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

Transcription

1 ibaan Decision Manager Business Templates User's Guide for BAAN IVc Replenishment 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: U7599A 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 Replenishment 2.0 business template 2-1 Introduction 2-1 Replenishment data-warehouse content 2-2 Universe 2-2 Replenishment OLAP Cubes 2-5 Cube delivery performance analysis 2-6 Cube Replenishment turnover analysis 2-10 Cube Transit Inventory analysis Examples 3-1 Transit Inventory Analysis 3-1 Replenishment Order Turnover Analysis 3-5 Loss in Transit inventory analysis 3-5 Replenishment order delivery performance 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. You can also use ibaan Decision Manager to 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 analysis. If you install Decision Manager, the Business Content is predefined for a number of areas. This manual describes the BAAN IVc Replenishment business template version 2.0. The business template is developed for Decision Manager 2.1. The manual describes what data you can use for performance analysis of the various aspects of Inventory, and provides some examples of business management questions that a specific type of analysis can answer. 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, BAAN IVc Replenishment 2.0 Business Template, provides a functional description of the data that is available for the analysis of your Replenishment performance. Chapter 3, Examples, Describes some example analysis that you can carry out based on the Business Content for Replenishment. For a more technical description of the Business Content for Replenishment, 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. 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 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. 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, the data warehouse data must be processed to prepare the data for analysis. You can process this data in two ways: By building and processing OLAP cubes. By designing 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 Navigator 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. Figure 1 shows how the various Decision Manager components are related to each other. OLAP Navigator Business Objects Reporting OLAP cube Universe Datawarehouse BAAN IV Replenishment 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 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 to support management s decision requirements. An end-to-end solution based on an open business-intelligence framework that contains best-ofbreed 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 requirements. 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, which consists 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 Replenishment 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. For Replenishment, the data that is extracted from BAAN IV is related to the Replenishment 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 delivery performance, inventory turnover, and transit inventory analysis. In delivery performance analysis, you can analyze several aspects related to the Replenishment function. These aspects are: Analysis of dispatch and receipt completeness. Schedule adherence analysis (% and in days) of Replenishment orders from dispatch, as well as receipt point of views. Schedule adherence analysis (%) of dispatched quantities. Comparison of actual transit time with planned transit time. In replenishment turnover analysis, you can analyze the following aspects at company level, as well as between warehouses: Dispatched quantities/value. Received quantities/value. Quantity/Amount lost in transit. In transit inventory analysis, you can analyze the following aspects at company level, as well as between warehouses: Quantity in transit. Value of transit inventory. For Replenishment, the data that is extracted from BAAN IV is related to the Replenishment Order History (tdrpl150) 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 Replenishment 2.0 business template Replenishment data-warehouse content For Replenishment analysis, several facts and dimensions are available. The facts are the numbers of interest related to the aspects mentioned previously. The dimensions define the aggregation levels on which the Replenishment data can be analyzed, as well as the angles from which the data can be analyzed. 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_REPLENISHMENT_HISTORY The fact table REPLENISHMENT_HISTORY contains the information on delivery level that enables a Replenishment Turnover analysis. The information in the fact table is extracted from the Replenishment Order History (tdrpl150) table using a Baan export session. Database view DW_ RPL_ DELIVERY_ PERFORMANCE This view is built on DW_ REPLENISHMENT_HISTORY, and contains information at Order line level. This view enables Delivery performance analysis. Database view RPL_ TRANSIT_ INVENTORY This view is built on DW_ REPLINISHMENT_HISTORY, and contains information in the form of a transit inventory snapshot between warehouses. This view enables transit inventory analysis. 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 Replinishmnent template can only be used for Replenishemnt delivery performance analysis. The Universe is based on the database view DW_ RPL_ DELIVERY_ PERFORMANCE. The measures from the other tables are made available through the OLAP Cubes. 2-2

13 BAAN IVc Replenishment 2.0 business template The measures that are available in the universe for reporting are explained in the following table: Measures Planned order qty in inv unit Early dispatched qty in inv unit Late Dispatched qty in inv unit On-time Dispatched qty in inv unit Order execution time Average planned transit time Average actual transit time No. of days order line dispatched late No. of days order line dispatched early Total no. of deliveries Early dispatched order lines Description Planned inventory quantity. This measure corresponds to the delivery line with minimum value. Dispatched quantity for those delivery lines for which: Planned dispatch date > Actual dispatch date. Dispatched quantity for those delivery lines for which: Planned dispatch date < Actual dispatch date. Dispatched quantity for those delivery lines for which: Planned dispatch date = Actual dispatch date. Difference between the date of last receipt and order date for a Replenishment order line that is completely dispatched. An order line is said to be dispatched completely if: Dispatch quantity for all the delivery lines >= Planned order qty. Average of Planned transit time for all the deliveries for an order line, where Planned transit time for the individual delivery line is the difference between the planned receipt date and the planned dispatch date. Average of actual transit time for all the deliveries for an order line, where actual transit time for the individual delivery line is the difference between the actual receipt date and the actual dispatch date. No. of days by which the replenishment order line is dispatched late. No. of days by which the replenishment order line is dispatched early. No. of deliveries for the replenishment order line. No. of order lines that are completely dispatched and are dispatched early. An order line is considered dispatched early when the last dispatched date falls before the target delivery date. Target delivery date is the maximum of all the planned delivery dates for that particular order. 2-3

14 BAAN IVc Replenishment 2.0 business template On-time dispatched order lines Late dispatched order lines Complete dispatched order lines Single shipment order lines No. of days order line received early No. of days order line received late Complete received order lines Early received order lines Late received order lines No. of orders that are completely dispatched and are dispatched on time. An order line is considered dispatched on time when the last dispatched date is the same as the target Delivery date. Target delivery date is the maximum of all the planned delivery dates for that particular order. No. of order lines that are completely dispatched and are dispatched late. An order line is considered dispatched late when the last dispatched date falls after the target delivery date. Target delivery date is the maximum of all the planned delivery dates for that particular order line. An order line is considered dispatched completely if: Dispatch quantity for all the delivery lines >= Planned order qty No of replenishment order lines which have been completed in a single delivery. No. of days by which the replenishment order is received early. No. of days by which the replenishment order is received late. An order line is said to have been received completely if: Received quantity for all the delivery lines >= Planned order qty. No. of order lines that are completely received and are received early. An order line is considered received early when the last receipt date falls before the target receipt date. Target receipt date is the maximum of all the planned receipt dates for that particular order line. No. of order lines that are completely received and are received late. An order line is considered received late when the last receipt date falls after the target receipt date. Target receipt date is the maximum of all the planned receipt dates for that particular order line. 2-4

15 BAAN IVc Replenishment 2.0 business template On time received order lines No. of order lines that are completely received and are received on time. An order line is considered received on time when the last receipt date is the same as the target receipt date. Target receipt date is the maximum of all the planned receipt dates for that particular order line. The available dimensions that are of interest for inventory transaction analysis are: Item dimension. Item group dimension. Company dimension. Replenishing warehouse dimension. Destination warehouse dimension. Time dimension. Order dimension. Replenishment OLAP Cubes The following cubes are available for Replenishment: Delivery performance analysis. Replenishment turnover analysis. Transit inventory analysis. Each cube contains measures and dimensions. The following section describes the measures and dimensions for the Cubes. 2-5

16 BAAN IVc Replenishment 2.0 business template Cube delivery performance analysis The Delivery performance analysis cube contains measures to analyze the delivery performance related to Replenishment orders. The following measures are made available for analysis: Measures Dispatch_Completeness_% Receipt_Completeness_% Early_Dispatched_Order_% Ontime_Dispatched_Order_% Late_Dispatched_Order_% Description Percentage of completely dispatched orders (Completely dispatched orders / Total number of orders), in which completely dispatched orders are those orders for which the total dispatched quantity is greater or equal to the planned order quantity. Percentage of completely received orders (Completely received orders / Total number of orders) Completely received orders are orders for which the total received quantity is greater or equal to the planned order quantity. Percentage of orders that were completely dispatched before the target delivery date (Number of completely dispatched orders before target delivery date / Total number of completely dispatched orders) The target delivery date is the latest of the Planned dispatch dates for that order. Percentage of orders that were completely dispatched on target delivery date. (Completely dispatched orders on target delivery date / Total number of completely dispatched orders) The target delivery date is the latest of the planned dispatch dates for that order. Percentage of orders that were completely dispatched after target delivery date (Completely dispatched orders after target delivery date / Total number of completely dispatched orders) The target delivery date is the latest of the planned dispatch dates for that order. 2-6

17 BAAN IVc Replenishment 2.0 business template Avg_Days_Dispatched_Early Avg_Days_Dispatched_Late Early_Received_Order_% Ontime_Received_Order_% Late_Received_Order_% Avg_Days_Received_Early Avg_Days_Received_Late Average number of days by which orders are dispatched early. An early dispatched order is an order that is completely dispatched before the target delivery date. (Total of days dispatched early / Total number of early dispatched orders) Average number of days by which orders are dispatched late. A late dispatched order is an order that is completely dispatched after target delivery date. (Total of days dispatched late / Total number of late dispatched orders) Percentage of orders that were completely received before the target receipt date. (Completely received orders before target receipt date / Total number of completely received orders) The target receipt date is the latest of the planned receipt dates for that order. Percentage of orders that were completely received on the target receipt date. (Completely received orders on target receipt date / Total number of completely received orders) The target receipt date is the latest of the planned receipt dates for that order. Percentage of orders that were completely received after the target receipt date (Completely received orders after target receipt date / Total number of completely received orders) The target receipt date is the latest of the Planned receipt dates for that order. Average number of days by which orders are received early. An early received order is an order that is completely received before target receipt date. (Total of days received early / Total number of early received orders) Average number of days by which orders are received late. A late received order is an order that is completely received after target receipt date. (Total of days received late / Total number of late received orders) 2-7

18 BAAN IVc Replenishment 2.0 business template Early_Dispatched_Qty_% Late_Dispatched_Qty_% Ontime_Dispatched_Qty_% Avg_Dispatches_per_Order Transit_Time_Variance Avg_Order_Execution_Time Single_Shipment_Order_% Percentage of total quantity, for all deliveries, dispatched before planned dispatch date (Total Quantity dispatched before planned dispatch date / Total Planned Order Quantity) Percentage of total quantity, for all deliveries, dispatched after planned dispatch date (Total Quantity dispatched after planned dispatch date / Total Planned Order Quantity) Percentage of total quantity, for all deliveries, dispatched on planned dispatch date (Total Quantity dispatched before on dispatch date / Total Planned Order Quantity) Average number of dispatches per order. Difference between Avg. actual transit time and Avg. planned transit time, in which Avg. actual transit time is the average of transit time (difference between receipt date and dispatch date for that delivery) for all the deliveries, and Avg. planned transit time is the average of planned transit time (difference between planned receipt date and planned dispatch date for that delivery) for all the deliveries Average of Order execution time for all completely dispatched orders, in which Order execution time is the difference between the order date and the date of the final receipt for completely dispatched orders. Percentage of orders that were completely dispatched in the single shipment (delivery). 2-8

19 BAAN IVc Replenishment 2.0 business template Dimensions available for delivery performance analysis cube are: Dimension Container.Item Time_Tar_Del_Date (Target Delivery Date) Time_Tar_Rec_Date (Target Receipt Date) Repl_Warehouse (Replenishment Warehouse) Dest_Warehouse (Destination Warehouse) Available levels for aggregation All - Item Group Item - Container All - Year - Half Year - Quarter - Month - Day All - Year - Half Year - Quarter - Month - Day All - Warehouse All - Warehouse In addition to these dimensions, two other dimensions are used only in multicompany cubes. Replenishment Warehouse Finance Company. Destination Warehouse Finance Company. 2-9

20 BAAN IVc Replenishment 2.0 business template Cube Replenishment turnover analysis The cube contains measures to analyze the Replenishment turnover.the measures that are available in this cube are: Measures Dispatched_Qty Dispatch_Value Received_Qty Receipt_Value Qty_lost_in_transit Amnt_lost_in_transit Qty_Lost_in_Transit_% Amt_Lost_in_Transit_% Description Dispatched Quantity in Inventory unit if slicing is at item level, or in terms of container unit if slicing is at container level. Value of Dispatched Quantity Received Quantity in Inventory unit if slicing is at item level, or in terms of container unit if slicing is at container level. Value of received quantity Quantity lost in transit in inventory unit if slicing is at item level, or in terms of container unit if slicing is at container level. Value of quantity lost in transit Percentage of quantity lost in transit (Quantity lost in transit / Dispatched quantity) Percentage of amount lost in transit (Value of quantity lost in transit / Value of dispatched quantity) Dimensions available for Replenishment turnover analysis cube are: Dimension Container.Item Time_Dispatch_Date Time_Receipt_Date Repl_Warehouse (Replenishment Warehouse) Dest_Warehouse (Destination Warehouse) Available levels for aggregation All Item Group Item - Container All Year - Half Year - Quarter - Month - Day All Year - Half Year - Quarter - Month - Day All - Warehouse All - Warehouse In addition to these dimensions, two other dimensions are used only in multicompany cubes. Replenishment Warehouse Finance Company. Destination Warehouse Finance Company. 2-10

21 BAAN IVc Replenishment 2.0 business template Cube Transit Inventory analysis The cube contains measures to analyze the Replenishment Turnover. The measures that are available in this cube are: Measures Avg_Transit_Qty Avg_Transit_Inv_Value Description Average transit inventory in inventory unit when slicing is at item level and in container unit when slicing is at container level. Value of the Average transit Inventory Dimensions available for Transit Inventory analysis cube are: Dimension Container.Item Time_Trans_Date Repl_Warehouse (Replenishment Warehouse) Dest_Warehouse (Destination Warehouse) Available levels for aggregation All - Item Group Item - Container All - Year - Half Year - Quarter - Month - Day All Warehouse All Warehouse In addition to these dimensions, two other dimensions are used only in multicompany cubes. Replenishment Warehouse Finance Company. Destination Warehouse Finance Company. 2-11

22 BAAN IVc Replenishment 2.0 business template 2-12

23 3 Examples This chapter describes some analysis 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, several issues can be clarified with help of the Business Content. Because these environments work with the notion of dimensions extensively, you can easily produce the information on the level you want. The Universe enables the user to create both single-company 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. 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. Transit Inventory Analysis Transit Inventory is one of the sizable components of the total inventory carried by the organizations. Management often must analyze the movement of this component of inventory over time and between warehouses for various products and product groups. The following example is provided to clarify the average transit inventory calculation. 3-1

24 Examples Calculation of transit inventory Consider the distribution structure of the organization provided here: W1(Central WH) W2(Regional WH) W3(Branch WH) W4(Branch WH) The following table provides the transactions that were recorded on delivery dates mentioned for the month of April 2000, after the replenishment order deliveries are processed. Order Pos. Del Item Repl wh Dest wh Delivery date Receipt date Delivery Qty X W1 W X W1 W X W2 W X W2 W

25 Examples This is translated into daily transit inventory snapshot for the combination of Item, Replenishment Warehouse, and Destination Warehouse. Item Repl WH Dest WH Transaction Date Transit_Inv_Quantity X W1 W X W1 W X W1 W X W1 W X W1 W X W1 W X W1 W X W1 W X W1 W X W1 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W X W2 W

26 Examples The average transit inventory for item X for the entire month is calculated as follows (from W1 to W2 only): Average Transit Inventory = Σ (Transit_Inv_Quantity) / Number of days for that Month = {100 + (150*5) + (50*3) + 0} / 30 = 1000/30 = In the area of transit inventory analysis, the answer to the following question is provided here: In which areas of my enterprise do I perform best or worst, with regard to the management of transit inventory? The measures that are available for this analysis are Avg_Tansit_Qty and Avg_Transit_Inv_Value. The quantity-related measures are available in inventory unit at Item level, as well as in container at container level. To find out the area where the performance is best or least good, you can use the item dimension, the Replenishing warehouse dimension, and the Destination warehouse dimension. The performance of these measures in relation to time is made available with the time dimension. The user can, for example, see movements of inventory levels with respect to time between the following pairs of warehouses, W1-W2, W2-W3, W2-W4, or W2-W3 andw4, or at an aggregate level for a particular company. 3-4

27 Examples Replenishment Order Turnover Analysis The turnover measures as available for OLAP analysis provide the user with the following information: Volume (quantity and value) of dispatches that have taken place from a Replenishing warehouse to a particular or a cluster of destination warehouse(s). This information can be made available for a particular product or a product group. In addition, if you select a time dimension for analysis, this gives you an idea of the trends. The volumes of dispatches for a particular product (item) are available in the inventory unit of that item. You can also have the quantities expressed in container units if the slicing is performed at container level. Volume of receipts that took place at a Destination warehouse from a particular or a cluster of Replenishing warehouse(s).this information can be made available for a particular product or a product group. Furthermore, if you select time dimension for analysis, this gives the user an idea of the trends. The volumes of receipts for a particular product (item) are available in the inventory unit of that item. You can also have the quantities expressed in container units if the slicing is performed at container level. Loss in Transit inventory analysis Management often needs to track the value of loss of inventory in transit. The user can obtain the following information with this analysis: The overall Value of loss in Transit Inventory during a particular period of time. This gives the user an idea of the trend, whether the situation is improving, or whether it is the same after a corrective action is taken to prevent loss. Product/product groups wise break up of the loss in transit. Furthermore, if the user slices on container level, the user can also obtain information in terms of container units, and the loss in Transit inventory. If users use Replenishing and Destination warehouses as dimension, they get an idea as to which part of the distribution structure mainly contributes to the loss in transit inventory. 3-5

28 Examples Replenishment order delivery performance analysis EXAMPLE NOTE The performance of the distribution network can be checked by the analysis of the following areas: Schedule adherence analysis (% and in days) of Replenishment orders from dispatch, as well as receipt point of views. Schedule adherence analysis (%) of dispatched quantities. Transit time variance analysis. Order execution time analysis. The significance of performance indicators that are required to do analysis in the areas listed previously is easier to understand with the help of the following example. The following information is available for a particular distribution structure, for a particular period of time. No. of Replenishment Orders Placed 100 Dispatched Complete 80 Orders. In the data warehouse, the definition of Dispatched complete is: All the orders that are processed and for which Dispatched Qty >= order line qty. The break of the dispatched complete orders is as follows: On time 50 Orders Early 20 Orders. Late 10 Orders. Schedule adherence analysis (% and in days) of Replenishment orders from dispatch as well as receipt point of views. The first set of performance indicators that the user can be interested in is: On Time Dispatched Order %= 50/80 Early Dispatched order % = 20/80 Late Dispatched Order % = 10/80 The user can also be interested in having the average lateness or average earliness of these orders from dispatch perspective. 3-6

29 Examples Consider the 10 replenishment order lines that are late as follows: Order line O1 O2 O10 No of Days late The Average lateness for the dispatches an item or for a range of items can be computed. Avg. Days Dispatched Late =( )/10= 27/10. Average Days Dispatched late = 2.7 days. The performance indicator average days dispatched early can be similarly computed. Another measure that can be of interest from the perspective of schedule adherence is the percentage of replenishment orders that were completely dispatched in a single shipment. (Single Shipment Order %). Schedule adherence analysis (%) of dispatched quantities. The orders discussed previously are possibly dispatched in multiple delivery lines. More important to the user is what % of the order quantity is supplied on time. The following performance indicators can be useful. Early Dispatched Qty %. On-time Dispatched Qty%. Late Dispatched Qty %. These measures are explained from the perspective of dispatch performance analysis. A similar set of measures are made available to perform the receipt performance analysis. Avg. No. Of Dispatches Per Order Line. This indicator indicates that, on average, the Replenishment order is dispatched in, for example, (x) number of deliveries. You can perform this analysis at item level or aggregate level. Transit Time Variance. The user possibly also wants to know what the average variance is between the planned transit time and actual transit time between various warehouses. 3-7

30 Examples Order execution time analysis. This measure is useful to give an idea of how responsive the distribution network or a particular part of the distribution network is. You can analyze the lead-time figure for each item or item group with the item dimension in this perspective. Several measures are made available to measure the delivery performance for the entire distribution structure or a particular part of the structure. To find out which part of the distribution structure is best or worst, in terms of delivery performance, you can use the item dimension, the replenishing, or the destination warehouse dimension. In addition, if you use the time dimension, the delivery performance by year, month, or any other time level can be made available. You can perform this analysis with either the OLAP client or the Business Object reporting tool. 3-8