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

Transcription

1 ibaan Decision Manager Business Templates User's Guide for BAAN IVc Inventory 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: U7598A 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 BAAN IVc Inventory 2.0 Business Template 2-1 Introduction 2-1 Inventory data warehouse content 2-2 Universe 2-2 Inventory OLAP Cubes 2-4 Inventory transaction analysis cube 2-4 Inventory on Hand Analysis cube Examples 3-1 Inventory transaction analysis 3-1 Inventory On-Hand Analysis 3-4 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 analyses. 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. If you install Decision Manager, the Business Content is predefined for a number of areas. This manual describes the BAAN IVc Inventory business template version 2.0. The business template is developed for Decision Manager 2.1. This manual describes what data you can use for performance analyses of the various aspects of Inventory, 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 Content for Inventory, provides a functional description of the data that is available for the analysis of your Inventory performance. Chapter 3, Examples, describes some example analyses that you can carry out based on the Business Content for Inventory. For a more technical description of the Business Content for Inventory, 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 into 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 for the analysis. 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. 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 customers, suppliers, warehouses, and time, that is related to the facts. For example Warehouse dimension to analyze which warehouses issue most, or none, of the goods. 1-1

8 Overview of ibaan Decision Manager 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 use either the Baan OLAP Client or Business Objects Reporting to analyze the data, you must process the data warehouse data must to prepare the data for analysis. You can process this data in two ways: Build 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 about trends, causes of events, exceptions, and other interesting facts. For instructions on how to use the OLAP Navigator, refer to the OLAP Client User Guide. Universes A Universe is an extra layer through which you can access the data in the data warehouse 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. The following figure (see Figure 1-1) shows how the various Decision Manager components are related to each other. OLAP Navigator Business Objects Reporting OLAP cube Universe Datawarehouse BAAN IV Inventory Figure 1-1 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 Datawarehouse Decision Manager Dimension Fact OLAP OLAP Navigator OLAP cube Universe Description A set of dimension and fact definitions, extraction schemes, cubes, and Universes that cover a particular business area. The total concept of data visualization and analysis. A set of applications for interactive reporting delivered by Business Intelligence vendor business objects. An integrated collection of data used in the support of management s decision needs. An end-to-end solution based on an open business intelligence framework containing best-of-breed components. Decision Manager also delivers ready-to-implement business content, designed to analyze key business processes in an enterprise. Point of view from which you can look at your data, for example, Time, Product, or Customer. Numeric information available in the data warehouse. Online Analytical Processing: a category of software technology that enables analysts and managers to gain insight into data through fact, 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. A Web-based, hyperrelational OLAP client that provides the business user overview and analytical support in addition to enterprise decision models. Using hyperdrill technology, the users can navigate intuitively between tactical and operational information needs. A set of related dimensions, which defines an n-dimensional manifold. The cube is the central metadata object recognized by OLE DB for OLAP. A semantic layer between the user and the data warehouse, consisting of classes and objects. The objects in the Universe map to data in the data warehouse, and enable the user to build queries on the Universe. A Universe represents a complete set of data and data operations. 1-4

11 2 BAAN IVc Inventory 2.0 Business Template 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 Inventory Transaction analysis and Inventory On Hand analysis. In Inventory Transaction analysis, you can analyze the following aspects related to the Inventory Transactions: Inflow and Outflow analysis for various transaction types in terms of quantity and value. A break up is made available for the Inflow and Outflow values in terms of their material and labour content along with the total value. The previously discussed analysis can be performed at the individual warehouse and at company level. In inventory on hand analysis, the following aspects, which would be required to gauge the performance of inventory movements, are made available. Average inventory for a particular item or at an aggregate level is available for a particular warehouse or at a company level for a particular period of time. This movement indicates the variability between demand and supply for a particular product over a period of time. Inventory turnover analysis is enabled at the company level or at individual warehouse level. Inventory movement analysis in terms of other measures, such as minimum/maximum/opening and closing levels of Inventory. For Inventory, the data that is extracted from BAAN IV is related to the inventory transaction history, on the Inventory Transaction by Item (tdinv700) table. The following section describes the content of the data warehouse, Business Universe, and the OLAP cubes. The section explains the use of the content from an end user perspective. 2-1

12 BAAN IVc Inventory 2.0 Business Template Inventory data warehouse content For Inventory transaction analysis, various facts and dimensions are available. The facts are the numbers of interest related to the aspects mentioned earlier. The dimensions define the aggregation levels on and angles from which you can analyze the Inventory transactions. 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. DW_INV_HISTORY This fact table contains the inventory level for a specific item/container/warehouse combination by date. The information in this fact table is extracted from the Inventory Transaction by Item (tdinv700) table using BAAN export sessions. DW_INV_SNAPSHOT This fact table contains the inventory level for a specific item/container/warehouse combination by date. This table is created from a view built on fact table DW_INV_HISTORY. For performance reasons, this table is created on the view. Universe A Universe is created to make the data-warehouse dimension and fact content available in the Business Objects reporting environment. The Universe made available for the BAAN IVc Inventory template can only be used for Inventory transaction analysis. A Universe is created for table INV_HISTORY. The information from view INV_SNAPSHOT is made available in the OLAP cubes. 2-2

13 BAAN IVc Inventory 2.0 Business Template The following table describes the measures that are available in the Universe for reporting: Measure Quantity_In Material_Value_In Operation_Value_In Total_Value_In Quantity_Out Material_Value_Out Operation_Value_Out Total_Value_Out Container conversion factor Description Quantity received in a warehouse or company as a result of a particular transaction type. The quantity is expressed in inventory unit. If a container is selected as one of the dimension and if the user desires to have the information in terms of container unit, then a customized Business Object report must be prepared using the container conversion factor measure provided in the Universe. Material value component associated with Quantity_In explained previously. Operation value component associated with Quantity_In explained earlier. Total value of Quantity_In based on the standard cost price of an item. Quantity dispatched from a warehouse or a company as a result of a particular transaction type. This quantity is expressed in inventory unit. If you select container as one of the dimensions and if the user wants to have the information in terms of container unit, then a customized Business Object report must be prepared using the Container conversion factor measure provided in the Universe. Material value component associated with Quantity_Out explained previously. Operation value component associated with Quantity_Out explained earlier. Total value of Quantity_Out based on the standard cost price of an Item. Conversion factor to convert quantity in Inventory unit to container unit. For example, one can = 10 lit. Conversion factor will be reflected as 10 in the Inventory transaction table. 2-3

14 BAAN IVc Inventory 2.0 Business Template The available dimensions that are of interest for inventory transaction analysis are: Item dimension. Item group dimension. Item type dimension. Item order system. Company dimension. Warehouse dimension. Time dimension. Transaction type dimension. Order dimension. Order type dimension. Inventory OLAP Cubes The cubes that are available for Inventory include: Inventory transaction analysis. On-hand inventory analysis. Each cube contains measures and dimensions. The measures and dimensions are described for the following two cubes: Inventory transaction analysis cube In Inventory transaction analysis, you can analyze following aspects related to the Inventory transactions: Inflow and outflow analysis for various transaction types in terms of quantity and value. A break up is made available for the inflow and outflow values in terms of their material and labor content along with the total value. The Inventory transaction analysis can be carried out at the Individual Warehouse and at Company Level. 2-4

15 BAAN IVc Inventory 2.0 Business Template The cube contains the following measures: Measure Quantity_In Material_Value_In Operation_Value_In Total_Value_In Quantity_Out Material_Value_Out Operation_Value_Out Total_Value_Out Description Quantity received in a warehouse or company as the result of a particular transaction type. The quantity is expressed in inventory unit if the slicing is at Item level and in terms of container unit if slicing is at container level. Material value component associated with Quantity_In explained previously. Operation value component associated with Quantity_In explained previously. Total value of Quantity_In based on the standard cost price of an item. Quantity dispatched from a warehouse or a company as a result of a particular transaction type. This quantity is expressed in Inventory unit if the slicing is at item level and in terms of container unit if the slicing is at container level. Material value component associated with Quantity_Out explained previously. Operation value component associated with Quantity_Out explained previously. Total value of Quantity_In based on the standard cost price of an item. NOTE The same measures apply to both single company and multicompany. The available dimensions for this cube are: Dimension Financial Companies Item Warehouse Transaction Type Item Type Time Available Level of details All Logical Company All - Item Group Item Container All Country-Warehouse Transaction Type All - Item Type All Year- Half Year - Quarter- Month-Date In the item dimension, if the slicing is carried out at container level, then Quantity_In and Quantity_Out are shown in the container unit or in the item inventory unit. 2-5

16 BAAN IVc Inventory 2.0 Business Template Inventory on Hand Analysis cube In Inventory on Hand analysis, the following aspects, which are required to gauge the performance of inventory movements, are made available. Average inventory for a particular item or at an aggregate level is available for a particular warehouse or at a company level for a particular period of time. This movement indicates the variability between demand and supply for a particular product over a period of time. Inventory turnover analysis is enabled at company level or at individual warehouse level. Further you can analyze the inventory movements in terms of other measures, such as minimum/maximum/opening and closing levels of inventory. The cube contains the following measures available for analysis: Measure Avg_OnHand_Qty Avg_OnHand_Value Min_OnHand_Qty Min_OnHand_Value Max_OnHand_Qty Description Average on-hand quantity for a period. This measure is not available for analysis at day level of the Time dimension. Average on-hand value for a period. This measure is not available for analysis at day level of the Time dimension. The value is at standard cost price. Minimum of on-hand quantity during a period. This measure is not available for analysis at day level of the Time dimension. While you calculate this measure for a period, the closing inventory level at day level is considered. Minimum value of on-hand quantity during a period. This measure is not available for analysis at day level of the time dimension. While calculating this measure for a period, the closing inventory level at day level is considered. Maximum of on-hand quantity during a period. This measure is not available for analysis at the day level of the time dimension. While calculating this measure for a period, the closing inventory level at day level is considered. 2-6

17 BAAN IVc Inventory 2.0 Business Template Max_OnHand_Value Opening_Balance_Qty Open_Balance_Value Closing_Balance_Qty Closing_Balance_Value Inventory_Turnover Maximum value of on-hand quantity during a period. This measure is not available for analysis at day level of the time dimension. While calculating this measure for a period, the closing inventory level at day level is considered. The on-hand quantity of last day of previous period The on-hand value of the last day of the previous period The on-hand quantity of the last day of the current period The on-hand value of the last day of the current period This value is defined as the rate at which the inventory moves through the warehouse or from the company. This value is defined as the ratio of annualized value of consumption of Inventory by the average value of inventory during a period. This value is calculated as: (Total_Value_Out * Periods_For_Year)/(Avg_OnHand_Value for that Period) The values are based on the standard cost price. To compute the annual consumption, the numerator of the ratio is a product of total value and periods for year, for example, if the total value out is for a month than Periods for year corresponds to 12. Total value out is the summation of values of all transactions that cause an effect of outflow of material at a warehouse level, as defined by transaction type. When the Inventory turnover ratio is calculated for a particular warehouse, then Total Value Out is used to compute the Inventory turnover ratio. OR (Total Value Out Co* Periods for year)/ (Avg_OnHand_Value for that Period) Total value out co is the total value out at company level. This value basically excludes all those outflow transactions, which are a result of material movement between warehouses. If the Inventory turnover ratio is calculated for a company, then Total Value Out Co. is used for computing the Inventory turnover ratio. 2-7

18 BAAN IVc Inventory 2.0 Business Template Sales_Turnover Inventory_Value_Change Total_Value_Sales_Out Days_For_Period Periods_For_Year This value is defined as the rate at which the goods are sold. This value is defined as the ratio of annualized value of goods sold by average value of inventory during a period. The values are based on the standard cost price (Total_Sales_Value_Out * Periods_For_Year) /(Avg_OnHand_Value for that period) Period For Year not clear here either. The change in the level of inventory of a particular period with respect to the on hand value of the previous period. Avg_OnHand_Value (Current period) Avg_OnHand_Value (Previous period) This value is the aggregation of all those transactions value for which the transaction type is sales issue. This value is also used to calculate the numerator of Sales_Turnover ratio. Number of days in time level. This value is used to calculate Avg_OnHand_Qty and Value For example, the number of days in the month of January 2000 is 31 days. Number of periods that make a year. For example: At month level, the value is reflected as 12, and at quarter level, the value is

19 BAAN IVc Inventory 2.0 Business Template The dimensions available with this cube are: Dimension Financial Companies Item Warehouse Item Type Time Available Levels of Detail All - Logical Company All - PhysComp - Item Group Item - Container All - PhysComp Country-Warehouse All - Item Type All Year- Half Year Quarter- Month NOTE The same measures apply to both single company and multicompany. In the item dimension, if the slicing is at container level, then the quantities are shown in container unit, unless the quantities are in Item Inventory units. In the warehouse dimension, if the slicing is at ALL Warehouse level, then the Total_Value_Out_Co is used for calculations instead of Total_Value_Out. Total_Value_Out does not consider value out due to replenishment. All the measures at day level in the time dimension are set to null. 2-9

20 BAAN IVc Inventory 2.0 Business Template 2-10

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 the business content for Inventory. Thanks to the flexibility of the OLAP environment and the reporting environment, you clarify several issues with the help of the business content. Because these environments work with the notion of dimensions extensively, you can easily produce information on the level you want. The Universe enables the user to create both single and multicompany reports. Standard, the report will contain data from multiple companies. With respect to amounts, make sure to use the DWC measures in a multicompany report. To create a single company report, select the Company number selected object in the query panel. When you run a query or refresh a report, this automatically prompts the user to select a company number from a list. The report will then only contain data for the selected company. The selected company number then appears in the report. Inventory transaction analysis Obviously, a business user is interested in the analysis of inflow and outflow movements of Inventory for various transaction types in terms of quantity and value. A break up is made available for the inflow and outflow values in terms of their material and labor content along with the total value. Depending on the transaction type, you can perform the following analyses: Purchase Receipt Analysis. You can reflect and compare the purchase receipts made for a particular item or item groups over a period of time. This analysis is possible if you slice on transaction type Purchase receipt, and select the Total_Value_In measure. To carry out this analysis at warehouse level, you can slice on a particular warehouse, or select all warehouses for which the values of Purchase receipts at company level are available. This analysis enables the user to focus on those items or item groups for which there was a high value of receipts during a period, for example, a list of top 10 items for the items receipt values. 3-1

22 Examples If you use the Business Object reporting tool, to drill down at order level, you can slice on Dimension Object Order. Alternatively, if you select the Total_Value_Out measure, you can analyze the movement of purchase returns. Production Receipt analysis. You can reflect and compare the production receipts made for a particular item or item groups over a period of time. To perform this analysis, you can slice on transaction type Production receipt, and select the Total_Value_In measure. You can also analyze the material and labour components of these receipts over a period of time. Production + Purchase receipt analysis. You can source a particular component from external vendors, or supply the component by the in-house manufacturing facility. As was discussed previously, the user can have separate reports for purchase and production receipts or a Business Object report for the aggregated value of receipts for an item or item group at warehouse or company level for a particular period. The following figure (see Figure 3-1) is an example of the production + purchase receipt analysis using Business Object reporting. Figure 3-1 Example of Production + Purchase Receipt Analysis 3-2

23 Examples Inventory adjustment order analysis. Applies to a user interested in details of Inventory adjustments that have taken place over a period of time. The user might want a list, for example, of the top 10 or 20 items for which inventory adjustment exist in a particular period. This analysis is available if you select the dimension, transaction type inventory adjustment, and measure as Total_Value_Out / Total_Value_In. In addition, if you select the dimension order in the Business Object report, the user can use order number and position number to retrieve the reason code associated with the Inventory adjustment order from the Baan database. Sales Issue Analysis. This analysis can provide the user with the sales issue for an item or a range of item over a period of time. To make this information available, you can select the transaction type sales issue. You can select the Material_Value_Out, Labour_Value_Out, andtotal_value_out measures. This information is available at company level or warehouse level. If you select the measures related to inflow, you can carry out a sales return analysis. Production issue analysis. This analysis provides the user with the production issue for an item or a range of item over a period of time. To make this information available, you can select the transaction type production issues. You can select the Material_Value_Out, Operation_Value_Out, and Total_Value_Out measures. This information is available at company level or warehouse level. Demand and supply variability analysis. You can compare the measure related to inflow and outflow of various transaction types. The following example shows how the OLAP cube is used to compare and analyze the measure for transaction type of purchase receipt and production issue Similarly, you can compare various transaction types such as sales issue and production receipt. 3-3

24 Examples Inventory On-Hand Analysis The important element of aggregate inventory control is measuring the inventory performance. The performance of an organization in terms of Inventory management can be measured by: Comparing the actual inventory with the budgeted values, which in term are arrived at keeping in mind the company s inventory policy. The budgeted values are currently not available in the data warehouse, therefore, you cannot compare the values with the actual values. Comparing the inventory levels for consecutive periods and parallel periods. Computing measures of inventory performance such as turnover rates which is defined as the ratio annual cost of consumption by average value of inventory during a period larger turnover usually means a better performance. The inventory turnover ratio is calculated for several categories of Inventories such as: Raw Material: Ratio of annualized value of consumption of components divided by the average value of inventory. This provides an idea of relative performance of inventory of components compared to that of the components consumption. Finished goods: Ratio of annualized cost of goods sold by the average inventory investment. This ratio provides an idea of relative performance of the inventory compared to that of sales. Time period coverage of current inventory investment. Days of supplies available. This can actually be computed from the same data used to compute the inventory turnover. Cost of goods sold for a year 12,000,000 and the average inventory investment is 4,000,000. The sales turn over ratio is 3.0. Days of supplies available can be then computed as 365/3 =122 days of supply. 3-4

25 Examples The Inventory template provides the following: Measures such as average inventory, maximum, minimum, opening and closing levels of inventory are made available. The user is in a position to analyze the movements of Inventory with respect to time using these measures. This analysis could be done at company level or at an individual warehouse level. Measures such as inventory turnover and sales turnover rate are made available by which relative performance of Inventory with respect to that of consumption / sales can be made available. Various options are available to the user to calculate the inventory turnover rate. The data used to calculate inventory performance could be handled differently depending on the business situation. For example, if an organization is interested in tracking the turnover rates each month. In that case, for analysis, the time dimension is required to be sliced at month level. The cost of goods that will be considered for calculation is the actual cost consumed/sold at the end of that month. The average inventory for that month is taken into account when computing the ratio. Options Time Dimension Slicing at following level Year Half Year Quarter Month Cost of goods Inventory 12 months actual for the year. Average for 12 months 6 months actual for the half year * 2. (annualized) Average for 6 months 3 months actual for the quarter * 4. (annualized) Average for past 3 months month actual * 12. (annualized) Average for month 3-5

26 Examples The template provides the user absolute measures of the inventory performance and also provides the measures of relative performance. Figure 3-2 Example of the calculation for sales turnover The previous figure illustrates the calculation for sales turnover for Item group = STEEL, for the months of Q3 of Sales_Turnover (month 07) = * 12 / = 7.02 The user can clearly see that a buildup of inventory occurs from month 7 through 9, however, a sudden dip in sales occurs for month 9, which is clearly reflected in the Sales_Turnover ratio. 3-6

27 Examples In the previous example, the management exercises inventory control by tracking the performance measure on a monthly basis. If the same calculation is carried out on a quarterly basis, then Sales_Turnover would be equal to Total_Value_Sales_Out_Hc (at quarter level) * 4 / Avg_OnHand_Value (at quarter level). Figure 3-3 Example of calculation of sales turnover The previous figure is an example to illustrate the calculation of Inventory_Turnover for a component belonging to an Item_Group, LABELS. As seen from above, the drop in the Inventory_Turnover ratio indicates a drop in the consumption of the component. Management of an organization must exercise caution when comparing the inventory performance data to other companies. The reason is that the other companies might not use the same methods to calculate their Inventory turnover rate. In addition, the operations, finances, management, and staff will differ. The other companies needs to supply the product to customers at a given service level might differ from your own. The turnover rate calculations are not the only measurements of success. These calculations are indicators of trend in inventory. 3-7

28 Examples 3-8