U.S. patent application number 09/987905 was filed with the patent office on 2002-07-25 for data warehouse system.
Invention is credited to Adendorff, Michael, Armstrong, Michael.
Application Number | 20020099563 09/987905 |
Document ID | / |
Family ID | 27427682 |
Filed Date | 2002-07-25 |
United States Patent
Application |
20020099563 |
Kind Code |
A1 |
Adendorff, Michael ; et
al. |
July 25, 2002 |
Data warehouse system
Abstract
A data warehouse system for managing performance of
organizations is provided. The data warehouse system comprises a
data model for storing data representing dimensions and measures
applicable for multiple organizations, and a configuration unit for
setting the placeholders such that the data model represents the
particular organization. The data model has placeholders settable
such that the data model represents a particular organization.
Inventors: |
Adendorff, Michael; (Ottawa,
CA) ; Armstrong, Michael; (Nepean, CA) |
Correspondence
Address: |
Finnegan, Henderson, Farabow,
Garrett & Dunner, L.L.P.
1300 I Street, N.W.
Washington
DC
20005-3315
US
|
Family ID: |
27427682 |
Appl. No.: |
09/987905 |
Filed: |
November 16, 2001 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
60262361 |
Jan 19, 2001 |
|
|
|
Current U.S.
Class: |
705/7.11 ;
705/348 |
Current CPC
Class: |
G06Q 10/067 20130101;
G06Q 10/063 20130101; G06Q 10/06 20130101; G06Q 10/103 20130101;
G06F 16/254 20190101 |
Class at
Publication: |
705/1 |
International
Class: |
G06F 017/60 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 1, 2001 |
CA |
2,339,063 |
May 31, 2001 |
CA |
2,349,277 |
Claims
What is claimed is:
1. A data warehouse system for managing performance of
organizations, the data warehouse system comprising: a data model
for storing data representing dimensions and measures applicable
for multiple organizations, the data model having placeholders
settable such that the data model represents a particular
organization; and a configuration unit for setting the placeholders
such that the data model represents the particular
organization.
2. The data warehouse system claimed in claim 1, wherein the data
model implements a business model for representing the dimensions
and measures applicable to the multiple organizations, the business
model comprising: a set of dimensions representing business
reference aspects of the multiple organizations, a subset of the
set of dimensions representing the business reference aspects of
the particular organization; a set of measures representing
measurements of business activity aspects of the multiple
organizations, a subset of the set of the measures representing the
measurements of business activity aspects areas of the particular
organization; and relationships between the set of dimensions and
the measures, the relationships allowing for the measures to use
common dimensions for cross-functional analysis.
3. The data warehouse system claimed in claim 2, wherein the
measures are grouped into functional areas of analysis to answer
business questions applicable to the multiple organizations, a
subset of the business questions used to analyze the particular
organization.
4. The data warehouse system claimed in claim 2, wherein one or
more dimensions contain one or more placeholders settable to
reflect at least one of: a fiscal pattern of the particular
organization; a common currency used by the data model; one or more
categories defined by a user, the categories used to analyze
information in the data model; and one or more multipliers used by
the data model.
5. The data warehouse system claimed in claim 2, wherein one or
more measures contain one or more placeholders settable to reflect
at least one of: a fiscal pattern of the particular organization; a
common currency used by the data model; one or more categories
defined by a user, the categories used to analyze information in
the data model; and one or more multipliers used by the data
model.
6. The data warehouse system claimed in claim 1, wherein the
configuration unit comprises at least one of: a fiscal pattern
settor for setting one or more placeholders in the data model to
reflect a fiscal pattern of the particular organization; a currency
settor for setting one or more placeholders in the data model to
reflect a common currency used by the data model; a user category
settor for setting one or more placeholders in the data model to
reflect a category defined by a user, the category used to analyze
information in the data model; and a multiplier settor for
aggregating amounts loaded into the data model.
7. The data warehouse system claimed in claim 1, further comprising
one or more connectors for extracting data from one or more data
source systems and loading the data into the data model, the
connectors having parameters settable such that connectors extract
data from a particular data source system.
8. The data warehouse system claimed in claim 7, wherein the
connectors contain one or more placeholders settable to specify the
particular data source system.
9. The data warehouse system claimed in claim 7, wherein the
connectors contain one or more placeholders settable to reflect
environmental settings of the particular data source system.
10. The data warehouse system claimed in claim 7, wherein the
configuration unit further sets the parameters in the connectors
for configuring the connectors to the particular data source
system.
11. The data warehouse system claimed in claim 7, wherein the
configuration unit comprises a source details settor for setting
one or more placeholders in the connectors to specify the
particular data source system.
12. The data warehouse system claimed in claim 7, wherein the
configuration unit comprises an environmental settor for setting
configuration options relating to the particular data source
system.
13. The data warehouse system claimed in claim 7, wherein the
connectors comprise extraction transformation loading (ETL)
software code.
14. The data warehouse system claimed in claim 7, wherein the
connectors comprise: a configuration ETL code unit for extracting
values from a data source system to set the placeholders in the
data model and to set the parameters in the configuration unit; and
a parameterized ETL code unit for using the values to extract
information from the data source system, transform the data and
load the data into the data model.
15. The data warehouse system claimed in claim 1, wherein the data
source systems comprise enterprise resource planning (ERP)
systems.
16. The data warehouse system claimed in claim 1 further comprising
an operational framework for managing the data warehouse system,
the operational framework comprising a console for providing a user
configuration options for configuring the data warehouse system,
wherein the configuration unit is provided in the operational
framework.
17. The data warehouse system claimed in claim 1, further
comprising a content explorer for generating reports based on the
analysis performed by the data model.
18. A method for configuring a data warehouse system, the method
comprising steps of: obtaining a data warehouse system comprising:
a data model for storing data representing dimensions and measures
applicable for multiple organizations, the data model having
placeholders settable such that the data model represents a
particular organization; and a configuration unit for setting the
placeholders such that the data model represents the particular
organization; and using the configuration unit to set one or more
data model placeholders in the data model of the data warehouse
system.
19. An operational framework for managing a data warehouse system,
the operational framework comprising: a console for configuring a
data model in the data warehouse system to a particular
organization and for configuring an extraction transformation
loading tool to a particular data source system; and a
configuration unit, the configuration unit comprising placeholders
settable to specify the particular data source system.
20. The operational framework claimed in claim 19, further
comprising a console for providing administrator access to
configure the data warehouse system.
21. A connector for extracting source data from multiple data
source systems and transforming the data for loading into
placeholders in a data model, the connector comprising: a
configuration ETL code unit for extracting values from a data
source system to set the placeholders in the data model and the
operational framework; and a parameterized ETL code unit for using
the values to extract information from the data source system,
transform the data and load the data into the data model.
22. A dimensional framework for use as a foundation of a data
warehouse system, the dimensional framework comprising a set of
dimensions representing business reference aspects of multiple
organizations, a subset of the set of dimensions representing the
business reference aspects of a particular organization, the
dimensions having placeholders settable set such that the
dimensional framework represents the particular organization.
23. A method of providing a data warehouse for managing performance
of organizations, the method comprising steps of: providing
placeholders in a data model, the data model for storing data
representing dimensions and measures applicable for multiple
organizations, the placeholders settable such that the data model
represents a particular organization; and providing a configuration
unit for setting the placeholders such that the data model
represents the particular organization.
24. The method claimed in claim 23, wherein the step of providing
placeholders comprises the step of providing placeholders in
dimensions of the data model, the dimensions representing business
reference aspects of the multiple organizations.
25. The method claimed in claim 23, wherein the step of providing
placeholders comprises the step of providing placeholders in
measures of the data model, the measures representing measurements
of business activity aspects of the multiple organizations, a
subset of the set of the measures representing the measurements of
business activity aspects areas of the particular organization.
26. The method claimed in claim 23, wherein the step of providing
placeholders comprises steps of: providing placeholders in
dimensions of the data model, the dimensions representing business
reference aspects of the multiple organizations; and providing
placeholders in measures of the data model, the measures
representing measurements of business activity aspects of the
multiple organizations, a subset of the set of the measures
representing the measurements of business activity aspects areas of
the particular organization.
27. The method claimed in claim 27, further comprising the step of
providing relationships between the set of dimensions and the
measures, the relationships allowing for the measures to use common
dimensions for cross-functional analysis.
28. The method claimed in claim 23, further comprising the step of
grouping the provided measures into functional areas of analysis to
answer business questions applicable to the multiple organizations,
a subset of the business questions used to analyze the particular
organization.
29. The method claimed in claim 23, wherein the step of providing
placeholders comprises at least one step of: providing one or more
placeholders in the data model to reflect a fiscal pattern of the
particular organization; providing one or more placeholders in the
data model to reflect a common currency used by the data model;
providing one or more placeholders in the data model to reflect a
category defined by a user, the category used to analyze
information in the data model; and aggregating amounts loaded into
the data model.
30. The method claimed in claim 23, further comprising the step of
providing one or more settable parameters in one or more
connectors, the connectors for extracting data from one or more
data source systems and loading the data into the data model, the
parameters settable such that the connectors extract data from a
particular data source.
31. The method claimed in claim 30, wherein the step of providing
settable parameters comprises the step of providing settable
parameters in the connectors for configuring the connectors to the
particular data source.
32. The method claimed in claim 30, wherein the step of providing
settable parameters comprises the step of providing one or more
settable placeholders in the data model for configuring the
connectors to the particular data source system.
33. The method claimed in claim 30, wherein the step of providing
settable parameters comprises the step of providing one or more
settable options in the configuration unit to reflect environmental
settings of the particular data source system.
34. The method claimed in claim 30, wherein the step of providing
parameters in one or more connectors comprises the step of
providing extraction transformation loading (ETL) software
code.
35. The method claimed in claim 30, wherein the step of providing
parameters in one or more connectors comprises steps of: providing
ETL code for extracting values from a data source system to set the
placeholders in the data model and to set the parameters in the
configuration unit; and providing ETL code for using the values to
extract information from the data source system, transform the data
and load the data into the data model.
36. The method claimed in claim 23, wherein the data source systems
comprise enterprise resource planning (ERP) systems.
37. The method claimed in claim 23, further comprising the step of
providing one or more reports generated based on the analysis
performed by the data model.
38. A method of providing a dimensional framework for use as a
foundation of a data warehouse system, the method comprising steps
of: providing placeholders in a set of dimensions, the dimensions
representing business reference aspects of multiple organizations,
a subset of the set of dimensions representing a particular
organization; and providing a configuration unit for setting the
placeholders such that the dimensional framework represents the
particular organization.
39. A computer data signal embodied in a carrier wave and
representing sequences of instructions which, when executed by a
processor, cause the processor to perform a method for providing a
data warehouse system adaptable for multiple organizations, the
data warehouse system for managing performance of a particular
organization, the method comprising steps of: providing
placeholders in a data model, the data model for storing data
representing dimensions and measures applicable for multiple
organizations, the placeholders settable such that the data model
represents a particular organization; and providing a configuration
unit for setting the placeholders such that the data model
represents the particular organization.
40. Computer-readable media for storing instructions or statements
for use in the execution in a computer of a method for providing a
data warehouse system adaptable for multiple organizations, the
data warehouse system for managing performance of a particular
organization, the method comprising steps of: providing
placeholders in a data model, the data model for storing data
representing dimensions and measures applicable for multiple
organizations, the placeholders settable such that the data model
represents a particular organization; and providing a configuration
unit for setting the placeholders such that the data model
represents the particular organization.
41. A computer program product for use in the execution in a
computer of a data warehouse system adaptable for multiple
organizations, the data warehouse system for managing performance
of a particular organization, the data warehouse system comprising:
a data model for storing data representing dimensions and measures
applicable for multiple organizations, the data model having
placeholders settable such that the data model represents a
particular organization; and a configuration unit for setting the
placeholders such that the data model represents the particular
organization.
42. A computer data signal embodied in a carrier wave and
representing sequences of instructions which, when executed by a
processor, cause the processor to perform a method for providing a
dimensional framework for use as a foundation of a data warehouse
system adaptable for multiple organizations, the data warehouse
system for managing performance of a particular organization the
method comprising steps of: providing placeholders in a set of
dimensions, the dimensions representing business reference aspects
of multiple organizations, a subset of the set of dimensions
representing a particular organization; and providing a
configuration unit for setting the placeholders such that the
dimensional framework represents the particular organization.
43. Computer-readable media for storing instructions or statements
for use in the execution in a computer of a method for providing a
dimensional framework for use as a foundation of a data warehouse
system data warehouse system adaptable for multiple organizations,
the data warehouse system for managing performance of a particular
organization the method comprising steps of: providing placeholders
in a set of dimensions, the dimensions representing business
reference aspects of multiple organizations, a subset of the set of
dimensions representing a particular organization; and providing a
configuration unit for setting the placeholders such that the
dimensional framework represents the particular organization.
44. A computer program product for use in the execution in a
computer of a dimensional framework for use as a foundation of a
data warehouse system adaptable for multiple organizations, the
data warehouse system for managing performance of a particular
organization, the data warehouse system comprising a set of
dimensions representing business reference aspects of multiple
organizations, a subset of the set of dimensions representing the
business reference aspects of a particular organization, the
dimensions having placeholders settable set such that the
dimensional framework represents the particular organization.
Description
FIELD OF THE INVENTION
[0001] This invention relates generally to business intelligence
systems and in particular to an integrated data warehouse
system.
BACKGROUND OF THE INVENTION
[0002] Many large organizations me enterprise resource planning
(ERP) systems to consolidate day-to-day transaction data and
streamline business functions such as manufacturing. With their
predefined, standard reporting capabilities, however, these ERP
systems are not optimized to support the flexible, ad hoc business
analysis and reporting businesses need to make strategic decisions
and improve business performance. Furthermore, ERP systems are not
intended to serve as e-business analysis and reporting
infrastructures.
[0003] For example, generating a report from an ERP system that
shows product line sales by region by sales person for the past
five years would typically be quite time-consuming. With their
multitude of tables, fields, and column names, ERP systems are not
well suited to end-user navigation. Without easy information
access, and the means to quickly analyze and report on findings,
users can overlook important business correlations or veer
off-track completely. Ultimately, the quality and speed of
decision-making suffer.
[0004] In addition, if hundreds or thousands of users were to
submit queries directly, ERP system performance would be impacted,
jeopardizing important production system functions. This, along
with the risks associated with giving the extended e-business
enterprise direct access to ERP systems, necessitates placing ERP
data into an environment that is not only optimized for business
analysis and reporting, but also for secure broad access. Seeking
predictable performance and desiring to give users all the
information they need quickly, many companies opt to build either
data warehouses or data marts.
[0005] Companies which have strived to develop decision support
systems that would support rich analysis and reporting realized
that operational reporting systems (e.g., ERP systems) were limited
in scope and the depth of insight they delivered. While optimized
for consolidating day-to-day transaction data and streamlining key
business functions, these systems offer but a fraction of the
reporting and analysis capabilities users need to fully comprehend
what drives business performance.
[0006] Many companies turned to developing data warehouses to fill
the requirement for consolidating data from across the
organization, with a single consistent historical view, and
designed for optimized reporting and analysis. The ultimate
objective of these systems was to ensure that the data needed to
answer the relevant business questions was captured and in a form
that would support timely information for decision-making. While
the intent was sound, the challenges of bringing together business
and IT to define best practices from both a business and technical
standpoint presented challenges. As a result projects failed
resulting in decision makers being left without crucial
information.
[0007] Created by extracting data from operational or transactional
systems (like ERP sources) and e-commerce systems and installing it
in a more analysis- and reporting-friendly database, data
warehouses are repositories of data that support management
decision-making. However, data warehouses are expensive to build
and time consuming. (For example, they can take 18 to 24 months to
create). Consequently, with enterprise information requirements
evolving so fast today, data warehouses often fail to meet
requirements when they are finally completed. Moreover, they
require specialized skills and experience to build
successfully.
[0008] Furthermore, due to their sheer scope, data warehouses
seldom produce the finely tuned analysis and reporting that
e-business decision-making depends upon. Intended to be all things
to all people, these warehouses focus on breadth of content, rather
than the depth of vital information sweet spots users need.
[0009] Unlike data warehouses that combine and make all corporate
data available across an enterprise, data marts focus more
narrowly, serving specific business areas or departments. Data
marts also take less time and money to build and can therefore
generate quicker payback than data warehouses.
[0010] Sound in principle, data mart creation can stumble in
practice. While data marts can be built incrementally, they do not
provide a holistic view of the enterprise. Companies will build a
data mart for sales, another for inventory, another for finance,
and so on. Unless these marts are coordinated, they act as
stovepipes and prevent users from sharing information across the
enterprise. They also duplicate data and lead to lengthy updates
because each mart must be refreshed individually. If companies
update the marts at different times, even just a couple of hours
apart, some users will have more current information than others.
This lack of synchronization can lead to inconsistent analysis
across the enterprise and cause users to question the integrity of
the analysis and reporting solution.
[0011] For instance, users of one mart might define a "large"
customer as one that generates more than $50,000 in revenue a
month. Users of another might define a large customer as one that
orders more than 100 units a month, which may only represent
$10,000. In these cases, people can mistakenly think that they are
discussing common ground. Not only may different marts define
dimensions differently, they can calculate measures differently as
well. For example, one department might compute "profit" by
including bad debts and another may exclude them. These types of
inconsistencies not only create misunderstandings, they can delay
schedules and increase costs, jeopardizing customer satisfaction
and profits.
[0012] There is a need for affordable data warehouse technology,
which an enterprise can use to achieve and maintain a complete view
of its operational and financial effectiveness, customer
relationships, and supply-side activities.
SUMMARY OF THE INVENTION
[0013] The invention solves one or more of the above mentioned
problems. In one embodiment of the invention, a configurable,
integrated data warehouse system is provided. This integrated data
warehouse system is rich and complete enough to be used by many
organizations. The integrated data warehouse is also configurable
to a particular organization. The initial steps of creating a data
warehouse are manifested in this system. The configuration of the
integrated data warehouse takes substantially less time to do than
creating a data warehouse from scratch. Thus, time and expenses are
saved with this invention.
[0014] The integrated data warehouse in another embodiment of this
invention allows for an incremental building of the data warehouse.
The system begins with a dimensional framework which represents an
organization. Areas of analysis can then be added to the
dimensional framework such that each area of analysis may be
compatible for cross-functional analysis. Thus, the incrementally
created data warehouse allows for an integrated analysis of an
organization's information.
[0015] In accordance with an aspect of the invention, a data
warehouse system for managing performance of organizations is
provided. The data warehouse system comprises a data model for
storing data representing dimensions and measures applicable for
multiple organizations, and a configuration unit for setting the
placeholders such that the data model represents the particular
organization. The data model has placeholders settable such that
the data model represents a particular organization.
[0016] In accordance with another aspect of the invention, there is
provided a method for configuring a data warehouse system. The
method comprises steps of obtaining a data warehouse system
comprising a data model for storing data representing dimensions
and measures applicable for multiple organizations and a
configuration unit for setting the placeholders such that the data
model represents the particular organization, and using the
configuration unit to set one or more data model placeholders in
the data model of the data warehouse system. The data model has
placeholders settable such that the data model represents a
particular organization.
[0017] In accordance with another aspect of the invention, there is
provided an operational framework for managing a data warehouse
system. The operational framework comprises a console and a
configuration unit. The console is used for configuring a data
model in the data warehouse system to a particular organization and
for configuring an extraction transformation loading tool to a
particular data source system. The configuration unit includes
placeholders settable to specify the particular data source
system.
[0018] In accordance with another aspect of the invention, there is
provided a connector for extracting source data from multiple data
source systems and transforming the data for loading into
placeholders in a data model. The connector comprises a
configuration ETL code unit for extracting values from a data
source system to set the placeholders in the data model and the
operational framework, and a parameterized ETL code unit for using
the values to extract information from the data source system,
transform the data and load the data into the data model.
[0019] In accordance with another aspect of the invention, there is
provided a dimensional framework for use as a foundation of a data
warehouse system. The dimensional framework comprises a set of
dimensions representing business reference aspects of multiple
organizations. A subset of the set of dimensions represents the
business reference aspects of a particular organization. The
dimensions have placeholders settable set such that the dimensional
framework represents the particular organization.
[0020] In accordance with another aspect of the invention, there is
provided a method of providing a data warehouse for managing
performance of organizations. The method comprises steps of
providing placeholders in a data model and providing a
configuration unit for setting the placeholders such that the data
model represents the particular organization. The data model is
used for storing data representing dimensions and measures
applicable for multiple organizations. The placeholders are
settable such that the data model represents a particular
organization.
[0021] In accordance with another aspect of the invention, there is
provided a method of providing a dimensional framework for use as a
foundation of a data warehouse system. The method comprises steps
of providing placeholders in a set of dimensions and providing a
configuration unit for setting the placeholders such that the
dimensional framework represents the particular organization. The
dimensions represent business reference aspects of multiple
organizations. A subset of the set of dimensions represent the
business reference aspects of a particular organization.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Embodiments of the invention will now be described with
reference to the accompanying drawings, in which:
[0023] FIG. 1 is a diagram showing a component overview of a data
warehouse system;
[0024] FIG. 2 is a diagram showing the configurable aspects of a
data warehouse system;
[0025] FIG. 3 is a diagram showing a component view of the
configuration environment of a data warehouse system;
[0026] FIG. 4 is a diagram showing a configuration view of a data
warehouse system;
[0027] FIG. 5 is a screen shot of an example of a set of
configuration placeholders of a data warehouse system;
[0028] FIG. 6 is a flow diagram showing the steps to configure a
data warehouse system;
[0029] FIG. 7 is a component view of a configuration unit of a data
warehouse system;
[0030] FIG. 8 is a flow diagram showing steps to configure a data
warehouse system;,
[0031] FIG. 9 is a diagram showing the structure of an example of a
business model of a data warehouse system;
[0032] FIG. 10 is an abstract model of a business model of a data
warehouse system;
[0033] FIG. 11 is a diagram showing an example of a business model
of a data warehouse system;
[0034] FIG. 12 is a diagram showing another example of a business
model of a data warehouse system;
[0035] FIG. 13 is a diagram showing an example of supply-side
performance management of a data warehouse system;
[0036] FIG. 14 is a diagram showing an example of demand-side
performance management of a data warehouse system;
[0037] FIG. 15 is a diagram showing an example of financial
performance management of a data warehouse system;
[0038] FIG. 16 is a diagram showing an example of a data model of a
data warehouse system;
[0039] FIGS. 17A to 17AE are diagrams showing examples of star
schemas of areas of analysis of a data model of a data warehouse
system;
[0040] FIG. 18 is a screen shot of a data warehouse system
console;
[0041] FIG. 19 is another screen shot of a data warehouse system
console.
[0042] FIG. 20 is a diagram showing a screen-shot of financial
analysis in a data warehouse application;
[0043] FIG. 21 is a diagram showing a screen-shot of sales analysis
in a data warehouse application;
[0044] FIG. 22 is a diagram showing a screen-shot of inventory
analysis in a data warehouse application;
[0045] FIG. 23 is a screen shot illustrating a step of generating a
report in a data warehouse system;
[0046] FIG. 24 is a screen shot illustrating another step of
generating a report in a data warehouse system;
[0047] FIG. 25 is a screen shot illustrating another step of
generating a report in a data warehouse system.
[0048] FIG. 26 is s flow chat showing the creation of a business
model of a data warehouse system; and
[0049] FIGS. 27A to 27E are flow charts showing the creation of a
data warehouse system.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0050] In this description, the term business will be used to
denote both commercial affairs and organizational affairs. The term
data warehouse system will be used to denote a system implemented
for the measurement and management of the performance of an
organization. The organization may be commercial or non-commercial.
A data warehouse system will include a data warehouse that is rich
and complete enough to be applicable to many organizations and
configurable to a specific organization. Finally, the term data
warehouse system also relates To a business performance management
system, including a business model and a query engine tool. The
term business model in a data warehouse system relate to a business
performance management model in a business performance management
system. The term business performance management refers to the
measurement and management of the performance of an organization
Referring to FIG. 1, a configurable data warehouse is described.
FIG. 1 shows a data warehouse environment including an enterprise
resource planner (ERP) data source 10, a user 20, an administrator
21 and a configurable data warehouse system 100. The user 20 refers
To the role of accessing the data warehouse system.
[0051] The administrator 21 refers to the role of administering the
data warehouse system. These roles may be performed by the same
person.
[0052] The configurable data warehouse system 100 includes a
business model 110, a data model 120, an operational framework 130,
connectors 140 and a content explorer 150. The business model 110
includes measures 111 and dimensions 112. The data model 120
includes fact tables 121 and dimension tables 122. The operational
framework 130 includes a console 133.
[0053] The configurable data warehouse system 100 is a system for
measuring the performance of an organization. The data warehouse
system 100 may be applicable to various organizations and is not
limited to only one organization. The data warehouse system 100 is
configurable to a specific organization. Preferably, the
configuration occurs after the installation of the system software
and before the operation of the system software. Re-configuration
may occur at any time thereafter.
[0054] The business model 110 includes the set of analytics and
paths used to measure the performance of an organization. The
business model 110 contains measures 111 which map the business
questions to which users 20 of a data warehouse may want answers.
The measures 111 represent measurements of business activity
aspects of an organization. For example, a business activity may be
a sales order. A measure 111 for a sales order may be sales order
volume. Another example of a measure 111 is inventory amounts. In
this example, inventory is the business activity measured.
[0055] Numerous business questions for numerous businesses are
categorized into different areas of analysis. The set of measures
111 in the business model 110 represents a union of measures used
to perform analysis for different organizations. Preferably, this
union of measures comprises the minimum set of measures 111 needed
to perform the desired analysis for all of the different
organizations to which the business model 110 applies. I.e.,
although not all organizations may requires each measure 111
available in the business model 110, the measures 111 they do
require will be available. The business model 110 also includes a
set of dimensions 112 which represent the structure of an
organization from an informational or dimensional viewpoint. I.e.,
the dimensions represent the business reference aspects of an
organization. An example of a dimension is the class of customers
of an organization. Further examples of dimensions and measures are
provided below.
[0056] The business model 110 is implemented in the data model 120.
The data model 120 is organized to facilitate the analysis
performed at the business model 110 level. The data model 120
contains fact tables which contain the measures used to measure the
performance of an organization. The data model 120 also includes a
set of dimension tables 122 which represents the structure of an
organization from a dimensional viewpoint. Another example of a
dimension is the class of employees of an organization.
[0057] Raw data information is collected from the organization ERP
10 and passed into the data model 120 through the connectors 140.
One way to build the connectors 140 is through an extraction,
transformation and loading (ETL) tool. The data warehouse system
100 is operated by an administrator 21 through the console 133 of
the operational framework 130. The operational framework 130 is
also used to configure the data warehouse system 100. Finally, the
content explorer 150 contains a set of reports used by the user 20
to review the analysis performed by the data warehouse system
100.
[0058] As has been stated above, the data warehouse system 100 is
designed to work for many different types of organizations and is
configurable to a specific organization. Preferably, the
configuration of the data warehouse system 100 occurs after the
installation of the system software and before the operation of the
system software. Configurability of the data warehouse system 100
is achieved by providing placeholders or parameters in various
components of the system such that these placeholders or parameters
are set during the configuration of the system software.
[0059] Referring to FIG. 2, the configuration 160 of the data
warehouse system 100 is described. There are configurable aspects
to the data model 120, the operational framework 130, and the
connectors 140. These configurable aspects are labeled on FIG. 2 as
125, 135 and 145, respectively.
[0060] In FIG. 3, the configuration 160 is enlarged. The
configuration 160 occurs when the operational framework
configurable aspects 135 interacts with placeholders located in the
data model configurable aspects 125 and the connectors configurable
aspects 145. These placeholders are set with data from an
organization ERP 10, preferably during the data warehouse system
100 configuration.
[0061] The connectors 140 contain ETL code, each connector having a
set of codes which perform a certain function. The ETL code
functions involve the extraction of data from the ERP 10 and the
loading of the data into the data model 120. The connectors may be
configurable to allow the data warehouse system 100 to operate with
different operational system, resource system, etc. The console
133, may provide the administrator 21 with a set of questions or
queries. The answer to these queries will define the configuration
options in the ETL 145. The console 133 may also prompt an
administrator 21 to specify values for the placeholders in the data
model 120.
[0062] Referring to FIG. 4, a configuration view 160 of an
integrated data warehouse system 100 is shown in more detail. FIG.
4 shows the configurable aspects of the data model 125, the
configurable aspect of the operational framework referred to as the
configuration unit 135, and the configurable aspects of the
connectors 145. The configurable aspects of the data model 125
includes data model placeholders 126. These placeholders 126
represent information that is completed in the data model 120
during configuration. The configuration unit 135 includes the
configurable portion of the console 133 and operational framework
placeholders 136. These placeholders 136 are stored in a set of
operational tables in the operational framework 130. The
configurable aspects of the connectors 145 includes configuration
ETL code 146 and parameterized ETL code 147. The configuration ETL
146 code may be used to extract values from the ERP 10 to set the
placeholders. The parameterized ETL 147 code may then use the
values of these placeholders 136 to extract information for the
data warehouse system that reflects the configuration for the
specific organization.
[0063] The configuration process involves setting the placeholders
126 and 136. There are two main methods to set the placeholders.
One method involves providing the administrator 21 with options
during the configuration. The administrator 21 may specify values
to options listed in the console 133 that represent the
characteristics of the organization that will have its performance
measured by the data warehouse system 100. FIG. 5 shows a screen
shot of an example of a set of a set of configuration placeholders
of a data warehouse system 100. A second method involves obtaining
the information used to set the placeholders directly from the
organization ERP 10. To achieve this, the configuration unit 135
creates a job that extracts the desired information from the
organization ERP 10 and loads it into the appropriate placeholder
126 or 136. Once the place holders are set, the data warehouse
system 100 may operate.
[0064] Referring to FIG. 6, a flowchart for configuring a
configurable data warehouse system 100 is shown. The first step
(501) involves installing the data warehouse system 100 software.
Once the system software is installed, the administrator 21 may
configure the system based on the ERP 10 environment (502). Once
the administrative selections are made, the connectors 140 may
access the ERP 10 and extract the desired information from the ERP
10. This information is loaded into placeholders in the data
warehouse system 100 (503). Once the information is loaded, the
data warehouse system 100 is ready to be used (504).
[0065] The configuration process 500 may be iterative. The
administrator 21 may initially choose to do steps 501 through 504
in sequence. However, at each step in the process new values for
the placeholders may be set. Thus, re-configuration of the data
warehouse system may be performed by the administrator 21.
[0066] Referring to FIG. 7, a component view of an embodiment of a
configuration unit 135 is shown. FIG. 7 represents an example of
the information which may be set with the configuration unit 135.
The configuration unit 135 includes a fiscal pattern settor 303, a
currency settor 304, a user defined category selector 305, a
multiplier settor 306, a source details selector 307, and an
environmental settor 308.
[0067] The configuration unit 135 may provide means to configure
the fiscal patterns to use in the data warehouse system 100. The
fiscal pattern settor 303 may be used to set one or more fiscal
patterns that reflect one or more fiscal calendars in use for an
organization. Fiscal patterns reflect the fiscal reporting
requirements of the organization. A fiscal pattern includes the
number of periods, the first period in the fiscal year and the
start date of each period. They are determined for an organization
by accounting practices.
[0068] A placeholder 136 in the configuration unit 135 may be set
to represent the identifiers of the fiscal patterns in the ERP data
source 10. The administrator 21 may set these placeholders. A
configuration ETL 146 job uses the information stored in the
placeholders 136 to extract this information from the ERP data
source system 10 and load the information into the fiscal variant
placeholders 126 in the date dimension of data model 125.
[0069] Another aspect of the configuration unit is the currency
settor. Many organizations have transactions in many currencies.
For analysis purposes, it is desirable for all amounts to be in the
same currency. To support cross-functional analysis, the currency
settor 304 may be used to set a currency to use for amounts subject
to analysis. This configuration allows a user to specify a currency
to be used for analysis. Amounts not in this currency may be
converted into this currency. Thus, one aspect of the business
model 110 is the notion of a common currency. This is represented
in the data model 120 by amounts that have been converted to the
proper currency. Within the operational framework 130 and the
configuration unit 135, common currency is represented by a
currency to which fiscal amounts are converted in order to analyse
the information in the data warehouse system 100. Common currency
is also represented by a financial currency conversion table that
determines the rate used to convert a transaction in one currency
to the common currency.
[0070] The configuration unit 135 may provide a means to set the
currencies to use in the data warehouse, the currency to use for
reporting, and the conversion rates to use to convert to the
reporting currency. A placeholder 136 in the configuration unit 135
is set to represent the currency to use for reporting. Additional
placeholders 136 may be specified to represent the currencies to
expect in the ERP data source 10. The administrator 21 may set
these placeholders.
[0071] A configuration ETL code 146 job may use the information
stored in the placeholder 136 to extract the required currency
conversion rates for the currencies, and to load this information
into the placeholders of the currency conversion table 126, which
is part of the data model 125. The connectors 140 load information
into the data warehouse by means of the parameterized ETL code 147.
The parameterized ETL code 147 may use information of the reporting
currency in the configuration unit placeholders 136 and the
conversion rates in the configured data model 126 to convert the
fiscal amount to the appropriate currency.
[0072] Another aspect of the configuration unit 135 is the user
category settor 305. Many organizations attach organization
specific classifiers to dimensions 112. For example, for one
organization, the color of hair of a customer may be important. The
configuration of user 20 specific categories allows such
organization specific classifiers to be part of the dimensional
framework. Such organization specific classifiers may be considered
as placeholders in the ERP 10. When analyzing a dimension 112, it
is desirable to use the same types of aspects across all of the
different analysis that will be performed.
[0073] The user category settor 305 may be used to select one or
more user defined categories in each dimension for analysis of an
organization. The configuration unit 135 may provide a means to set
the user categories that are to be used to analyse information in
the data warehouse. Placeholders 136 in the configuration unit 135
are set to reflect the user categories from the ERP 10 that are of
interest to the organization for the purposes of business
performance management and the placeholder 126 in the data model
125 where the user category is to be placed. The parameterized ETL
code 147 of the connector 140 uses the placeholders 136 to
determine which user category to select from the source ERP and
where to store the information in the data model 125 using the
placeholders 126.
[0074] Another aspect of the configuration unit is the multiplier
settor. The multiplier settor 306 may be used to set multipliers
for use during transaction aggregation and rollups. Organizations
may attach meaning to specific quantities. For instance, a
transaction may be a credit or a debit. The configuration of
multipliers allows an organization to attach different meanings to
values in business transactions which are organization
specific.
[0075] The configuration unit may provide a means to set
multipliers that are used to aggregate and attach meaning to the
amounts which are loaded into the data warehouse system 100. One
aspect of the configurable data model 125 is the placeholders 126
which are used to specify the multipliers to use for business
performance management by the organization. The values attached to
these placeholders are unique to an organization. The configuration
unit 135 may use a configuration ETL code 146 job to set the values
of the placeholders 126 representing multipliers in the data model,
based on default information in the ERP data source 10. The
administrator 21 through the console 133 component of the
configuration unit 135 may then review and change the values (i.e.,
override the default values) of these placeholders in the data
model 126 to reflect the organization's needs for business
performance management.
[0076] Another aspect of the configuration unit 135 is the source
details settor 307. In an organization each ERP data source 10 is
configured to meet the operational needs of the organization. There
may be more than one location in the ERP 10 to store information.
For example the ERP 10 may represent the relationships between
business entities such as customers in a separate relationships
table or by direct reference from one customer to another customer.
Similarly the ERP system 10 may store all information related to
several business entities in a single source table, and user
defined (configurable) codes are used to specify which objects
represent which types of business entities. For example all
business entities associated with an address may be stored in a
single table with codes representing which addresses represent
customers and which represent vendors. As another example all sales
activity may be stored in a single table, but different types of
sales activities (order, direct shipments, etc.) may be identified
through specific codes.
[0077] When the connectors 140 load information from the ERP data
source 10 into the data warehouse system 100 they should know what
information to extract for the purposes of business performance
management. The configuration unit 135 may provide a means to
specify this information. Placeholders 136 in the configuration
unit 135 are used to specify the ERP 10 specific values that are to
be used to extract the appropriate information from the ERP 10. The
administrator 21, through the console 133 may set the values of
these placeholders 136 to represent the ERP data source 10 for the
specific organization. When the data warehouse system 100 is loaded
from the ERP data source 10, the parameterized ETL code 147 of the
connectors 140 uses the placeholders 136 in the configuration unit
135 to extract the appropriate information.
[0078] There are other configuration 160 options that have not been
mentioned. These configuration options involve ERP 10 specific
issues such as: What is the date format? These options may also
include physical implementation details such as the name of the
library where these tables exist, etc. This class of configuration
options is referred to as the environmental configuration options.
The environmental configuration settor 308 allows for the
configuration of such options. The environmental configuration
settor 308 may also be used to handle the hardware configuration,
the operating system configuration, and the database configuration,
i.e., how dates are stored for obtaining the date.
[0079] The configuration unit may provide a means to set various
environmental placeholders. Placeholders 136 in the configuration
unit 135 are used to represent the values of the environmental
setting. The administrator 21 sets the placeholders 136 in the
configuration unit 135 using the console 133. The parameterized ETL
code 147 may then use this information to reflect the environment
in which the data warehouse system 100 is operating.
[0080] Referring to FIG. 8, a flowchart for configuring a data
warehouse system 100 is shown. Once the data warehouse system 100
software is ready to be configured (701), one or more fiscal
patterns that reflects one or more fiscal calendars used by the
specific organization may be set (702). A currency to use for all
amounts in the dimensional framework may be set to support
cross-functional analysis (703). One or more user defined
categories in each dimension 112 may be selected for analysis
(704). Multipliers may be set for use during transaction
aggregation and rollups (705). Source details may be set to
identify what information to extract from which ERP 10 (706).
Environmental configuration options may be set (707). These steps
may be performed in alternative order. Furthermore, steps may be
re-performed by the administrator 21. Once these steps are
completed, the data warehouse system 100 is ready to be used
(708).
Dimensional Framework
[0081] Another aspect of an example of an embodiment of the
invention relates to the fact that the data warehouse system 100
analyses and measures a complete organization environment. I.e.,
the data warehouse contains the dimensions, tables, entities, etc.,
to reflect any one of an identified group of organizations. This
analysis and measurement is performed through the concept of a
dimensional framework.
[0082] The dimensional framework manifests itself in the dimensions
112, in the dimension tables 122. The dimensions 112 of the
business model 110 are a set of business entities, components or
dimensions, such as customers, suppliers, vendors, material,
employees, time, organization, etc. These types of business
entities or dimensions are commonly used to analyze a business or
organization in a data warehouse. However, in the configurable data
warehouse system 100, the set of dimensions may be applicable to
many different organizations; rather than custom-built for one
particular organization.
[0083] The dimension tables 122 of the data model 120 are connected
to fact tables 121, preferably, in a star schema format. This
allows the same dimension table 122 to be used to represent the
same dimension 112 in any fact table 121 which uses that dimension
112.
[0084] The operational framework 130 allows for the handling of
hierarchies in the dimensional framework. The dimensional framework
has common ways of handling hierarchies. I.e., customers contained
within higher customer groups, or materials contained within higher
material groups. The handling of hierarchies allows for consistency
for analysis in the dimensional framework. From a configuration
point of view, the dimensional framework may include certain
details or aspects of specific placeholders in the dimension tables
that are in the data model 120.
[0085] In one example of an embodiment of the present invention,
the set of dimensions 112 includes 39 dimensions: company
consolidation 320, profit center 321, cost center 322, business
area 323, GL budget version 324, chart of accounts 325, accounting
document class 326, sales document class 327, movement document
328, material movement class 329, quotation activity document 331,
purchase order activity document 332, requisition activity document
333, contract activity document 334, procurement document class
335, vendor 336, material 337, customer 338, employee 339,
organization 340, plant 341, material storage 342, storage bin 343,
shipping point 344, AR activity document 345, GL activity document
346, AP activity document 347, all time (time, fiscal) 348, unit of
measure 349, financial currency conversion 350, unit of measure
conversion 351, user category 352, flexi-dimension 353, forecast
version 354, sales status 355, procurement status 356, release
strategy 357, valuation 358, batch 359, and stock class 360.
Dimensions 112 may be added or removed from this set of dimensions
112.
[0086] This set of dimensions 112 may be applicable as part of a
dimensional framework to many organizations. These dimensions 112
may also be configured to a specific organization through the use
of a configuration unit 135. As described above, the configuration
unit 135 may include a fiscal pattern settor 303, a currency settor
304, a user category settor 305, a multiplier settor 306, a source
details settor 307, and an environmental settor 308.
[0087] The dimensional framework may contain one or more dimensions
contain one or more placeholders settable to reflect a fiscal
pattern of the particular organization. The dimensional framework
may contain one or more dimensions contain one or more placeholders
settable to reflect a common currency used by the data warehouse
system. The dimensional framework may contain one or more
dimensions contain one or more placeholders settable to reflect one
or more categories defined by a user, the categories used to
analyze information in the data warehouse system. The dimensional
framework may contain one or more dimensions contain one or more
placeholders settable to reflect one or more multipliers used by
the data warehouse system.
[0088] Providing a dimensional framework for use as a foundation of
a data warehouse system may include one or more of the following
steps:
[0089] providing placeholders in a set of dimensions, the
dimensions representing business reference aspects of multiple
organizations, a subset of the set of dimensions representing a
particular organization;
[0090] providing a configuration unit for setting the placeholders
such that the dimensional framework represents the particular
organization;
[0091] providing placeholders comprises the step of providing one
or more placeholders in the dimensional framework to reflect a
fiscal pattern of the particular organization;
[0092] providing placeholders comprises the step of providing one
or more placeholders in the dimensional framework to reflect a
common currency used by the data warehouse system;
[0093] providing placeholders comprises the step of providing one
or more placeholders in the dimensional framework to reflect a
category defined by a user, the category used to analyze
information in the data warehouse system; and
[0094] providing placeholders comprises the step of aggregating
amounts loaded into the dimensional framework..
Components of the Data Warehouse System
[0095] Components of the configurable data warehouse system 100
will now be described in further detail. Built upon an operational
framework 130 and a robust production environment, the data
warehouse system 100 helps decision-makers derive business value
from their enterprise data. By using the data warehouse system 100,
organizations receive a wide, cross-functional view of their ERP 10
and e-business data, which provides a strategic perspective on key
performance indicators (KPIs). And they reduce implementation costs
and effort, which accelerates time to results.
[0096] An aspect of the data warehouse system 100 also relates to
the challenges that organizations face when implementing data
warehouses and traditional "stove pipe" data marts. A solution is
provided, i.e., the integrated data warehouse, which comprises a
series of coordinated data marts. These coordinated data marts
allow organizations to deliver value-laden enterprise-wide data
warehouse solutions that are important to competitive advantage in
the e-business economy.
[0097] One advantage of the data warehouse system 100 lies in the
quality of its business content. It is the business content that
gives end users the ability to answer complicated questions
involving numerous business dimensions 112 and quickly gain the
insight required to make strategic decisions. The basis of this
content combines business intelligence expertise established by
broad studies and best practices proven by experience, including
strategies which have helped many of the world's leading companies
generate maximum decision-making value from their data. This
business content is reflected in the business model 110, as
described below.
The Business Model 110
[0098] By using the data warehouse system 100 according to the
embodiment of the present invention, users may answer in-depth
questions such as: "Which customers in the western sales region
have increased their purchases by more than 30 percent in the past
three years?" or "How much revenue did we generate from
international sales of Product X last November?" These types of
complex queries, involving time, geography, product lines,
revenues, and other business variables, require that multiple
dimensions and levels of detail be examined. The data warehouse
system 100 allows users to make connections between these
cross-functional variables, connections that will provide insight
into what is driving the business.
[0099] The business model 110 is based on comprehensive information
about the business questions that users 20 in functional areas of
an organization face, including hundreds of function-specific
questions common to business people in many industries. In other
words, someone who manages a sales force for a pharmaceutical
company will face many of the same business challenges as someone
who manages a sales force at a textile company or a semiconductor
company. These questions can also be the basis of the business
measures, dimensions, and attributes. Business rules that govern
how to derive measures such as "net profit margin" or "inventory
balances", i.e., measures that do not appear in ERP systems 10 and
should be created, are also established in the business model
110.
[0100] Based on how companies manage their workflows within each
functional area, the business questions can be categorized as
strategic, tactical, or operational. Information needs associated
with each category are reflected in the business model 110. For
example: What level of data granularity do users require? How much
history do they need? Five years? Three years? How often do they
need to refresh data? Do they have to know what happened yesterday
to answer a given business question or can they wait until the end
of the week?
[0101] The structure of the business model 110 is presented in FIG.
9. The business model 110 is made up of multiple business
functional areas 202 (e.g., sales, accounts receivable (AR),
general ledger (GL), accounts payable (AP), procurement, inventory,
e-commerce, etc.) and a set of dimensions 112 reflecting the
business model 110 manifestation of the dimensional framework. As
has been stated above, the data warehouse system 100 may assist in
the management of the performance of many types of organizations,
including, but not limited to, not-for-profit organizations,
for-profit businesses, charities, governmental organizations, etc.
Thus, the business functional areas 202 include functional areas of
organizations that are not necessarily commercial enterprises.
[0102] For the purpose of data warehouse analysis, each business
functional area (or functional area) 202 is divided into areas of
analysis 203. In an embodiment of the invention, there are over 30
areas of analysis, but this number may change as the business model
110 evolves. The content 204 of an area of analysis 203 may include
the KPIs, measures, attributes that are used to support the
business analysis that can be performed. The functional areas 202,
the areas of analysis 203 and the KPIs, measures, dimensions and
attributes 204 may be arranged as shown in FIG. 9.
[0103] Analytical functions may be added to the set of dimensions
112 to provide the business performance management offered in the
data warehouse system 100.
[0104] Referring to FIG. 10, a business model 110 rich and complete
enough to be applicable to many organizations is described. The
business model 110 comprises a set of functional areas 202, a set
of dimensions 112, and relation indicators 390 showing a
relationship between the dimensions and the functional areas of
analysis. A functional area 202 is a set of areas of analysis 203.
Each area of analysis contains measures 111, and may use many
dimensions 112. The individual dimensions are labeled D.sub.1 to
Dn. The notations m, and n refer to integers where m is greater
than 0, n is greater than m. Thus, in this abstract representation
of a business model 100 rich and complete enough to be used by many
organizations, there are n dimensions. Similarly, the functional
areas 202 are labeled A.sub.l to A.sub.y. The notations x and y
refer to integers where x is greater than 0, and y is greater than
x. Thus, in this business model 100, there are y functional areas
of analysis 202.
[0105] Not all dimensions 112 or areas of analysis 203 will
necessarily be used by all organizations to which this model
applies. However, all dimensions and areas of analysis are
available for the organizations. Most organizations will use most
of the dimensions 112. As has been stated above, the dimensions 112
are used by the measures 111 and areas of analysis 203. The
differences between organizations may be reflected in the areas of
analysis 203 selected by the organizations. These areas of analysis
203 may then use the appropriate dimensions 112 to answer business
questions of the organization. An organization may use all of the
dimensions 112 and/or all of the measures 111.
[0106] The abstract business model 110 in FIG. 10 shows how it is
possible for one model to encompass all the dimensions 112 and
functional areas 202 necessary for a group of organizations. The
more dimensions 112 and the more functional areas 202 added to the
model, the richer and more complete the model will be, so as to
allow for other organizations to use it. Having one business model
110 which is rich and complete enough to be used by multiple
organizations is advantageous because the business model 110 only
need to be built once and then configured to a particular
organization.
[0107] FIG. 11 shows an example of a business model 110. This
business model 110 includes a set of dimensions 112 and six
functional areas 202 including sales analysis 401, AR analysis 402,
GL analysis 403, AP analysis 404, inventory analysis 405 and
procurement analysis 406. The functional areas 202 are comprised of
areas of analysis 203. The areas of analysis 203 are comprised of
measures.
[0108] The area of analysis 203 of the sales analysis 401
functional area helps analyze sales raw data to increase sales.
Companies may select from a host of key performance metrics and
decision-ready reports that enable them to analyze forecast
accuracy and pipeline volume, profile leads, calculate average deal
size, and examine revenues and profitability. With the sales
analysis 401 functional area, companies may:
[0109] Evaluate discount practices, target customers who generate
the highest margins, and spot clients who cost the most;
[0110] Know about prospects, customers, and product performance;
and
[0111] Identify opportunities, increase revenues, minimize costs,
and shorten the sales cycle.
[0112] The areas of analysis 203 of the sales analysis 401
functional area may include the following: sales functional
performance analysis 410, customer sales analysis 411, product
sales analysis 412, sales organizational effectiveness analysis
413, and shipping performance analysis 414. Other areas of analysis
may be added, such as e-commerce analysis. In this example, this
functional area relates to 100 business questions, 80 KPIs, 11
dimensions, and 43 reports.
[0113] The area of analysis 203 of the AR analysis 402 functional
area helps analyze raw AR sub-ledger transaction level data to
manage a corporate asset. The AR analysis 402 functional area helps
restructure AR data into key measurable facts used for strategic
planning, program management and execution, and AR performance
monitoring and reporting. Companies may select from a host of key
performance metrics and decision-ready reports that enable them to
continuously analyze the effectiveness of their AR function,
performance of existing resources, and fully understand the
existing customer base.
[0114] The areas of analysis 203 of the AR analysis 402 functional
area may include the following: AR functional performance analysis
420, customer credit analysis 421, AR corporate self-appraisal
analysis 422, AR cash inflow analysis 423, and AR organizational
effectiveness analysis 424. Other areas of analysis may be added,
such as quality of AR analysis. In this example, this functional
area relates to 77 business questions, 71 KPIs, 12 dimensions, and
28 reports.
[0115] The areas of analysis 203 GL analysis 403 functional area
helps analyze raw GL transaction level data to manage a corporate
asset. The GL analysis 403 functional area helps restructure GL
data into the key measurable facts used for strategic planning,
program management and execution, and financial performance
monitoring and reporting. Companies may select from a host of key
performance metrics and decision-ready reports that enable them to
continuously analyze their company's financial health.
[0116] The areas of analysis 203 of the GL analysis 403 functional
area may include the following: financial performance reporting and
analysis 430, budget analysis 431, key financial ratio reporting
and analysis 432, and operational performance and analysis 433.
Other areas of analysis may be added, such as sales functional
performance. In this example, this functional area currently
relates to 60 business questions, 50 KPIs, 11 dimensions, and 24
reports.
[0117] The areas of analysis 203 AP analysis 404 functional area
helps analyze raw AP sub-ledger transaction level data to manage a
corporate asset. The AP analysis 404 functional area helps
restructure AP data into the measurable facts used for strategic
planning, program management and execution, and AP performance
monitoring and reporting. Companies may select from a host of key
performance metrics and decision-ready reports that enable them to
continuously analyze the effectiveness of their AP function,
performance of existing resources, and enhance understanding of the
existing vendor base.
[0118] The areas of analysis 203 of the AP analysis 404 functional
area may include the following: AP performance analysis 440, AP
vendor account analysis 441, AP cash outflow analysis 442, and AP
organizational effectiveness analysis 443. Other areas of analysis
may be added. In this example, this functional area relates to 80
business questions, 64 KPIs, 12 dimensions, and 28 reports.
[0119] The areas of analysis 203 inventory analysis 405 functional
area helps deliver value to managers by helping turn raw data into
information used to take action. The inventory analysis 405
functional area helps provide a host of key performance metrics and
decision-ready reports that enable companies to analyze forecast
accuracy, stock levels and valuations, stock fluctuations (e.g.,
minimum and maximum stock levels, stock outs), and key inventory
analytics (e.g., ABC analysis, inventory turns, and stock
coverage).
[0120] The areas of analysis 203 of the inventory analysis 405
functional area may include the following: stock overview and
valuation analysis 450, material movement activity analysis 451,
demand analysis 452, material reservations analysis 453, physical
inventory analysis 454, and inventory forecast analysis 455. Other
areas of analysis may be added. In this example, this functional
area relates to 150 business questions, 100 KPIs, 15 dimensions,
and 49 reports.
[0121] The areas of analysis 203 of the procurement analysis 406
functional area helps deliver value to managers by turning raw data
into the information used to take action. The procurement analysis
406 functional area helps provide a host of key performance metrics
and decision-ready reports that enable users to analyze purchasing
volumes and patterns across commodities, analyze performance of the
buying organization, deliver vendor scorecarding, review
comparative vendor performance, and assess operational
effectiveness.
[0122] The areas of analysis 203 of the procurement analysis 406
functional area may include the following: material expenditure
analysis 460, material demand analysis 461, procurement vendor
analysis 462, procurement process effectiveness analysis 463, and
procurement organizational effectiveness analysis 444. Other areas
of analysis may be added, such as bill of material analysis, and
e-procurement analysis.
[0123] In this example, this functional area relates to 180
business questions, 139 KPIs, 15 dimensions, and 35 reports.
[0124] FIG. 12 shows a business model 110 where the dimensions are
grouped according to the following groupings of dimensions 112:
organizational dimensions for financial analysis 391, functional
document dimensions 392, master dimensions 393, operational entity
dimensions 394, financial transaction activity 395, universal
dimensions 396, and functional specific dimensions 397.
[0125] The dimensions 112 are linked with the functional areas and
areas of analysis for the purpose of reporting and analysis. For
example, FIGS. 11 and 12 show that the sales analysis-401 uses the
sales document class 327, material 336, customer 337, employee 338,
organization 339, shipping point 342, all time (time, fiscal) 347,
unit of measure 348, unit of measure conversion 350, and sales
status 354 dimensions. Other functional areas of analysis may use
different dimensions. The relationship between functional areas and
dimensions are shown in FIGS. 11 and 12 by way of connecting lines
390.
[0126] The business model 110 is extensible and scalable: it may be
expanded to include more functional areas, more areas of analysis
and more KPIs, measures, dimensions and attributes. Other examples
of business model functional areas 202 and their respective areas
of analysis 203 include:
[0127] Human Resource Analysis
[0128] Payroll Analysis
[0129] Professional Development Analysis
[0130] Recruiting Effectiveness Analysis
[0131] Financial Controlling Analysis
[0132] Cost Analysis
[0133] Profitability Analysis
[0134] Customer Relationship Intelligence
[0135] Customer Profiling
[0136] Customer Base Demographics
[0137] Marketing Analysis
[0138] Process Effectiveness Analysis
[0139] Customer Satisfaction
[0140] Supply Chain Intelligence
[0141] Vendor Scorecarding
[0142] Demand Forecasting Analysis
[0143] Process Effectiveness
[0144] Inventory Status Analysis
[0145] Procurement Activity Profiling
[0146] The set of dimensions 112 may also be used with a subset of
functional areas 202 or areas of analysis 203 or with other
functional areas of analysis. Such examples include
cross-functional performance management, among others: supply-side
performance management (see FIG. 13), demand-side performance
management (see FIG. 14), and financial performance (or GL)
management (see FIG. 15). The business model 110 would itself also
supports the above areas of cross-functional performance
management, among others, including individual functional areas
akin to a data mart.
[0147] FIG. 13 shows an embodiment of supply-side performance
management as containing the following functional areas 202: AP
analysis 404, inventory analysis 405, and procurement analysis 406.
The relevant areas of analysis 203 and dimensions 300 are also
displayed in the format of the business model 110 as shown in FIG.
11. FIG. 14 shows an embodiment of demand-side performance
management as containing the following functional areas 202: sales
analysis 401, and AR analysis 402. The relevant areas of analysis
203 and dimensions 300 are also displayed in the format of the
business model 110 as shown in FIG. 11. FIG. 15 shows an embodiment
of financial performance management as containing: AR analysis 402,
GL analysis 403, and AP analysis 404. The relevant areas of
analysis 203 and dimensions 300 are also displayed in the format of
the business model 110 as shown in FIG. 11.
[0148] The business model 110 is extensible. As has been described,
administrators 21 may add new functional area data marts to further
enhance their enterprise analysis and reporting. Administrators 21
may broaden the source data collection points beyond the ERP 10
system to gain a more complete view of the enterprise and customer
relationships. Components of the data warehouse system 100 are also
designed for high scalability. Organizations may also increase the
number of users that the system supports, accommodating corporate
expansion without the growing pains.
[0149] The areas of analysis in the business model 110 exemplified
above may each be one of a series of pre-packaged data marts aimed
at meeting the market demand for cross-functional business
intelligence (BI) against data held within corporate ERP 10 systems
and other sources of data within the enterprise. Each component
contributes to the core functional information requirements of an
enterprise, taking its place within the data warehouse system 100
"backbone" which is comprised of data marts targeting other core
data including sales, distribution, billing, inventory, financial
and cost accounting, and human resource management.
[0150] The sales analysis functional area 401, AR analysis
functional area 402, GL analysis functional area 403, AP analysis
functional area 404, inventory analysis functional area 405,
procurement analysis functional area 406, and e-commerce analysis
functional area questions listed above represent a sampling of the
type of valuable information available in the respective analysis
of the data warehouse system 100, information that business
professionals desire to effectively manage their roles and
responsibilities. The questions address the desire for information
regarding the following:
[0151] sales, shipping and billing portion of the sales cycle;
[0152] demand for information regarding the organization's ability
to meet collection expectations, customer profiling, and analyst
performance;
[0153] demand for information regarding the GL;
[0154] demand for information regarding the organization's ability
to meet payment expectations, vendor profiling, and analyst
performance;
[0155] demand for information regarding the investment in stock,
process effectiveness, use of resources, and the effectiveness to
meet the demand of internal and external customers;
[0156] demand for information regarding the commodities purchased,
vendor activity and performance, analysis of internal demand;
and
[0157] the demand for information regarding the e-commerce order
taking process of the e-commerce cycle.
[0158] It should be noted that more analysis is possible. The
multi-dimensional nature of the sales, AR, GL, AP, inventory,
procurement and e-commerce analyses components, along with the
power of business intelligence tools, offers robust analysis around
any single question, further expanding the knowledge gained from
The data extracted from the source ERP 10 system.
[0159]
The Data Model 120
[0160] The following will describe an embodiment of this invention
using a star schema. It should be noted that this invention is not
limited To a star schema data model. The invention may be applied
to other types of data models.
[0161] FIG. 16 shows an embodiment of a data model 120. In this
embodiment, the data model 120 implements the business model 110.
The data model 120 includes a set of dimension tables 122
corresponding to the dimensions 112 of the business model, and fact
tables 121 which are analogous to the functional areas 202 of the
business model 110. The fact tables 121 may relate to a data mart,
multiple data marts, or an integrated data warehouse. Furthermore,
the configurable dimensional framework allows for more fact tables
121 to be added to the data model 120.
[0162] In this example, the fact tables are divided into six
functional areas 202; sales analysis 901, AR analysis 902, CTL
analysis 903, AP analysis 904, inventory analysis 905 and
procurement analysis 906. FIGS. 17A to 17AE show the individual
star schemas for each individual areas of analysis 203 as reflected
in the data model 120 The areas of analysis 203 of the function
areas 202 and their measures 111 are listed below.
[0163] The components of The data model 120 may be provided as part
of a pre-packaged solution. Some components may be provided
separately and integrated with other components of a data model
120. Each component of the data model 120 is designed from careful
consideration of the dimensions 112 or measures 111 that are common
To each functional area 202 of the business or organization. Based
an common terms and common information, these dimensions 112 ensure
that users in relevant departments approach business issues using
the same references.
[0164] For example, the dimension "customer"337 means precisely the
same thing to a sales manager as it does to an inventory warehouse
manager or a face vice president. Without conforming dimensions,
each department would likely develop different definitions,
hierarchies, terms, and dimensions for many of the same business
measures, an inefficiency that can sidetrack productivity and
hamper decision-making.
[0165] Incorporating common dimensions 112 means that IT builds the
tables (121 and 122) only once, less redundancy because data is
stored once, and shorter time to update because updated data is
loaded once. Moreover, multiple star schemas can leverage the
shared dimensions 112 to reduce update time and resources. For
example, updates occur once for a change to a dimension table 112
that is shared by five fact tables 121, not five times, which
speeds the update process. In addition, common dimensions save disk
space, reduce redundancy, and ensure that data is consistent from
one data mart, or functional area of analysis 203, to the next.
[0166] The data marts, or functional areas of analysis 203, perform
business performance management faster than traditional ERP 10
systems which distribute data fields among thousands of tables.
Finding the fields that describe a given query in an ERP 10 system
often requires joining copious tables, a time-consuming step that
slows analysis and drains database processing power. The data
warehouse system 100 incorporates a star schema architecture that
accelerates query performance and produces fast business insight
for high-speed analysis and reporting.
[0167] Star schema architectures contain two types of tables: fact
tables 121 and dimension tables 122. A fact table 121 comprises the
transaction history associated with each activity being modeled.
These fact tables 121 store the numerical measurements of the
business and include an ID field for each dimension that they
represent. For instance, a sales fact table 121 might include
fields for Customer ID, Sales-person ID, Product ID, Quantity Sold,
Discount, and Total Amount, etc. The fact tables 121 are linked to
several dimension tables 122 that qualitatively describe the fact
table 121 fields in more detail. For instance, the Salesperson ID
dimension table might include Salesperson ID, Salesperson Name,
Phone Number, Sales Office, and Employee Number, etc.
[0168] This star structure, with the fact tables 121 surrounded by
satellite dimension tables 122, allows users to drill down quickly
into the data to uncover correlations between dimensions 112 and
elements in the fact table 121. Forming queries involves a set of
simple one-way joins, from the fact table 121 to each dimension
112, rather than complex multi-step joins through multiple levels
of tables. Users 20 get the information they need quickly, allowing
them to solve business problems, spot trends, or act on
opportunities.
[0169] Traditional stovepipe data warehouse applications, such as
traditional data marts, may serve certain departmental
decision-making needs, but they fail to offer a variety of
important enterprise-wide views. By incorporating common
dimensions, the data model 120 allows knowledge workers to share
information across departments and gain important decision-making
synergies. Based on common terms and common information, common
dimensions ensure that users in relevant departments or functional
areas approach business issues using the same references.
[0170] To solve a business problem, sometimes decision-makers want
to see transaction details, not just higher level summaries. For
this reason, the components of the data warehouse system 100, which
contain both relational and OLAP data, extract the most granular
data from the source ERP 10 systems and use it to populate the data
marts. Decision-makers may therefore access transaction-level
detail and gain a micro view of the business issues at hand.
[0171] Offering detailed granularity takes pressure off the source
ERP system 10 as well. Rather than query the production system
every time they need to perform detailed analysis, decision makers
may simply query the components of the data warehouse system 100
and glean the insight they desire.
[0172] One embodiment of the present invention provides a
configurable dimensional framework to be used as a base for a data
mart, multiple data marts, or an integrated data warehouse
application 100, which offers the benefits of both data warehouses
and data marts, i.e., the breadth of an enterprise-wide data
warehouse and the luxury of incremental data mart implementation.
This structure enables an organization to maximize the return on
its ERP 10, e-commerce, and other source data system investments.
Released from the analysis and reporting confines of ERP 10
systems, users 20 can now creatively explore business problems and
make equally creative and effective business decisions.
[0173] Moreover, users may incrementally add data marts over time,
expanding the integrated data warehouse system 100 at their own
pace. Each new data mart fits seamlessly with its predecessors,
extending the scope of the data warehouse system 100 to produce
effective cross-functional business content, e.g., the fundamental
information users need to understand their business drivers.
[0174] For example, if the inventory turnover rate suddenly
dropped, users would want to know why. With an integrated data
warehouse system 100 comprised of several subject-specific data
marts, users could explore whether the root of the problem lies in
sales or in inventory, perhaps the result of a change in the
company sales compensation plan or a tightening of credit policy.
By sharing the same conforming dimensions 112 (for instance,
"product") in both the sales and inventory marts, users could
generate these types of revealing cross-functional views. The
result: enterprise-wide decision-making is improved.
The Connectors 140
[0175] The business driven extractions and source-to-target
mappings are labeled as connectors 140 on FIG. 1. Business-driven
extractions and source-to-target mappings incorporate business
rules that unravel ERP systems 10 such as SAP R/3 (.TM.), Oracle
Applications (.TM.), and J. D. Edwards (.TM.), and are open to
alternative sources.
[0176] A complex part of building a traditional data mart involves
extracting the right data from the source system, transforming it
into the desired form, and loading it into the data marts. To
facilitate and expedite this process, a repository is built for the
data warehouse system 100 connectors 140. The connectors 140
understand both the source ERP system 10 and the targets. This
repository uses business rules to transform data from the ERP
system 10 to the targets.
[0177] The data warehouse system 100 simplifies the complex process
of extracting data from specific source systems such as J.D.
Edwards, SAP R/3, and Oracle, overcoming the technical hurdles and
addressing the unique characteristics involved in each system.
[0178] Extracting data may involve in-depth knowledge about the
underlying source system. Traditionally, developers of data
warehousing needed to know where the relevant data comes from and
what the specific data structures look like. They also needed to
know about the technical hurdles specific to their source systems
10. The data warehouse system 100 has functions to adapt to a
variety of source systems. An embodiment is based on extensive
experience with SAP, Oracle, and J. D. Edwards ERP systems 10. For
example, SAP uses pooled and clustered table structures, Oracle
provides "flex" fields, and J. D. Edwards maintains address books
in a special way. Each system contains unique characteristics that
affect data mart building. The data warehouse system 100 addresses
these source features. This inherent source system intelligence of
the data warehouse system 100 spares users 20 from having to spend
time analyzing complex ERP and e-business systems.
[0179] In addition to speeding the extraction process, the
connectors 140 incorporate safeguards to protect data integrity. As
data comes across from the source system 10, the connectors 140
look for specific conditions. If these conditions are absent, the
connectors generate an error log and lists the missing data,
simplifying system administration and trouble-shooting. Missing
data, incomplete data, or inaccurate data may degrade the quality
of a business performance management solution and substantially
hinder the business results.
[0180] To generate consistently high data quality, the connectors
140 contain transformation functions that format and integrate
source data before it is stored in a data mart. This process might
involve any number of functions: restructuring data files, records,
and fields; removing superfluous data; decoding and translating
field values to enhance data; improving data readability;
validating data; calculating new values from one or more source
columns; simplifying data; and changing data types. The
transformation process may also reject records that do not satisfy
business rules. As part of the transformation process, the data
warehouse system 100 may employ surrogate keys that substitute for
natural keys to improve processing performance.
[0181] Once the source data has been transformed, the data
warehouse system 100 loads it into the destination data marts and
make the data available to users 20 for analysis and reporting. The
data model 120 may be considered as an abstract collection of data
marts.
[0182] The components of the data warehouse system 100 may apply
different updating rules to different tables depending on the
nature of the component data. By tailoring the data-loading process
to the data, the data warehouse system 100 updates information
faster with less demand on the target system. For instance, tables
defined as "static" contain data that changes infrequently and
therefore needs refreshing only on an ad hoc basis. Tables that
require more frequent refreshing can be treated differently as
well, according to the characteristics of their data. Users 20 may
perform a complete refresh, a changed-data capture, or a slowly
changing dimension.
[0183] The data warehouse also includes stop-recover strategy,
which allows extraction jobs that have been interrupted to be
restarted. This feature saves administrators time and helps ensure
data integrity.
[0184] To help ensure that an integrated data warehouse accurately
captures changes to dimensions 112 that vary infrequently, such as
product hierarchies, sales regions, and so on, the data warehouse
system 100 may accommodate slowly changing dimensions. This feature
offers two primary benefits. First, it may allow users 20 to go
back and find out what was going on at a point in corporate
history, In other words, although employees may have moved or sales
territories may have been redrawn, the system 100 may present
information about these slowly changing dimensions as they existed
at the time of interest. This may allow users 20 to derive
consistent, repeatable results, solidifying the value of their
decision support system by preserving history.
[0185] Second, users 20 may see values or changes over time. This
capability furnishes the insight to uncover longer-term trends and
business impacts. If users 20 have incomplete historical
information, they may end up making improper assumptions and
compromising the quality of their decisions. Whereas ERP systems 10
may typically archive all but the most recent year or two's worth
of data without access to supporting details, the data warehouse
system 100 allows users 20 to dig into an issue's past several
years or more to gain revealing perspectives about its present.
This trend-analysis capability allows companies to track the impact
of decisions over time.
[0186] In the data warehouse system 100, if a sales person
transfers to a different region in mid year, the data marts may
allow an organization to record the move and reflect the change in
their database. Without record of this slowly changing dimension, a
year-end revenue summary by region may allocate their entire year's
sales to the new regional manager, overstating their
accomplishments and understating the previous manager's
performance. Companies that make decisions based on this type of
misleading information may end up making incorrect assumptions and
that can result in costly mistakes.
[0187] With slowly changing dimensions, the revenue that the sales
person generated before their departure will properly accrue to the
previous regional sales manager, and the revenue that they generate
after the move will be credited to the new manager. Over time,
certain dimensions such as employees, products, and customers may
change, and the data warehouse system 100, by creating another
dimension record, has the flexibility to accommodate these changes
and produce an accurate view of business performance.
[0188] The data warehouse system 100 handles slowly changing
dimensions so that the integrated data warehouse accurately
captures infrequent but important data changes. Users 20 can rely
on the integrity of the data.
[0189] The data warehouse system 100 may also include changed-data
capture, the capacity to periodically update the data marts with
current information without rebuilding them from the ground up.
Changed-data capture detects new, modified, or deleted records in
source systems 10 and updates the data marts with those
changes.
[0190] To improve updating speed, the data warehouse system 100
splits the changed-data capture function into two. One inserts new
data incrementally in bulk, a quick and efficient approach that
eases the pressure on processing resources. The other step updates
changes to existing data, a process that involves going into the
database, finding the modified row, updating it, and then saving
the change. Given that changes are less voluminous than new data,
the data warehouse system 100 handles the majority of updating with
the more efficient and speedier process. Updating may therefore be
conducted successfully even in the face of continually shrinking
update windows.
[0191] To further its efficiency, the data warehouse system 100 may
look only at the data that has changed in the ERP system 10.
Recognizing the date and time of the last update, the ETL tool 140
requests only records from that update forward. Asking what records
have changed and determining whether the changed records are of
interest may filter this subset further. This approach demands far
fewer CPU (central processing unit) resources than may be required
to extract all the ERP 140 data, to compare it to the data mart,
and to load the difference; a process that would involve examining
every row in the ERP 140 system. Consequently, changed-data capture
improves system performance and speeds updates. Changed-data
capture allows users to periodically update data marts without
reloading them from scratch.
The Operational Framework 130
[0192] The operational framework 130 of the data warehouse system
100 reflects how the data warehouse system 100 may be productized.
The operational framework 130 allows the administrator 21 to:
[0193] Customize the data warehouse system 100 to reflect their
unique ERP 10 environment;
[0194] Controls the operation of the data warehouse system 100 in a
production environment, and contains a component which includes
stop-recover strategy; and
[0195] Handles exceptions during data mart updates.
[0196] The operational framework 130 provides functionality that
makes the data warehouse system 100 responsive to the variations of
ERP 10 implementations. The operational framework 130 uses
information stored in the operational framework schema to adjust
the business-driven extractions and source-to-target mappings
business rules of the connectors to reflect the requirements of the
particular ERP 10 implementation. The operational framework 130
uses information stored in placeholders in the operational
framework schema to determine the status of the extracts that load
the data mart and to determine what new data needs to be extracted
to the data mart.
[0197] The data warehouse system console 133 employs easy-to-use
configuration parameters to help administrators 21 tailor
components of the data warehouse system 100 to their environment.
As has been stated above, the system console 133 assists in the
configuration of the dimensional framework.
[0198] As has been described, the operational framework may include
a configuration unit 135. An administrator 21 may likely customize
their SAP, Oracle, or J.D. Edwards source system. If so, their
hierarchies, hierarchy types, status codes, charts of accounts,
exchange rates types, and other fields may differ from the source
system defaults. The system console 133 has parameters which help
users configure the data warehouse system 100 to reflect these
changes. This convenience saves an administrator 21 effort, speeds
configuration, and delivers business performance management value
faster.
[0199] FIG. 18 is a screen shot of the system console 133 which
enables administrators 21 to augment the data warehouse system 100
to reflect their particular implementation through configuration
parameters. The system console 133 matches the configuration to the
user's 20 target database and equipment. For example, whether
Oracle RBDMS (.TM.) or Microsoft SQL Server (.TM.) on NT or Unix
platforms are used, the data warehouse system console 133 may
tailor its implementation to the physical environment.
[0200] The system console 133 enables users to import historical
ERP 10 data at a pace convenient to their business. This initial
load job may take a long time, a potential problem if
administrators 21 attempt to import all this data during a single
extended window. Using the system console 133, however,
administrators 21 may schedule the loading to occur in phases which
users set and populate the data marts during slow network activity
periods. This convenience avoids saddling users with degraded
network performance while the loading occurs.
[0201] Administrators 21 may also use the system console 133 to
simplify the ongoing ETL processes 140. It may help administrators
21 sequence jobs and determine which are to run, what data they are
to extract, and when they are to run (i.e., date ranges). The
system console 133 may also enable administrators 21 to run ad hoc
jobs or put scheduled jobs on hold.
[0202] Moreover, the system console 133 may equip administrators 21
to maintain their system. In the data warehouse system 100,
administrative tables within the relational database store
information pertaining to the system's 100 operation. The console
133 uses this information to generate job status reports and error
reports, giving administrators 21 a firm handle on their system at
all times.
[0203] The engine behind the data warehouse system 100 resides
within the system console 133, an easy-to-use production control
environment that simplifies the up front installation,
configuration, and loading of the data warehouse system 100. It
also makes maintaining the data marts easier once they are up and
running.
[0204] Administrators 21 may use the system console 133 to set
extraction sequences, and establish dependencies and priorities. It
may also enable organizations to implement coordinated analytic
applications incrementally and manage them centrally.
[0205] As has been stated, the system console 133 may be considered
part of the operational framework 130. The system console 133
provides intelligent connector 140 job control for ad hoc or
scheduled data loads, sequences extraction jobs, and defines
extract dates. It allows an administrator 21 to set configuration
parameters so that the data warehouse system 100 reflects ERP 10
site-specific configurations. FIG. 19 is a screen shot of the
system console 133 that manages connector 140 processes
automatically.
The Content Explorer 150
[0206] The data warehouse system 100 may also provide packaged
reports, OLAP cubes, and catalogs 151 that offer business insight
and reflect the information and KPIs used to manage, measure, and
improve business performance in each functional area. These reports
may be included in the content explorer 150.
[0207] Users 20 may generate an array of reports, such as OLAP,
relational, standard, ad hoc, time trend, etc., to meet information
requirements, for positions in the organization. Moreover, these
reports are also easy to change. Decision makers can easily adapt
them to manage, measure, and improve business performance in their
functional areas, greatly reducing the burden on IT. Either way,
knowledge workers gain key business insight and derive immediate
productivity gains.
[0208] Furthermore, the data warehouse system 100, which may be
extended to include scorecarding and visualizations, provide the
right report for the right users on the client platform of choice:
e.g., Windows, Excel, or Web browser, whether users are LAN-based
or working remotely.
[0209] The data warehouse system 100 contains a number of packaged
reports that reflect the business requirements for important areas
such as finance, sales, and inventory. FIG. 20 is a screen shot of
an example of a report of the financial (or GL) analysis 403
functional area. This report helps speed reconciliations,
period-end closings, and financial reporting and distribution by
giving managers the information they use to analyze income
statements, balance sheets, cash flows, key financial ratios, or
currency rate conversions.
[0210] Types of financial reports available to end users
include:
[0211] Overview reports, such as income statement and balance
sheet;
[0212] Income statement analysis;
[0213] Balance sheet analysis;
[0214] Budget analysis;
[0215] Analysis by legal entity;
[0216] Analysis by management entity; and
[0217] Operational reports, such as cost center and GL Analysis
303.
[0218] FIG. 21 is a screen shot of an example of a report of the
sales analysis 401 functional area. This report allows users to
analyze forecast accuracy and sales volume, calculate average deal
size, and examine revenues and profitability, etc.
[0219] Types of sales reports available to end users 20
include:
[0220] Reports by customer, such as customer sales ranking or
customer sales by region;
[0221] Reports by product, such as order summary, or product sales
ranking;
[0222] Reports by sales organization, such as orders by reps or by
country;
[0223] Reports by profit; and
[0224] Reports by quantity sold.
[0225] FIG. 22 is a screen shot of an example of a report of the
inventory analysis 405 functional area. This report provides
inventory managers with the information they use to understand
supply chains and assess demand forecasting accuracy, inventory
carrying costs, supplier performance, and warehouse performance,
etc.
[0226] Types of inventory reports available to end users
include:
[0227] Inventory performance, such as stock level overview or
profile of plants by stock level;
[0228] Demand analysis, such as stock usage comparisons, or
materials profile of demand;
[0229] Material tracking;
[0230] Vendor analysis by stock movements; and
[0231] Resource activity, such as activity comparisons or
plant/employee analysis.
[0232] The data warehouse system 100 also allows for ease of report
generation. FIGS. 23 to 24 illustrate the ease with which a series
of reports may be generated from any starting point. For example,
FIG. 23 shows a screen shot of a report highlighting sales revenues
over the past several years By Division (identified by arrow 2501).
A user 20 may decide that it would be interesting to view revenues
over these periods by sales office within the sales organization.
To generate this report, the user 20 would simply move the cursor
over the Sales Office folder, shown by circle 2502, then drag and
drop it on the Divisions column shown within circled 2503.
[0233] This single step presents the user with a new report which
represents sales revenues over time by sales office within, in this
example, the Germany Sales Organization. This analysis may be taken
one step further by dragging and dropping the materials file
(identified by circle 2601 in FIG. 24) to the nested row position
in the report, (identified by thick vertical line 2602 within
circle 2603). FIG. 25 shows a screen shot of the result: a new
report, identified by arrow 2701, highlighting how revenues are
distributed by material groups across sales offices within, in this
example, the German sales organization.
[0234] Thus, with three clicks, a user 20 is able to view three
reports, each of which offer sales related information. Similarly,
each of these reports are only clicks away from more varied and
valuable analysis.
[0235] Other components can be added to the data warehouse system
100 environment.
Methodology for Creating the Data Warehouse System
[0236] Creating and implementing a successful traditional
integrated data warehouse involves a lengthy series of complex
steps and activities, and requires expertise in numerous highly
specialized areas.
[0237] Despite the substantial hurdles, some information technology
(IT) departments elect to build data warehouses themselves. It is
not unusual for these projects to end up over budget, miss major
milestones, or even fail due to the unanticipated complexity of
extracting, transforming, and loading the right data.
[0238] The data warehouse system 100 offers an integrated analytic
solution, rich and complete enough for multiple organizations to
use it, that allows IT departments to provide users with high
quality cross-functional business performance management in a short
time, freeing up specialized IT resources for immediate impact. The
data warehouse solution puts robust decision-making solutions in
the hands of users quickly and cost-effectively.
[0239] A data warehouse system 100 rich and complete enough to be
used by multiple organizations may save users a complete business
cycle in deploying and extending their integrated data warehouse
solution. A complete business cycle can be spent on establishing
end-user needs, data mart design, source system analysis, data mart
creation, target system and configuration environment, data mart
operation, and business analysis and report. The data warehouse
system 100 (including the initial load, user acceptance, and
implementation) requires considerably less time to install than
conventional solutions creating an integrated data warehouse from
scratch.
[0240] The development of an effective data warehouse system 100
includes several key components, such as:
[0241] Business decision maker requirements (both functional and
cross-functional) defining the type of analysis required based on
best practices
[0242] A technical design which ensures consolidated data from
across the organization (i.e., ERPs and other data sources),
delivering consistent and reliable results
[0243] A strategic architecture which allows for incremental
implementation business performance management by functional
area
[0244] Enterprise Business Intelligence (EBI) designed to deliver
rich analysis and reporting, with the functionality to share
information across the organization, as well as across corporate
intranets and extranets with key business partners
[0245] The data warehouse system 100 may be viewed as a series of
business analytical solutions designed to deliver key information
to an organization's core business functions, including sales,
accounts receivable (AR), general ledger (GL), accounts payable
(AP), inventory management and procurement. While each application
includes rich functional analysis, applications can be used
together to join other operational data from across the demand and
supply sides of the organization for a coordinated enterprise view
of performance.
[0246] Each data warehouse system 100 business analytical solution
may be built on three pillars:
[0247] Rich business content with predefined BI reports based on
best practices as defined through research with industry
experts
[0248] Robust technical architecture, ERP source analysis,
installation wizards, and production system management
[0249] Conforming design allowing for the combination of multiple
applications based on common dimensions (e.g., customers, products,
vendors)
[0250] The data warehouse system 100 brings together the components
used to deliver the important business analysis required for
effective decision making. This includes source ERP system 10
analysis, data extraction and transformation, best practices, data
architecture and EBI.
[0251] Before attempting to build an integrated data warehouse, IT
departments should fully assess the obstacles and risks involved.
An integrated data warehouse project uses a diverse array of skills
and experience. The following six skill-sets are important to a
successful implementation.
[0252] 1. Business Requirements Analyst:
[0253] Acts as liaison between the data warehouse project team and
the warehouse's end users. This person identifies and documents the
needs of the business and produces a plan for addressing these
needs using the data warehouse. The Business Requirements Analyst
should have excellent communications skills and an ability to
assess business information needs.
[0254] 2. Subject Matter Experts:
[0255] Typically end users who are familiar with the information
and business needs of the internal groups or areas that they
represent and who have significant knowledge of the data. These
people help standardize on different aspects related to the data
and work to resolve issues across business areas.
[0256] 3. Source Systems Experts:
[0257] Identifies source fields based on the requirements specified
for the warehouse. Also identifies the source hurdles that will
need to be overcome in order to implement.
[0258] 4. Data Architect:
[0259] The Data Architect develops and maintains the logical and
physical data models of the warehouse, and is able to identify the
most valuable data, integrate it, and develop the correlating data
model. Also responsible for recommending the optimal system of
record, the Data Architect should ensure the company's business
needs are incorporated into a technical solution.
[0260] 5. Data Acquisition Developer and Architect:
[0261] Responsible for extracting data from a source system,
performing associated transformations, and making the data
available for loading into the data warehouse. The Data Acquisition
Developer and Architect should understand extraction and
transformation, identify transformations, and define
source-to-target mappings.
[0262] 6. Business Intelligence (BI) Developer:
[0263] Develops solutions that allow end users to easily and
consistently access the data warehouse. The BI Developer should
understand the business needs, be able to incorporate these into
technical solutions, and be skilled in end-user access, reporting,
and analysis tools.
[0264] Assembling the necessary skills and expertise is the first
step of many involved in the process of successfully developing an
integrated data warehouse. Building an integrated data warehouse
includes the following process.
[0265] 1. Establishing End-User Needs
[0266] Business requirements analysis
[0267] 2. Data Mart Design
[0268] Logical data model
[0269] Physical data model
[0270] 3. Source System Analysis
[0271] Source system analysis and mappings
[0272] 4. Data Mart Creation
[0273] Data acquisition process design
[0274] Data acquisition construction
[0275] 5. Target System and Configuration Environment
[0276] Technical architecture design
[0277] 6. Data Mart Operation
[0278] Maintenance and administration
[0279] 7. Business Performance Management (which includes Business
Analysis and Reporting) (Business Intelligence) and other features
described herein
[0280] Data access design
[0281] Data access construction
[0282] Establishing end user needs through assessing business
requirements may take up to 50% of the entire effort of building a
warehouse.
[0283] An IT department should know its users'business
requirements. How will people use information? What questions do
they need answered? Do they want high-level views or transaction
details? Will they use this information in their offices or on the
road? By exploring users'business requirements, and fully
understanding how the departments of the enterprise interact, a
user will be ready to create the appropriate metrics and business
rules an effective analysis and reporting solution requires.
Including the content in the warehouse that effectively supports
business goals is a key to achieving maximum return on
investment.
[0284] Designing data marts involves turning the business needs
that have been identified into useful data. The process involves
designing the data mart logical data model and the subsequent
physical data model. Many questions should be answered at this
stage: Which end users should be involved during the design
sessions? Do data sources exist for some or all of the intended
data? Have they chosen an ETL tool? Will the initial design include
metadata? If so, will it comprise technical metadata, business
metadata, or both?
[0285] Once these questions are addressed, to optimize the solution
for business performance management, a high-speed star schema data
marts that logically arrange data and allows for cross-functional
views of business operations should be designed. Simply put, the
star schema data marts, based on relational data, use shared,
conformed dimensions to achieve a unified view of traditional
processes. In effect, a Sales data mart would define "Product X"
the same way that the Inventory data mart does. These marts should
also be scalable and contain embedded knowledge of the data
warehouse applications they will serve.
[0286] The next step, source system analysis, should be undertaken
by someone who is familiar with the user's ERP, e-commerce, and
other source systems as well as any modifications that they have
made to them. This expertise is used to identify which data to
extract and how to extract it.
[0287] The source system expert should understand the unique
parameters, fields, hierarchies, and technical approaches that
characterize each ERP solution. Many organizations outsource the
initial design of their ERP and e-commerce systems to consultants
who take their source expertise with them once the contract is
completed. This, coupled with the high rate of movement of in-house
IT resources leaves companies with a knowledge gap regarding these
complex source systems. The solution is typically to retain
consulting expertise, which can become prohibitively costly and,
depending on a consultant's availability, even delay the solution
delivery date.
[0288] Once one knows where to look for data in the source systems,
their next step is to develop source to target mappings and ensure
that they extract, transform, and load ERP and other data into
their data marts. Poor source data quality, missing source data,
and redundant source data, among other challenges, can complicate
this process.
[0289] Ultimately, the ETL system should flag errors during the ETL
process, minimize computing resources, maximize automation, and
incorporate best warehousing practices such as slowly changing
dimensions, history preservation, and changed-data capture.
Delivering these capabilities will ensure that the process runs as
smoothly as possible and that the data generated is accurate.
[0290] One should also know how to incrementally add data mans. For
instance, if a user adds an inventory mart To their existing sales
and fiance at, he user should be careful to avoid creating data
definition conflicts between The mars. Synchronization and
coordination are key because problems at this stage can sabotage
data integrity
[0291] The target system and configuration environment need to be
checked. For example, is one using an NT application server lo run
an ETL code and populating an Oracle database on a Unix platform?
Or are they running their ETL code on Unix and populating a
Microsoft SQL server on NT? Depending on the platform and database,
one will have to vary the way that they install and configure their
solution.
[0292] Tasks associated with operating, managing, and maintaining
the integrated data warehouse include loading data marts from
operational systems, troubleshooting the system, restarting failed
jobs, and scheduling jobs so that they minimize impact on source
systems. Building an integrated data warehouse from scratch
requires substantial IT expertise, not to mention substantial time
and money.
[0293] A preferred methodology 3000 for creating a business model
110 in accordance with the present invention will be described
referring to FIG. 26. The first step involves selecting a market
(3001). In this market, organizations to which the business model
110 will apply are identified (3002). An identified organization in
the market is analyzed to collect organizational information
(3003). Then business questions are determined based on the
collected organizational information (3004). This collection of
organizational information and determination of business questions
is performed for each identified organization in the selected
market (3005). The business questions of the organizations in the
selected market are then merged into areas of analysis (3006). The
areas of analysis are then decomposed into dimensions, measures and
attributes to help answer the business questions (3007). The
business model may now be designed based on the dimensions,
measures and attributes (3008).
[0294] A preferred methodology 2000 for creating a data warehouse
system 100 in accordance with the present invention is described
referring to FIGS. 27A to 27E. There are four main steps in this
methodology: business model development (2001), requirements
definition (2011), configurable data warehouse model specification
(2020), and configurable data warehouse product development and
packaging (2030)The
[0295] The first two main steps (2001) and (2011) create the
business model 110. The first main step is to develop an
organizational model (2001). The second main step is to define
requirements of an organization (2011). In the development of an
organizational model (2001), many organizations in a selected
market are analysed to determine their requirement in a business
model 110. In the requirements definition (2011), the requirements
determined in the first main step (2001) are defined into
components of a business model 110, i.e., dimensions 112 and areas
of analysis 111.
[0296] The first main step establishes the correct framework for
analyzing, grouping and managing business performance measurements.
This framework is responsive to a "horizontal" view of the
industries of interest. The first step in developing an
organizational model is to select a market (2002). The selected
market will help to define the set of characteristics that will
determine the types of organizations to which the business model
110 will apply, i.e., define the target sectors and industries. The
next step is to identify development partners who are
representative of aspects of the selected market (2003).
Development partners include end user companies, industry experts,
and related professional associations and/or organizations.
[0297] The next step is to investigate the key business drivers of
the selected market (2004). This step may be broken down into
investigating the corporate imperatives and investigating best
business practices of organizations in the selected market.
Identifying the key business drivers and best business practices
defines the highest level of the "metric" framework. This process
provides focus and scope to the set of measures that are necessary
to the successful data warehouse system solution.
[0298] The next step is to identify the "big" questions (i.e.,
business questions) that should be answered to manage the business
performance of organizations in the selected market (2007). This
establishes the high level "areas of analysis" within the business
model 110. An area of analysis 203 can represent the set of metrics
required to answer one or more business questions.
[0299] The next step is to define an organizational model that
represents the typical business functions of the organizations in
the selected market (2008). This step may involve defining the main
functions as well as the main business functions of the
organizations. This step combines all the findings of the previous
steps into a business model 110 that represents the core functional
areas 202 typical to a company within the target market industries
and sectors.
[0300] As has been stated above, once the requirements of the
organizations in the selected market have been determined (2001),
the requirements may be defined into components of a business model
110. Each functional area of the organizations, determined in the
organizational model development (2001) should be developed into a
data warehouse system 100 application. The first step in the
requirements definition is to select a functional area to develop
into a data warehouse system 100 application (2012). The business
process of the selected area may be divided into activities and
tasks (2013). The decomposition of the functional area 202 is used
to understand the workflow, business process and roles that are to
be measured and managed in a data warehouse system 100. Then key
roles involved in the data warehouse system 100 application should
be identified (2014). Next, a list of business questions to be
answered during business performance management may be developed
(2015). Steps (2012) to (2015) are repeated for each functional
area determined in the first main step (2001). The set of business
questions represents the questions that should be answered to
determine if the objectives and goals of performance (typically
established in the corporate imperatives) are being met. Each
question should be stated according to a standard specification. A
question should contain one and only one metric, and may contain
one or more dimensions, and attributes.
[0301] The business questions determined in step (2015) may now be
grouped into areas of analysis 111 (2016). The business questions
(often hundreds) are grouped into their related area of analysis
203. The areas of analysis 203 were defined during the creation of
the business model 110. Grouping business questions this way can be
used to verify the business model 110 and establish the
corresponding data model 120 parameters. The business questions may
also be decomposed into measures, dimensions and attributes (2017)
and documented as business requirements (2018). Finally, functional
requirements which define how the questions are to be answered may
be documented (2019). Documenting the functional requirements
includes the identification of configuration options necessary to
support multiple organizations.
[0302] Referring back to FIG. 10, the functional areas determined
in step (2001) are denoted as A.sub.1 to A.sub.y, The dimensions
determined in step (2017) are denoted as D.sub.1 to Dn. Multiple
areas of analysis determined in step (2016) are included in the
functional areas A.sub.1 to A.sub.y. Multiple measures and
attributes determined in step (2017) are also included in the
functional areas A.sub.1 to A.sub.y. The connecting lines 390 show
which dimensions 112 are used with each functional area 111 to
answer the business questions determined in step (2015). Finally,
the boxes outlining the different organizations show which
dimensions are functional areas are needed by a particular
organization.
[0303] The creation of the business model 110 was the first two
steps in the methodology of the development of the data warehouse
system 100. Once the business model 110 has been created, a data
model 120 may be created to implement the business model 110.
Moreover, the data model 120 may be configurable and joined to a
data warehouse system 100.
[0304] The third main step in the development of a data warehouse
system 100 is to create a configurable data warehouse model
specification (2020). In this main step, a data warehouse model
specification is designed (2021) to answer the business questions
determined in step (2015). A high level functional specification
may also be designed to show how these business questions are to be
answered (2022). The next step is to analyze selected source
systems 10 to determine how and what to extract to meet the defmed
requirements (2023) for: business concepts, business processes,
data entities, and data life cycle information. The source system
analysis step is used to identify the configuration options used to
support the possible implementation specific variations of multiple
organizations. Then source system 10 specific variations should be
identified for each source system 10 (2028). Finally,
implementation specific various should be identified within each
source system 10 (2029).
[0305] The last main step in the development of a data warehouse
system 100 takes all the information and analysis performed thus
far and develops a product. This last main step involves the
configurable data warehouse product development and packaging
(2030). This first step in this last main step is to design the
configurable version of the data model 120 and connectors 140
(i.e., ETL code) (2031). This involves designing the configurable
target elements (i.e., the fact tables 121 and dimension tables
122) and designing the configurable extraction code used by the
ETL. Next, the configurable aspects of the solution are implemented
in the configuration unit 135 (2034). Finally, the complete
solution may be packaged as a product (2035). This last step
results in the specification for the configuration unit 135 which
enables the selection of the various configure options in the data
model 120 and the connectors 140.
Further Information Regarding an Example of an Embodiment of a Data
Warehouse System 100
Dimensions
[0306] The following is a listing of dimensions which may be used
in a data warehouse system 100:
[0307] ACCOUNT CATEGORY PARTY
[0308] ACCOUNTING DOCUMENT CLASS
[0309] ALL TIME
[0310] AP ACTIVITY DETAIL
[0311] AP ACTIVITY DOCUMENT
[0312] AP DAILY ACTIVITY SUMMARY
[0313] AP INVOICE SUMMARY
[0314] AP MONTHLY ACCOUNT SUMMARY
[0315] AP MONTHLY ACTIVITY SUMMARY
[0316] AP PAYMENT SUMMARY
[0317] AR ACTIVITY DETAIL
[0318] AR ACTIVITY DOCUMENT
[0319] AR DAILY ACTIVITY SUMMARY
[0320] AR INVOICE SUMMARY
[0321] AR MONTHLY ACCOUNT SUMMARY
[0322] AR MONTHLY ACTIVITY SUMMARY
[0323] AR PAYMENT SUMMARY
[0324] BATCH
[0325] BUDGET VERSION
[0326] BUSINESS AREA
[0327] CHART OF ACCOUNT
[0328] COMMITTMENT ACTIVITY DETAIL
[0329] COMMITTMENT ACTIVITY DOCUMENT
[0330] COMPANY CONSOLIDATION
[0331] CONTRACT ACTIVITY DETAIL
[0332] CONTRACT ACTIVITY DOCUMENT
[0333] CONTRACT DOCUMENT SUMMARY
[0334] CONTROLLING COST OBJECT
[0335] CONTROLLING COST OBJECT GROUP MEMBER
[0336] COST ACCOUNT ACTUAL
[0337] COST ACCOUNT ACTUAL DOCUMENT
[0338] COST ACCOUNT PLAN ITEM
[0339] COST ACCOUNT PLAN ITEM HEADER
[0340] COST ACCOUNT PLAN VERSION
[0341] COST CENTER
[0342] COST CLASS
[0343] COST ELEMENT
[0344] COST ELEMENT GROUP MEMBER
[0345] COSTING GROUP
[0346] COSTING PROJECT
[0347] CUSTOMER
[0348] CUSTOMER DEMOGRAPHIC
[0349] EMPLOYEE
[0350] EURO CURRENCY RATE
[0351] FINANCIAL CURRENCY CONVERSION
[0352] FISCAL
[0353] FLEXIDIM
[0354] GL ACTIVITY DETAIL
[0355] GL ACTIVITY DOCUMENT
[0356] GL BALANCE
[0357] GL BUDGET
[0358] MATERIAL
[0359] MATERIAL MOVEMENT
[0360] MATERIAL MOVEMENT DOCUMENT
[0361] MATERIAL MOVEMENT DOCUMENT CLASS
[0362] MATERIAL MOVEMENT DOCUMENT SERIAL NUMBER
[0363] MATERIAL RESERVATION
[0364] MATERIAL STORAGE
[0365] ORGANIZATION
[0366] PHYSICAL INVENTORY
[0367] PLANT
[0368] PROCUREMENT ACTIVITY PERIODIC SUMMARY
[0369] PROCUREMENT DOCUMENT CLASS
[0370] PROCUREMENT STATUS
[0371] PROFIT CENTER
[0372] PROMOTION
[0373] PURCHASE ORDER ACTIVITY DETAIL
[0374] PURCHASE ORDER ACTIVITY DOCUMENT
[0375] PURCHASING ORGANIZATION GROUP
[0376] QUOTATION ACTIVITY DETAIL
[0377] QUOTATION ACTIVITY DOCUMENT
[0378] RELEASE STRATEGY
[0379] REQUISITION ACTIVITY DETAIL
[0380] REQUISITION ACTIVITY DOCUMENT
[0381] SALES ACTIVITY PERIODIC SUMMARY
[0382] SALES BILLING
[0383] SALES BILLING DOCUMENT
[0384] SALES CONTRACT
[0385] SALES CONTRACT DOCUMENT
[0386] SALES DISTRIBUTION
[0387] SALES DISTRIBUTION DOCUMENT
[0388] SALES DOCUMENT CLASS
[0389] SALES ORDER
[0390] SALES ORDER DOCUMENT
[0391] SALES ORGANIZATION
[0392] SALES STATUS
[0393] SHIPPING POINT
[0394] STOCK CLASS
[0395] STOCK LEVEL DAY
[0396] STOCK LEVEL MONTH
[0397] STOCK LEVEL WEEK
[0398] STOCK OPENING BALANCE
[0399] STOCK OVERVIEW
[0400] STOCK USAGE FORECAST
[0401] STOCK USAGE FORECAST VERSION
[0402] STOCKOUT
[0403] STORAGE BIN
[0404] TIME
[0405] UNIT OF MEASURE
[0406] UNIT OF MEASURE CONVERSION
[0407] USER CATEGORY
[0408] VALUATION
[0409] VENDOR
[0410] VENDOR PROFILE
[0411] WORK ORDER
Functional Areas 203, Areas of Analysis 202, and Measures 111
[0412] Referring back to FIG. 16 and FIGS. 17A to 17AE, the
following is a listing of areas of analysis 202 and their measures
111 which may be used in a data warehouse system 100:
Sales
[0413] The sales functional area 1703 data model component may
include Sales Distribution Detail 1722, Sales Billing Detail 1723,
and Sales Order Detail 1724 data structures. The sales functional
area 1703 data model component may also include Sales Contract
Detail and Sales Activity Summary data structures.
[0414] The Sales Distribution Detail 1722 data structure may
comprise:
[0415] Actual Delivered Base Unit Quantity
[0416] Actual Delivered Sale Unit Quantity
[0417] Company Code
[0418] Actual Goods Issue Date Sid
[0419] Changed Date
[0420] Complete Delivery Indicator
[0421] Created Date
[0422] Delivered Date Sid
[0423] Distribution Channel Code
[0424] Document Currency Code
[0425] Group To Document Currency Conversion Rt
[0426] Document Currency Extended Cost Amount
[0427] Document Currency Extended Net Price Amt
[0428] Document Currency Extended Net Value Amt
[0429] Document Item Number
[0430] Document Number
[0431] Document Type Code
[0432] Group Currency Code
[0433] Group Currency Extended Net Price Amount
[0434] Group Currency Extended Net Value Amount
[0435] Loaded Date Sid
[0436] Local Currency Code
[0437] Group to Local Currency Conversion Rate
[0438] Local Currency Extended Net Price Amount
[0439] Local Currency Extended Net Value Amount
[0440] Next Planned Shipping Date Sid
[0441] Order Combination Indicator
[0442] Planned Goods Issue Date Sid
[0443] Priority Delivery Code
[0444] Requested Delivery Date Sid
[0445] Scheduled Transportation Date Sid
[0446] The Sales Billing Detail 1723 data structure may
comprise:
[0447] Adjustment Identifier
[0448] Changed Date
[0449] Created Date
[0450] Customer Transaction Line Number
[0451] Customer Transaction Number
[0452] Document Currency Code
[0453] Group to Document Currency Exchange Rate
[0454] Document Currency Extended Cost Amount
[0455] Document Currency Extended Price Amount
[0456] Document Currency Cash Discount Amount
[0457] Document Currency Freight Amount
[0458] Document Currency Tax Amount
[0459] Document Item Number
[0460] Document Number
[0461] Document Type Code
[0462] Group Currency Code
[0463] Group Currency Discount Amount
[0464] Group Currency Extended Price Amount
[0465] Group Currency Cash Discount Amount
[0466] Group Currency Freight Amount
[0467] Group Currency Profit Margin Amount
[0468] Group Currency Tax Amount
[0469] Local Currency Code
[0470] Group to Local Currency Exchange Rate
[0471] Local Currency Extended Price Amount
[0472] Local Currency Cash Discount Amount
[0473] Local Currency Freight Amount
[0474] Local Currency Tax Amount
[0475] The Sales Order Detail 1724 data structure may comprise:
[0476] Changed Date
[0477] Created Date
[0478] Document Currency Code
[0479] Group To Document Currency Conversion Rt
[0480] Document Currency Discount Amount
[0481] Document Currency Extended Cost Amount
[0482] Document Currency Extended Price Amount
[0483] Document Currency Profit Margin Amount
[0484] Document Currency Freight Amount
[0485] Document Currency Tax Amount
[0486] Document Item Number
[0487] Document Number
[0488] Document Type Code
[0489] Group Currency Code
[0490] Group Currency Discount Amount
[0491] Group Currency Extended Cost Amount
[0492] Group Currency Extended Price Amount
[0493] Group Currency Freight Amount
[0494] Group Currency Profit Margin Amount
[0495] Group Currency Tax Amount
[0496] Local Currency Code
[0497] Group to Local Currency Conversion Rate
[0498] Local to Document Currency Conversion Rt
[0499] Local Currency Discount Amount
[0500] Local Currency Extended Cost Amount
[0501] Local Currency Extended Price Amount
[0502] Local Currency Freight Amount
[0503] Local Currency Profit Margin Amount
AR
[0504] The AR functional area 1705 data model component may include
AR Activity Detail 1728, AR Daily Activity Summary 1729, AR Monthly
Activity Summary 1730, and AR Monthly Account Summary 1731 data
structures. The AR functional area 1705 data model component may
also include AR Invoice Summary and AR Payment Summary data
structures.
[0505] The AR Activity Detail 1728 data structure may comprise:
[0506] Debit Multiplier
[0507] Credit Multiplier
[0508] Local Currency Amount
[0509] Local Currency Net Amount
[0510] Local Currency Tax Amount
[0511] Local Currency Discount Amount
[0512] Local Currency Cost Amount
[0513] Local Currency Freight Amount
[0514] Local Currency Profit Margin Amount
[0515] Group Currency Amount
[0516] Group Currency Net Amount
[0517] Group Currency Tax Amount
[0518] Group Currency Discount Amount
[0519] Group Currency Cost Amount
[0520] Group Currency Freight Amount
[0521] Group Currency Profit Margin Amount
[0522] Created Date
[0523] Changed Date
[0524] The AR Daily Activity Summary 1729 data structure may
comprise:
[0525] Daily Open Transaction Count
[0526] Daily New Transaction Count
[0527] Daily Total Open Item Amount
[0528] Daily Total New Item Amount
[0529] Daily Total Transaction Amount
[0530] Daily Total Gross Sales Revenue Amount
[0531] Daily Total Net Sales Revenue Amount
[0532] Daily Total Revenue Amount
[0533] Daily Average Transaction Amount
[0534] Daily Average Gross Sales Revenue Amount
[0535] Daily Average Net Sales Revenue Amount
[0536] Daily New To Open Amount Ratio
[0537] Daily New To Open Count Ratio
[0538] The AR Monthly Activity Summary 1730 data structure may
comprise:
[0539] Monthly Open Transaction Count
[0540] Monthly New Transaction Count
[0541] Monthly Discount Taken Transaction Count
[0542] Monthly Discount Refused Transaction Count
[0543] Monthly Total Transaction Amount
[0544] Monthly Profit Amount
[0545] Monthly Average Transaction Count
[0546] Monthly Average Transaction Amount
[0547] Monthly New to Open Transact Count Ratio
[0548] Monthly New to Open Transac Amount Ratio
[0549] Monthly Average Daily Sales Volume
[0550] Monthly Average Collection Period
[0551] Monthly Value Past Due Amount
[0552] Monthly Trade Discount Cost Amount
[0553] Monthly Effect on Bottom Line Amount
[0554] Monthly Collection Effectiveness Index
[0555] Dollar Weighted Avg Days Outstanding Amt
[0556] Dollar Weighted Avg Days Beyond Term Amt
[0557] Dollar Weighted Average Days to Pay Amt
[0558] Monthly Net Credit Period
[0559] Monthly AR Account Balance Amount
[0560] Created Date
[0561] Changed Date
[0562] The AR Monthly Account Summary 1731 data structure may
comprise:
[0563] Monthly Average Cost To Serve Amount
[0564] Monthly Avg Invoice Payment Day Count
[0565] Monthly Cost to Serve Amount
[0566] Monthly Average Daily Sales Volume
[0567] Monthly Average Collection Period
[0568] Monthly Value Past Due Amount
[0569] Monthly Trade Discount Cost Amount
[0570] Monthly Effect on Bottom Line Amount
[0571] Monthly Average Deliquent Day Count
[0572] Monthly Collection Effectiveness Index
[0573] Dollar Weighted Avg Days Outstanding Amt
[0574] Dollar Weighted Avg Days Beyond Term Amt
[0575] Dollar Weighted Average Days to Pay Amt
[0576] Monthly AR Account Balance Amount
GL
[0577] The GL functional area 1704 data model component may include
GL Activity Detail 1725, GL Balance 1726, and GL Budget 1727 data
structures.
[0578] The GL Activity Detail 1725 data structure may comprise:
[0579] Local Currency Amount
[0580] Local Currency Credit Amount
[0581] Local Currency Debit Amount
[0582] Local Currency Net Amount
[0583] Group Currency Credit Amount
[0584] Group Currency Debit Amount
[0585] Group Currency Net Amount
[0586] Changed Date
[0587] Created Date
[0588] The GL Balance 1726 data structure may comprise:
[0589] Changed Date
[0590] Created Date
[0591] Group Currency Close Bal Amount
[0592] Group Currency Period Credit Amount
[0593] Group Currency Period Debit Amount
[0594] Group Currency Period Net Activity Amt
[0595] Group Currency Period Open Bal Amount
[0596] Group Currency Year Open Bal Amount
[0597] Group Currency YTD Credit Amount
[0598] Group Currency YTD Debit Amount
[0599] Group Currency YTD Net Activity Amount
[0600] Local Currency Close Bal Amount
[0601] Local Currency Period Credit Amount
[0602] Local Currency Period Debit Amount
[0603] Local Currency Period Net Activity Amt
[0604] Local Currency Period Open Bal Amount
[0605] Local Currency Year Open Bal Amount
[0606] Local Currency YTD Credit Amount
[0607] Local Currency YTD Debit Amount
[0608] Local Currency YTD Net Activity Amount
[0609] Year End Indicator
[0610] The GL Budget 1727 data structure may comprise:
[0611] Changed Date
[0612] Created Date
[0613] Group Currency Close Bal Amount
[0614] Group Currency Period Activity Amount
[0615] Group Currency Period Open Bal Amount
[0616] Group Currency Year Open Bal Amount
[0617] Group Currency YTD Activity Amount
[0618] Local Currency Close Bal Amount
[0619] Local Currency Period Activity Amount
[0620] Local Currency Period Open Bal Amount
[0621] Local Currency Year Opening Bal Amount
[0622] Local Currency YTD Activity Amount
AP
[0623] The AP functional area 1706 data model component may include
AP Activity Detail 1732, AP Monthly Activity Summary 1733, AP
Monthly Account Summary 1734, and AP Daily Activity Summary 1735
data structures. The AP functional area 1706 data model component
may also include AP Invoice Summary and AP Payment Summary data
structures.
[0624] The AP Activity Detail 1732 data structure may comprise:
[0625] Local Currency Amount
[0626] Local Currency Net Amount
[0627] Local Currency Tax Amount
[0628] Local Currency Discount Taken Amount
[0629] Local Currency Discount Allowed Amount
[0630] Local Currency Freight Amount
[0631] Group Currency Amount
[0632] Group Currency Net Amount
[0633] Group Currency Tax Amount
[0634] Group Currency Discount Taken Amount
[0635] Group Currency Discount Allowed Amount
[0636] Group Currency Freight Amount
[0637] Total Payment Days Count
[0638] Payment Term Day Count
[0639] Payment Discount Day Count
[0640] Created Date
[0641] Changed Date
[0642] The AP Monthly Activity Summary 1733 data structure may
comprise:
[0643] New Transaction Count
[0644] Open Transaction Count
[0645] Discount Taken Transaction Count
[0646] Discount Refused Transaction Count
[0647] New Transaction Amount
[0648] Open Transaction Amount
[0649] Discount Taken Amount
[0650] Discount Available Amount
[0651] Created Date
[0652] Changed Date
[0653] The AP Monthly Account Summary 1734 data structure may
comprise:
[0654] AP Account Balance Amount
[0655] Average Days Past Due Count
[0656] Average Collection Period
[0657] Bad Debt Amount
[0658] Invoice Count
[0659] Invoice Amount
[0660] Payment Count
[0661] Payment Amount
[0662] Adjustment Count
[0663] Adjustment Amount
[0664] Best Possible DPI Ratio
[0665] Bottom Line Effect Amount
[0666] Payment Effectiveness Index
[0667] Cost To Serve Amount
[0668] Days of Purchases Instanding Ratio
[0669] Net Credit Purchases Amount
[0670] Past Due Amount
[0671] Trade Discount Profit Amount
[0672] Trade Discount Offered Amount
[0673] Dollar Weighted Avg Days Beyond Term Amt
[0674] Past Due Count
[0675] Dollar Weighted Avg Days Outstanding Amt
[0676] The AP Daily Activity Summary 1735 data structure may
comprise:
[0677] Open Transaction Count
[0678] New Transaction Count
[0679] Discount Taken Transaction Count
[0680] Discount Refused Transaction Count
[0681] Discount Taken Amount
[0682] Discount Available Amount
[0683] Open Transaction Amount
[0684] New Transaction Amount
[0685] Total Gross Sales Revenue Amount
[0686] Total Net Sales Revenue Amount
[0687] Total Revenue Amount
[0688] Past Due Amount
[0689] Average Transaction amount
[0690] Average Gross Sales Revenue Amount
[0691] Average Net Sales Revenue Amount
Inventory
[0692] The Inventory functional area 1702 data model component may
include Stock Usage Forecast 1713, Physical Inventory 1714,
Material Reservation 1715, Stock Overview 1716, and Material
Movement 1721 data structures.
[0693] The Stock Usage Forecast 1713 data structure may
comprise:
[0694] Forecast First Day Date
[0695] Modified Forecast First Day Date
[0696] Forecast Period Number
[0697] Forecast Value
[0698] Corrected Value
[0699] Seasonal Index Value
[0700] Created Date
[0701] Changed Date
[0702] The Physical Inventory 1714 data structure may comprise:
[0703] Document Number
[0704] Document Item Number
[0705] Inventory Fiscal Year
[0706] Book Stock Level Count
[0707] Book Stock Document Cur Extndd Val Amt
[0708] Book Stock Group Currency Extndd Val Amt
[0709] Book Stock Local Currency Extndd Val Amt
[0710] Physical Inventory Count
[0711] Physical Inventory Grp Cur Extnd Val Amt
[0712] Final Count Indicator
[0713] Absolute Stock Accuracy Percentage
[0714] Relative Stock Accuracy Percentage
[0715] UserName
[0716] Last Count Date Sid
[0717] The Material Reservation 1715 data structure may
comprise:
[0718] Document Number
[0719] Document Item Number
[0720] Reservation Date
[0721] Reserved Quantity
[0722] Reserved Quantity Doc Cur Extndd Val Amt
[0723] Reserved Quantity Grp Cur Extndd Val Amt
[0724] Reserved Quantity Lcl Cur Extndd Val Amt
[0725] Confirmed Quantity
[0726] Withdrawn Quantity
[0727] Confirmed Quantity Grp Cur Extnd Val Amt
[0728] Withdrawn Quantity Grp Cur Extnd Val Amt
[0729] Document Currency Code
[0730] Document Currency Conversion Rate
[0731] Group Currency Code
[0732] Local Currency Code
[0733] Local Currency Conversion Rate
[0734] User Name
[0735] Deletion Indicator
[0736] Final Issue Indicator
[0737] The Stock Overview 1716 data structure may comprise:
[0738] Calendar Month
[0739] Absolute Stock Accuracy Percentage
[0740] Average Stock Level
[0741] Average Unrestricted Stock Level
[0742] Closing Stock Level
[0743] Closing Unrestricted Stock Level
[0744] Cumulative Usage Quantity
[0745] Forecast Usage Quantity
[0746] Last Used Date
[0747] Maximum Stock Level
[0748] Maximum Unrestricted Stock Level
[0749] Minimum Stock Level
[0750] Minimum Unrestricted Stock Level
[0751] Moving Average Stock Level
[0752] Moving Average Usage Quantity
[0753] Moving Avg unrestricted Stock Level
[0754] Opening Stock Level
[0755] The Material Movement 1721 data structure may comprise:
[0756] Purchase Order Number
[0757] Purchase Order Item Number
[0758] Document Date
[0759] Expiration Date
[0760] Group Currency Value
[0761] Movement Quantity
[0762] Created Date
[0763] Changed Date
Procurement
[0764] The Procurement functional area 1701 data model component
may include Procurement Activity Periodic Summary 1707, Requisition
Activity Detail 1708, Quotation Activity Detail 1709, Purchase
Order Activity Detail 1710, Contract Activity Detail 1711, and
Contract Document Summary 1712 data structures.
[0765] The Procurement Activity Periodic Summary 1707 data
structure may comprise:
[0766] Open Entered Document Count
[0767] Open Blocked Document Count
[0768] Open Approved Document Count
[0769] Completed Closed Document Count
[0770] Completed Cancelled Document Count
[0771] Total Document Open Days Count
[0772] Remaining Document Dollar Amount
[0773] Total Document Value
[0774] Changed Date
[0775] Created Date
[0776] The Requisition Activity Detail 1708 data structure may
comprise:
[0777] Group To Local Exchange Rate
[0778] On Hold Quantity
[0779] Open Quantity
[0780] Received Quantity
[0781] Relieved Quantity
[0782] Requested Transaction Quantity
[0783] Group Currency Estimated Unit Price Anit
[0784] Group Currency Extended Price Amount
[0785] Group Currency Other Expenses Amount
[0786] Group Currency Total Landed Cost Amount
[0787] Group Currency Tax Amount
[0788] Group Currency Duty Amount
[0789] Group Currency Freight Amount
[0790] Touch Count
[0791] Correction Count
[0792] Adjustment Count
[0793] Created Date
[0794] Changed Date
[0795] The Quotation Activity Detail 1709 data structure may
comprise:
[0796] Transaction Quantity
[0797] On Hold Quantity
[0798] Open Quantity
[0799] Received Quantity
[0800] Relieved Quantity
[0801] Group Currency Unit Price Amount
[0802] Group Currency Extended Price Amount
[0803] Group Currency Other Expenses Amount
[0804] Group Currency Total Landed Cost Amount
[0805] Group Currency Tax Amount
[0806] Group Currency Duty Amount
[0807] Group Currency Freight Amount
[0808] Group To Local Exchange Rate
[0809] Touch Count
[0810] Correction Count
[0811] Adjustment Count
[0812] Created Date
[0813] Changed Date
[0814] The Purchase order Activity Detail 1710 data structure may
comprise:
[0815] Transaction Quantity
[0816] On Hold Quantity
[0817] Open Quantity
[0818] Received Quantity
[0819] Relieved Quantity
[0820] Group Currency Unit Price Amount
[0821] Group Currency Extended Price Amount
[0822] Group Currency Other Expenses Amount
[0823] Group Currency Total Landed Cost Amount
[0824] Group Currency Tax Amount
[0825] Group Currency Duty Amount
[0826] Group Currency Freight Amount
[0827] Group To Local Exchange Rate
[0828] Touch Count
[0829] Correction Count
[0830] Adjustment Count
[0831] Created Date
[0832] Changed Date
[0833] The Contract Activity Detail 1711 data structure may
comprise:
[0834] Transaction Quantity
[0835] On Hold Quantity
[0836] Open Quantity
[0837] Relieved Quantity
[0838] Cumulative Received Quantity
[0839] Received Quantity
[0840] Group Currency Unit Price Amount
[0841] Group Currency Target Commitment Amount
[0842] Group To Local Exchange Rate
[0843] Touch Count
[0844] Correction Count
[0845] Adjustment Count
[0846] Created Date
[0847] Changed Date
[0848] The Contract Document Summary 1712 data structure may
comprise:
[0849] Total Contract Dollar Value
[0850] Remaining Dollar Value
[0851] Created Date
[0852] Changed Date
Reports
[0853] The following is a listing of some of the reports and
groupings of reports for some functional areas:
[0854] Procurement Reporting:
[0855] MATERIAL DEMAND ANALYSIS
[0856] Internal Customer Profile and Ranking
[0857] Material Demand Analysis and Trends
[0858] Demand Rationalization
[0859] VENDOR PROFILE
[0860] Vendor Ranking
[0861] Vendor Expenditure Overview
[0862] Contract Activity Analysis
[0863] Contract Analysis
[0864] Vendor - Material Rationalization
[0865] Vendor Profiling
[0866] OPERATIONAL EFFECTIVENESS
[0867] Procurement Activity Overview
[0868] Buyer Account Management Status
[0869] Buyer Comparisons
[0870] Procurement Process Efficiency
[0871] Buyer Activity Overview
[0872] Contract Usage Analysis
[0873] Release Strategies
[0874] OPERATIONAL REPORTING
[0875] Document Lists
[0876] Inventory Reporting:
[0877] INVENTORY PERFORMANCE
[0878] Stock Level Overview and Comparisons
[0879] Stock Level Analysis (Plant, Material)
[0880] Detailed Storage Stock Levels
[0881] DEMAND ANALYSIS
[0882] Stock Usage Overview and Comparisons
[0883] Stock Usage Analysis
[0884] Detailed List of Usage
[0885] MATERIAL TRACKING
[0886] Material Movement Overview and Comparisons
[0887] Movements Analysis
[0888] RESOURCE ACTIVITY
[0889] Resource Activity Overview
[0890] Activity Comparisons
[0891] Plant/Employee Analysis
[0892] STOCK ACCURACY
[0893] Stock Overview
[0894] Stock Comparisons
[0895] Stock Analysis
[0896] RESERVATIONS
[0897] Reservations Overview
[0898] Reservations Comparisons
[0899] Reservations Analysis
[0900] FORECASTS
[0901] Stock Forecast Overview and Comparisons
[0902] Stock Forecast Analysis
[0903] Stock Forecasts Profile
[0904] VENDOR ANALYSIS (MOVEMENTS)
[0905] Vendor Overview and Comparisons
[0906] Vendor Analysis
[0907] Vendor Activity Profile
[0908] AP Reporting:
[0909] AP MANAGEMENT OVERVIEW
[0910] Ageing Overview
[0911] Payments Analysis
[0912] Quality of Accounts Receivable
[0913] Bad Debt Analysis
[0914] VENDOR ACCOUNT MANAGEMENT
[0915] Vendor A/P Overview
[0916] Vendor Ageing
[0917] Top Ten Vendor Activity Report
[0918] Overdue Accounts
[0919] Vendor Account Overview
[0920] Vendor Transaction Summary
[0921] Vendor Activity Analysis
[0922] Analysis of Adjustments
[0923] Vendor Profile Status
[0924] VENDOR PAYABLES SCORECARDING
[0925] Vendor Cost Analysis
[0926] Discount Analysis
[0927] OPERATIONAL EFFECTIVENESS
[0928] Organizational Overview
[0929] Account Management Status
[0930] Analyst Activity Overview
[0931] Analyst Profile Overview
[0932] Analyst Profile Status
[0933] Document Flow Report
[0934] CASH OUTFLOW MANAGEMENT
[0935] Payment Schedule
[0936] Cash Outflow Forecasts
[0937] GL Reporting:
[0938] INCOME STATEMENT ANALYSIS
[0939] Income Statement Time Comparisons
[0940] Vertical Analysis
[0941] Detailed Income Statement
[0942] Income Statement Budget Variances
[0943] BALANCE SHEET ANALYSIS
[0944] Balance Sheet Time Comparisons
[0945] Balance Sheet Time Trends
[0946] Detailed Balance Sheet
[0947] Balance Sheet Budget Variance
[0948] FINANCIAL/LEGAL ENTITY ANALYSIS
[0949] Company, Profit and Cost Center Comparison of Financial
Reports
[0950] Company, Profit Center and Cost Center Rankings and
Comparisons
[0951] Ratio Trends
[0952] BUDGET ANALYSIS
[0953] Customer Profitability Analysis
[0954] Customer Cost Analysis
[0955] Discount Analysis
[0956] OPERATIONAL REPORTS
[0957] Cost Center Analysis
[0958] Account Analysis
[0959] Trial Balance
[0960] General Ledger Detail
[0961] KEY FINANCIAL RATIOS
[0962] Multi-dimensional analysis of key financial ratios:
[0963] Leverage Ratios including Debt to Asset and Times Interest
Earned
[0964] Liquidity Ratios including Current, Quick Ratio, Fixed Asset
Turnover, Total Asset Turnover
[0965] Profitability or Efficiency Ratios including Profit Margin,
Inventory Turnover, Return on Assets, Return on Equity
[0966] Sales Reporting:
[0967] SALES ORDER LIFE CYCLE
[0968] Sales Orders Overview and Comparison
[0969] Sales Orders Analysis
[0970] Sales Order List By Customer
[0971] Customer Order Profiles
[0972] CUSTOMER BUYING TRENDS
[0973] Customer Buying Overview and Comparisons
[0974] Trends Analysis
[0975] Billings List By Customer
[0976] Customer Ranking
[0977] SALES/PRODUCT PERFORMANCE
[0978] Sales and Product Overview/Comparison
[0979] Sales and Product Performance Analysis
[0980] Sales Office and Sales Rep Performance Profiles
[0981] Product Sales List
[0982] Product Performance Profile
[0983] SHIPPING CHANNEL TREND/DRIVERS
[0984] Shipping Overview and Analysis
[0985] Shipping Channel Comparisons
[0986] Shipping Performance Overview/Comparisons
[0987] Shipping Profile and Document List by Product
[0988] CHANNEL PERFORMANCE
[0989] Channel Overview and Comparisons
[0990] Channel Performance Analysis
[0991] Billing List by Channel; Channel Profile
[0992] DELIVERY/ON-TIME DELIVERY ANALYSIS
[0993] Delivery Effectiveness Overview and Comparisons
[0994] Delivery Effectiveness Analysis
[0995] Shipping Point Profile
[0996] AR Reporting:
[0997] AR MANAGEMENT OVERVIEW
[0998] Ageing Overview
[0999] Collection Analysis
[1000] Quality of Accounts Receivable
[1001] Bad Debt Analysis
[1002] CUSTOMER COLLECTION MANAGEMENT
[1003] Customer A/R Overview
[1004] Customer Ageing
[1005] Top Ten Customer Activity Report
[1006] Overdue Accounts
[1007] CUSTOMER ACCOUNT MANAGEMENT
[1008] Customer Account Overview
[1009] Customer Transaction Summary
[1010] Customer Activity Analysis
[1011] Analysis of Adjustments
[1012] Customer Profile Status
[1013] CUSTOMER SCORECARDING
[1014] Customer Profitability Analysis
[1015] Customer Cost Analysis
[1016] Discount Analysis
[1017] OPERATIONAL EFFECTIVENESS
[1018] Organizational Overview
[1019] Account Management Status
[1020] Analyst Activity Overview
[1021] Analyst Profile Overview
[1022] Analyst Performance Comparison
[1023] Document Flow Report
[1024] AR AND SALES ANALYSIS
[1025] Accounts Receivable and Sales Related KPIs
[1026] Customer AR Sales Overview
[1027] The data warehouse system of the present invention may be
implemented by any hardware, software or a combination of hardware
and software having the above described functions. The software
code, either in its entirety or a part thereof, may be stored in a
computer readable memory. Further, a computer data signal
representing the software code which may be embedded in a carrier
wave may be transmitted via a communication network. Such a
computer readable memory and a computer data signal are also within
the scope of the present invention, as well as the hardware,
software and the combination thereof.
[1028] While specific embodiments of the present invention have
been described, various modifications and substitutions may be made
to such embodiments. Such modifications and substitutions are
within the scope of the present invention, and are intended to be
covered by the following claims.
* * * * *