Download 18. How to Improve Forecast Accuracy With SAP APO Demand Planning 26125209...
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How to Improve Forecast Accuracy with SAP APO Demand Planning
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Dr. Gerald Heisig SAP AG
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In This Session, You’ll Get An Overview About ...
SAP APO Demand Planning (DP) basic architecture Key DP features
Promotion Planning Lifecycle Management Seasonal Planning Etc.
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
© SAP 2007-2008 / 2
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What We’ll Cover …
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Introduction to Demand Planning Understanding the basic structures and architecture Tapping into Demand Planning features Looking at different forecasting techniques Demonstration: Demand Planning Wrap-up
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© SAP 2007-2008 / 3
Pain Points in Demand Planning
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Actual
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Planned
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8 Differences in planned demand and actual sales 8 Incorporation of all necessary demand information like promotions, product lifecycles, or other events in your demand plan
8 Demand visibility and consistency across all your
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departments and users
8 Sophisticated statistical forecasting
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© SAP 2007-2008 / 4
Implications for Your Demand Management
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Pla nned
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8 Bad forecast quality 8 Incomplete and inaccurate demand 8 High number of stock outs 8 High inventory levels 8 Slow response to changing market 8 Lack of information for right planning decisions
© SAP 2007-2008 / 5
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SAP APO Demand Planning (DP) Calculates future demand as accurate as possible Features
Key Benefits
Comprehensive forecasting toolset
Improved forecast quality
Statistical forecasting with causal and time-series methods
One tool for power and business user
Automatic outlier detection available
Consolidated demand plan (different regions, countries, departments, … )
Highly configurable planning books with macro functionality Supporting aggregation/ disaggregation logic
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Lifecycle Planning
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Plan promotions separately from the rest of your forecast
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Offline Planning Seasonal Planning
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Collaborative Demand Planning © SAP 2007-2008 / 6
DP Interactive Planning
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© SAP 2007-2008 / 7
What We’ll Cover …
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Introduction to Demand Planning Understanding the basic structures and architecture Tapping into Demand Planning features Looking at different forecasting techniques Demonstration: Demand Planning Wrap-up
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© SAP 2007-2008 / 8
Integration Between SAP SCM and SAP NetWeaver Business Intelligence (SAP NetWeaver BI)
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SAP SCM
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External SAP BI
With internal SAP BI
_________________________________ Forecasting results
Central data store for reporting and analyzing
InfoCube
APO Demand Planning
POS data Cost information Order and shipping data
Demand History
Demand History
Demand History InfoCube
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Source systems include: SAP ERP Excel Non-SAP systems
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© SAP 2007-2008 / 9
Data in SAP Supply Chain Management (SAP SCM) SAP ERP
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SAP SCM
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Transactional Data
SAP BI extraction structures
SAP BI
_________________________________ DP master Data: CVCs
Master Data ATP
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DP
LC Transactional Data
CIF
Master Data
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PP/DS
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Location Product Resource PPM/PDS
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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 /=10Product Data Structure © SAP 2007-2008
Demand Planning Master Data: Characteristic Value Combinations Characteristics
Characteristic values
Product
Prod01 Prod02 Prod03
Location
Customer
© SAP 2007-2008 / 11
DC01 DC02 DC03 Cust01 Cust02 Cust03
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Characteristic value combinations (CVCs)
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Prod01, DC01, Cust01 Prod01, DC01, Cust02 Prod01, DC01, Cust03 Prod01, DC02, Cust01 Prod01, DC03, Cust02 Prod01, DC03, Cust03 …
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Planning Object Structure and Planning Area
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The planning object structure is an APO InfoCube saved in the database of the internal SAP NetWeaver® BI system The characteristic value combinations can be created automatically Optionally, for better performance you can create additional aggregates The planning object structure with the relevant characteristic value combinations (DP master data) is assigned to a planning area
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© SAP 2007-2008 / 12
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 here It 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
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Planning book
Characteristics
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Key figures
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Planning area
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Characteristics
Key figures
Planning version
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© SAP 2007-2008 / 13
Planning Book
A planning book is based on information or a subgroup of Sales Manager Sales Rep Data View information from a planning area Data View In the planning book, select the Key figures Key figures characteristics and key figures required for the demand planner’s individual tasks Planning book Each planning book can contain several views where you can store key figures for detailed analyses Characteristics Key figures and planning tasks In each view you must also Planning area determine the planning horizon and time buckets profile Planning A planning area can have more Characteristics Key figures version than one planning book, but a planning book can be linked to only one planning area
© SAP 2007-2008 / 14
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SAP NetWeaver BI and Demand Planning Reporting Reporting for Demand Planner and Sales Reps Regional Forecast Reporting
Planned Actual Deviation
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Top 10 Deviations Planned/Actual
Forecast Accuracy
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D M I
Historical
Demand Planning
Planned
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DP delivers planning data through Data Mart Interface (DMI) SAP NetWeaver BI InfoCube for information consumers
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© SAP 2007-2008 / 15
What We’ll Cover …
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Introduction to Demand Planning Understanding the basic structures and architecture Tapping into Demand Planning features Looking at different forecasting techniques Demonstration: Demand Planning Wrap-up
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© SAP 2007-2008 / 16
Interactive Planning Flexibility
Free definition of planning books and data views Creation of data groups (selections) and user-specific assignment Multi-level planning with full visibility (drill up/down) Supporting different aggregation/disaggregation logic Data representation on different periodicities and horizons Text can be added to any cell (notes management) Copy and paste (within grid and from/to Microsoft Excel) Graphic with data manipulation possibilities User-specific customization
© SAP 2007-2008 / 17
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Interactive Planning (cont.)
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Macros
Enable any kind of calculation
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Can be started any time on any level
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© SAP 2007-2008 / 18
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 user Any kind of check carried out by a macro
Alerts are communicated to the user by:
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Visualization in the alert monitor Mail SMS message
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© SAP 2007-2008 / 19
Lifecycle Management Actuals for old product
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Lifecycle Planning simulates the launch, growth, maturity, and discontinuation phases of different products Mimics the sales curve that you expect the product to display during the following phases:
Launch and growth Discontinuation
Forecast for new product
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Like Modeling Lifecycle
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Phase-out profile
Realignment
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Generate new characteristic value combinations based on existing combinations
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The key figures for realignment can be selected
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An empty Excel file can be created with the structure of the required realignment steps to upload and execute realignment
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© SAP 2007-2008 / 21
Promotion Planning
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Attempts to predict the outcome of the effect of an event, e.g., an annual promotion or advertising campaign Characteristics of promotion planning include:
Separation of base sales data from changes caused by the event Evaluating the effect of the promotional spending Corrected forecast + promotions
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History (with promotions)
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Corrected forecast
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© SAP 2007-2008 / 22
Future
Promotions can be imported from SAP CRM Marketing Planner
Cannibalization
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You use cannibalization groups to model the impact of a promotion on sales of related products Sales for special offer product
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M M 07/03 08/03
M 09/03
M 10/03
Time
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Original forecast Corrected forecast M M 07/03 08/03 © SAP 2007-2008 / 23
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Corrected forecast
Original forecast
Sales for similar product
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M 09/03
M 10/03
Time
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Seasonal Planning
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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
Product A (Apparel)
SRING '03
SUMMER '03
Product B (Apparel)
SRING '03
Product 1 (Footwear)
SEASON C '02
Product 2 (Footwear)
SEASON C '02
FALL '03
SUMMER '03
FALL '03
SEASON A '03
SEASON A '03
HOLIDAY '03
SRING '04
HOLIDAY '03
SUMMER '04
SRING '04
SEASON B '03
SEASON C '03
SEASON B '03
SEASON C '03
FALL '04
SUMMER '04
HOLIDAY '04
FALL '04
SEASON A '04
SEASON A '04
Jan
HOLIDAY '04
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SEASON B '04
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SEASON B '04
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Apparel Planning Year Footwear Planning Year
Freely definable seasons and planning years are introduced that can be flexibly assigned to characteristic combinations
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© SAP 2007-2008 / 24
Automatic Outlier Correction
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4 Tolerance range = ep ± σ *1.25* MAD
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ep = ex-post forecast
1 0
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Today © SAP 2007-2008 / 25
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 framework Most DP processes are enabled for use by this framework
Automatic parallelization is offered for most of the DP processes
© SAP 2007-2008 / 26
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Offline Planning 1. Download data
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2. Working on the file
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3. Upload file data
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© SAP 2007-2008 / 27
Collaborative Planning
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Any planning book can be accessed through the Internet © SAP 2007-2008 / 28
Duet™ Demand Planning 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 plan Use MS Excel features like additional lines, columns, graphics, and formulae Save local file, distribute (e.g., by mail) with possibility for a later data synchronization, work online or offline
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© SAP 2007-2008 / 29
Consensus Demand Planning Objective
Create a demand plan by integrating all available information Collaborative process to gain “one number” consensus from sales, marketing, operations
Combine various data:
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Forecast Promotions
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Budgets, sales plans, etc.
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Manual changes
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What We’ll Cover …
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Introduction to Demand Planning Understanding the basic structures and architecture Tapping into Demand Planning features Looking at different forecasting techniques Demonstration: Demand Planning Wrap-up
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© SAP 2007-2008 / 31
Forecasting
Forecasting predicts future demand based on historical and judgmental data Forecasts can be created in various ways
Statistical methods
Human judgment
Combination of above
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© SAP 2007-2008 / 32
Use of Statistical Methods
Demand Planning
≠
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Forecasting
Statistical methods can support the planning process but they cannot solve basic planning problems Powerful 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 forecasts
With 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
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© SAP 2007-2008 / 33
Data Preparation
Statistical methods can only run on appropriate data Adaptations may be necessary for:
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Start of real history
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Negative/zero values
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Missing values
Special events (e.g., strike, promotions, …)
Causal effects
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© SAP 2007-2008 / 34
Different Demand Patterns
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© SAP 2007-2008 / 35
Statistical Forecasting Methods Constant Exponential smoothing Moving average Weighted moving average
Multiple Linear Regression (MLR) Influence variables
Trend
Exponential smoothing Linear regression
Season (without trend)
P I C K
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Causal Analysis
A U T O M A T E D
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Exponential smoothing
Climate (e.g., temperature) Price Advertising Distribution ...
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Trend – Season
Exponential smoothing Manual forecasting Seasonal linear regression
B E S T
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Composite Forecast Combine different forecasts Weight each forecast (time independent or dynamic)
Others
Croston method (sporadic demand) History No forecast External forecast
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© SAP 2007-2008 / 36
Selection of Forecasting Methods
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It is not appropriate to use the same forecasting method for all items Basic classifications include:
Forecast/Planning – Horizon: short ↔ medium ↔ long Linear ↔ Nonlinear development of the trend Univariate forecast ↔ Causal analysis (MLR) Product type: New, mature, sporadic Different parameter settings
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Product classification (e.g., spare part, standard product) Planning purpose/business requirements
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Pilot study –
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Logical reasons –
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The assignment can be based on:
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Grouping of products Assignment of parameters
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Ex-post error measures, but should not be the only criteria
The assignment should be checked in regular intervals
© SAP 2007-2008 / 37
(Weighted) Moving Average
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Only
suitable for constant demand patterns (with no trend-like or season-like patterns)
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(Weighted) Moving Average: Gt+1 = Σ (Wt-j+1) Vt-j+1 / n
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j=1
Period 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Demand 95,1 94,9 94,6 94,7 95,2 95,6 95,7 95,6 95,5 95,3 95,9 96,2 96,4 96,3 96,1 95,9
Moving Average
Forecast (M)
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95,1 95,2 95,3 95,4 95,5 95,7 95,8 95,9 96,0 96,0
n=7
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95,5
Demand Forecast
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Periods
© SAP 2007-2008 / 38
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First-Order Exponential Smoothing
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Only
suitable for constant demand patterns (with no trend-like or season-like patterns)
Gt+1(t) = (1- α)Gt(t-1) + αVt Period 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Demand 20 22 18 23 19 17 20 24 23 18 16 23 22 17 20 21
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for all t = 2,…..,n ; G1 = V1
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Forecast Forecast 20,0 20,6 19,8 20,8 20,8 19,6 19,7 21,0 21,6 20,5 19,2 20,3 20,8 19,7 19,8
20,0 20,2 20,0 20,3 20,3 20,0 20,0 20,4 20,6 20,4 19,9 20,2 20,4 20,1 20,1
α = 0.3 α = 0.1
Exponential Smoothing
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30 25
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20 Demand 15
Forecast (alpha = 0.3)
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Forecast (alpha = 0.1)
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10 5
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0 1
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Periods © SAP 2007-2008 / 39
Exponential Smoothing
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In a trend, seasonal, or seasonal trend model
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© SAP 2007-2008 / 40
Linear Regression
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For demand patterns
With trend With trend + season
∑ (t n
b1 =
i =1
i
∑ (t n
i =1
− t)
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t 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 © SAP 2007-2008 / 41
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b0 = y −b1t
2
i
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seasonal adjustment factor for each period within the season
− t )(y i − y )
y 1048,35 1102,06 1155,77 1209,48 1263,19 1316,90 1370,60 1424,31 1478,02 1531,73 1585,44 1639,15 1692,86 1746,57 1800,28 1853,99 1907,70 1961,41
Linear Regression
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2100 2000 1900 1800
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1700 1600 y
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yt = b0 + b1* t yt = b0 + b1* t + Smod, t
1500
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1400 1300 1200 1100
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1000 900 800 0
2
4
6
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10 Periods
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Croston Method
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Useful for intermittent, erratic, or slow-moving demand
Example: When demand is zero most of the time (say 2/3 of the time) Might be caused by:
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Short forecasting intervals (e.g., daily) A handful of customers that order periodically Aggregation of demand elsewhere (e.g., reorder points)
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An intermittent Demand Series
3.5
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De m a nd
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Choose Forecasting Model – Overview
Exponential Sm. (Croston)
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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 patterns The 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 trend Test for season Test for trend and season Seasonal model and test for trend Trend model and test for seasonal pattern
© SAP 2007-2008 / 44
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Lifecycle
Phase-In/Out
Trend + Season
Seasonal Linear Regression
Linear Regression
Intermittent Demand
First Order Expon. Sm. (Winters)
© SAP 2007-2008 / 43
Second Order Expon. Sm. (Holt)
Additionally, lifecycle profiles can be added to simulate phasein/out of products
First Order Exponential Sm.
Regular Demand Different univariate forecasting Constant Trend methods can be assigned based on regular and intermittent demand patterns
(Weighted) Moving Average
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Automatic Model Selection (cont.)
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Procedure 1 The relevant forecast parameters (alpha, beta, and gamma) are constant No consideration of error measure
Tests 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
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Procedure 2
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Procedure 2 is more precise than Procedure 1, but it takes longer
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© SAP 2007-2008 / 45
Multiple Linear Regression (MLR)
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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 coefficients
The input data for the MLR (i.e., the modeling of the causal effects) is the key issue Typical 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)
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Substantial experience is required for modeling causal effects!
© SAP 2007-2008 / 46
Composite Forecast
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Combine different forecasts
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Own defined model selection based on error measure
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Weight each forecast (time independent or dynamic) Enables the combination of different forecasts with a constant or time-dependent weighting
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The weighting will, in general, be purely arbitrary
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Univariate
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1 Forecast
Univariate ... MLR
n © SAP 2007-2008 / 47
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Combine Combine and and Reconcile Reconcile
Result MLR
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Forecasting and Forecast Errors
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© SAP 2007-2008 / 48
Error Measures
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Look at errors over time
Cumulative 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)
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Measure Bias
Smoothed measures reflects errors in the recent past
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Mean Absolute Deviation (MAD)
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Error Measures (cont.)
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Look at errors over time
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Cumulative measures summed or averaged over all data
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Error Total (ET) Mean Percentage Error (MPE) Mean Absolute Percentage Error (MAPE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE)
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Smoothed measures reflects errors in the recent past
Mean Absolute Deviation (MAD)
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Measure error magnitude © SAP 2007-2008 / 50
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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
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The results should be documented and archived
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© SAP 2007-2008 / 51
What We’ll Cover …
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Introduction to Demand Planning Understanding the basic structures and architecture Tapping into Demand Planning features Looking at different forecasting techniques Demonstration: Demand Planning Wrap-up
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© SAP 2007-2008 / 52
Demonstration: Demand Planning
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1.
DP Interactive Planning
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2.
DP Features and Forecasting Run
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© SAP 2007-2008 / 53
What We’ll Cover …
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Introduction to Demand Planning Understanding the basic structures and architecture Tapping into Demand Planning features Looking at different forecasting techniques Demonstration: Demand Planning Wrap-up
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© SAP 2007-2008 / 54
Resources help.sap.com
SAP Business Suite Æ SAP Supply Chain Management Æ SAP SCM 5.0 Æ Application Help Æ EN Æ SAP Advanced Planning and Optimization (SAP APO) Æ Demand Planning Æ Demand Planning Process
Comparison of the Planning Methods
SAP Business Suite Æ SAP Supply Chain Management Æ SAP SCM 5.0 Æ Application Help Æ EN Æ SAP Advanced Planning and Optimization (SAP APO) Æ Demand Planning Æ Technical Aspects of Demand Planning
SAP Notes
SAP Note 832393 (Release Restrictions for SCM 5.0) SAP Note 576015 (Collective Consulting Note for Demand Planning)
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© SAP 2007-2008 / 55
7 Key Points to Take Home
Demand Planning (DP) is mid- to long-term planning that will help you to create forecasts for your products Demand 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 management DP offers a broad range of statistical forecasting methods that are applicable for all kinds of demand patterns Usually, the same statistical forecasting method cannot be used for all products Quality of resulting forecasts should be evaluated regularly DP is tightly integrated to SAP NetWeaver BI, SAP ERP, and other SAP APO modules like SNP, PP/DS, and GATP
© SAP 2007-2008 / 56
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Your Turn!
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How to contact me: Gerald Heisig
[email protected]
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© SAP 2007-2008 / 57
Copyright 2007-2008 SAP AG All Rights Reserved No 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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© SAP 2007-2008 / 58
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