Description
Supply Chain Business Intelligence Model
Supply Chain Business Intelligence Model
Nenad Stefanovic
1
, Vidosav Majstorovic
2
, Dusan Stefanovic
1
1
Information Systems Division, Zastava Automobiles, Inc., Kragujevac, Serbia and Montenegro
2
Laboratory for Production Metrology and TQM, Mechanical Engineering Faculty, University of Belgrade,
Serbia and Montenegro
3
Department for Informatics, Faculty of Science, University of Kragujevac, Serbia and Montenegro
Abstract
This paper discusses the need for Supply Chain Business Intelligence and driving forces for its adoption,
and presents BI development life cycle along with quality success factors. Also, Internet-based supply
chain BI model that enables many-to-many, loosely-coupled information exchange for collaborative
business analysis and decision making is described. Finally, the main elements of the developed BI
solution and Enterprise Portal based on the native web technologies will be presented.
Keywords
SCM, BI, Model
1 INTRODUCTION
The demands for a high quality product and services are
rising. Customers want the right product, at the right place
and in timely fashion. Modern manufacturing has driven
down the time and cost of the production process, leaving
supply chains as the final frontier for cost reduction and
competitive advantage. In such environment organization
can not be viewed as a single business entity, but rather
as a part of the supply chain that is competing with other
chains on the market [1]. Supply chain management
(SCM) was seen as an opportunity for cost reduction
through optimization, and real-time collaboration with
trading partners.
Given the increasing competition in today’s tough
business climate, it is vital that organizations provide cost-
effective and rapid access to business information for a
wide range of business users, if these organizations are to
survive into the new millennium. The solution to this issue
is a business intelligence (BI) system, which provides a
set of technologies and products for supplying users with
the information they need to answer business questions,
and make tactical and strategic business decisions.
Data is an asset to any organization. However, its value is
realized only if it is in a ready to use form. Hence,
gathering, managing and utilizing data has been a major
activity for all the organizations over the years. Giving a
meaningful shape to disparate sources of data lying at
different operational systems, databases and applications,
is very difficult task.
Being able to consolidate and analyze this data for better
business decisions can often lead to a competitive
advantage, and learning to uncover and leverage those
advantages is what business intelligence is all about.
By providing wider visibility to plans and supporting data,
BI tools increase the return on existing SCM applications
because they help companies understand where and how
they deviate from their plan objectives. In addition, they
provide shared data availability that encourages a global
perspective on business performance.
Supply Chain Intelligence (SCI) is a new initiative that
provides the capability to reveal opportunities to cut costs,
stimulate revenue, and increase customer satisfaction by
utilizing collaborative decision making [2]. SCI takes
broader, multidimensional view of supply chain in which,
using patterns and rules, meaningful information about the
data can be discovered.
The following megatrends can be identified [3]:
• Information democracy - Companies are putting
business intelligence tools and dashboards in the
hands of hundreds of white-collar employees, not just
a few marketing or financial analysts.
• Unstructured data - Tomorrow's data warehouses will
have free-form text -- like notes from the call centre
agent about why the customer hated your product --
and even images.
• Predictive analytics - Tools that can predict what your
customers are likely to buy, and when they're likely to
defect, will be extremely powerful.
• Integration - BI software will be blended into regular
operations to the point where managers will be able to
monitor business activity throughout the day and some
business decisions will be automated.
Regulatory concerns and an increasing quantity of data
caused BI to retain the top spot in planned purchases.
Demand for financial applications also stayed on top in
2005 with 4 percent growth [4]. There is also trend of
recognizing information as a strategic part of the business
[5]. Companies are implementing enterprise-wide BI into
other key enterprise projects that promise to optimize
business processes and deliver benefits to the bottom
line.
Businesses collect large quantities of data in their day-to-
day operations: data about orders, inventory, accounts
payable, point-of-sale transactions, and of course,
customers. In addition, businesses often acquire data,
such as demographics and mailing lists, from outside
sources. Being able to consolidate and analyze this data
for better business decisions can often lead to a
competitive advantage, and learning to uncover and
leverage those advantages is what business intelligence
is all about. Some examples are:
• Achieving growth in sales, reduction in operating
costs, and improved supply management and
development.
• Using OLAP to reduce the burden on the IT staff,
improve information access for business processing,
uncover new sources of revenue, and improve
allocation of costs.
• Using data mining to extract key purchase behaviors
from customer survey data.
When implemented, a BI system should help decision
makers extend information access and analysis
capabilities to a broader user base, as well as capture and
share individual expertise to benefit the enterprise.
613
2 BI DEVELOPMENT
2.1 BI development life cycle
Almost every kind of engineering project—structural
engineering as well as software engineering—goes
through six stages between inception and implementation:
Stage 1.
Justification: Assess the business need that gives rise to
the new engineering project.
Stage 2.
Planning: Develop strategic and tactical plans, which lay
out how the engineering project will be accomplished and
deployed.
Stage 3.
Business analysis: Perform detailed analysis of the
business problem or business opportunity to gain a solid
understanding of the business requirements for a potential
solution (product).
Stage 4.
Design: Conceive a product that solves the business
problem or enables the business opportunity.
Stage 5.
Construction: Build the product, which should provide a
return on investment within a predefined time frame.
Stage 6.
Deployment: Implement or sell the finished product, then
measure its effectiveness to determine whether the
solution meets, exceeds, or fails to meet the expected
return on investment.
BI development steps are shown in Figure 1. They span
the whole product development lifecycle from business
case assesment to implementation.
Design
ETL Design
Justification
Business Case
Assesment
Planning
Project Planning
Enterprise
Infrastructure
Evaluation
Business Analysis
Project Requirements
Definition
Data Analysis
Application
Prototyping
Meta Data Repository
Analysis
Database Design Meta Data Repository
Design
Construction
Application
Development
Data Mining
ETL Development
Meta Data repository
Development
Deployment
Implementation
Release evaluation
Figure 1: BI Development Stages
Because there is a natural order of progression from one
engineering stage to another, certain dependencies exist
between some of the development steps. Steps stacked
on top of each other in the diagram are performed
relatively linearly (with less overlap) because of their
dependencies, while steps that appear to the right or left
of each other can be performed simultaneously.
Since BI is an enterprise-wide evolving environment that
is continually improved and enhanced based on feedback
from the business community, the system development
practices of the past are inadequate and inappropriate.
Unlike static stand-alone systems, a dynamic, integrated
BI decision-support environment cannot be built in one big
bang. Data and functionality must be rolled out in iterative
releases, and each deployment is likely to trigger new
requirements for the next release. A waterfall
methodology is not suitable for the iterative releases of BI
decision-support applications, but an agile and adaptive
development guide specifically geared toward BI decision-
support applications is.
While some development steps are clearly project-
specific, most development steps must be performed from
a cross-organizational perspective. Thus the focus of
those project activities takes on a cross-functional
dimension, and the reviewers of those activities should
include business representatives from other lines of
business. The main task for the business representatives
from the other lines of business is to validate and ratify the
strategies, policies, business rules, and standards either
being used or being developed during the BI project.
Building a BI decision-support environment is a never-
ending process. Unlike most operational systems, which
have sharply defined functionalities, BI applications must
evolve to handle emerging information needs. As the
needs and goals of your organization change, so must the
BI decision-support environment. There is no practical
way to anticipate all possible questions in the initial design
of the BI decision-support environment or of any BI
application. The best you can do at any given time is to
have an environment that supports the current
organizational goals and that can be easily adapted to
new goals. Plan to design flexible and easy-to-change BI
applications so that you have the ability to modify them
when the organization's goals change. This applies to all
BI initiatives, from small departmental data marts to large
industrial-strength enterprise data warehouses. Be
prepared to modify all BI applications and BI target
databases in future releases in order to provide new query
and reporting capabilities and more data.
2.2 Critical factors for a BI Solution
The most important guidelines for achieving project
success are [6]:
• Scope the project to be able to deliver within at least
six months.
• Select a specific business subject area; do not try to
solve all business requirements within one project.
• Find a sponsor from the upper management of the
business side of the company.
• Involve the sponsor throughout the project.
• Establish a sound information and communication
structure that includes business and technical staff
inside and outside the project.
• Define the contents and type of the deliverables of the
project as early and in as much detail as possible.
• Together with the end users validate the results of the
analysis phase (the initial dimensional models) against
the deliverables definition.
• Deploy the solution quickly to a limited audience and
iterate development.
614 PROCEEDINGS OF LCE2006
• Establish commonly agreed on business definitions for
all items within the scope of the project.
• Validate the quality and correctness of the information
before making it available to the end user community.
• Keep the end users involved and informed throughout
the project.
• Be prepared for political and cultural obstacles
between business departments or between business
and IT departments.
Also, there are a number of success indicators such as:
• Return on investment (ROI) – Lower costs, improved
productivity, increased revenue.
• The data warehouse utilization.
• The data warehouse usefulness.
• The project is delivered on time.
• The project is delivered within budget.
• There is improved user satisfaction.
• There are additional requests for data warehouse
functions and data.
• Goals and objectives are met.
• Business opportunity is realized.
• Business performance-based benchmarks.
Some of the indications of failure are:
• Users are unhappy with the quality of the data.
• Project is out of budget.
• Only a small percentage of users take advantage of
the data warehouse.
• Users are unhappy with the analytical tools.
• Integration is not achieved.
3 SUPPLY CHAIN BI MODELING
In order to achieve SCI objectives we need a supply
chain-wide business excellence model that will provide
consistent framework for establishing, modelling,
managing, measuring, and improving supply chain
processes. SCI model is based on the global supply chain
excellence which unifies the business domain (modelling,
people, existing supply chain and business process
models, best practices, and quality management models),
and the functional domain (information technology
infrastructure, modern object-oriented development
methods, and patterns) [7] and methodology for supply
chain process integration [8].
3.1 Process Approach
Processes are important assets. They are a company’s
core competencies and determine business performance.
Managing and measuring business processes is critical
for SCI.
Supply Chain Operations Reference (SCOR) model
integrates the well-known concepts of business process
reengineering, benchmarking, and process measurement
into a cross-functional framework, and represents an
industry standard [9]. SCOR model is also very useful in
achieving a common understanding of SCM domain from
the software agent point of view [10]. It contains standard
descriptions of management processes, a framework of
relationships among the standard processes, standard
metrics to measure process performance, and
management practices that produce best-in-class
performance. SCOR is based on five distinct management
processes: plan, source, make, deliver, return. Thanks to
the standardization and metrics system, partners in supply
chain can communicate more unambiguously,
collaboratively measure, manage, and control their
processes, and establish benchmarking for performance
comparison and uncover best business practices for
gaining competitive advantage.
However, SCOR does not address important areas such
as organization-wide training and development, tools and
methodologies focused on process execution, project
management, and problem solving techniques [11]. These
problems can be surpassed by incorporating different
quality management initiatives like ISO 9000, Lean, and
Six Sigma, thus creating comprehensive integrated
management system.
3.2 Modelling and Design Methodology
For every supply chain, it is essential to make a model of
the business. Models provide visualization (visualize final
situation, show relationships between “objects”),
complexity management (focus on one aspect at a time,
reuse patterns/objects), and communication (standard
symbols, go in details). A business model is composed of
the views, diagrams, and objects and processes. It helps
to better understand the key mechanisms of an existing
supply chain, to act as the basis for creating suitable
information systems that support the business, to act as
the basis for improving the current business structure and
operation, and decision-making. Modelling and analysing
the logistic interdependencies across supply chains
enables supply chains to better control their process
reliability [12]. Modelling approaches are essential for
developing and benchmarking autonomous logistic
processes [13]. However, the traditional modelling
techniques and notations do not satisfy modelling
demands of complex processes found in supply chains.
An advantage of modelling in a language such as UML
(Unified Modelling Language) is that it visually depicts
functions and relationships that are usually difficult to
visualize clearly and offers standard notation throughout
lifecycle, both for the business people and the software
specialists. The UML consists of nine different diagram
types, and each diagram shows a specific static or
dynamic aspect of a system. Using the technologies
based on XML such as XMI (Extensible Model
Interchange), models can be exchanged among different
teams across the supply chain and different CASE
(Computer Aided Software Engineering) tools. Common
Warehouse Metadata (CWM) addresses the metadata
definition issue for the business intelligence field,
including OALP, data mining, transformation, and so on
[14]. It is also based on UML.
Using the object-oriented modelling techniques to
describe the business has several advantages: concepts
similar to real-life, well-proven established techniques,
standard notation, short learning curve, and new and
easier ways to view an organization or a business.
We based our supply chain modelling on UML, and
techniques like RUP (Rational Unified Process) [15] and
EUP (Enterprise Unified Process) which includes new
disciplines and phases, and should be tailored into the
standard RUP, making it more effective [16]. RUP uses
the iterative approach and it allows to be extended and
configured for the particular use.
3.3 SCI Model
The proposed modelling approach enables the creation of
RUP plug-in tailored for SCM and based on the SCOR.
With this process framework supply chain processes can
be modelled. Each supply chain can decide how much of
the process to implement and which roles, activities,
artefacts, and workflows it will use - Business modeling,
Requirements, Development & Analysis, Implementation,
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CIRP INTERNATIONAL CONFERENCE ON LIFE CYCLE ENGINEERING 615
Test, Deployment, Project management, Configuration
and Change Management, and Environment.
The global SCI model is shown in Figure 2. It starts with
defining business objectives and eliciting requirements,
and followed with the creation of supply chain
configuration based on SCOR Metamodel.
We have created the SCOR Metamodel (Figure 3) which
enables creation of any supply chain configuration and it
is the basis for further modelling. The Metamodel is
normalized and contains all SCOR elements such as
processes, metrics, best practices, inputs and outputs. It
also incorporates business logic through relationships,
cardinality, and constrains.
The object model and UML notation allow us to use
object-oriented techniques such as inheritance,
encapsulation, abstraction, generalization, etc. Also,
patterns can be identified that offer model and software
reuse.
The Metamodel is extended with additional entities to
support supply network modelling. That way, processes,
metrics and best practices can be related to specific
nodes and tiers in the supply network.
SCOR defines processes in three levels of details. With
this Metamodel, lower-level processes also can be
modelled thus providing more detailed view of supply
chain processes.
In the next phase, further modelling is performed in
relation to specific business subject. The resulting
artefacts are different types of UML diagrams (use case,
activity, class, component, database, etc.) which are basis
for the operational data store (ODS), ERP applications,
and SCI solutions.
After we have created supply chain model from our
metamodel (we have metrics system for each of the
process level and the nodes) and forward engineered
ODS from the model, the next step is to design data
warehouse metadata. Now, we can design star schemas
and snowflakes with particular fact tables, dimensions,
measures, hierarchies, and aggregations. BI Metadata is
very important as it represents the foundation for a further
analysis. Since the BI is domain specific, it is necessary to
involve business analysts during the modelling in order to
ensure that metadata supports the real supply chain. The
well designed and collaboratively maintained metadata
ensures the data quality and provides a single version of
the truth.
Figure 2: SCI Global Model.
The last step is creating the front-end analysis
applications such as KPI (Key Performance Indicator)
systems, balance scorecards, reporting systems, and data
mining solutions. These tools provide end-users with
predefined and ad-hoc reports, help them to measure and
monitor progress toward organizational goals, and
discover meaningful information about the data. When
being web-enabled they can be consumed by any client
application and platform.
Figure 3: SCOR Metamodel.
616 PROCEEDINGS OF LCE2006
4 SUMMARY
Automotive industry, as one of the most complex, faces
considerable challenges. SCI can help supply chain
participants to integrate their systems and achieve a
sophisticated level of data analysis and control of their
information bases.
The proposed SCI model is used to develop the SCI
solution in one big Serbian automotive company. This
company has typical supply chain which consists of six
internal factories and many external partners. The
objective was to create SCI solution that would provide
collaborative and global analysis and decision making.
The existing system infrastructure which included one
huge central relational database for internal factories, but
also the data in different sources (flat files, spreadsheets,
raw files, and databases) had to be taken into account.
The proposed model allow us to configure the design
processes according to concrete business requirements,
but also to design a solution which can be easily changed
and scaled in the future. This comprehensive, integrated
approach helps to seamlessly build and deploy robust
business intelligence applications to transform information
into better business decisions across the supply chain and
at all organizational levels. These kinds of projects are
very complex and multidisciplinary, with many of the
technical details, so only main characteristics will be
presented.
The project started with the following steps:
• Project management plan, Team creation, and
Requirements definition.
• Supply chain modelling based on SCOR Metamodel.
• Detailed modelling with UML and according to the
unified process.
The knowledge gained from the previous modelling
represent the inputs for the further SCI design: ETL
packages, OLAP cubes, facts, dimensions, measures,
hierarchies, KPIs, and reports.
Some of the interesting and useful features of the solution
are:
• Allows business rules to be captured in the model to
support richer analysis.
• Allows the user model to be greatly enriched.
• Supports real-time analysis using the proactive
caching.
• Hierarchies which are imply a sequence of attributes
that can then be used in queries to ease such drill-
down/drill-up scenarios.
• Measures, hierarchies, and other objects are grouped
into folders meaningful to the user, allowing the
reporting tool to display large numbers of attributes in
a manageable way.
• Allows translations of metadata to be provided in any
language.
• Perspectives, each one presenting only a specific
subset of the model (measures, dimensions,
attributes, and so forth) that is relevant to a particular
group of users.
• KPI system where each performance metric contains:
actual value, goal, value, status, and trend as shown
in Figure 5.
• Users can take action based upon the data they see
• Security: Roles can be defined, permissions granted to
the roles, and users included as members of each
role.
Figure 5: KPI System
Figure 6: SCI Web Portal
• SCI web portal with main features (Figure 6.): role-
based security, customization, personalisation,
support for collaboration, document exchange,
connection to different data sources (cubes,
databases, XML web services), views, analytical tools
such as ad-hoc multidimensional queries (MDX), cell
colouring, drilldown, etc.
5 SUMMARY
Business intelligence has become essential in most
organizations. BI is not constrained to individual
departments or organizations, but rather is viewed as
essential at the supply chain level with many
organizations now focusing on growing their BI maturity
vis-a-vis prior states as well as peer organizations.
This paper analyzed the need for supply chain BI and
presented the development methodology that incorporate
iterative and cross-functional approach.
The introduced SCI model promotes the unified approach
for supply chain modelling and BI design according to real
business requirements. It reduces the development
lifecycle by optimizing the iterative and incremental
delivery mechanism and workflow and utilizes best
practices and standards in formulating the processes in
supply network. Supply chain standard process reference
model with its metrics and best practices, quality
management, and object-oriented modelling provide
extensive knowledge which can be used in the design of
data warehouse metadata, data mining models and
multidimensional analysis.
Supply chain business intelligence reveals opportunities
to reduce costs and stimulate revenue growth and it
enables companies to understand the entire supply chain
from the customer’s perspective.
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CIRP INTERNATIONAL CONFERENCE ON LIFE CYCLE ENGINEERING 617
REFERENCES
[1] Lambert, M.-D., Cooper, C.-M., Pagh, D.-J., 1998,
Supply Chain Management: Implementation Issues
and Research Opportunities, The International
Journal of Logistics Management, 44/2:1-19.
[2] Haydock, P.-M., 2003, Supply Chain Intelligence,
ASCET, 5:15-21.
[3] Bets M., Get Smart About Business Intelligence,
2005, Computerworld Inc., Framingham, Mass., 4-6.
[4] Uncapher M., 2005, ITAA E-Letter, Information
Technology Association of America.
[5] Top 10 Trends in Business Intelligence and Data
Warehousing for 2005, Knightbridge Solutions LLC,
IL, USA.
[6] Reinschmidt J., Francoise A., 2000, Business
Intelligence Certification Guide, IBM Corp.
[7] Stefanovic, N., Majstorovic, V., Stefanovic, D., 2005,
Research and Development of Digital Quality Model
in SCM, Third International Working Conference TQM
– Advanced and Intelligent Approaches, Belgrade,
Serbia and Montenegro. 189-197.
[8] Stefanovic, D., Majstorovic, V., Stefanovic, N., 2005,
Methodology for Process Integration in Supply
Networks, The 38
th
CIRP ISMS, Florianopolis, Brazil.
[9] Supply-Chain Operations Reference-Model Overview
Version 7.0, 2005, Supply Chain Council,
www.supply-chain.org.
[10] Scholz-Reiter, B., Höhns, H., Hamann, T., 2004,
Adaptive Control of Supply Chains: Building blocks
and tools of an agent-based simulation framework,
Annals of the CIRP, 53/1:353.
[11] [Recker, R., Bolstroff, P., 2003, Integration of SCOR
with Lean & Six Sigma, Advanced Integrated
Technologies Group, Inc.
[12] H-P. Wiendahl H.-P., von Cieminski, G., Begemann,
C., 2003, A Systematic Approach for Ensuring the
Logistic Process Reliability of Supply Chains, Annals
of the CIRP, 52/1:375.
[13] Scholz-Reiter, B., Freitag, M., de Beer, C., Jagalski,
T., 2005, Modelling Dynamics of Autonomous
Logistic Processes: Discrete-event versus
Continuous Approaches, 54/1:413.
[14] The Common Warehouse Metamodel, Object
Management Group, Inc., MA, www.omg.org/cwm.
[15] Rational Unified Process Home Page, 2004, IBM,
www.rational.com/products/rup/index.jsp.
[16] Ambler W.-S., Nalbone, J., Vizdos, J.-M., 2005, The
Enterprise Unified Process: Extending the Rational
Unified Process, Pearson Education, Prentice Hall
PTR, Upper Saddle River, New Jersey.
618 PROCEEDINGS OF LCE2006
doc_439056424.pdf
Supply Chain Business Intelligence Model
Supply Chain Business Intelligence Model
Nenad Stefanovic
1
, Vidosav Majstorovic
2
, Dusan Stefanovic
1
1
Information Systems Division, Zastava Automobiles, Inc., Kragujevac, Serbia and Montenegro
2
Laboratory for Production Metrology and TQM, Mechanical Engineering Faculty, University of Belgrade,
Serbia and Montenegro
3
Department for Informatics, Faculty of Science, University of Kragujevac, Serbia and Montenegro
Abstract
This paper discusses the need for Supply Chain Business Intelligence and driving forces for its adoption,
and presents BI development life cycle along with quality success factors. Also, Internet-based supply
chain BI model that enables many-to-many, loosely-coupled information exchange for collaborative
business analysis and decision making is described. Finally, the main elements of the developed BI
solution and Enterprise Portal based on the native web technologies will be presented.
Keywords
SCM, BI, Model
1 INTRODUCTION
The demands for a high quality product and services are
rising. Customers want the right product, at the right place
and in timely fashion. Modern manufacturing has driven
down the time and cost of the production process, leaving
supply chains as the final frontier for cost reduction and
competitive advantage. In such environment organization
can not be viewed as a single business entity, but rather
as a part of the supply chain that is competing with other
chains on the market [1]. Supply chain management
(SCM) was seen as an opportunity for cost reduction
through optimization, and real-time collaboration with
trading partners.
Given the increasing competition in today’s tough
business climate, it is vital that organizations provide cost-
effective and rapid access to business information for a
wide range of business users, if these organizations are to
survive into the new millennium. The solution to this issue
is a business intelligence (BI) system, which provides a
set of technologies and products for supplying users with
the information they need to answer business questions,
and make tactical and strategic business decisions.
Data is an asset to any organization. However, its value is
realized only if it is in a ready to use form. Hence,
gathering, managing and utilizing data has been a major
activity for all the organizations over the years. Giving a
meaningful shape to disparate sources of data lying at
different operational systems, databases and applications,
is very difficult task.
Being able to consolidate and analyze this data for better
business decisions can often lead to a competitive
advantage, and learning to uncover and leverage those
advantages is what business intelligence is all about.
By providing wider visibility to plans and supporting data,
BI tools increase the return on existing SCM applications
because they help companies understand where and how
they deviate from their plan objectives. In addition, they
provide shared data availability that encourages a global
perspective on business performance.
Supply Chain Intelligence (SCI) is a new initiative that
provides the capability to reveal opportunities to cut costs,
stimulate revenue, and increase customer satisfaction by
utilizing collaborative decision making [2]. SCI takes
broader, multidimensional view of supply chain in which,
using patterns and rules, meaningful information about the
data can be discovered.
The following megatrends can be identified [3]:
• Information democracy - Companies are putting
business intelligence tools and dashboards in the
hands of hundreds of white-collar employees, not just
a few marketing or financial analysts.
• Unstructured data - Tomorrow's data warehouses will
have free-form text -- like notes from the call centre
agent about why the customer hated your product --
and even images.
• Predictive analytics - Tools that can predict what your
customers are likely to buy, and when they're likely to
defect, will be extremely powerful.
• Integration - BI software will be blended into regular
operations to the point where managers will be able to
monitor business activity throughout the day and some
business decisions will be automated.
Regulatory concerns and an increasing quantity of data
caused BI to retain the top spot in planned purchases.
Demand for financial applications also stayed on top in
2005 with 4 percent growth [4]. There is also trend of
recognizing information as a strategic part of the business
[5]. Companies are implementing enterprise-wide BI into
other key enterprise projects that promise to optimize
business processes and deliver benefits to the bottom
line.
Businesses collect large quantities of data in their day-to-
day operations: data about orders, inventory, accounts
payable, point-of-sale transactions, and of course,
customers. In addition, businesses often acquire data,
such as demographics and mailing lists, from outside
sources. Being able to consolidate and analyze this data
for better business decisions can often lead to a
competitive advantage, and learning to uncover and
leverage those advantages is what business intelligence
is all about. Some examples are:
• Achieving growth in sales, reduction in operating
costs, and improved supply management and
development.
• Using OLAP to reduce the burden on the IT staff,
improve information access for business processing,
uncover new sources of revenue, and improve
allocation of costs.
• Using data mining to extract key purchase behaviors
from customer survey data.
When implemented, a BI system should help decision
makers extend information access and analysis
capabilities to a broader user base, as well as capture and
share individual expertise to benefit the enterprise.
613
2 BI DEVELOPMENT
2.1 BI development life cycle
Almost every kind of engineering project—structural
engineering as well as software engineering—goes
through six stages between inception and implementation:
Stage 1.
Justification: Assess the business need that gives rise to
the new engineering project.
Stage 2.
Planning: Develop strategic and tactical plans, which lay
out how the engineering project will be accomplished and
deployed.
Stage 3.
Business analysis: Perform detailed analysis of the
business problem or business opportunity to gain a solid
understanding of the business requirements for a potential
solution (product).
Stage 4.
Design: Conceive a product that solves the business
problem or enables the business opportunity.
Stage 5.
Construction: Build the product, which should provide a
return on investment within a predefined time frame.
Stage 6.
Deployment: Implement or sell the finished product, then
measure its effectiveness to determine whether the
solution meets, exceeds, or fails to meet the expected
return on investment.
BI development steps are shown in Figure 1. They span
the whole product development lifecycle from business
case assesment to implementation.
Design
ETL Design
Justification
Business Case
Assesment
Planning
Project Planning
Enterprise
Infrastructure
Evaluation
Business Analysis
Project Requirements
Definition
Data Analysis
Application
Prototyping
Meta Data Repository
Analysis
Database Design Meta Data Repository
Design
Construction
Application
Development
Data Mining
ETL Development
Meta Data repository
Development
Deployment
Implementation
Release evaluation
Figure 1: BI Development Stages
Because there is a natural order of progression from one
engineering stage to another, certain dependencies exist
between some of the development steps. Steps stacked
on top of each other in the diagram are performed
relatively linearly (with less overlap) because of their
dependencies, while steps that appear to the right or left
of each other can be performed simultaneously.
Since BI is an enterprise-wide evolving environment that
is continually improved and enhanced based on feedback
from the business community, the system development
practices of the past are inadequate and inappropriate.
Unlike static stand-alone systems, a dynamic, integrated
BI decision-support environment cannot be built in one big
bang. Data and functionality must be rolled out in iterative
releases, and each deployment is likely to trigger new
requirements for the next release. A waterfall
methodology is not suitable for the iterative releases of BI
decision-support applications, but an agile and adaptive
development guide specifically geared toward BI decision-
support applications is.
While some development steps are clearly project-
specific, most development steps must be performed from
a cross-organizational perspective. Thus the focus of
those project activities takes on a cross-functional
dimension, and the reviewers of those activities should
include business representatives from other lines of
business. The main task for the business representatives
from the other lines of business is to validate and ratify the
strategies, policies, business rules, and standards either
being used or being developed during the BI project.
Building a BI decision-support environment is a never-
ending process. Unlike most operational systems, which
have sharply defined functionalities, BI applications must
evolve to handle emerging information needs. As the
needs and goals of your organization change, so must the
BI decision-support environment. There is no practical
way to anticipate all possible questions in the initial design
of the BI decision-support environment or of any BI
application. The best you can do at any given time is to
have an environment that supports the current
organizational goals and that can be easily adapted to
new goals. Plan to design flexible and easy-to-change BI
applications so that you have the ability to modify them
when the organization's goals change. This applies to all
BI initiatives, from small departmental data marts to large
industrial-strength enterprise data warehouses. Be
prepared to modify all BI applications and BI target
databases in future releases in order to provide new query
and reporting capabilities and more data.
2.2 Critical factors for a BI Solution
The most important guidelines for achieving project
success are [6]:
• Scope the project to be able to deliver within at least
six months.
• Select a specific business subject area; do not try to
solve all business requirements within one project.
• Find a sponsor from the upper management of the
business side of the company.
• Involve the sponsor throughout the project.
• Establish a sound information and communication
structure that includes business and technical staff
inside and outside the project.
• Define the contents and type of the deliverables of the
project as early and in as much detail as possible.
• Together with the end users validate the results of the
analysis phase (the initial dimensional models) against
the deliverables definition.
• Deploy the solution quickly to a limited audience and
iterate development.
614 PROCEEDINGS OF LCE2006
• Establish commonly agreed on business definitions for
all items within the scope of the project.
• Validate the quality and correctness of the information
before making it available to the end user community.
• Keep the end users involved and informed throughout
the project.
• Be prepared for political and cultural obstacles
between business departments or between business
and IT departments.
Also, there are a number of success indicators such as:
• Return on investment (ROI) – Lower costs, improved
productivity, increased revenue.
• The data warehouse utilization.
• The data warehouse usefulness.
• The project is delivered on time.
• The project is delivered within budget.
• There is improved user satisfaction.
• There are additional requests for data warehouse
functions and data.
• Goals and objectives are met.
• Business opportunity is realized.
• Business performance-based benchmarks.
Some of the indications of failure are:
• Users are unhappy with the quality of the data.
• Project is out of budget.
• Only a small percentage of users take advantage of
the data warehouse.
• Users are unhappy with the analytical tools.
• Integration is not achieved.
3 SUPPLY CHAIN BI MODELING
In order to achieve SCI objectives we need a supply
chain-wide business excellence model that will provide
consistent framework for establishing, modelling,
managing, measuring, and improving supply chain
processes. SCI model is based on the global supply chain
excellence which unifies the business domain (modelling,
people, existing supply chain and business process
models, best practices, and quality management models),
and the functional domain (information technology
infrastructure, modern object-oriented development
methods, and patterns) [7] and methodology for supply
chain process integration [8].
3.1 Process Approach
Processes are important assets. They are a company’s
core competencies and determine business performance.
Managing and measuring business processes is critical
for SCI.
Supply Chain Operations Reference (SCOR) model
integrates the well-known concepts of business process
reengineering, benchmarking, and process measurement
into a cross-functional framework, and represents an
industry standard [9]. SCOR model is also very useful in
achieving a common understanding of SCM domain from
the software agent point of view [10]. It contains standard
descriptions of management processes, a framework of
relationships among the standard processes, standard
metrics to measure process performance, and
management practices that produce best-in-class
performance. SCOR is based on five distinct management
processes: plan, source, make, deliver, return. Thanks to
the standardization and metrics system, partners in supply
chain can communicate more unambiguously,
collaboratively measure, manage, and control their
processes, and establish benchmarking for performance
comparison and uncover best business practices for
gaining competitive advantage.
However, SCOR does not address important areas such
as organization-wide training and development, tools and
methodologies focused on process execution, project
management, and problem solving techniques [11]. These
problems can be surpassed by incorporating different
quality management initiatives like ISO 9000, Lean, and
Six Sigma, thus creating comprehensive integrated
management system.
3.2 Modelling and Design Methodology
For every supply chain, it is essential to make a model of
the business. Models provide visualization (visualize final
situation, show relationships between “objects”),
complexity management (focus on one aspect at a time,
reuse patterns/objects), and communication (standard
symbols, go in details). A business model is composed of
the views, diagrams, and objects and processes. It helps
to better understand the key mechanisms of an existing
supply chain, to act as the basis for creating suitable
information systems that support the business, to act as
the basis for improving the current business structure and
operation, and decision-making. Modelling and analysing
the logistic interdependencies across supply chains
enables supply chains to better control their process
reliability [12]. Modelling approaches are essential for
developing and benchmarking autonomous logistic
processes [13]. However, the traditional modelling
techniques and notations do not satisfy modelling
demands of complex processes found in supply chains.
An advantage of modelling in a language such as UML
(Unified Modelling Language) is that it visually depicts
functions and relationships that are usually difficult to
visualize clearly and offers standard notation throughout
lifecycle, both for the business people and the software
specialists. The UML consists of nine different diagram
types, and each diagram shows a specific static or
dynamic aspect of a system. Using the technologies
based on XML such as XMI (Extensible Model
Interchange), models can be exchanged among different
teams across the supply chain and different CASE
(Computer Aided Software Engineering) tools. Common
Warehouse Metadata (CWM) addresses the metadata
definition issue for the business intelligence field,
including OALP, data mining, transformation, and so on
[14]. It is also based on UML.
Using the object-oriented modelling techniques to
describe the business has several advantages: concepts
similar to real-life, well-proven established techniques,
standard notation, short learning curve, and new and
easier ways to view an organization or a business.
We based our supply chain modelling on UML, and
techniques like RUP (Rational Unified Process) [15] and
EUP (Enterprise Unified Process) which includes new
disciplines and phases, and should be tailored into the
standard RUP, making it more effective [16]. RUP uses
the iterative approach and it allows to be extended and
configured for the particular use.
3.3 SCI Model
The proposed modelling approach enables the creation of
RUP plug-in tailored for SCM and based on the SCOR.
With this process framework supply chain processes can
be modelled. Each supply chain can decide how much of
the process to implement and which roles, activities,
artefacts, and workflows it will use - Business modeling,
Requirements, Development & Analysis, Implementation,
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CIRP INTERNATIONAL CONFERENCE ON LIFE CYCLE ENGINEERING 615
Test, Deployment, Project management, Configuration
and Change Management, and Environment.
The global SCI model is shown in Figure 2. It starts with
defining business objectives and eliciting requirements,
and followed with the creation of supply chain
configuration based on SCOR Metamodel.
We have created the SCOR Metamodel (Figure 3) which
enables creation of any supply chain configuration and it
is the basis for further modelling. The Metamodel is
normalized and contains all SCOR elements such as
processes, metrics, best practices, inputs and outputs. It
also incorporates business logic through relationships,
cardinality, and constrains.
The object model and UML notation allow us to use
object-oriented techniques such as inheritance,
encapsulation, abstraction, generalization, etc. Also,
patterns can be identified that offer model and software
reuse.
The Metamodel is extended with additional entities to
support supply network modelling. That way, processes,
metrics and best practices can be related to specific
nodes and tiers in the supply network.
SCOR defines processes in three levels of details. With
this Metamodel, lower-level processes also can be
modelled thus providing more detailed view of supply
chain processes.
In the next phase, further modelling is performed in
relation to specific business subject. The resulting
artefacts are different types of UML diagrams (use case,
activity, class, component, database, etc.) which are basis
for the operational data store (ODS), ERP applications,
and SCI solutions.
After we have created supply chain model from our
metamodel (we have metrics system for each of the
process level and the nodes) and forward engineered
ODS from the model, the next step is to design data
warehouse metadata. Now, we can design star schemas
and snowflakes with particular fact tables, dimensions,
measures, hierarchies, and aggregations. BI Metadata is
very important as it represents the foundation for a further
analysis. Since the BI is domain specific, it is necessary to
involve business analysts during the modelling in order to
ensure that metadata supports the real supply chain. The
well designed and collaboratively maintained metadata
ensures the data quality and provides a single version of
the truth.
Figure 2: SCI Global Model.
The last step is creating the front-end analysis
applications such as KPI (Key Performance Indicator)
systems, balance scorecards, reporting systems, and data
mining solutions. These tools provide end-users with
predefined and ad-hoc reports, help them to measure and
monitor progress toward organizational goals, and
discover meaningful information about the data. When
being web-enabled they can be consumed by any client
application and platform.
Figure 3: SCOR Metamodel.
616 PROCEEDINGS OF LCE2006
4 SUMMARY
Automotive industry, as one of the most complex, faces
considerable challenges. SCI can help supply chain
participants to integrate their systems and achieve a
sophisticated level of data analysis and control of their
information bases.
The proposed SCI model is used to develop the SCI
solution in one big Serbian automotive company. This
company has typical supply chain which consists of six
internal factories and many external partners. The
objective was to create SCI solution that would provide
collaborative and global analysis and decision making.
The existing system infrastructure which included one
huge central relational database for internal factories, but
also the data in different sources (flat files, spreadsheets,
raw files, and databases) had to be taken into account.
The proposed model allow us to configure the design
processes according to concrete business requirements,
but also to design a solution which can be easily changed
and scaled in the future. This comprehensive, integrated
approach helps to seamlessly build and deploy robust
business intelligence applications to transform information
into better business decisions across the supply chain and
at all organizational levels. These kinds of projects are
very complex and multidisciplinary, with many of the
technical details, so only main characteristics will be
presented.
The project started with the following steps:
• Project management plan, Team creation, and
Requirements definition.
• Supply chain modelling based on SCOR Metamodel.
• Detailed modelling with UML and according to the
unified process.
The knowledge gained from the previous modelling
represent the inputs for the further SCI design: ETL
packages, OLAP cubes, facts, dimensions, measures,
hierarchies, KPIs, and reports.
Some of the interesting and useful features of the solution
are:
• Allows business rules to be captured in the model to
support richer analysis.
• Allows the user model to be greatly enriched.
• Supports real-time analysis using the proactive
caching.
• Hierarchies which are imply a sequence of attributes
that can then be used in queries to ease such drill-
down/drill-up scenarios.
• Measures, hierarchies, and other objects are grouped
into folders meaningful to the user, allowing the
reporting tool to display large numbers of attributes in
a manageable way.
• Allows translations of metadata to be provided in any
language.
• Perspectives, each one presenting only a specific
subset of the model (measures, dimensions,
attributes, and so forth) that is relevant to a particular
group of users.
• KPI system where each performance metric contains:
actual value, goal, value, status, and trend as shown
in Figure 5.
• Users can take action based upon the data they see
• Security: Roles can be defined, permissions granted to
the roles, and users included as members of each
role.
Figure 5: KPI System
Figure 6: SCI Web Portal
• SCI web portal with main features (Figure 6.): role-
based security, customization, personalisation,
support for collaboration, document exchange,
connection to different data sources (cubes,
databases, XML web services), views, analytical tools
such as ad-hoc multidimensional queries (MDX), cell
colouring, drilldown, etc.
5 SUMMARY
Business intelligence has become essential in most
organizations. BI is not constrained to individual
departments or organizations, but rather is viewed as
essential at the supply chain level with many
organizations now focusing on growing their BI maturity
vis-a-vis prior states as well as peer organizations.
This paper analyzed the need for supply chain BI and
presented the development methodology that incorporate
iterative and cross-functional approach.
The introduced SCI model promotes the unified approach
for supply chain modelling and BI design according to real
business requirements. It reduces the development
lifecycle by optimizing the iterative and incremental
delivery mechanism and workflow and utilizes best
practices and standards in formulating the processes in
supply network. Supply chain standard process reference
model with its metrics and best practices, quality
management, and object-oriented modelling provide
extensive knowledge which can be used in the design of
data warehouse metadata, data mining models and
multidimensional analysis.
Supply chain business intelligence reveals opportunities
to reduce costs and stimulate revenue growth and it
enables companies to understand the entire supply chain
from the customer’s perspective.
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