Susanne Hess, Stefanie Lenz, Jochen Scheibler. Sales and Distribution Controlling with SAP. NetWeaver BI. Bonn Boston

Transcription

1 Susanne Hess, Stefanie Lenz, Jochen Scheibler Sales and Distribution Controlling with SAP NetWeaver BI Bonn Boston

2 Contents Acknowledgments Introduction Goals and Basic Principles Sales in SAP ERP with the Sales and Distribution Component Overview of SAP NetWeaver BI SAP NetWeaver BI and Its Components Benefits and Advantages of a Data Warehouse Summary SAP NetWeaver BI The Basic Principles of Modeling in SAP NetWeaver BI Data Structures in a Data Warehouse Extract, Transform, and Load (ETL) The Basic Principles of Data Retrieval Extraction from SAP Source Systems Document Extraction versus Extraction from SIS Delta Capability of DataSources Extension of DataSources The Extraction Process Reporting Options in Microsoft Excel Defining Queries in the BEx Query Designer Executing Queries with the BEx Analyzer Web Reporting Web Application Designer Web Analyzer Report Designer SAP NetWeaver Visual Composer Summary

3 Contents 3 SD Sample Scenario The Basic Principles of Data Models DataSources Data Staging DSOs for the Sales Document Area DSOs for the Delivery Area DSOs for the Order Delivery Area DSO Objects for the Billing Document Area InfoProviders MultiProviders Extended Data Model Incoming Orders/Orders on Hand Cross-Company Stock Transfers/Purchase Orders Follow-on Documents Summary Key Figures in the Sales and Distribution Process Sales and Distribution Key Figures General Basics Quotation Key Figures Quotation Value, Quotation Quantity, Number of Quotations Order Probability and Expected Order Value Open Quotations Success Quota and Accepted Quotation Rejected Quotations Expired Quotations Sales Cycle Order Key Figures Incoming Orders and Orders on Hand Orders with Delivery or Billing Blocks Analysis of Business Processes Excursus: Orders on Hand and Incoming Orders via the SIS Delivery Key Figures Delivery Quantity, Delivery Value, and Documents On-Time Delivery Performance Delivery Time

4 Contents 4.6 Billing Key Figures Sales, Sales Volume Pricing Analysis Margin Key Figures in the Cross-Company Business Scenarios of the Cross-Company Processes System Examples Summary Implementing the Key Figures in SAP NetWeaver BI Quotation Key Figures Quotation Value, Quotation Quantity, Number of Quotations Order Probability and Expected Order Value Open, Rejected, and Accepted Quotations Success Quota and Rejection Quota Expired Quotations Sales Cycle Order Key Figures General Order Key Figures Incoming Orders/Orders on Hand Implementing Different Standard Processes Consignment as an Example of Standard Processes Evaluations for Outline Agreements Orders with Billing or Delivery Blocks Delivery Key Figures Outstanding Deliveries per Order Delivery Quantity, Volume, Number of Deliveries Delivery Performance (On-Time Delivery Performance, Delivery Time) Billing Key Figures General Billing Key Figures Key Figures from Billing Conditions Margin Analysis Key Figures in the Cross-Company Business Cross-Company Sales

5 Contents Cross-Company Stock Transfer Summary Cross-Sectional Evaluations Tools Evaluation via Visual Composer Details per Material Details per Customer Technical Background of the Visual Composer Evaluation via the Web Application Designer The Sales and Distribution Overview Web Template Summary Summary The Authors Index

6 2 SAP NetWeaver BI This chapter introduces you to SAP NetWeaver Business Intelligence (SAP NetWeaver BI) by first describing the basic principles of modeling in the SAP Business Information Warehouse (SAP BW) as well as the basic elements of data retrieval. Finally, the chapter describes the reporting options in Microsoft Excel and through the web. 2.1 The Basic Principles of Modeling in SAP NetWeaver BI The following sections provide an overview of the various SAP NetWeaver BI components, that is, the system s data structures and the corresponding modeling or the definition of the basic objects in SAP BW. The description includes characteristics and key figures, as well as the DataStores that can integrate extracted and generated data. You also define rules that must be adhered to when aggregating, correcting, changing, consolidating, and updating extracted data. This is part of the extract, transform, and load (ETL) concept, which describes the areas of data extraction, data transformation, and data loading. The ETL concept also comprises the phases in which the data is extracted from the source systems and transferred into a SAP BW target structure according to specific transformation rules. Another basic area of SAP NetWeaver BI is reporting. The goal and purpose of every SAP BW system is to store particular data in a format optimized for reporting. Based on this data, the evaluations are defined and made available to the users. The users have numerous navigation options to easily generate the required view for their data. They can also create new evaluations on the basis of existing data structures. Reporting can either be carried out in a web-based portal or by means of a Microsoft Excel-based tool called Business Explorer Analyzer. Sections 2.3, Reporting Options in Microsoft Excel, and 2.4, Web Reporting, provide more information on reporting. 19

7 2 SAP NetWeaver BI Data Structures in a Data Warehouse In this context, we must first distinguish between the basic objects in SAP BW, that is, the InfoObjects and the InfoProviders that are necessary to store data. InfoObjects are the basic meta objects the smallest units in SAP BW which are key figures and characteristics. Key figures are data fields that represent values or quantities. Examples of key figures are amounts, such as sales order value or billing values; quantities, such as the delivered quantity; or numbers, such as the number of orders. The defining feature of a key figure is its aggregation behavior, which describes how a key figure is to be aggregated. Usually, the individual data records are summed; however, you can also use exception aggregations, for example, Last Value via an Aggregation Reference Characteristic. The Last Value via the Month Characteristic option can be used for balance key figures because you can only create a correct balance in the course of time. Characteristics further specify the key figures and define which view is supposed to be evaluated in reporting. They are the criteria that can be used to analyze the data. For example, the sales order value key figure can be analyzed per customer or per material. In this case, customer and material are the characteristics. Characteristics can also have texts, attributes, and hierarchies. Texts are the descriptions of the characteristic attributes, for example, the name of the customer. Attributes are information that logically belongs to the characteristic. For the example of the customer characteristic, attributes could be the corresponding location, the sales district, the customer group, and so on. The hierarchy refers to the hierarchical structure of the characteristic s attributes, which are related to one another. For example, the customer characteristic can have a customer hierarchy that groups all customers of the enterprise by their location in the different countries. Key figures and characteristics are summarized here: Key figures Data fields that represent values and quantities (amounts, quantities, numbers) Particular aggregation behavior Example: sales order value, delivered quantity, number of billing documents 20

8 The Basic Principles of Modeling in SAP NetWeaver BI 2.1 Characteristics Business reference objects for the key figures Can have texts, attributes, and hierarchies Example: customer The DataStore is a further important component in the data structure area. SAP BW includes various objects that can store data. Here, you must differentiate among the following data storage levels: 1. PSA Tables 2. DSOs 3. InfoCubes PSA (Persistent Staging Area) tables are the lowest storage level. These flat tables depend on the source system and contain the fields that are provided by the source system. The data is stored unmodified and hasn t been transformed in SAP BW yet. However, you can t use the data within these tables for reporting. The data is only available as a kind of interim state for further updates within SAP BW. It may also be useful to analyze the data in the PSA tables for checking and testing purposes. The DSOs (DataStore objects) make up the next data storage level, which are also flat tables. However, in contrast to the PSA tables, they can be used for reporting. DSOs contain the already mentioned InfoObjects as characteristics and key figures. DSOs are used in SAP BW for two reasons: E E Storage of detailed data They enable you to store detailed data, usually at document level. If necessary, you can use this data for reporting. However, this is more of an exception because evaluations are usually based on aggregated data. For example, if you want to analyze particular facts in a special case, you can access the detailed data (document level) by navigating to the data of a DSO. E E Consolidation and enrichment of data DSOs allow for a consolidation and enrichment of data across multiple levels. In this context, the lowest level usually includes data at document level. This data can be consolidated in several transformation steps and enriched with additional information. The resulting data is stored in additional DSOs, that is, the data is available at all stages in SAP BW and can be used for detailed or aggregated reporting as required. 21

9 2 SAP NetWeaver BI Finally, you can store the data in InfoCubes, which are based on a multi-dimensional data model that enables optimum access to the data for reporting. An InfoCube also consists of various InfoObjects. Several dimension tables link the characteristics to the key figures that are defined in the fact table. This distribution of the characteristic combinations into various smaller dimension tables enables performance-optimized access to the data. In reporting, the characteristics contained in an InfoCube are used to access the attributes, texts, or hierarchies of this characteristic that are separately stored in master data, text, or hierarchy tables. Figure 2.1 shows an example of an InfoCube database schema. Like DSOs and InfoObjects, InfoCubes are referred to as InfoProviders in the SAP BW system because they provide information for reporting. Master Data Table Master Data Table Customer Customer Number City, Street, Customer Group Dimension Table Customer Customer Dimension ID Customer Number Master Data Table Sales Organization Sales Organization Country of Sales Organization Dimension Table Sales Area Sales Area Dim. ID Division Distribution Channel Sales Organization Company Code Master Data Table Company Code Company Code Country of Company Code Text Table Dimension Tables Fact Table Text Table Sales & Distribution Staff Sales Staff Number Sales Staff Name Dimension Table Sales & Distr. Staff Sales Staff Dim. ID Sales Staff Number Dimension Table Units/Currency Unit Dimension ID FACT TABLE Document Currency Sales Unit Customer Dimension ID Sales Area Dim. ID Sales Staff Dim. ID Product Dimension ID Time Dimension ID Unit Dimension ID Package Dimension ID Net Value of Billing Item Quantity in Sales Unit Costs Net Weight Number of Billing Items Dimension Product Product Dimension ID Product Hierarchy Material Number Dimension Table Time Time Dimension ID Calendar Year/Month Calendar Year Month Quarter Calendar Day Dimension Table Package Package Dimension ID Change Run ID Record Type Request ID Master Data Table Material Material Number Material Grouping Material Type Figure 2.1 Database Schema of an InfoCube 22

10 The Basic Principles of Modeling in SAP NetWeaver BI 2.1 In addition to the InfoCubes and DSOs, you can define MultiProviders in SAP BW. A MultiProvider links the data of multiple InfoProviders to allow cross-company reporting. You can use a MultiProvider, for example, to link the billing documents to the open items of a customer to evaluate this information together in reporting. MultiProviders don t store data; instead, the data is read from the underlying Info- Cubes, DSOs, and InfoObjects for evaluations. MultiProviders only provide the structure that is used to link the data Extract, Transform, and Load (ETL) Now that you re familiar with the data structures of SAP BW objects, you have to bring them to life, that is, fill them with data. A critical prerequisite for extracting data from a source system is the DataSource. It contains all characteristics and key figures that are supposed to be extracted and filled via specific extraction logic. You ll find further information in Section 2.2, The Basic Principles of Data Retrieval. To load sales order values and quantities into the SAP BW system, for example, you need a DataSource in the source system. Irrespective of whether a Business Content DataSource is available or whether you must define your own DataSource, this DataSource is made known in SAP BW through replication, which means the information on the delivered characteristics and key figures is provided in the source system and in SAP BW. In the next step, you define the link between the information from the source system and the SAP BW objects. You must specify which characteristics and key figures of the DataSource correspond to the InfoObjects in the DataStores of the SAP BW system. This mapping is defined in transformation rules, which link a Data- Source to a data target, such as DSOs or InfoCubes. There are various options to map information in accordance with these transformation rules: E E E E 1:1 mapping A characteristic from the source system is mapped to an InfoObject in SAP BW without logic. This is the simplest transformation rule. Mapping fixed values Fixed values are mapped to the InfoObjects, for example, for which no information from the source system can be delivered. 23

11 2 SAP NetWeaver BI E E Creating a routine A routine enables you to use your own ABAP source code to map complex transformation logic. Routines are provided both for the individual InfoObjects that are contained in the data target and globally for the entire transformation (start, end, and expert routine), for example, to enable cross-company calculations. The transformation rules enable you to clean up, enrich, consolidate, or aggregate the data delivered from the source system. Consequently, the data can be stored in SAP BW in the best possible way according to your specific requirements. In some cases, you may have to transform data across multiple stages, for example, when complex formatting and aggregations are required in addition to the actual logic or when data from different source systems is supposed to be standardized first and then processed using the same logic. To map such requirements, there are two different methods: E E E E Storage via interim steps To store data in interim steps means to use several DSOs in which data is respectively stored after a transformation step and then updated with the next transformation. This option can be used, for example, if you require stored data after specific transformation steps. Interposing an InfoSource If you don t have to temporarily store data, you can also interpose an InfoSource. An InfoSource is a structure that consists of InfoObjects and can t store data itself. It only provides the option to link two transformations to each other. First, the data is transformed from the source object (DataSource or DSO) to the InfoSource, and then transformed from the InfoSource to the target object (DSO or InfoCube). To trigger and control the loading of data from the source system into the SAP BW system, two additional object categories are needed: InfoPackages Data transfer processes (DTPs) 24

12 The Basic Principles of Modeling in SAP NetWeaver BI 2.1 InfoPackages are used to request data from a source system and store it in the PSA tables in SAP BW. You can start the data extraction immediately or schedule it in the background, periodically if required. In the InfoPackage, you can use specific criteria to filter the data that you want to request. For this purpose, you re provided with the fields that were selected as selection fields in the DataSource of the source system. For example, if the Sales Organization field is a selection field, you can restrict the InfoPackage to a specific sales organization to load only this data into the SAP BW system. The system stores the imported data in the corresponding PSA table. From there, you can update and store the data in further DataProviders within the SAP BW system. Data transfer processes (DTPs) control the update of the data. Via DTPs, data originally stored in the PSA table are updated into the data targets. For this process, you need an active transformation rule for the respective data target. If you must update data from a DSO or an InfoCube into additional data targets, both an active transformation rule and a DTP are required here as well. The system can supply only one data target with data for each DTP. If various data targets must be supplied from one source, you will need to create multiple DTPs. You can load the data online via DTPs or by means of process chains by scheduling the process periodically in the background. A process chain bundles several process steps and can include the entire data flow from the source system to the update of the data in the InfoCubes of SAP BW. A process chain can comprise various loading steps through InfoPackages, DTPs, or post-processing of the data targets, for example, setting up database indices and statistics or compressing data to ensure optimal performance. You can execute process chains online with a direct start or initiate them in the background. Usually, they are periodically scheduled, for example, to supply the SAP BW system each night with the most up-to-date data from the source systems. The data flow from the source system to the data targets in SAP BW is illustrated in Figure

13 2 SAP NetWeaver BI Business Warehouse System Process Chain InfoCube InfoCube Transformations Data Transfer Process DSO Object Transformations PSA Tables InfoPackage Source System DataSource Extraction Logic Figure 2.2 Data Flow from the Source System to the Data Targets in SAP BW This section described the data flow from the source systems to the DataStores in SAP BW. The process and background of data retrieval in the source system were only outlined here, but the following section provides detailed information on these aspects. 2.2 The Basic Principles of Data Retrieval As already mentioned, SAP BW objects have to be supplied with data. The data supply begins with extracting the data from a source. The DataSource is the central object in the data supply process because it provides the data for the extraction. The DataSource has a specific structure that is defined by the extract structure and is identical in the source and in SAP BW. An extractor selects and formats the data in the source system and transfers the data from the source to the input storage location of the SAP BW system, the PSA tables. The data is stored in the format in which it was determined by the extractor in the source and transferred into the SAP BW system through the DataSource. Data formatting as it s carried out in transformations isn t possible here. 26

14 4 Key Figures in the Sales and Distribution Process The previous chapters introduced you to SAP NetWeaver Business Intelligence (SAP NetWeaver BI) and provided a sample scenario. To set up a key figure system in SAP NetWeaver BI, you must know the development process of data in the source system. This chapter deals with the key figures relevant for the sales and distribution (S&D) process. Initially, we address the general fundamentals of how basic information in SAP ERP is developed and look at which data is required for creating key figures in SAP NetWeaver BI. This is followed by a description of quotation key figures, order key figures, delivery key figures, billing key figures, and key figures for the cross-company business. 4.1 Sales and Distribution Key Figures General Basics In general, a key figure is an indicator for evaluating a subject. Business key figures are aggregated data, which is usually evaluated with reference to a period at characteristic level in order to measure the success or attainment of goals in specific areas. For the various enterprise areas (production, material management, sales, accounting and balancing, controlling, etc.), key figure systems have been developed that correlate the individual key figures to achieve transparency of the success contribution of the respective subareas. For the subarea of S&D, numerous key figures and key figure systems have been developed. Every enterprise must decide individually which key figures are supposed to be considered for the evaluation of S&D. The following criteria can be decisive: Related industry Business model Size of the enterprise 101

15 4 Key Figures in the Sales and Distribution Process Market situation Sales strategy and concept From this, it follows that for each enterprise, a suitable set of key figures needs to be configured, or rather that the relevant indicators must be selected from the existing key figure definitions. At this point, we don t want to discuss a complete key figure system and map it in the system. We d rather show you, using some sample key figures, how raw data is created in the S&D process of the SAP ERP application, which then is processed into key figures in SAP NetWeaver BI. For this purpose, the S&D process is subdivided into the following steps: quotation, order, delivery, and billing. After we describe the corresponding key figures for each step, we outline the specifics in the cross-company business. 4.2 Quotation Key Figures The following key figures form the basis for evaluations in the quotation area: E E Quotation value E E Quotation quantity (total of all items) Number of quotation documents These key figures form the basis for the success analysis of the sales activity in this phase of the S&D process. The aim is to determine an enterprise s success in the market, find the reasons for this success, and define the problem areas. Building on that, the following questions must be considered for the success analysis: E E What s the total amount of quotation value, quotation quantity, and number of documents (or number of document items) within a certain period? E E What are the absolute numbers of the open quotation value (or quotation quantity, number of documents, number of document items)? E E What s the quota of open quotations? What s the absolute value of accepted quotations? What s the quota of accepted quotations (success quota of quotations)? 102

16 Quotation Key Figures 4.2 What s the absolute value of rejected quotations? What s the quota of rejected quotations? What s the value of all quotations that have already expired? What s the quota of expired quotations? For the description of key figures, the following pattern is used. First, you receive a brief description of the business background of key figures. Then, we outline where you can find the required raw data in the SAP ERP source documents for calculating the key figures. Following that, you learn which characteristics are interesting in addition to the classic characteristics (customer, country, material, sales area, etc.). The entire discussion is rounded off with a system example Quotation Value, Quotation Quantity, Number of Quotations Quotation value, quotation quantity, number of quotation documents, and number of quotation items are counted among to the basic key figures in S&D. Here, no distinction is made with regard to the status ( open, accepted, rejected ). Rather, all quotations are considered that have been recorded within the analysis period specified as the selection period by the user. These values are also suitable to make period comparisons as well as target/actual comparisons. Period comparisons and target/actual comparisons provide early indicators for the demand for products and services offered by the enterprise. The quotation value is the net value of all chargeable items of the quotation. The quotation quantity is the cumulative quantity of all items of a document. Here, you must make sure to not only evaluate the cumulative value of all items. Usually, the users want to know how the quotation value is distributed to the materials (or material groups and product hierarchies). This necessitates a quotation value for each quotation item. We now gradually examine a source document in SAP ERP based on a system example. Figure 4.1 shows the item overview of a quotation. 103

17 4 Key Figures in the Sales and Distribution Process Figure 4.1 Item Overview in the Quotation in the SD Component (Transaction VA22) The quotation for the customer (DROBEDA Drugstore Trade Ltd. with Sold-toparty-number 1040) contains two items: one for material PR4711 and one for material PR4712. The Net value field in the item row indicates the quotation value for the individual items. This value is stored in Table VBAP (Sales Documents: Item Data) in the NETWR field. The order quantity is stored in the KWMENG field of the same table. This information represents the source information for the key figures, quotation value and quotation quantity. Figure 4.2 shows the same quotation as illustrated in Figure 4.1; now, however, the Sales tab is activated. The Order reason dropdown list provides the input options available, which are defined in the Customizing area of SAP ERP (Field AUGRU in Table VBAK Sales Document Header Data). This field enables you to analyze the key figures quotation value, quotation quantity, and number of documents according to order reasons in addition to the classic key figures (sales area, country, customer, customer group, material, material group, etc.). This allows you to also evaluate the efficiency of the different sales instruments (advertisement, field sales, and campaigns). The fact that the field is called Order reason and not quotation reason is confusing in this context because you need to specify here which sales activity the quotation is based on. In SAP ERP, however, all S&D documents (quotations, orders, returns orders, credit memo and debit memo requests ) are mapped in the same tables so that the fields are identical. 104

18 Quotation Key Figures 4.2 Figure 4.2 VA22) Selecting the Quotation Reason, Order Reason in the SD Component (Transaction The order reasons are maintained in the sales and distribution (SD) component via the Customizing path SPRO Sales and Distribution Sales Sales Documents Sales Document Header Define Order Reasons. You must select the order reason during the quotation entry Order Probability and Expected Order Value You can specify the order probability for each item as an additional value in the quotation document. Via the order probability and based on the quotation value, the system calculates the expected order value. Calculating the Expected Order Value Net value : 4,300 EUR Order probability: 70 % Expected order value: 3,010 EUR In the following sections, we continue with this example. Figure 4.1 shows that an order probability of 70% is specified for each item respectively (VBAP-AWAHR 105

19 Index A ABAP source code, 24 After image, 90 Aggregated storage, 87 Analysis, 83, 87 Analysis of days overdue, 231, 232, 235 Append structure, 32 Attribute, 20 Award, 98 B Balance, 231 Before image, 90 BEx Analyzer, 37 BEx Broadcaster, 237 BEx Suite, 72 Billing, 102, 121, 134, 148 Order-related billing, 154 Billing block, 124, 139, 200, 202 Evaluation, 201 Billing condition, 213 Billing document, 85, 121, 124, 125, 128, 133, 134, 225, 230, 237 Delivery-related, 122, 154 External, 156 Internal, 156 Order-related, 130 Billing item, 86, 211, 215 Billing key figure, 154, 211, 212 Selection screen, 211 Billing quantity, 230 Business Content, 23, 27, 32, 76, 82, 85, 88, 89, 100 Business Explorer Analyzer, 15, 33 Business Explorer Broadcaster, 16 Business Explorer Maps, 15 Business Explorer Query Designer, 33 Business process, 139 Button bar, 236, 237 C Cash discount, 213, 230, 231 Characteristic, 20, 21, 45, 103, 119, 141 Free, 35 Restrictions, 35 Routine, 97 Chart view, 235 Company code, 95 Complexity, 83 Condition, 54 Condition data, 86 Condition type, 78, 87, 155, 159 PI01, 159 VPRS, 155 Consignment, 131, 232 Consignment business, 134 Consignment evaluation, 194 Consignment fill-up, 131, 132, 140, 193 Consignment issue, 131, 132, 133, 193 Consignment pick-up, 131, 193 Consignment process, 14 Consignment returns, 131, 134, 193 Consignment standard process, 193 Consignment stock, 133, 223, 239 Consignment store, 131, 132, 140 Consolidation, 21 Constant selection, 189, 197 Container item, 65 Contract, 136, 138, 194, 195, 196 Contribution margin, 215, 216, 225 Credit memo, 124 Credit memo item, 126 Quantity credit memos, 125 Value credit memo, 125 Credit memo and debit memo, 14 Processing, 123 Request, 104 Credit memo request, 123, 124 Credit memos on hand, 125 Cross company, 156 Business, 216 Cross-company 245

20 Index Process, 14, 144 Sales, 94, 156, 157, 217 Stock transfer, 89, 94, 219, 220, 157 Currency translation type, 214 Currency unit, 172 Customer discount, 213 Customer master, 106 D Data aggregation, 119 Database schema, 22 Data extraction, 77 Data fields that represent values and quantities, 20 Data flow, 25, 72, 81 Data model, 75, 76, 82, 93, 95, 100 Multi-dimensional, 22 DataProvider, 62 Data retrieval, 26 Dataset Development, 91 Data source, 27 DataSource, 31, 32, 76, 77, 81, 83, 85, 86 Customer-specific, 29 Generic, 29 Data staging, 77 DataStore, 21 Data structure, 20 Data transfer process, 25 Data warehouse, 16, 19, 163 Debit memos, 126 Default value, 35 Delay in delivery, 229 Deletion record, 91 Delivery, 78, 102, 121, 122, 133, 142, 152, 158 Free of charge, 134 Open, 144 Picked, 144 Delivery block, 139, 200 Delivery date, 137, 230 Delivery detail, 83 Delivery deviation, 239 Delivery document, 121, 122, 146 Delivery header data, 81 Delivery key figures, 144, 148, 204 Delivery performance, 207 Delivery processing, 144 Delivery quantity, 144 Open, 146, 149 Delivery quantity performance, 203 Delivery status, 206 Delivery time, 153 Delivery value, 144, 147, 149 Determine, 144 Open, 146, 149 Delta creation, 82 Delta direct, 32 Delta management, 79 Delta procedure, 32 Delta update, 77 Design, 236 Detailed data, 21 Determination Contribution margin, 215 Expected sales order value, 98 Incoming orders value, 91 Margin, 215 Number of quotations, 166 On-time delivery performance, 150, 207 Open quotations, 176 Rejected quotations, 175 Target value, 198 Weighted average score, 210 Dimension table, 22, 87 Document extraction, 28 Document flow, 110, 120, 122, 127, 131, 135 Document item, 96 Document status, 110, 114 DSO, 21, 22, 25, 27, 75, 77, 78, 81, 90, 93, 99 E Early indicators, 103, 118 End customer, 128 End routine, 78 ERP system, 12 ETL, 19, 23 Evaluation, 33 Exception,

21 Index Exception aggregation, 167, 171 Extraction, 96 Extractor, 26, 27, 30 Extract structure, 26 F Flexibility, 75 Follow-on document, 89, 98 Formula variable, 168, 169, 170, 171, 178 Form view, 235 Form view element, 234, 235 Search manually, 234 Function module, 29 G Goods issue date, 152 Goods issue posting, 121, 132, 133, 147 Goods receipt posting, 126 H Header status, 78 Hierarchy, 20 I Incoming orders, 89, 90, 93, 118, 120, 124, 130, 132, 134, 135, 138, 156, 157, 159, 184, 186 InfoCube, 21, 22, 25, 27, 75, 80, 87, 93 InfoObject, 20, 22 InfoPackage, 25 InfoProvider, 20, 22, 87 Information structures, 141, 144 InfoSet, 29 InfoSource, 24 Initialization, 32, 77 Internal billing document, 217, 219 Item, 152 Item category, 126, 128, 129, 134, 139, 187, 188 Item overview, 114 iview element, 234, 235 K Key figure routine, 84 Key figures, 20, 101, 104, 141 Basic key figures, 103 Billing key figures, 101 Condition key figures, 214 Delivery key figures, 101 Limit, 174, 187, 193 Quotation key figures, 101 Totals key figures, 192 Key figure system, 101 L Line-item dimension, 87 Loading performance, 75, 87 Local formula, 51 LO Data Extraction Customizing Cockpit, 28 M Mapping fixed values, 23 Margin, 155, 215, 216, 225 Master data, 239 Master data ID table, 87 Material management (MM), 128 Material requirements planning (MRP), 12 Menu area, 237 Model, 221, 233 Modeling, 19, 75, 234 Monitor, 33 MultiProvider, 75, 87, 88 N Navigation attribute, 88 Net value, 104, 105, 108, 198, 213, 215, 216, 218, 229,

22 Index New record, 90 Number of accepted items, 173 Number of billing documents, 211, 212 Number of deliveries, 144 Number of delivery items, 144 Number of documents, 102 Number of quotation items, 164, 165, 167 Number of quotations, 103, 164, 165, 166, 167, 229 Formula, 166 Number quotation documents, 102 Number range, 97 numbers), 20 O Offset, 185, 186 OLAP function, 17 OLTP system, 16 On-order stock, 129 On-time delivery performance, 84, 141, 150, 203, 207, 208, 210, 229, 230 Formula, 207 Scoring scheme, 151 Order, 95, 102, 200, 229, 239 Open, 141, 143, 144 Rejected, 183 Order condition, 77 Order item, 142, 150, 229 Order key figure, 118, 180 Order on stock, 134 Order probability, 105, 106, 107, 108, 168, 169, 170, 171, 178 Selection screen, 168 Order quantity, 180, 225 Expected, 168, 170, 171 Order reason, 104, 105, 108, 110, 113, 124 Order schedule line, 79, 82, 83 Orders on hand, 76, 89, 93, 118, 120, 124, 130, 132, 135, 138, 156, 157, 159, 184, 186 Determine, 118 Orders with delivery or billing blocks, 139 Order type, 129, 134, 139, 188 Order value, 105, 180, 181, 229 Dynamic calculation, 107 Expected, 107, 168, 170, 171 Outline agreement, 14, 134, 194 Overhead rate, 213 P Performance, 22, 75, 77 Period, 101 Portal role, 222, 236 Preceding document, 98 Pricing analysis, 155 Pricing procedure, 155 Process chain, 25 Product hierarchy, 182, 206 PSA table, 25 Purchase order, 128 Purchase order history, 130 Purchase order value Business volume, 130 Purchase requisition, 128 Purchasing document, 97 Q Quantities, 20 Quantity conversion type, 215 Quantity reliability, 151 Query, 29, 34, 79 Query definition, 35, 40, 50, 68, 83, 163, 179, 210 Target value, 198 Query Designer, 34 Query property, 47 Queued delta, 32 Quota, 184 Quota of open quotations, 102 Quotation, 102, 103, 104, 112, 117, 120, 122, 239 Accepted, 109, 173, 229 Expired, 117, 177 Key figure, 228 Open, 107, 173, 176, 207 Rejected, 173, 175, 229 Value, 229 Quotation item, 103, 111, 114, 115, 166,

23 Index Rejected, 173 Quotation key figure, 101, 102, 164, 165, 166 Quotation quantity, 102, 103, 104, 164 Accepted, 173, 175 Open, 108 Rejected, 175 Quotation value, 102, 103, 104, 107, 114, 164, 171, 173 Accepted, 173 Open, 102, 108 Quote, 176 R Rate, 229 Raw data, 119 Reason for rejection, 98, 115, 116, 174 Rebate, 213 Record mode, 90 Reduced workload, 83 Refresh a query, 53 Rejection quota, 176 Release order, 134, 136 Release order statistics, 136 Replication, 23, 32 Report, 87, 93 Report Designer, 71 Reporting, 19, 33 Reporting performance, 75, 87 Requested delivery date, 150, 151, 164, 207, 230 Returns credit memo, 127 Returns delivery, 126 Returns order, 104, 126 Returns process, 14 Returns processing, 126 Returns stock, 126 Revenue, 85, 184, 185, 191, 211, 218, 225, 231 Reverse image, 90 Routine, 24, 77 S Sales, 122, 123, 124, 125, 126, 130, 141, 144, 154, 159 Sales and distribution key figures, 101 Sales and distribution process, 101 Sales and distribution (SD), 14, 105 Sales area, 95 Sales cycle, 117, 229 Average per customer, 179 Sales document, 77, 89 Item, 78 Sales document type, 106 Sales Information System (SIS), 119, 141, 156, 157, 184 SIS update, 119 Sales order, 110, 111, 113, 117, 120, 121, 122, 133, 134, 142, 143, 146, 158, 218, 219 Item, 89 Quantity, 98 Value, 98 Sales quantity, 154, 211 Sales volume, 154 SAP Business Information Warehouse (SAP BW), 15 SAP ERP, 104 SAP NetWeaver Portal, 222 Schedule lines, 138 Scheduling agreement, 134, 137, 195, 198 Item, 137 Processing, 138 Selection screen, 37, 227 Quotation, 165 Signal element, 234 Source code, 79 Source document, 103, 126 Source system, 19, 75, 77 SQL statement, 87 Staging area, 77, 78 Standard order processing, 120 Standard process, 187, 193 Start routine, 90, 95, 96, 98 Statistics date, 91 Status information, 78 Stock, 223 Stock overview, 132 Stock transfer order, 156,

24 Index Stock transport order, 96, 219 Submission of quotation, 98 Subtotal, 213 Success analysis, 102 Success quota, 109, 112, 113, 115, 117, 176, 177, 184 T Tab design, 65 Table view, 29 Table view element, 234 Top 10, 235 Target/actual comparisons, 103 Third-party business processing, 14 Third-party item, 128, 129 Third-party order item, 130, 131 Third-party order processing, 127, 140 Third-party purchase order, 130 Time characteristic, 89 Toolbox BEx Analysis, 37 BEx Design, 37 Tool tip, 223 Top 10 list, 227 Totals row, 170 Transaction LBW0, 30 LBWE, 28 VA02, 113, 123, 133, 136 VA22, 105, 108, 111, 114, 115 VA32, 137, 138 VA42, 135, 136 VD02, 107 VOV8, 106 Transaction data, 27, 223 Transformation, 23, 76, 77, 78, 79, 93, 98 Transparency, 83 U Unit of measure, 172, 215 Update, 76, 81, 119, 143, 144 Update rules, 144 Usage level, 136 V Value chain, 13 Value contract, 135, 137 Vendor invoice, 129, 130, 162 Visual Composer, 72, 221, 222, 233, 235 BI kit, 72 Model-driven architecture, 72 W Web Analyzer, 70 Web Application Designer, 60, 221, 235, 237 Web item, 62, 237, 239 Web reporting, 59, 222 Web template, 60, 221, 222, 231, 235, 238, 239 Wizard, 227 Workbook, 58 X XHTML code,