ibaan Decision Manager Business Templates User's Guide for BAAN IVc Purchase 2.0

Transcription

1 ibaan Decision Manager Business Templates User's Guide for BAAN IVc Purchase 2.0

2 A publication of: Baan Development B.V. P.O.Box AC Barneveld The Netherlands Printed in the Netherlands Baan Development B.V All rights reserved. The information in this document is subject to change without notice. No part of this document may be reproduced, stored or transmitted in any form or by any means, electronic or mechanical, for any purpose, without the express written permission of Baan Development B.V. Baan Development B.V. assumes no liability for any damages incurred, directly or indirectly, from any errors, omissions or discrepancies between the software and the information contained in this document. Document Information Code: U7597A US Group: User Documentation Edition: A Date: April, 2001

3 Table of contents 1 Overview of ibaan Decision Manager 1-1 Definitions, acronyms, and abbreviations Business template BAAN IVc Purchase Introduction 2-1 Purchase data warehouse content 2-2 Universe 2-3 Purchase OLAP cubes 2-5 Vendor Reliability Analysis cube 2-5 Purchase Order Process Analysis cube Examples 3-1 Vendor reliability analysis 3-1 Supplier Delivery Performance analysis 3-1 Supplier s Quality Performance analysis 3-4 Purchase Order Process analysis 3-6 i

4 Table of contents ii

5 About this document ibaan Decision Manager is a decision-support solution that you can use to extract and analyze key data from your business management system and obtain the information that you need to support your decisions and to take measures in time to avoid bottlenecks in your business processes. For example, you can use Decision Manager for various types of sales performance analyses, production performance analyses, and inventory data analysis. For each area that you want to analyze, the Business Content defines what data is extracted from the source system and how Decision Manager presents the data to you for further analyses. When you install Decision Manager, the Business Content is predefined for a number of areas. This manual describes the business template BAAN IVc Purchase 2.0. The business template is developed for Decision Manager 2.1. The manual describes what data you can use for performance analyses of the various aspects of Purchase, and provides some examples of business management questions that can be answered by a specific type of analysis. Chapter 1, Overview of ibaan Decision Manager, briefly describes the Decision Manager concepts and components and explains the function of the business contents in the Decision Manager solution. Chapter 2, Business template BAAN IVc Purchase 2.0, provides a functional description of the data that is available for the analysis of your Purchase performance. Chapter 3, Examples, provides some example analyses that you can carry out based on the business content for Purchase. For a more technical description of the business content for Purchase, refer to the online metadata. iii

6 About this document iv

7 1 Overview of ibaan Decision Manager ibaan Decision Manager is a decision-support solution that you can use to: Extract key data from your business management system. Store the data in a data warehouse and OLAP databases. Analyze the data. Present the analysis in the form of reports and graphs. For each area that you want to analyze, for example, sales, or production, the business content defines what data is extracted from the source system, how the data is stored in the data warehouse, and how the information is made available to the analyses. The business content consists of: Extraction, Transformation, and Loading (ETL) scripts. OLAP cubes. Universes. This chapter briefly explains the various Decision Manager concepts and components related to the business content. ETL scripts The ETL scripts extract the data from the source systems, transform the data into useful information for decision making, and load the information in a data warehouse. After loading, the data warehouse contains the required fact and dimension data for analysis. Facts Facts reflect the events in your business that you want to analyze. The facts consist of the dynamic data that Decision Manager extracts from your BAAN database, such as order quantities, delivery dates, invoiced amounts, and completed orders. 1-1

8 Overview of ibaan Decision Manager Dimensions Dimensions represent a point of view from which you can analyze the data. The dimensions consist of comparatively static data, or master data, such as business partners, warehouses, and time, that is related to the facts. For example, a Warehouse dimension to analyze which warehouses issue most, or none, of the goods. Data warehouse The fact data and dimension data is stored in fact tables, and dimension tables in a data warehouse. The ETL scripts update the data in the warehouse with the data from the live database. Before you can analyze the data with either the Baan OLAP Client or Business Objects reporting, the data warehouse data must be processed to prepare it for analysis. You can do this in either of the following ways: Building and process OLAP cubes. Design Universes. OLAP cubes The OLAP cubes contain a combination of fact data and dimension data as well as the results of computations, such as totals, average numbers, and percentages. To access the OLAP cubes, you can use the OLAP client as well as the Business Object reporting tool. ibaan OLAP Navigator You can use the ibaan OLAP Navigator to view the data in the OLAP cubes from various angles. You can apply formulas, filters, graphs, and so on, to the data in OLAP cubes to obtain information on trends, causes of events, exceptions, and other interesting facts. For instructions on how to use the OLAP Navigator, refer to OLAP Navigator User Guide. Universes A Universe is an extra layer through which you can access the data in the datawarehouse by using the Business Objects reporting environment. The Universe consists of objects that are mapped on the data in the data-warehouse. You can build queries on the data warehouse through the Universe and present the results in reports compiled by Business Objects reporting. 1-2

9 Overview of ibaan Decision Manager Business Objects Reporting You can use Business Objects Reporting to access and print reports of the information in a data warehouse and in the OLAP cubes. The available Business Objects Reporting user manuals describe how you can define queries to analyze the data and to build reports. Figure 1 shows how the various Decision Manager components are related to each other. OLAP Navigator Business Objects Reporting OLAP cube Universe Data warehouse BAAN IV Purchase Figure 1 High-level Decision Manager architecture overview. 1-3

10 Overview of ibaan Decision Manager Definitions, acronyms, and abbreviations Term Business template Business Intelligence Business Objects reporting Data warehouse Decision Manager Dimension Fact Measure OLAP Description A collection (or subset) of: export session, Baan ETL model, dimension and cube definitions, and a Business Objects Universe that cover a particular business area. For example, the Purchase template. Business templates are used to extract data from an operational source system (such as BaanERP) and store it in the data warehouse from where it can be analyzed by using OLAP and/or Business Objects technology. The total concept of data visualization and analysis. A set of applications for interactive reporting delivered by Business Intelligence vendor Business Objects. A solution that maintains integrated data and metadata in an open RDBMS environment. The data warehouse is an integrated, time-variant, nonvolatile collection of data in support of management s decision needs. The data warehouse is ready for analysis with business-intelligence tools. It contains atomic and summarized data. MS SQL Server 7 and 2000 are the RDBMS to use. An end-to-end solution based on an open Business Intelligence framework that contains best-of-breed components. Decsion Manager also delivers ready-toimplement Business Content, designed to analyze key business processes in an enterprise. A point of view from which you can look at your data, for example, sales figures by time, by product, or by customer. A cube and a Business Objects Universe contains a set of dimensions. The measures in the cube or in the universe, originating from a fact table, are analyzed against the dimensions. Synonym for measure; see also measure. A quantity that you can analyze. Each cube or Universe has at least one measure. For example, sales, costs, and gross margin. Synonym: Fact. Acronym for Online Analytical Processing; a software technology that enables analysts and managers to gain insight into data through fast, consistent, and interactive access, and a wide variety of possible views of information. OLAP functionality is characterized by dynamic multidimensional analysis of consolidated enterprise data that reflects the real dimensionality of the enterprise as understood by the user. Baan Decision Manager uses Microsoft OLAP technology. 1-4

11 Overview of ibaan Decision Manager OLAP Navigator A web-based, hyperrelational OLAP client that provides the business user overview and analytical support in addition to enterprise decision models. With hyperdrill technology, the users can navigate intuitively between tactical and operational information requirements. OLAP cube A set of related dimensions, which defines an n- dimensional manifold. The cube is the central metadata object recognized by OLE DB for OLAP. Universe A means for Business Objects applications to read data from the data warehouse through. A Universe is a metadata layer on top of a database. The Universe translates a technical data model into a functional data model that is easier to understand by end users. The end users create reports by selecting elements from the Universe. By applying the Universe concept, Business Objects removes the complexity of creating SQL statements including joins from the end users. Every Baan DM2.0 business template contains one or more own Business Objects Universes. 1-5

12 Overview of ibaan Decision Manager 1-6

13 2 Business template BAAN IVc Purchase 2.0 Introduction The template is built for Decision Manager 2.1. The data that is extracted from BAAN IVc is transformed and loaded into the data warehouse. The data is stored in data warehouse tables and OLAP cubes that serve as the basis for Purchase Order Process analysis and Vendor Reliability analysis. For Purchase, the data that is extracted from BAAN IV is related to the Purchase order history. The data is transformed and loaded into the data warehouse. The data is stored in data warehouse tables and OLAP cubes that serve as the basis for Purchase Order Process analysis and Supplier Reliability analysis. In Purchase Order Process analysis, several aspects related to the Purchase function can be analyzed. These aspects are: Purchase process: flow in terms of quantity and value of orders placed, order cancellations, and orders for which receipts are made. Purchase order portfolio: quantities and amounts related to Purchase orders that are not yet completed. Purchase Variance analysis : Difference between the Purchase Price and the Standard cost. Purchase Price movements: The variation of price for items supplied by different suppliers over a period of time can be analyzed. 2-1

14 Business template BAAN IVc Purchase 2.0 In Vendor Reliability analysis, the following aspects that are required to gauge the performance of the supplier can be analyzed. Supplier Quality performance: Suppliers can be analyzed for the quality in terms of volume of rejections. Supplier Delivery performance: Timeliness and completeness of receipts related to purchase orders. The following section describes the content of the data warehouse, a Business Object Universe, and the OLAP cubes. The section explains the use of the content from an end-user perspective. Purchase data warehouse content The data warehouse contains several dimension tables and the following tables and views that contain the facts required to carry the analysis in the area of Inventory. For Purchase Order Process analysis, several facts and dimensions are available. The facts are the numbers of interest related to the aspects previously mentioned. The dimensions define the aggregation levels on and angles from which the purchase performance data can be analyzed. The data warehouse contains several dimension tables and the following tables and views that contain the facts required to enable the analysis in the area of Purchase. DW_PUR_HISTORY (contains records from tdpur050 and tdpur051). This table contains the measures on order-line transaction level. On this table, one view (PUR_ORDERLINES) is created after a series of intermediate views. This view is converted into a table (DW_PUR_ORDERLINES_FINAL) to improve the performance. DW_PUR_ORDERLINES_FINAL: The view PUR_ORDERLINES aggregates all the records that correspond with one purchase order line and calculates the supplier reliability statistics for the order lines. 2-2

15 Business template BAAN IVc Purchase 2.0 Universe A Universe is created to make the data warehouse dimension and fact content available in the Business Objects reporting environment. A Universe is created in the PUR_Orderlines_Final table to make the data warehouse dimension and fact content available in the Business Objects reporting environment. The information in the other tables is available through the OLAP cubes. The measures that are available in the Universe for reporting are explained later. The Universe enables Vendor reliability analysis. Measure No. of receipts. No. of rejected order lines No. of rejected receipts Completely received order lines Received order lines No. of early order lines No. of on time order lines No. of late order lines Description Number of purchase receipts. Number of purchase order lines of which at least one of their receipts is rejected. Number of rejected receipts. Number of purchase order lines for which the Total qty Approved >= Order line qty. Number of purchase order lines for which at least one receipt exists. Number of orders that are completely received and are received early. An order is received early if the actual receipt date of the last receipt transaction for that order occurs earlier than the target receipt date (= maximum of the planned receipt dates of the transactions for the same order line). Number of orders that are completely received and are received on time. An order is received on time if the actual receipt date of the last receipt transaction for that order is equal to the target receipt date (= maximum of the planned receipt date of the transactions for the same order line). Number of orders that are completely received and are received early. An order is received early if the actual receipt date of the last receipt transaction for that order occurs earlier than the target receipt date (= maximum of the planned receipt date of the transactions for the same order line). 2-3

16 Business template BAAN IVc Purchase 2.0 No. of days order lines were received early No. of days order lines were received late Approved quantity in inventory unit RejectedQty in inventory unit OrderLineQty in inventory unit Received quantity in inventory unit Early quantity in inventory unit On time quantity in inventory unit Late quantity in inventory unit Number of days by which the orders are early. Number of days by which the orders are late. Approved quantity for the purchase receipts. Rejected quantity for the purchase receipts. Order line quantity of the purchase orders placed. For the return order, the order line quantity is considered as 0. Approved + rejected quantity. Approved quantity for those receipt lines for which: actual receipt date < target receipt date. Approved quantity for those receipt lines for which: actual receipt date = target receipt date. Approved quantity for those receipt lines for which: actual receipt date > target receipt date. The available dimensions that are of interest for Vendor Reliability analysis are: Item dimension. Item group dimension. Employees. Line of business. Company dimension. Warehouse dimension. Time dimension. Price lists. Statistic groups. Suppliers. Country. Financial supplier group. Order dimension. 2-4

17 Business template BAAN IVc Purchase 2.0 Purchase OLAP cubes The following cubes are available for Purchase: Purchase Order Process analysis. Vendor Reliability analysis. Each cube contains measures and dimensions. The measures and dimensions are described for these two cubes: Vendor Reliability Analysis cube This cube contains measures to analyze the reliability of the supplier. The measures that are available in this cube are: Measures ApprovedQuantity Rejected Quantity Order Line Quantity Received Quantity Early Quantity On Time Quantity Late Quantity Early Quantity Percentage On Time Quantity Percentage Late Quantity Percentage Received Order Line Approved % Order Receipt Approved % Approved Quantity % Avg. No. of Deliveries Per OrderLine Description Approved quantity for all the purchase receipts. Rejected quantity for all the purchase receipts. Order line quantity of all the purchase orders placed. For return order the order line quantity is considered as 0. Approved + rejected quantity. Approved quantity for those receipt lines for which: actual receipt date < target receipt date. Approved quantity for those receipt lines for which: actual receipt date = target receipt date. Approved quantity for those receipt lines for which: actual receipt date > target receipt date. Percentage of the received quantity that is received early. (Early Quantity / Received Quantity) Percentage of received quantity that is received on time. (OnTime Quantity / Received Quantity) Percentage of received quantity that is received late. (Late Quantity / Received Quantity) No. of purchase order lines for which at least one receipt exists. Percentage of order lines for which not a single receipt is rejected. Percentage of receipts that is not rejected. Percentage of received quantity that is approved. Average No. of receipts made per order line. 2-5

18 Business template BAAN IVc Purchase 2.0 Early Order Percentage On Time Order Percentage Late Order Percentage Avg. Lateness by Order Avg. Earlyness by Order Percentage of completed orders for which all the receipts are made before the target receipt date. Or: Percentage of orders for which lateness is negative. Lateness is defined as difference between last receipt date and target receipt date. Percentage of completed orders for which the last receipt date is equal to the target receipt date. Percentage of completed orders for which the last receipt is made after the target receipt date. Average No. of days by which the late orders are late. Average No. of days by which the early orders are early. The following dimensions are available for this cube: Dimension Item.ItemGroup Item.StatGrpPur OrderSystem ItemType Suppliers.FinSupplGrp Supplier.Country Warehouse TargetReceiptDate Pur Normal Return Order Available levels of detail All Item Group - Item All StatGrpPur - Item All ordersystem All ItemType All FinSupplGrp - Supplier All Country - Supplier All Warehouse All Year - Quarter - Month - Day All Normal Return Order Type 2-6

19 Business template BAAN IVc Purchase 2.0 Purchase Order Process Analysis cube You can use the purchase order process cube to analyze the purchase order process, which consists of order intake procedures, cancellation procedures, and order receipt procedures. The measures available to perform this analysis are described here: The cube contains the following measures: Measures Quantity_Order_Received Amount _Order_Received Discount_amount_For Received_Quantity Total_Cost_For_Goods_ Received Quantity_Order_Placed Amount_Order_Placed Quantity_Order_Deleted Amount_Order_Deleted Open Order Quantity Open Order amount Variance Net_Price Description Received quantity in inventory unit. Net value of the received quantity. Discount amount for the received quantity. Total cost of good received. Quantity in inventory unit for the purchase orders placed. Net value of the purchase orders placed. Deleted quantity in inventory unit. Net value of deleted qty. This is the quantity of open order till date (at the end of a specific period) in terms of inventory unit. This is the value of open orders till date (at the end of a specific period) in terms of inventory unit. Difference between Total_Cost_For_Goods_ Received and Amount_Order_Received. This is calculated by dividing Amount _Order_Received by Quantity_Order_Received. The available dimensions for purchase order process analysis are: Dimension Item.ItemGroup Item.StatGrpPur OrderSystem ItemType Suppliers.FinSupplGrp Supplier.Country TransactionDate Pur Normal Return Order Available levels of detail All - Item Group Item All - StatGrpPur Item All - ordersystem All ItemType All FinSupplGrp supplier All Country supplier All - Year Quarter - Month - Day All Normal Return Order Type 2-7

20 Business template BAAN IVc Purchase

21 3 Examples This chapter describes some analyses that you can carry out based on the Business Content described in the previous chapter. The following examples show the capabilities of Business Content Purchase. Due to the flexibility of the OLAP environment and the reporting environment, issues can be clarified with Business Content. As these environments extensively work with the notion of dimensions, you can easily produce the information on the level you want. The Universe enables the user to create both single and multi-company reports. Standard, the report contains data from multiple companies. With respect to amounts, make sure you use the DWC measures in a multi-company report. To create a single company report, select the Company number selected object in the query panel. If you run a query, or refresh a report, this automatically prompts the user to select a company number from a list. The report then only contains data for the selected company. The selected company number can then be displayed in the report. Vendor reliability analysis The reliability of a supplier can be judged by the performance of the supplier in the following areas: The suppliers conformance to the delivery schedule. The suppliers conformance to the quality of the delivered goods. Supplier Delivery Performance analysis The level of the supplier s adherence to the delivery schedule can be determined with this analysis. The user wants to find the number of purchase orders that were received on time, late, or early, and investigate the causes. You can view the information for a specific supplier (or for all suppliers) and for purchase orders with a Target Delivery Date that falls in a specific date range. The other performance indicators that are required can be understood with the following example. 3-1

22 Examples The following example shows information available for a particular supplier, for a particular period. No. of Orders Placed 100 Received Complete 80 Orders. (in the data warehouse, the definition of received complete is: all the orders for which Approved Qty >= order line qty) The break of the received complete orders is as follows: On time 50 Orders Early 20 Orders. Late 10 Orders. The first set of the performance indicator that the user is possibly interested in, is: On Time Order %= 50/80 Early order % = 20/80 Late Order % = 30/80 The user can also be interested in the average lateness, and earliness of these orders. Consider the 10 late order lines, as follows: Purchase Order line No of days late O1 O2 O The average lateness of the referred supplier for an item, or for a range of items can be computed. ( )/10= 27/10. Average lateness = 2.7 days. This reads: this supplier has supplied 10/80 orders late by 2.7 days. Similarly, the performance indicator Avg. Earliness can be computed. 3-2

23 Examples These orders can be received in multiple receipts. What is more important to the user is what % of the Order Qty is supplied on time. The following performance indicators can be useful. The users must slice the Pur_Normal_Return_Order dimension, for normal orders. Early order Qty %. On time Order Qty%. Late Order Qty %. The other performance indicator that the end user can use is: Avg. No. Of Deliveries Per Order Line = Number Of Receipts / Received Order Line Count This indicator indicates that on average, the supplier has completed the orders in: (x) No. of Deliveries. This analysis can be performed on item level or on aggregate level. This analysis is an example of an analysis that can be performed with the Vendor Reliability cube provided in the OLAP client by selecting the performance indicators explained previously, and by slicing on dimensions such as Supplier, Item, and Target Receipt date. The user can also use the Business Object reporting tool to create various reports based on the Universe that is built on the data warehouse of DM1.3. The Dimension Objects that you can select to create such a report, are: Item Group. Item. Supplier. Period. The report that indicates these performance indicators can be available for an item or item group for various suppliers. 3-3

24 Examples Supplier s Quality Performance analysis The user can be interested in analyzing the quality performance related to deliveries of a particular item from various suppliers over a particular period of time. This can be made available with the OLAP cube; alternately, a report can be built with the contents of the Business Objects Universe. The following performance indicators are available in the Vendor Reliability cube that assists the user in analyzing the vendor in terms of the quality of the deliveries. 1>Order Line Approval % = ((Received Order line Count - Rejected Line Count) /Received Order line Count ) * 100 2>Order Receipt Approval % = ((Number Of Receipts - Rejected Receipt Count) /Number Of Receipts) * 100 3> Approved Qty % = (Approved Qty/Received Qty) * 100 The following example explains the difference between the first two measures: A supplier(s1) delivered 100 order lines for a particular item. These orders are delivered in 400 receipts. 80 of these 100 order lines only have a single receipt with rejection. Thus the order line approval % is 20. The remaining 20 order lines each have 2 receipts with rejection. Therefore, the total number of receipts with rejection is: * 2=120. For example, ( )/300 60% of the receipts are approved versus 20% of the order lines that are aproved. 3-4

25 Examples The significance of these measures can be understood by considering the pros and cons of the measures, explained in the following table. These three measures provide a good view of the quality of supplies from the vendor. Measure Pros Cons Order Line Approval % Order Receipt Approval % Both Approved Qty % This help you determine the number of purchase order lines rejected. This measure takes into account all partial deliveries and all partially rejected deliveries. Therefore, this is more precise than QR 1. This is from EPM/EIS. As this measure is based purely on quantities and not on counting order lines or partial deliveries, it does not suffer from any of the count-related problems mentioned previously. Even if a small part of one partial delivery is rejected, the complete order line is counted as rejected. A supplier with many very small mistakes gets a very low score based on this measure. If quantities are being rejected after taking them into inventory, they are shipped back by a return order. If one return order is shipping back quantities from multiple receipts, the measures Rejected Line count and Rejected Receipt count can be misleading. As quantity values depend on the inventory unit of the item, items with a small unit (such as gram) have large quantity values, and items with a big unit (such as ton) have a small quantity value. If you aggregate QR 3 over multiple items, the items with a small unit overemphasize the result. 3-5

26 Examples Purchase Order Process analysis The user can perform the following analysis with this cube: Comparison of the quantities and values for orders for orders placed and received in a period of time. This sort of analysis enables the user to compare the value of business that is offered to various suppliers on an aggregate level, for the Various Item group, or at an item level over a period of time. Purchase Order Variance analysis: This enables the management of an organization to compare the value of goods received with the cost of goods during a period of time. This difference (variance) can be a positive or negative variance, depending on which the management takes corrective action. Open order analysis: This cube can provide the user with the quantity of open items and its value at the end of a particular period of time. This can be seen at an aggregated level for all the suppliers for item/item group/all items, or for an individual supplier. Movement of price: The user can see the movement of the net price for a particular item, supplied by various suppliers over a period of time. Note that the net price is calculated by dividing the net value of received items during a period by the quantity received during that period. For a particular date, if no receipts of a particular item are available, the net price is then reflected as NULL in the OLAP client. 3-6