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SAP SCM 7.0

August 2011

English

Demand Planning Analytics (Y99)

SAP AGDietmar-Hopp-Allee 1669190 WalldorfGermany

Business Process Documentation

SAP Best Practices Demand Planning Analytics (Y99): BPD

Copyright© 2012 SAP AG. All rights reserved.

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All other product and service names mentioned are the trademarks of their respective companies. Data contained in this document serves informational purposes only. National product specifications may vary.

These materials are subject to change without notice. These materials are provided by SAP AG and its affiliated companies ("SAP Group") for informational purposes only, without representation or warranty of any kind, and SAP Group shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP Group products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty.

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Icons

Icon Meaning

Caution

Example

Note

Recommendation

Syntax

External Process

Business Process Alternative/Decision Choice

Typographic Conventions

Type Style Description

Example text Words or characters that appear on the screen. These include field names, screen titles, pushbuttons as well as menu names, paths and options.

Cross-references to other documentation.

Example text Emphasized words or phrases in body text, titles of graphics and tables.

EXAMPLE TEXT Names of elements in the system. These include report names, program names, transaction codes, table names, and individual key words of a programming language, when surrounded by body text, for example, SELECT and INCLUDE.

Example text Screen output. This includes file and directory names and their paths, messages, source code, names of variables and parameters as well as names of installation, upgrade and database tools.

EXAMPLE TEXT Keys on the keyboard, for example, function keys (such as F2) or the ENTER key.

Example text Exact user entry. These are words or characters that you enter in the system exactly as they appear in the documentation.

<Example text> Variable user entry. Pointed brackets indicate that you replace these words and characters with appropriate entries.

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Content

1 Purpose........................................................................................................................................ 5

2 Preliminary Steps......................................................................................................................... 5

2.1 User Roles............................................................................................................................. 5

2.2 Logging on to Systems..........................................................................................................5

3 Demand Planning Analytics Overview..........................................................................................6

3.1 Dashboard Summary............................................................................................................6

3.1.1 Dashboard: Historical Forecast Review.........................................................................7

3.1.2 Dashboard: Future Forecast Review..............................................................................7

3.1.3 BW Report on the Portal................................................................................................8

3.2 Overview Key Figures...........................................................................................................8

3.3 Overview Characteristics.......................................................................................................9

4 Process Steps.............................................................................................................................. 9

4.1 Dashboard: Historical Forecast Review..............................................................................10

4.1.1 Define the Selection Condition and Group by Characteristic........................................10

4.1.2 Check Top 20 Forecast Error with Two Measurements...............................................11

4.1.3 Check Forecast Error with Bar Chart...........................................................................11

4.1.4 Check Plan vs. Actual with Line Chart..........................................................................12

4.2 Dashboard: Future Forecast Overview................................................................................13

4.2.1 Define the Selection Condition and Group by Characteristic........................................13

4.2.2 Review Final Demand Plan over the Next 12 Months with Bar Chart..........................14

4.2.3 Compare the Forward View with Historical Trend and Pattern with Line Chart............14

4.3 BW Report on Portal...........................................................................................................15

4.3.1 Query – Sales and Plan...............................................................................................15

4.3.2 Query – Forecast Error.................................................................................................16

5 Appendix (Optional)....................................................................................................................17

5.1 Source Data........................................................................................................................ 17

5.2 Create Dashboard Demo Transaction Data........................................................................17

5.2.1 Create Sales Order / Delivery Note in the SAP ECC System and Transfer to SAP SCM17

5.2.2 Upload Demo Sales History Data from Flat CSV File into SCM...................................20

5.2.3 Load Sale History Data to Planning Area by Process Chain........................................20

5.2.4 Execute Forecasting Run by Process Chain................................................................21

5.2.5 Transfer Planning Result from Planning Area to Back Up InfoCubes..........................21

5.2.6 Upload Forecast Result to Newest Forecasting Cube Directly (Optional)....................22

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1 PurposeThis document describes how to work with the different reports of the Demand Planning Analytics scenario.

2 Preliminary Steps

2.1 User Roles

The user roles in the portal are all the same. They can access both dashboards. The following two portal roles are necessary for all users:

pcd:portal_content/com.sap.pct/platform_add_ons/com.sap.ip.bi/Roles/com.sap.ip.bi.bi_showcase - This role is relevant for BI specific functions.pcd:portal_content/administrator/super_admin/super_admin_role – This role is relevant for systems communication.

The detail data the user can review is controlled by the mapped user in the backend SCM system:

Business Role User ID Linked Role in SCM

gg LOC_PLN_AME YDP_IP_LOC_AME

Local Planner APJ LOC_PLN_APJ YDP_IP_LOC_APJ

Local Planner EU LOC_PLN_EU YDP_IP_LOC_EU

Central Planner All CTR_PLN_ALL YDP_IP_CTR_ALL

DP Administrator DP_ADMIN YDP_MP_ALL_ADMIN

2.2 Logging on to Systems

UseThis section describes how to log on to the portal where the dashboards are stored.

PrerequisitesThe dashboards are published in the portal, for example http://vmw3180.wdf.sap.corp:58800.

Dashboards are assigned in iviews. You find the Demand Planning Analytics tab page after logging on to the portal. You can also directly access the dashboard by the URL link.

Your browser should be the Internet Explorer, and the latest version of the Adobe Flash Player should be installed.

Your user ID on the portal has been properly configured and mapped to the backend system user. The backend system user should have the proper roles assigned.

Procedure1. Log on to your enterprise portal, with you user ID and password.

2. Choose the Demand Planning Analytics tab page.

3. You can choose the different sub tab pages to access the two dashboards and BW reports.

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http://vmw3180.wdf.sap.corp:58800/


You can also directly access the dashboard by the URL link. Of course, you still need your portal user and password.

1. Open the link to the dashboard in your browser. For information on this link, contact your system administrator.

Example for the direct URL link to the dashboard:

http://vmw3180.wdf.sap.corp:58800/irj/servlet/prt/portal/prtroot/pcd!3aportal_content!2fcom.sap.pct!2fplatform_add_ons!2fcom.sap.ip.bi!2fiViews!2fcom.sap.ip.bi.bex?system=SH1CLNT185&DASHBOARD=ZBPR_Y97_DPA_XD01


2. Enter your username and password, and choose Log On.

3. The reports are directly displayed with blank tables and charts.

3 Demand Planning Analytics Overview

3.1 Dashboard Summary

The dashboards are designed to provide the demand planner an analysis of historical forecast performance and visibility to future demand trends.

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The Historical Forecast Review dashboard displays a comparison of past forecasts against actual and summarizes the accuracy of past forecasts. This dashboard allows the planner to assess how reliable forecasts are based on previous periods and quickly identify opportunities for improvement.

The Future Forecast Review dashboard provides snapshots of the most up to date demand plan. The dashboard provides quick access to the demand plan along with a comparison to the historical data.

On both dashboards, the user is given the option to select the data to display and the level at which the data are displayed. This gives the planner the flexibility to review the data across different dimensions and levels of detail as required.

3.1.1 Dashboard: Historical Forecast Review

The dashboard allows the user to compare past forecasts with actual sales. The dashboard displays both the system generated statistical forecasts and the final demand plan which incorporates any manual adjustments made to the system generated forecasts. The dashboard displays the total sales and forecasts at the aggregate level over two horizons: the last 3 months or the last 12 months. For the selected grouping level (for example, region, product line), the users can compare the historical forecasts and sales across the different groups.

The dashboard also displays the average forecast accuracy over the same horizon. Measurement of the forecast accuracy serves two objectives: (1) to understand the reliability of the current forecasts, and (2) to improve the accuracy of future forecasts. The dashboard displays two error measurements:

Mean absolute percentage error (MAPE). The MAPE measures the absolute difference of the forecast versus the actual sales.

Mean percentage error (MPE). The MPE is calculated to identify any consistent bias to over forecast or under forecast.

MAPE / MPE calculated in the dashboard does not match MAPE/MPE calculated in DP forecasting in DP throwing the forecast alert. Here, we compare the newest forecast with actual sales. In the DP forecast, MAPE/MPE is for the ex-post forecast.

The errors are measured at the level that the forecasts are generated and released to supply planning, at the product-location level. The error measure at the aggregate level is the average of the errors calculated at the product-location level. The error calculations are averaged and displayed over the last 3 months or the last 12 months. The errors across different aggregate levels are displayed side by side for comparison (for example different product groups, regions). Displaying the average errors at the aggregate level provides the planner an insight into which area or group of products to focus on for forecast improvements.

In addition to showing the average errors, the dashboard also displays the individual product-locations that have the lowest forecast accuracy. This list identifies the specific product-locations that the planner can focus on for adjustments or improvements.

3.1.2 Dashboard: Future Forecast Review

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The Future Forecast Review dashboard is designed for planners to review the most current demand plan. The dashboard displays the last generated forecasts for the next 12 months. In contrast to the historical forecast review which displays past forecasts and realized sales, this dashboard is a projection of the demand for the future. The forecasts are displayed in terms of quantities (for example pieces) and in terms of equivalent currency values.

The dashboard is not intended to replace the demand planning books, rather it is meant to complement the planning books. The dashboard does not display the lowest level of detail available in the planning book. The dashboards are designed to summarize the forecasts at aggregate levels. This dashboard can be used to communicate the final demand plan across the organization for sales and operations planning.

3.1.3 BW Report on the Portal

Two BW reports (query) are also provided on the portal for further detailed analysis purposes.

Users can use Query – Sales and Plan to check the detailed demand plan and actual sales figures in a free table for the last 12 months. A few Free characteristics are provided for users to drill down to the detail level.

Users can use Query –Forecast Error to check the full list of Forecast Error and bios at the product location level for the last month. It uses the same logic as the table Top 20 Forecast Error for last month included in the dashboard Historical Forecast Review. The only difference is that this query provides the complete list but not only limited to the top 20.

3.2 Overview Key FiguresKey Figure Unit Business Definition Calculation Used in

Actual Sales Quantity

EA Historical customer request quantity in Sale Order

Extract exact data from ECC system

Historical Forecast Review.

Future Forecast Review

Statistic Forecast Quantity

EA System generated statistic forecast based on Univariate Forecast method.

Calculated by Type of Forecast Model: Auto. Model Sel. 2, based on corrected historical sales quantity



Final Demand Plan EA Final demand plan, which will be released for supply network planning or production planning

Manual Forecast + Local/Central Planner Adjustment + Additional Demand



Final Demand Plan w/o Additional Demand

EA Final demand plan without additional demand

Manual Forecast + Local/Central Planner Adjustment


Forecast Error(MAPE): Statistic Forecast vs. Actual Sales

% Measure absolute difference of the forecast versus the actual sales On product location

level.


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Key Figure Unit Business Definition Calculation Used in

F: Statistic Forecast

A: Actual Sales

Forecast Error(MAPE): Final Demand vs. Actual Sales Quantity

% Measure absolute difference of the final demand versus the actual sales On product location

level.

D: Final Demand

A: Actual Sales


Forecast Error(MPE): Final Demand vs. Actual Sales (BIAS)

% Measure the relative difference of the final demand versus the actual sales

(Σ D / Σ A)– 1

D: Final Demand

A: Actual Sales


Final Demand Plan in Value

EUR Measure the final demand plan in terms of currency value.

Final demand plan quantity * standard price (at product or location level)


The MAPE/MPE key figure calculation is carried out in each single month. The bar chart of the Forecast Error Report displayed on the dashboard for the last 3 months or 12 months means the average of the percentages for 3 months or 12 months.

3.3 Overview CharacteristicsField Name Description Default Value Used In Comment

Product Line Product line information

All Filter Normally extracted from product hierarchy field in material master in ECC

ABC Indicator Product Importance Indicator

All Filter Normally extracted from ABC indicator field in material master in ECC

Sales Org Sales Organization All Filter From Sale Order

Customer Class.

Customer Classification Indicator

All Filter Normally extracted from customer class. Field of customer master data in ECC

Area Group of Region All Filter that is, EU for Europe, APJ for Asia-Pacific Japan.

Region Country of Ship-to Party

All Filter Normally extracted from customer master data in ECC, that is, CN for China, DE for Germany

4 Process Steps

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4.1 Dashboard: Historical Forecast Review

UseUsers can review the historical forecasts against the historical sales and display the calculated forecast errors. Users display the forecasts and errors at aggregate levels to identify the areas that require attention.

The selection filter and grouping characteristic allows users to review the data first at an aggregate level and drill down to lower levels based on their analysis requirements.

For example, the demand manager for the Asia Pacific Japan (APJ) area may choose to display the data for his or her area. He or she selects the APJ data and group the data by region. Grouping the data by region allows him or her to compare the historical performance of each country in the APJ area. If the demand manager identifies that a specific country’s accuracy is significantly lower than the other countries, he or she may then select that region’s data and group by product lines in that region.

Within the range of his or her data selection, the list of product-locations with the lowest forecast accuracy is also displayed. This allows the user to identify the specific product-locations that have the most potential for improvement.

PrerequisiteThe demand planning process in APO-DP has finished, and the latest forecast has been backed up to the InfoCube.

4.1.1 Define the Selection Condition and Group by Characteristic

UseUsers specify the combination of characteristics to select the scope of data that they want to review. In addition to specifying the filter criteria, users also select the grouping characteristic to specify the aggregation level to display the data.

Procedure1. In the header of the dashboard, choose the target value for each attribute:

Product line: Choose All or specific value

ABC Indicator: Choose All or specific value

Sales Org: Choose All or specific value

Customer Class: Choose All or specific value

2. For Area and Region attribute, you can choose the following possibilities:

If you want to see all areas’ plan, choose All.

If you want to see a certain area’s plan, choose the area and leave region as All.

If you want to see a certain region’s plan, choose the area and the region.

3. Select the Group By value for the plan value distribution in the selected category. Then choose Refresh. There are four choices:

Product Line

ABC Indicator

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Customer Class

Region

ResultThe users define the Selection Condition and Group by Characteristic based on their own criteria.

4.1.2 Check Top 20 Forecast Error with Two Measurements

UseWithin the scope of data selected on the dashboard, users can review in two separate columns the list of:

20 product-locations with the biggest Forecast Error measurement for the last month.

20 product-locations with the biggest Forecast Bias measurement for the last month.

The user can sort the list in ascending or descending order for each column.

PrerequisiteThe selection conditions and group by characteristic has been defined.

Procedure1. Choose the FC Error column and check the list with the top 20 MAPE at the product/location

level within the selection criteria.

2. Choose the Bias column, and check the list with the top 20 MPE at the product/location level within the selection criteria.

3. Choose the table header to sort with product, location, ABC Indicator, MAPE or MPE in ascending or descending order.

ResultThe users have a draft picture of the worst forecast by product and location combination for the last month. He or she can also know whether there is a consistent bias for certain product and location. If needed, the user can go to BW report to display the full list report for all product and location and for each of the last 12 months to get more information.

4.1.3 Check Forecast Error with Bar Chart

UseIn this activity, the users can review various forecast error reports based on the selection condition and grouping characteristic.


Procedure

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1. Choose the Last 12 Months tab pages or Last 3 Months for the time span.

2. Choose Error (MAPE) to show the average forecast error for the last 12 or 3 months.

3. In the bars displayed side by side for the different Group By characteristic values (for example different area), the user can find out the improvement area and also identify whether manual adjustment makes forecast even worse.

4. Choose Bias (MPE) to show average bias for the last 12 or 3 months.

5. Repeat step 3.

6. Choose Quantity to show average quantities of the statistics forecast, final demand and actual sales for the last 12 or 3 months.

7. Repeat step 3.

Redefine the Selection Condition and Group by Characteristic to check other forecast error reports. Consequently identify the forecast improvement area.

ResultThe managers know where to improve the forecast accuracy.

4.1.4 Check Plan vs. Actual with Line Chart

UseCompared with the Forecast Error Chart that shows the average forecast error or quantity for the last 3 or 12 months. The line chart (check plan vs. actual) at the lower part of the dashboard is used to show the same key figures for each of the last 12 months.


Procedure1. Keep the checkbox Show All selected.

2. Make sure that the Error (MAPE) radio button in the Forecast Error chart is chosen. In the Plan vs. Actual chart, the forecast error (MAPE) for each of the last 12 months is displayed.

3. You can use the checkbox at the bottom of the chart to choose which key figures you want to show in the chart.

4. Deselect the Show All checkbox, and in the Forecast Error chart, choose a suitable place in one bar to select the drilldown value. The Plan vs. Actual chart is synchronized to show the key figures only for the drilldown characteristic value.

You can see the selected value in line chart’s subtitle.

5. Check the Bias (MPE) radio button. Repeat the steps 1 to 4 to review the forecast error (MPE) for each of the last 12 months.

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6. Check the Quantity radio button, repeat the steps 1 to 4 to review the statistic forecast, final demand plan and actual sales for each of the last 12 months.

ResultThe managers can see the change or trend of the forecast errors with two measurements (MAPE and MPE), and the quantity difference between planned and actual for each of the last 12 months. If needed, the users can go to the BW report to display the plan and actual report for all products and locations of the last 12 months.

4.2 Dashboard: Future Forecast Overview

UseThis dashboard is intended to provide a high level picture of the demand distribution over the next 12 months, and to compare the forward view with the historical trend and pattern based on the selection and group by conditions.


4.2.1 Define the Selection Condition and Group by Characteristic

UseThe users can use the selection condition combination to filter the objects they want to review, and then group the filtered objects with group by characteristic.

The selection and group by characteristic is the same as the first dashboard.

Procedure1. In the header of the dashboard, choose the target value for each attribute:

Product line: Choose All or specific value

ABC Indicator: Choose All or specific value

Sales Org: Choose All or specific value

Customer Class: Choose All or specific value

2. For the Area and Region attribute, you can choose the following possibilities:

If you want to see all areas’ plan, choose All.

If you want to see a certain area’s plan, choose the area and leave the region as All.

If you want to see a certain region’s plan, choose the area and the region.

3. Select the Group By value for the plan value distribution in the selected category. Then choose Refresh. There are four choices:

Product Line

ABC Indicator

Customer Class

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Region

ResultThe users define the Selection Condition and Group by Characteristic based on their own criteria.

4.2.2 Review Final Demand Plan over the Next 12 Months with Bar Chart

UseThe users can use the bar chart to have a forward picture of the demand distribution over the next 12 months based on the defined selection condition and group by characteristic. The user can choose to display the chart in terms of quantity or currency value by the radio button.

The Sales value forecast is calculated by multiplying forecast quantity with price, at product-location level. The price data can be extracted from ECC (that is stand material price or historical sales price). The price data is saved in a DSO at the product-location level. The data model of DSO can be changed according to customers’ requirements.


Procedure1. Make sure the Quantity radio button is selected.

2. After the user has chosen Refresh in the selection area, the bar chart is updated.

3. The final demand plan for the next 12 months is displayed in the bar chart. For each bar, the demand is distributed by the defined group by characteristic.

4. At the same time, the Average Final Demand Plan Quantity over next 12 months information is also updated and displayed at the left side of the dashboard. Each group by characteristic value is displayed.

5. Check Values. The bar chart shows the final demand plan in terms of currency value over the next 12 months. The information area at the left side of the dashboard is also updated to show the value instead of the quantity.

ResultThe users have reviewed the final demand plan over the next 12 months in terms of quantity and currency.

4.2.3 Compare the Forward View with Historical Trend and Pattern with Line Chart

UseWith a line chart at the lower part of the dashboard, the user can compare the historical trend and pattern with future demand plan. The data spans from the last 12 months to the next 12 months. The user can also choose the year over year display mode to compare on an annualized basis.

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Procedure1. After the user has chosen Refresh in the selection area, the line chart is updated.

2. Keep the checkbox Show All selected, and YoY deselected. The line chart displays the total quantity for the group by characteristic.

3. Actual sales are displayed for the last 12 months, and the planned demand is displayed for the next 12 months. You can use the scroll bar at the bottom of the chart to display more or less periods at the main line chart.

4. All four key figures are displayed in the chart by default. You can use the checkbox at the bottom of the chart to choose which key figures you want to show in the chart.

5. Deselect the Show All checkbox, and in the Final Demand Plan over Next 12 Months chart, choose a suitable place in one bar to select the drilldown value. The Plan vs. Actual chart is synchronized to display the key figures only for the drilldown characteristic value.

You can see the selected value in the subtitle of the following chart.

6. Check the YoY radio button. The user can compare the actual and planned demand on an annualized basis.

ResultThe users compare the historical trend and pattern with future demand plan in the YoY view or in a linear time stream view.

4.3 BW Report on Portal

UseThere are two BW queries embedded in the portal for the further root-cause analysis. Users can drill down to detail level to check the forecast result.


4.3.1 Query – Sales and Plan

UseUsers can use this query to check the detailed demand plan and actual sales figures in a free table, for the last 12 months. The default rows are the location and product level. Users can switch or add other characteristics as rows from the Free characteristics list on the left of the report.

The operation of the query embedded in the portal is the same as the query in the BEx Analyzer.

Procedure

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1. Choose the BW Report tab page below the Demand Planning Analytics tab page. Choose the Query – Sales and Plan in the Detailed Navigation window on the left of the Internet Explorer window. (The query is selected by default when you choose the BW Report tab page).

The query automatically gives the result of the Demand Plan, Actual Sales and calculated Bias at the product location level for the last 12 months.

2. If you want to drill down to a more detailed level, for example, Customer Class level, you can drag the Customer Class from the Free characteristics list, and drop it in the row head area of the table, for example, between Location and Products. Then you can get the detailed report on the Product, Customer Class, and Location level.

3. If you want to drill up and do not need to display the product in the table, you can drag the Product from the table and drop it to the Free Characteristic list. Then you get the report on the Customer Class and Location level.

4. If you want to filter for a certain characteristic, for example, Customer Class, you only need to check the data of A-Customer. You can right-click the characteristic Customer Class (either in the table area, or in the Rows list area), choose Filter → Select Filter Value. In the dialog box, you can mark A-Customer and choose Add to move it to the Selections table on the right. Choose OK to adopt your selection. Then you find the data for A-customer only in the table.

5. If you want to sort for a certain characteristic, for example, Product, you can choose the small triangle on the right side to characteristic Product in the table area. Then the data is sorted according to the name of the product.

ResultThe user can drill down and select the detail data as desired to find out the root cause of an unreasonable forecast error.

4.3.2 Query – Forecast Error

UseUsers can use this query to check the full list of Forecast Error and Bias at product location level for the last month. It uses the same logic as the table Top 20 Forecast Error for last month included in the dashboard Historical Forecast Review. The only difference is that this query provides the complete list but not only limited to the top 20.

Procedure1. Choose the BW Report tab page below the Demand Planning Analytics tab page. Choose the

Query – Forecast in the Detailed Navigation window on the left of the Internet Explorer window.

The query automatically gives the result of the calculated Deviation, Bias and Abs(Bias) at the product location level for the last month.

2. If you want to filter for a certain characteristic, for example, Location, you only need to check the data of Plant 2400. You can right-click the characteristic Plant (either in the table area, or in the Rows list area), choose Filter → Select Filter Value. In the dialog box, you can mark Plant 2400 and choose Add to move it to the Selections table on the right. Choose OK to adopt your selection. Then you find the data for Plant 2400 only in the table.

3. If you want to sort for a certain calculated key figure, for example, Bias, you can choose the small triangles on the right side of key figure Bias in the table area. Then the data is sorted according to the value of Bias. Choosing the triangles again switches the sorting between ascend and decent.

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ResultThe user can check the full list of forecast deviation and bias for the last month and sort as desired.

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5 Appendix (Optional)

5.1 Source DataData Type Description Where Used Comment

MultiCube ZY92_MP01: forecasting and actual

Dashboard: Historical Forecast Overview

The MultiCube includes InfoCubes: ZY92_IC01 (Newest Forecast), ZY91_IC01 (Sales Historical)

MultiCube ZY92_MP02: future forecasting review

Dashboard: Future Forecast Review

The MultiCube includes InfoCubes: ZY91_IC02 (Backup InfoCube for planning area YDP_PA), ZY91_IC01 (Sales Historical)

5.2 Create Dashboard Demo Transaction Data

Use

The dashboards need to access the query of SCM system, and the query in SCM needs Historical Actual Sales, Statistical Forecast, Final Demand Plan.

The source of Historical Sales quantity is the transaction data in SAP ECC, the data is extracted by SCM BW to the InfoCube for query to access. To simplify the demo data preparation process, you can also directly upload the data from the flat CSV file to the cube instead.

The Statistic Forecast quantity and Final Demand quantity are generated by the forecast planning run or interactive planning. Then the figures are backed up from the planning area to the backup InfoCube. Meanwhile, the latest forecast result is also backed up into another InfoCube for the query purpose.

To simplify the demo data preparation process, you can also directly upload the data from the flat CSV file to the backup InfoCube.

5.2.1 Create Sales Order / Delivery Note in the SAP ECC System and Transfer to SAP SCM

You can execute this step to prepare transcation data in SAP ECC and transfer them to the SCM InfoCube. You can also skip this step and excute the next step to upload the data from the flat file instead.

Prerequisites

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The material master data and customer mater data should be maintained in the SAP ECC system, and material master data and plant master data has been transferred to SAP SCM system via CIF.

The pricing condition type PR00 for the material should be maintained.

There should be enough stock of the material, and the customer should have enough credit limit for the delivery to be carried out.

Procedure 1. Access the activity using the following navigation options:

Transaction Code VA01

SAP ECC Menu Logistics Sales and Distibution Sales Order Create

2. On the Create Sales Order initial screen, make the following entries:

Field Name Description User Action and Values Comment

Order Type OR

Sale Organization that is, 1000

Distribution Channel that is, 01

Division that is, 01

Sales Office that is, 1000

Sales Group that is, 100

3. Choose Enter, then proceed to the Create Standard order Overview screen, and make the following entries:


Standard Order Order Number Leave blank

Sold-to Party that is, 100110

Ship-to Party that is, 100110

PO Number that is, 000111 Any Free text

Req. deliv. date that is, 20.04.2011 Default value: date of document entry date

Deliver. Plant that is, 1000

Material Material Number to be sold to customer

that is, L101

Oder Quantity that is, 1000

Unit that is, EA Default value take from mater data if leave blank, after press enter

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4. Choose Enter. The system reads the master data and carries out the ATP Check and pricing procedure.

5. Choose Save. The system provides the sales order number you have created.

6. Access the activity using the following navigation options:

Transaction Code VL01N

SAP ECC Menu Logistics Logistic Execution Outbound Process Goods Issue for Outbound delivery CreateSingle Document With Reference to Sales Order

7. On the Create Outbound Delivery With Order Reference screen, make the following entries:


Shipping Point that is, 1000

Selection Date that is, 20.04.2011 Should be equal or after the required delivery date

Order Input the SO number you get from the last steps

From item Leave blank

To item Leave blank

Delivery Type Leave blank

8. Choose Enter to go to the next screen.

9. Choose the Picking tab page and make the make the following entries:


SLOC Storage Location

that is, 0002

Picked Qty that is, 1000 Normally the same quantity with the Deliv. Qty

10. Choose Post Goods Issue.

11. The Delivery Note is created and the Delivery Note number is given by SAP ECC.

12. You can repeat all the steps above to create as many sales orders and delivery notes as needed.

13. Access the activity using the following navigation options:

SAP SCM Menu Advanced Planning and Optimization Demand Planning Environment Maintain Process Chains

Transaction Code RSPC

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14. Choose the process chain DPA – Data Extraction from ECC, and choose Execute (F8).

15. Assign the priority for the job Job Higher Priority A.

16. Choose Log View to monitor the status of the process chain execution.

17. In the Date Selection dialog box, choose Today.

Expand the tree to find the Log for the process, and choose the row. The detailed status of each step in the process chain is displayed on the right window. If everything is correct, each step is given a green check, and the step box is green, too.

5.2.2 Upload Demo Sales History Data from Flat CSV File into SCM

UseYou can execute this step to upload the demo data of the Sales History to the InfoCube instead from ECC.


Transaction Code RSA1

SAP SCM Menu Information Systems Business Information Warehouse Modeling Data Warehousing Workbench: Modeling

2. Choose InfoProvider.3. Expand the InfoArea Tree: RDS Demand Planning.

4. Expand the InfoCube: Sales Historical .

5. Expand the Data Transfer processes.

6. Expand the RSDS ZBPR_Y91_DP_DS01 / FLAT_FILE → ZY91_IC01_D.

7. Expand the DataSource: ZBPR_Y91_DP_DS01.

8. Choose the InfoPackage: ZBPR_Y91_ZBPR_Y91_DP_DS01_IP01_F.

9. Choose the Extraction tab page.

10. Choose the data file you have prepared in the File Name field.

11. Choose the Schedule tab page, and choose Start.

12. Wait until the Data Transfer Process is finished.

5.2.3 Load Sale History Data to Planning Area by Process Chain




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2. Choose the process chain DPA – Data load and preparation, and choose Execute (F8).


4. Choose Log View to monitor the status of process chain execution.


6. Expand the tree to find the Log for the process, and choose the row. The detailed status of each step in the process chain is displayed in the right window. If everything is correct, each step is given a green check, and the step box is green, too.

5.2.4 Execute Forecasting Run by Process Chain




2. Choose the process chain DPA – Forecasting, and choose Execute (F8).




Expand the tree to find the Log for the process, and choose the row. The detailed status of each step in the process chain is displayed in the right window. If everything is correct, each step is given a green check, and the step box is green, too.

6. Choose the process chain DPA – Consolidate. Choose Execute (F8).





5.2.5 Transfer Planning Result from Planning Area to Back Up InfoCubes




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2. Choose the process chain DPA – Backup Forecast. Choose Execute (F8).





6. Choose the process chain DPA – Update Last Forecast cube. Choose Execute (F8).





10. After this step, all the necessary data for the dashboard demonstration is complete. If you need more data for the forecast result, it is an option to continue the next step to upload more forecast result from the flat CSV file.

5.2.6 Upload Forecast Result to Newest Forecasting Cube Directly (Optional)

UseYou can execute this step to simplify the demo data preparation by upload forecast result directly into the Newest Forecast Cube.


Transaction Code RSA1

SAP SCM Menu Information Systems Business Information Warehouse Modeling Data Warehousing Workbench: Modeling

2. Choose InfoProvider.3. Expand the InfoArea Tree: RDS Demand Planning.

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4. Expand the InfoCube: Newest Forecasting.

5. Expand the Data Transfer processes.

6. Expand the RSDS ZBPR_Y91_DP_DS01 / FLAT_FILE → ZY91_IC01_D.

7. Expand the DataSource ZBPR_Y91_DP_DS01.

8. Choose the InfoPackage ZBPR_Y91_ZBPR_Y91_DP_DS01_IP01_F.

9. Choose the Extraction tab page.

10. Choose the data file you have prepared in the File Name field.

11. Choose the Schedule tab page, and choose Start.

12. Wait until the Data Transfer Process is finished.

13. Choose RSDS ZBPR_Y91_DP_DS01 / FLAT_FILE → ZY91_IC01_D.

14. Choose the Execute tab page and choose Execute.

15. Confirm the system message (monitor the data transfer process). This leads you to the window monitoring status update of the data transfer process. Choosing Refresh from time to time keeps the status updated. When all the lights are green, the process is over and all the data has been loaded.

ResultThe transaction data for the dashboard demonstration is ready.

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