains
A job scheduling tool for creation, scheduling, and monitoring complex job chains is offered
The SAP NetWeaver BI tool for process chains is implemented as a frameworkMost DP processes are enabled for use by this framework
Automatic parallelization is offered for most of the DP processes
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© SAP 2007-2008 / 27
Offline Planning
1. Download data
2. Working on the file
3. Upload file data
© SAP 2007-2008 / 28
Collaborative Planning
Any planning book can be accessed through the Internet
© SAP 2007-2008 / 29
Duet™ Demand Planning enables sales and planners to utilize the full Microsoft Excel capabilities as an intuitive planning frontend for SAP SCM
Load data from APO Demand Planning Analyze and contribute to demand planUse MS Excel features like additional lines, columns, graphics, and formulaeSave local file, distribute (e.g., by mail) with possibility for a later data synchronization, work online or offline
Duet™ Demand Planning
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© SAP 2007-2008 / 30
Consensus Demand Planning
ObjectiveCreate a demand plan by integrating all available informationCollaborative process to gain “one number” consensus from sales, marketing, operations
Combine various data: – Forecast– Promotions– Budgets, sales plans, etc.– Manual changes
© SAP 2007-2008 / 31
What We’ll Cover …
Introduction to Demand PlanningUnderstanding the basic structures and architectureTapping into Demand Planning features Looking at different forecasting techniquesDemonstration: Demand PlanningWrap-up
© SAP 2007-2008 / 32
Forecasting
Forecasting predicts future demand based on historical and judgmental dataForecasts can be created in various ways
Statistical methodsHuman judgmentCombination of above
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© SAP 2007-2008 / 33
Use of Statistical Methods
Statistical methods can support the planning process but they cannot solve basic planning problemsPowerful forecasting software can calculate millions of forecasts on the lowest level of detail but this is not always the appropriate planning level
Nobody can control/check millions of forecastsWith millions of data sets everything happens (Murphy’s law)It is sometimes better to plan on a higher (controllable) level and break down the results into details using fixed rules
Demand Planning ≠ Forecasting
© SAP 2007-2008 / 34
Data Preparation
Statistical methods can only run on appropriate dataAdaptations may be necessary for:
Start of real historyNegative/zero valuesMissing valuesSpecial events (e.g., strike, promotions, …)Causal effects
© SAP 2007-2008 / 35
Different Demand Patterns
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© SAP 2007-2008 / 36
ConstantExponential smoothingMoving averageWeighted moving average
TrendExponential smoothingLinear regression
Season (without trend)Exponential smoothing
Trend – SeasonExponential smoothingManual forecastingSeasonal linear regression
OthersCroston method (sporadic demand)History No forecastExternal forecast
Causal Analysis
Multiple Linear Regression (MLR)Influence variables
Climate (e.g., temperature)PriceAdvertisingDistribution...
AUTOMATED
PICK
BEST
Composite ForecastCombine different forecastsWeight each forecast (time independent or dynamic)
Statistical Forecasting Methods
© SAP 2007-2008 / 37
Selection of Forecasting Methods
It is not appropriate to use the same forecasting method for all itemsBasic classifications include:
Forecast/Planning – Horizon: short ↔ medium ↔ longLinear ↔ Nonlinear development of the trendUnivariate forecast ↔ Causal analysis (MLR)Product type: New, mature, sporadic Different parameter settings
The assignment can be based on:Logical reasons– Product classification (e.g., spare part, standard product)– Planning purpose/business requirementsPilot study– Grouping of products– Assignment of parameters Ex-post error measures, but should not be the only criteria
The assignment should be checked in regular intervals
© SAP 2007-2008 / 38
(Weighted) Moving Average
Moving Average
93,5
94
94,5
95
95,5
96
96,5
97
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Periods
DemandForecast
Period Demand Forecast (M)1 95,12 94,93 94,64 94,7 95,15 95,2 95,26 95,6 95,37 95,7 95,48 95,6 95,59 95,5 95,710 95,3 95,811 95,9 95,912 96,2 96,013 96,4 96,014 96,315 96,116 95,9 n = 7
(Weighted) Moving Average: Gt+1 = Σ (Wt-j+1) Vt-j+1 / nj=1
n
Only suitable for constant demand patterns (with no trend-like or season-like patterns)
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© SAP 2007-2008 / 39
First-Order Exponential Smoothing
Exponential Smoothing
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Periods
DemandForecast (alpha = 0.3)Forecast (alpha = 0.1)
Period Demand Forecast Forecast 1 202 22 20,0 20,03 18 20,6 20,24 23 19,8 20,05 19 20,8 20,36 17 20,8 20,37 20 19,6 20,08 24 19,7 20,09 23 21,0 20,410 18 21,6 20,611 16 20,5 20,412 23 19,2 19,913 22 20,3 20,214 17 20,8 20,415 20 19,7 20,116 21 19,8 20,1
α = 0.3 α = 0.1
Gt+1(t) = (1- α)Gt(t-1) + αVt for all t = 2,…..,n ; G1 = V1
Only suitable for constant demand patterns (with no trend-like or season-like patterns)
© SAP 2007-2008 / 40
Exponential Smoothing
In a trend, seasonal, or seasonal trend model
© SAP 2007-2008 / 41
Linear Regression
For demand patterns With trendWith trend + season
t y1 1048,352 1102,063 1155,774 1209,485 1263,196 1316,907 1370,608 1424,319 1478,02
10 1531,7311 1585,4412 1639,1513 1692,8614 1746,5715 1800,2816 1853,9917 1907,7018 1961,41
Linear Regression
800900
100011001200130014001500160017001800190020002100
0 2 4 6 8 10 12 14 16 18
Periods
y
( )( )
( )∑
∑
=
=
−
−−= n
ii
n
iii
tt
yyttb
1
2
11
tbyb 10 −=
yt = b0 + b1* tyt = b0 + b1* t + Smod, t
seasonal adjustment factor for each period within the season
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© SAP 2007-2008 / 42
Croston Method
Useful for intermittent, erratic, or slow-moving demandExample: When demand is zero most of the time (say 2/3 of the time)Might be caused by:
Short forecasting intervals (e.g., daily)A handful of customers that order periodicallyAggregation of demand elsewhere (e.g., reorder points)
An intermittent Demand Series
0
0.5
1
1.5
2
2.5
3
3.5
1 14 27 40 53 66 79 92 105
118
131
144
157
170
183
196
209
222
235
248
261
274
287
300
313
326
339
352
365
378
391
Period
Dem
and
© SAP 2007-2008 / 43
(Wei
ghte
d) M
ovin
g A
vera
ge
Firs
t Ord
er E
xpon
entia
l Sm
.
Intermittent Demand
Exp
onen
tial S
m. (
Cro
ston
)
Phas
e-In
/Out
Sec
ond
Ord
er E
xpon
. Sm
. (H
olt)
Line
ar R
egre
ssio
n
Regular Demand
Life-cycle
Constant Trend Trend+ Season
Sea
sona
l Lin
ear R
egre
ssio
n
Firs
t Ord
er E
xpon
. Sm
. (W
inte
rs)
Choose Forecasting Model –Overview
Different univariate forecasting methods can be assigned based on regular and intermittent demand patternsAdditionally, life-cycle profiles can be added to simulate phase-in/out of products
© SAP 2007-2008 / 44
Automatic Model Selection
You can choose Automatic Model Selection if there is no knowledge of the patterns in the historical data
The historical data are checked for constant, trend, seasonal, and seasonal trend patternsThe forecasting model that corresponds most closely to the pattern detected is applied
If no regular pattern is detected, the system runs the forecast as if the data revealed a constant pattern
You can also restrict the Automatic Model Selection to:Test for trendTest for seasonTest for trend and seasonSeasonal model and test for trend Trend model and test for seasonal pattern
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© SAP 2007-2008 / 45
Automatic Model Selection (cont.)
Procedure 1The relevant forecast parameters (alpha, beta, and gamma) are constantNo consideration of error measure
Procedure 2Tests for constant, trend, seasonal, and seasonal trend patterns, using all possible combinations for the alpha, beta, and gamma smoothing factors The model with the lowest error measure customized (e.g., Mean Absolute Deviation [MAD]) is chosen
Procedure 2 is more precise than Procedure 1, but it takes longer
© SAP 2007-2008 / 46
Multiple Linear Regression (MLR)
MLR can assess how the development of one (dependent) variable can be explained by several (independent) variables (and a constant value)For a causal analysis, MLR does the final calculation of the regression coefficientsThe input data for the MLR (i.e., the modeling of the causal effects) is the key issueTypical variables:
Trend Seasonality Climatic conditions (e.g., temperature, precipitation)Economy (e.g., GDP, inflation, unemployment rate)Product specific (e.g., price/costs, new model/version, marketing activities)Demography (e.g., population in age classes)Others (e.g., lifecycle, replacement demand, distribution)
Substantial experience is required for modeling causal effects!
© SAP 2007-2008 / 47
Composite Forecast
Combine different forecastsOwn defined model selection based on error measure Weight each forecast (time independent or dynamic)Enables the combination of different forecasts with a constant or time-dependent weightingThe weighting will, in general, be purely arbitrary
Forecast1
n
... Combine and
Reconcile
Combine and
Reconcile
Univariate
MLR
UnivariateResultMLR
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© SAP 2007-2008 / 48
Forecasting and Forecast Errors
© SAP 2007-2008 / 49
Error Measures
Look at errors over timeCumulative measures summed or averaged over all data
Error Total (ET)Mean Percentage Error (MPE)Mean Absolute Percentage Error (MAPE)Mean Squared Error (MSE)Root Mean Squared Error (RMSE)
Smoothed measures reflects errors in the recent pastMean Absolute Deviation (MAD)
Measure Bias
© SAP 2007-2008 / 50
Error Measures (cont.)
Look at errors over timeCumulative measures summed or averaged over all data
Error Total (ET)Mean Percentage Error (MPE)Mean Absolute Percentage Error (MAPE)Mean Squared Error (MSE)Root Mean Squared Error (RMSE)
Smoothed measures reflects errors in the recent pastMean Absolute Deviation (MAD)
Measure errormagnitude
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© SAP 2007-2008 / 51
Evaluation
The planning/forecasting process has to be reviewed permanently/in regular intervals
This can include the analysis of:KPIs (e.g., service level, out of stock)Financial data (e.g., turnover, profit)Promotions/advertising Special influences (e.g., strike)Causal effects
The results should be documented and archived
© SAP 2007-2008 / 52
What We’ll Cover …
Introduction to Demand PlanningUnderstanding the basic structures and architectureTapping into Demand Planning features Looking at different forecasting techniquesDemonstration: Demand PlanningWrap-up
© SAP 2007-2008 / 53
Demonstration: Demand Planning
1. DP Interactive Planning
2. DP Features and Forecasting Run
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© SAP 2007-2008 / 54
What We’ll Cover …
Introduction to Demand PlanningUnderstanding the basic structures and architectureTapping into Demand Planning features Looking at different forecasting techniquesDemonstration: Demand PlanningWrap-up
© SAP 2007-2008 / 55
Resources
help.sap.comSAP Business Suite SAP Supply Chain Management SAP SCM 5.0 Application Help EN SAP Advanced Planning and Optimization (SAP APO) Demand Planning Demand Planning Process
Comparison of the Planning MethodsSAP Business Suite SAP Supply Chain Management SAP SCM 5.0 Application Help EN SAP Advanced Planning and Optimization (SAP APO) Demand Planning Technical Aspects of Demand Planning
SAP NotesSAP Note 832393 (Release Restrictions for SCM 5.0)SAP Note 576015 (Collective Consulting Note for Demand Planning)
© SAP 2007-2008 / 56
7 Key Points to Take Home
Demand Planning (DP) is mid- to long-term planning that will help you to create forecasts for your productsDemand Management is not just forecasting – data preparation is key to getting reasonable forecasts DP offers a broad range of features for demand management like promotion or lifecycle managementDP offers a broad range of statistical forecasting methods that are applicable for all kinds of demand patternsUsually, the same statistical forecasting method cannot be used for all products Quality of resulting forecasts should be evaluated regularlyDP is tightly integrated to SAP NetWeaver BI, SAP ERP, and other SAP APO modules like SNP, PP/DS, and GATP
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© SAP 2007-2008 / 57
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How to contact me:Gerald Heisig
© SAP 2007-2008 / 58
Copyright 2007-2008 SAP AGAll Rights ReservedNo part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG. The information contained herein may be changed without prior notice.Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors.SAP, R/3, mySAP, mySAP.com, xApps, xApp, SAP NetWeaver, Duet, Business ByDesign, ByDesign, 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 and associated logos displayed are the trademarks of their respective companies. Data contained in this document serves informational purposes only. National product specifications may vary.
The information in this document is proprietary to SAP. This document is a preliminary version and not subject to your license agreement or any other agreement with SAP. This document contains only intended strategies, developments, and functionalities of the SAP® product and is not intended to be binding upon SAP to any particular course of business, product strategy, and/or development. SAP assumes no responsibility for errors or omissions in this document. SAP does not warrant the accuracy or completeness of the information, text, graphics, links, or other items contained within this material. This document is provided without a warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability, fitness for a particular purpose, or non-infringement.SAP shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials. This limitation shall not apply in cases of intent or gross negligence.The statutory liability for personal injury and defective products is not affected. SAP has no control over the information that you may access through the use of hot links contained in these materials and does not endorse your use of third-party Web pages nor provide any warranty whatsoever relating to third-party Web pages.
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