apo dp delta scm70

APO 7.0 Demand PlanningDelta Overview

SCM Solution ManagementOctober, 2008

© SAP 2008 / Summary developments APO 7.0 /

1. Customer Forecast Management2. Enhancements in Demand Forecasting3. Release and Consumption of Forecasts with Configuration

for VC4. Enhancements in APO Alert Monitor

Agenda


Customer Forecast Management

1. Overview2. Basic settings3. Inbound processing4. Customer forecast analysis5. Waterfall analysis

© SAP 2008 / Summary developments APO 7.0 / © SAP 2008 / CFM - Claus Bosch, Page 4


My Company Customer

SAP APO

Forecast


CFMAnalyze customer

forecast

DPUse forecast for

downstreamplanning


Traditional Customer Forecast ManagementProcess

CustomerSupply Chain

Planner

Generatematerial

requirementsplan

Sendforecast to

supplier Use customerforecast fordownstreamsupply chain

planning

Key Pain PointsToo much variability in customer forecast impacting the MRP/Supply Planning processNo ability to intercept and analyze the incoming forecast (EDI/B2B) from customer -some companies adopt a manual Rip-and-Read processNo ability to track whether the new updated forecast has been received or not


Customer Forecast Management Process

Customer ServiceManager

Customer Supply ChainPlanner

Generatematerial

requirementsplan

Sendforecast to

supplier

Analyze Original Customer ForecastCheck forecast data for readinessand completenessCheck tolerances to comparedifferent versions of customerforecastsUse alerts and monitoringcapabilities

Load Original Customer ForecastUse IDoc functionality, Excel uploador manual input for entering data

Approve Customer Forecast for DemandPlanning

Integrate either original or adjustedcustomer forecast in demand and supplyplanning

Perform Waterfall AnalysisDownload customer forecast to Excelfor detailed analysis of data

Usecustomer

forecast forsupply chain

planning


Increased visibility of real customerdemandHigher responsiveness to short-termdemand fluctuationIncreased sales and lower lost salesthrough prevention of stock-outsOverall processing speed is improvedData entry errors and customer ordererrors are reduced due to automatedcommunicationImproved cooperation relationshipbetween the supplier and the customer

SAP APO Customer Forecast Management(CFM)

Powerful customer forecast analysisdashboardIntercept customer forecast and analyzetolerances, readiness and completenessof dataEfficient inbound processing – Usestandard IDoc functionality or CSV fileCustomizable profiles for forecastanalysisWaterfall analysis in structured ExcelformatStandard integration to APO DemandPlanning - use customer forecast tosense demand

Key BenefitsFeatures

Allows efficient management and understanding of the customerforecast by controlling, intercepting and analyzing incomingcustomer forecast.


Example scenario:Integrated scenario CFM with Consigned VMI


Definition of basic settings

Define BI structures and objects to be used for datastorage and mapping You specify the settings for the

Demand Planning area from whichstatistical forecast can be read andused in forecast comparison

Specify tolerance value for bucketoffset in the inbound processing offorecast dataSpecify what to do if forecast datais incomplete


Maintenance of Forecast Release Profile

Define the release parameters for the release from Customer ForecastManagement to Demand PlanningUse BAdI /SAPAPO/CFM_DATA_RELEASE (CFM Data Release to SpecifiedDestination) if you want to release data to other applications than APO DP (e.g.APO SPP, SNP or ERP Demand Management)


Maintenance of Forecast Analysis Profile

Expected frequency ofreceiving customer

forecast

Expected horizon ofcustomer forecast

Definition of short-term,mid-term, long-term

horizon

Acceptable upside anddownside tolerance per

horizon

A product-location can be set to require a mandatory manual reviewDifferent versions of uploaded data can be compared

Make the settings necessary for performing an analysis of customer forecast data


Maintenance of key figure comparison andassignment

Determine the key figures to be compared and assign them to the forecast analysis profile


Assignment of release and forecast analysisprofile

You assign release and forecast analysis profile to location product master data onproduct, location or on a product group level.

Manual assignmentMass assignment (e.g. per customer location)


Inbound Processing

You receive and store incoming original customer forecast data, and link it to aBusiness Intelligence (BI) InfoCube. This enables the vendor to process the data inCustomer Forecast Management.

Inbound processing supports both IDoc types PROACT and DELFOR with a newmessage variant CFM for both message types PROACT and DELINS.Additionally, data can be uploaded via CSV file.

If the forecast data provided by the customer has the form of an IDoc, the systemfirst loads the data to a temporary table (/SAPAPO/CFM_IDOC), and then abackground job uploads it to the InfoCube.

You can also enter the data manually on the CustomerForecast Management screen.


ManualAdjustment

Customer Forecast ManagementInbound Dataflow

Standard InfoCube9ACFM_C

ODS Object9ACFM_DS

MultiProvider9ACFM_MP

CFM application layercalculates reverse and

new records

Customer Forecast Management UI:Manual adjustmentof adjusted forecast

Customer Forecast Management UI:Manual adjustmentof adjusted forecast

Real Time InfoCube9ACFM_RC

Direct write accessagainst

Real Time InfoCube

IDoc table

/SAPAPO/CFM_IDOC

Takeover Adjusted Forecastkey figure from

previous version, if it was setin the CFM profile.

CSV File

Upload


BI structures

Predefined BI structures (InfoCubes,ODS,…) are available in thedata warehousing workbench

Predefined process chains are available toupload customer forecast either in a CSV file, oras an IDoc


Upload CSV file

For CSV upload use data format of data source 9ACFM_CSVMaintain the right file name in InfoPackage 9ACFM_CSV_LOAD or copyInfoPackage


Forecast Analysis Run

You run a forecast analysis manually, or schedule it as a background job.The system checks forecast data for readiness and completeness, and comparesdifferent versions of forecasts performing a tolerance check.If violations occur, the system generates and outputs alerts.


Customer Forecast Analysis Dashboard

View customer forecast data grouped according to alerts generated during the forecastanalysis run.You can filter the forecasts, change predefined queries or create new ones, and you canalso personalize the table for displaying the results of the query.

Forecast notreceived per

expected frequency

Forecast durationnot for entire

expected horizon

New forecastexceeds specified

tolerance limits

New forecast received;requires mandatory manual

review/approval

GUI alternatives:NetweaverBusiness ClientInternet Browser


CFM Dashboard Detailed View

Per profile: Comparison ofnew customer forecast with

last customer forecast

Per profile: Comparison ofnew customer forecast with

new adjusted forecast

Adjust customer forecastas needed

Approve and release fordownstream planning

Make the necessary adjustments to the data, creating adjusted customer forecasts.Release the data to Demand Planning by approving the data on the CustomerForecast Management overview or detail screen.


Release customer forecast to DemandPlanning

You can release the customer forecast data in either of the following ways:You schedule an automatic forecast release run.You can specify the parameters for the run under Advanced Planning andOptimization Demand Planning Customer Forecast Management ForecastRelease Run (transaction /SAPAPO/CFM_FC_REL) .You manually approve the forecasts on the Customer Forecast Management UIscreen, and thus trigger a release of the data to the DP planning area defined inthe forecast release profile.

After releasing the data, the system automatically deletes the relevant forecasts fromthe work list.


CFM Perform waterfall analysis

When needed, you can perform a waterfall analysis on the forecast data, whichenables you to view forecast quantities at different points in time.The output is exported to an Excel sheet.




Agenda


Enhancements in Demand Forecasting

1. Forecasting Enhancement “MLR with POS”New Forecasting Algorithm to Incorporate AggregatedPOS Data in Forecasting

2. Statistical Forecast EnhancementReinitialization and trend dampening


Using POS in Demand Planning & ForecastingMotivation and Business Benefits

MotivationLeveraging POS data in supply chain processes is becoming more important than ever to increase revenue,decrease costs and increase efficiency.

Consumer Products and Hi Tech companies continue to struggle with demand latency – „in both segments it takesmore than 2 weeks to sense channel sales.“ (Lora Cecere, AMR Research, May 2007)

Increasingly more CP companies are receiving consistent daily or weekly point-of-sale data directly from ansignificant number of retailers.

Demand Signal Repository (DSR) applications have reached a maturity level that allows POS data to be cleansedand mapped with the high level quality that is necessary for supply chain applications and processes.

Business BenefitsImprove demand visibility and response

typically shipment history includes effects such as logistic rounding, shipment scheduling, productsubstitution and availability effects and does not reflect the original demand.including POS data in forecasting counters the effects of historical inaccuracies and is closer to a forwardlooking view of demand patterns to come

Higher forecast accuracythe reduced demand latency and improved demand visibility leads to a higher forecast accuracy in the theshort to medium term forecast horizon, typically 2-8 weeks.including POS data in forecasting dampens the short term variability („bull whip effect“) in all stages of thesupply chain – and makes supply chain planning more stable – even down to the production schedule.


A Quick Look at Different Approaches to Use POSin Forecasting

Different approaches to using POS in forecasting:

„exact“ methodusing detailed POS data (e.g. by store/sku/day) to create a store level POS forecast and includereplenishment parameters to calculate impact on manufacturer supply chainhighest benefit potential, but . . .time & resource-intensive – cpu & user

„qualtitative“ methodsincluding POS data in existing processes with no or marginal process extensionbenefit potential maybe not as high as exact methods, but . . .faster and easier to implement – „lowhanging fruit“

Approach is often a question of cost-benefit trade-off and strategic importance to company andrelationship to customer/retailer)


Highlights of the new MLR Forecasting Method„MLR with POS“

combines statistical forecasting with causalanalysis

calculates fluctuation due to inventory in theretail supply chain(which is not visible to the manufacturer)

works with Life Cycle Planning

has its own alertsdiagnosis group using MAPEnew MLR with POS alert types in Alert Monitor


MLR With POS - Algorithm in a Nutshell

Several sets of MLR iterations

1st regression run:dependent variable: week-to-week changes in (shipment) historyindependent variables: lagged week-to-week changesin shipment history and POS with different lagscalculated coefficients: inventory fluctuation

2nd regression runinventory fluctuation and statistical forecast used to calculateflux-adjusted forecastdependent variable: shipment historyindependent variables: expost forecast, inventory fluctuations

Check whether the data is sufficient to run the algorithmmin. 52 weeks or 364 daysfor seasonal: 2 seasonsotherwise error

Statistical forecasting methods:constant (min. 25 periods)seasonalno trend


MLR Forecasting Using POSEnhancements in SCM 7.0

The scope of the new development includes:

New forecasting algorithm for POS dataincorporation

UI adjustment for the end user to set up theforecasting method as well as changing parameters ininteractive planning

New alerts for forecasting errors


MLR Profile Enhancements for MLRForecasting Using POS Data

Current MLR Profile usedAdditional subscreen to select MLR methodNew settings:

History key figure and versionPOS key figure and versionForecast models: constant or seasonalOther parameters:– Save flux to key figure– Ignore leading zeros– Diagnosis group for interactive alerts


Forecast in Interactive Planning

Run & adjust forecast ininteractive planning

Key figures:ForecastShipment historyPOS historyStatistical forecastInventory flux

Adjust forecast parameters ininteractive planning on the fly:

Forecast / history horizon ornumber of periodsStatistical forecast model &smoothing factorsKey Figure & versionOther parameters

View messages


New Alert Types

Alerts

Insufficient data to execute algorithm

MAPE upper limit exceeded

Calculated MAPE greater than statistical forecast MAPE


Enhancements in Demand Forecasting

1. Forecasting Enhancement “MLR with POS”New Forecasting Algorithm to Incorporate AggregatedPOS Data in Forecasting

2. Statistical Forecast EnhancementReinitialization and trend dampening


Motivation and Business Benefits

MotivationCompanies struggle with dynamic market conditions leading to changes in sales history basis used for statisticalforecasting e.g.

changing numbers of ship-to destinations by changing customer locations

fast growth by entering new markets

In such an environment traditional forecast methods can lead to over forecast by not considering dampening trends

Improvements in competitive, state-of-the-art and scientific algorithms should be reflected as enhancements in DPforecasting

Business Benefits

Improve forecasting accuracy in a dynamic supply chain environmentShift planner’s time to more value added analytical tasksLess over forecasting in an automated system forecast


SAP APO DP Forecast Improvements (1)

Introduce new means of limiting or dampening the trend while also providing a means tomake more generally applicable settings

Settings will be possible to maintain for a planning area as well as for UnivariateProfile

There are several possibilities to dampen the trend:use a trend dampening profileset an upper limit for the trend valueset an upper limit for the forecastuse the trend dampening factor Phi (applicable only for exponential smoothingmethods).



Improve reaction of exponential smoothing methods (for certain smoothing parameters) tostructural changes in the data by reinitialization.Reinitialization means the re-estimation of the forecast parameters basic value, trendvalue and seasonal indices when a structural change is detected in the historical data.Reinitalization settings apply only to exponential smoothing class of forecast methods.

A structural change is detected in the historical data by means of 2 measures:tracking signal (TS)normalized error (NE)

When the structural change was identified the forecast parameters (basic value, trendvalue, seasonal indices) are reinitialized from the historical values of the period that wasprocessed and the periods that follow.


Structural change in history

© SAP 2008 / Page 41

Structural change



Offer new BAdI method for additional white noise test calculationNew white noise test is based on the Box-Pierce testUse BADI method /SAPAPO/SCM_FCSTPARA -> WHITE_NOISE_TEST to activate theBADI implementation containing the new white noise test




Agenda


Overview

Motivation for CBCL Planning

Globalization leads to planning across locations and visibility of the entire supplynetwork.

Increasing customization of products leads to increasing requirement of planningwith characteristics.

ObjectiveTo enhance the cross location planning functionalities for the industries that are usingcharacteristics (e.g. Industries: Mill, Machinery & Components)To enable the industries, using cross location planning, to use characteristics(e.g. Industries: High Tech, Chemicals, Life Sciences)


Release and Consumption of Forecasts withConfiguration for VC

Sales Order CreationForecasting

Demand Planning

Sales Orderwith Rqmt. Class Det.

Assignment ofCharacteristic Values

ATP Check

Delivery

Detailed Scheduling

Production Planning

Sales ProductionDistribution

Distribution Planning

Production ExecutionDeployment

Transport Load Building

Transport Execution

Forecast Release

Forecast ConsumptionIntegrated Distribution & Production Planning


Release and Consumption of Forecasts withConfiguration for VC

ObjectiveEnable the planner to release forecasts with their own configuration for the VC schemeThese released forecasts can be consumed by the sales orders subsequently in caseof identical configuration

FeaturesThe planner can choose between releasing forecasts with own configuration or not, inthe customizing.

© SAP 2008 / Summary developments APO 7.0 / © SAP 2008 / CBCL Planning in APO 7.0 / Page 47

Characteristics Based Forecasting

Forecasting done using the CBF (Characteristics Based Forecasting) technique

© SAP 2008 / Summary developments APO 7.0 / © SAP 2008 / CBCL Planning in APO 7.0 / Page 48

Release of Forecasts with Configuration

Forecasts are released to SNP orPP/DS for planning (usingtransaction: /SAPAPO/MC90)Configuration are created accordingto the values in the CBF profile forthe characteristics restricted by theconsumption group.Requirement Strategy group chosen:35 (Planning Without Final AssemblyFor Configuration


Forecasts with Configuration for VC

Released forecasts are available in a new planning segment –“Characteristic Based Planning without Final Assembly”

– The segment can contain multiple configurations– Stock transfer demands and dependent demands can also be created in this

segment


Planning for Forecasts with Configuration forVC

– Forecasts in the segment can be planned with CTM and PP/DS planning run– Orders within this planning segment are not integrated with ERP


Consumption of Forecasts with Configurationfor VC

Consumption of the forecasts by the sales orders happen only for matchingconfiguration

Possibility to release back the orders to DP/CBF considering configurationForecasts can be created from dependent demands for the components for the sameplanning version (using transaction /SAPAPO/DMP2)Filter for configuration available in Product View

Limitations:Multi-level configuration is not supported




Agenda


Enhancements in APO Alert Monitor

1. Alert Profile Maintenance2. Overall Profile Maintenance3. Application Profile Maintenance


APO Alert Monitor

Improve usability of alert profile maintenanceIn order to display and manage alerts the Alert Monitor uses alert profiles that specifyselection criteria for the checked business area. The alert profiles are created and maintainedby the users. In order to make the process of alert profile maintenance easier and friendlier anew user interface is to be implemented.

Improve performance of alert determination processIn case of large amount of alerts it can happen that the alert determination process runs for along time. In order to reduce response time of populating the overview screen of alert monitor,collection the detailed alert information, not necessary to populate the overview screens, willbe omitted.Moving the execution of alert determination process from ABAP to liveCache, as other way ofimproving performance, is also part of the development.


Alert Profile Maintenance (main screen)

Check area:

When profile check performed,the result is displayed in thecheck area.

Profile can be activated only if noerror item is generated.

Profile Selection:

List existing profiles

Profile Detail:

Display and maintain profilesettings


Alert Profile Maintenance (toolbar)

General profile functions (also available from context menu of selection tree)

• Display/change mode

• Create new profile

• Copy existing profile

• Delete profile

• Transport profile

• Search profile based on different criteria (name, description, alert type, transported, assignment, etc…)

• Check profile (consistency check, performance prediction, etc …)

• Activate profile (check performed successfully, ready to use)

• Run overall/application profile (for application profile „planning version + period” asked)

• User specific settings (favorite profiles, hierarchy, substitute settings)


Alert Profile Maintenance (profile tree)

Provides an easy way to access alert profiles (overall & application)

Displays profiles grouped by profile type (overall, application)

Context menus for accessing general functions (create, copy, delete,etc) are available


Overall Profile Maintenance

The overall alert profilecollects application-specific alert profiles anddefines the time periodfor which the systemdetermines alerts.

In the overall alert profile,you make the followingsettings:

• Period for which thesystem determines alerts

• Planning version

• Application-specificalert profiles assignment


Application Profile Maintenance

Defines application-specificselection criteria for alertdetermination.

If you want to use the AlertMonitor to monitor anapplication, you must define anapplication-specific alert profilefor the application.

If you are working with SAPAPO, the following alert profilesare available to you:

• Available-to-Promise (ATP)Alert Profile

• Forecast Alert Profile

• Supply and Demand Planning(SDP) Alert Profile

•Transport Load Builder (TLB)Alert Profile

• Production Planning andDetailed Scheduling (PP/DS)Alert Profile

• Vehicle Scheduling (VS) AlertProfile

• Vendor-Managed Inventory(VMI) Alert Profile


Application Profile Maintenance (attributes)

General attributes of application profile:

• Name (unique)

• Description (language dependent)

• Application

• Administrative data (actions performed by/at)


Application Profile Maintenance (alert types)

Select alert types

• Displayed alert types are applicationdependent

• Configurable hierarchy ?

(corrently only fix hierarchy: alertobject type/alert type)


Application Profile Maintenance (selection)

Specify selection:

Application dependent selection criteria


Application Profile Maintenance (overallprofile assign)

Overall Profiles:

• List of overall profiles the application profile isassigned to

• Direct navigation to overall profile screen


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