Decision Support and
Business Intelligence
Systems
Chapter 1:
Decision Support Systems
and Business Intelligence
Learning Objectives
1-2
Understand today's turbulent business
environment and describe how organizations
survive and even excel in such an environment
(solving problems and exploiting opportunities)
Understand the need for computerized support
of managerial decision making
Understand an early framework for managerial
decision making
Learn the conceptual foundations of the
decision support systems (DSS)
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Learning Objectives – cont.
1-3
Describe the business intelligence (BI)
methodology and concepts and relate them to
DSS
Describe the concept of work systems and its
relationship to decision support
List the major tools of computerized decision
support
Understand the major issues in implementing
computerized support systems
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Opening Vignette:
“Norfolk Southern Uses BI for Decision
Support to Reach Success”
Company background
Problem
Proposed solution
Results
Answer and discuss the case questions
1-4
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Changing Business Environment
Companies are moving aggressively to
computerized support of their
operations => Business Intelligence
Business Pressures–Responses–Support
Model
1-5
Business pressures result of today's
competitive business climate
Responses to counter the pressures
Support to better facilitate the process
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Business Pressures–Responses–
Support Model
1-6
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The Business Environment
The environment in which organizations
operate today is becoming more and
more complex, creating:
Business environment factors:
1-7
opportunities, and
problems
Example: globalization
markets, consumer demands, technology,
and societal…
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Business Environment Factors
FACTOR
Markets
Consumer
demand
Technology
Societal
1-8
DESCRIPTION
Strong competition
Expanding global markets
Blooming electronic markets on the Internet
Innovative marketing methods
Opportunities for outsourcing with IT support
Need for real-time, on-demand transactions
Desire for customization
Desire for quality, diversity of products, and speed of delivery
Customers getting powerful and less loyal
More innovations, new products, and new services
Increasing obsolescence rate
Increasing information overload
Social networking, Web 2.0 and beyond
Growing government regulations and deregulation
Workforce more diversified, older, and composed of more women
Prime concerns of homeland security and terrorist attacks
Necessity of Sarbanes-Oxley Act and other reporting-related legislation
Increasing social responsibility of companies
Greater emphasis on sustainability
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Organizational Responses
Be Reactive, Anticipative, Adaptive, and
Proactive
Managers may take actions, such as
1-9
Employ strategic planning
Use new and innovative business models
Restructure business processes
Participate in business alliances
Improve corporate information systems
Improve partnership relationships
Encourage innovation and creativity …cont…>
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Managers actions, continued
1-10
Improve customer service and relationships
Move to electronic commerce (e-commerce)
Move to make-to-order production and on-demand
manufacturing and services
Use new IT to improve communication, data access
(discovery of information), and collaboration
Respond quickly to competitors' actions (e.g., in
pricing, promotions, new products and services)
Automate many tasks of white-collar employees
Automate certain decision processes
Improve decision making by employing analytics
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Closing the Strategy Gap
1-11
One of the major objectives of
computerized decision support is to
facilitate closing the gap between the
current performance of an organization
and its desired performance, as
expressed in its mission, objectives, and
goals, and the strategy to achieve them
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Managerial Decision Making
Management is a process by which
organizational goals are achieved by
using resources
1-12
Inputs: resources
Output: attainment of goals
Measure of success: outputs / inputs
Management Decision Making
Decision making: selecting the best
solution from two or more alternatives
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Mintzberg's 10 Managerial Roles
Interpersonal
1. Figurehead
2. Leader
3. Liaison
Informational
4. Monitor
5. Disseminator
6. Spokesperson
1-13
Decisional
7. Entrepreneur
8. Disturbance handler
9. Resource allocator
10. Negotiator
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Decision Making Process
Managers usually make decisions by
following a four-step process (a.k.a. the
scientific approach)
1.
2.
3.
4.
1-14
Define the problem (or opportunity)
Construct a model that describes the realworld problem
Identify possible solutions to the modeled
problem and evaluate the solutions
Compare, choose, and recommend a
potential solution to the problem
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Decision making is difficult, because
1-15
Technology, information systems, advanced search
engines, and globalization result in more and more
alternatives from which to choose
Government regulations and the need for compliance,
political instability and terrorism, competition, and
changing consumer demands produce more
uncertainty, making it more difficult to predict
consequences and the future
Other factors are the need to make rapid decisions,
the frequent and unpredictable changes that make
trial-and-error learning difficult, and the potential costs
of making mistakes
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Why Use Computerized DSS
Computerized DSS can facilitate
decision via:
1-16
Speedy computations
Improved communication and collaboration
Increased productivity of group members
Improved data management
Overcoming cognitive limits
Quality support; agility support
Using Web; anywhere, anytime support
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A Decision Support Framework
(by Gory and Scott-Morten, 1971)
1-17
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A Decision Support Framework – cont.
Degree of Structuredness (Simon, 1977)
Decision are classified as
Types of Control (Anthony, 1965)
1-18
Highly structured (a.k.a. programmed)
Semi-structured
Highly unstructured (i.e., non-programmed)
Strategic planning (top-level, long-range)
Management control (tactical planning)
Operational control
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Simon’s Decision-Making Process
1-19
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Computer Support for Structured
Decisions
Structured problems: encountered
repeatedly, have a high level of structure
It is possible to abstract, analyze, and
classify them into specific categories
1-20
e.g., make-or-buy decisions, capital
budgeting, resource allocation, distribution,
procurement, and inventory control
For each category a solution approach is
developed => Management Science
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Management Science Approach
Also referred to as Operation Research
In solving problems, managers should
follow the five-step MS approach
1.
2.
3.
4.
5.
1-21
Define the problem
Classify the problem into a standard category (*)
Construct a model that describes the real-world
problem
Identify possible solutions to the modeled problem
and evaluate the solutions
Compare, choose, and recommend a potential
solution to the problem
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Automated Decision Making
A relatively new approach to supporting
decision making
Applies to highly structures decisions
Automated decision systems (ADS)
(or decision automation systems)
An ADS is a rule-based system that
provides a solution to a repetitive
managerial problem in a specific area
1-22
e.g., simple-loan approval system
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Automated Decision Making
ADS initially appeared in the airline
industry called revenue (or yield)
management (or revenue optimization)
systems
1-23
dynamically price tickets based on actual
demand
Today, many service industries use
similar pricing models
ADS are driven by business rules!
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Computer Support for
Unstructured Decisions
1-24
Unstructured problems can be only
partially supported by standard
computerized quantitative methods
They often require customized solutions
They benefit from data and information
Intuition and judgment may play a role
Computerized communication and
collaboration technologies along with
knowledge management is often used
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Computer Support for
Semi-structured Problems
1-25
Solving semi-structured problems may
involve a combination of standard
solution procedures and human
judgment
MS handles the structured parts while
DSS deals with the unstructured parts
With proper data and information, a
range of alternative solutions, along with
their potential impacts
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Automated Decision-Making
Framework
1-26
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Concept of Decision Support Systems
Classical Definitions of DSS
1-27
Interactive computer-based systems, which help
decision makers utilize data and models to solve
unstructured problems" - Gorry and Scott-Morton, 1971
Decision support systems couple the intellectual
resources of individuals with the capabilities of the
computer to improve the quality of decisions. It is a
computer-based support system for management
decision makers who deal with semistructured
problems
- Keen and Scott-Morton, 1978
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DSS as an Umbrella Term
The term DSS can be used as an
umbrella term to describe any
computerized system that supports
decision making in an organization
1-28
E.g., an organization wide knowledge
management system; a decision support
system specific to an organizational function
(marketing, finance, accounting,
manufacturing, planning, SCM, etc.)
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DSS as a Specific Application
In a narrow sense DSS refers to a
process for building customized
applications for unstructured or semistructured problems
Components of the DSS Architecture
1-29
Data, Model, Knowledge/Intelligence, User,
Interface (API and/or user interface)
DSS often is created by putting together
loosely coupled instances of these
components
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High-Level Architecture of a DSS
1-30
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Types of DSS
Two major types:
Evolution of DSS into Business Intelligence
1-31
Model-oriented DSS
Data-oriented DSS
Use of DSS moved from specialist to managers,
and then whomever, whenever, wherever
Enabling tools like OLAP, data warehousing, data
mining, intelligent systems, delivered via Web
technology have collectively led to the term
“business intelligence” (BI) and “business analytics”
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Business Intelligence (BI)
1-32
BI is an umbrella term that combines
architectures, tools, databases, analytical
tools, applications, and methodologies
Like DSS, BI a content-free expression, so it
means different things to different people
BI's major objective is to enable easy access
to data (and models) to provide business
managers with the ability to conduct analysis
BI helps transform data, to information (and
knowledge), to decisions and finally to action
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A Brief History of BI
The term BI was coined by the Gartner
Group in the mid-1990s
However, the concept is much older
1-33
1970s - MIS reporting - static/periodic reports
1980s - Executive Information Systems (EIS)
1990s - OLAP, dynamic, multidimensional, ad-hoc
reporting -> coining of the term “BI”
2005+ Inclusion of AI and Data/Text Mining
capabilities; Web-based Portals/Dashboards
2010s - yet to be seen
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The Evolution of BI Capabilities
1-34
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The Architecture of BI
A BI system has four major components
1-35
a data warehouse, with its source data
business analytics, a collection of tools for
manipulating, mining, and analyzing the
data in the data warehouse;
business performance management (BPM)
for monitoring and analyzing performance
a user interface (e.g., dashboard)
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A High-Level Architecture of BI
1-36
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Components in a BI Architecture
1-37
The data warehouse is a large repository of
well-organized historical data
Business analytics are the tools that allow
transformation of data into information and
knowledge
Business performance management (BPM)
allows monitoring, measuring, and comparing
key performance indicators
User interface (e.g., dashboards) allows access
and easy manipulation of other BI components
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Styles of BI
MicroStrategy, Corp. distinguishes five
styles of BI and offers tools for each
1.
2.
3.
4.
5.
1-38
report delivery and alerting
enterprise reporting (using dashboards
and scorecards)
cube analysis (also known as slice-anddice analysis)
ad-hoc queries
statistics and data mining
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The Benefits of BI
The ability to provide accurate information
when needed, including a real-time view of
the corporate performance and its parts
A survey by Thompson (2004)
1-39
Faster, more accurate reporting (81%)
Improved decision making (78%)
Improved customer service (56%)
Increased revenue (49%)
See Table 1.3 for a list of BI analytic
applications, the business questions they
answer and the business value they bring
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The DSS–BI Connection
1-40
First, their architectures are very similar
because BI evolved from DSS
Second, DSS directly support specific decision
making, while BI provides accurate and
timely information, and indirectly support
decision making
Third, BI has an executive and strategy
orientation, especially in its BPM and
dashboard components, while DSS, in
contrast, is oriented toward analysts
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The DSS–BI Connection – cont.
1-41
Fourth, most BI systems are constructed with
commercially available tools and components,
while DSS is often built from scratch
Fifth, DSS methodologies and even some tools
were developed mostly in the academic world,
while BI methodologies and tools were
developed mostly by software companies
Sixth, many of the tools that BI uses are also
considered DSS tools (e.g., data mining and
predictive analysis are core tools in both)
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The DSS–BI Connection – cont.
Although some people equate DSS with BI,
these systems are not, at present, the same
1-42
some people believe that DSS is a part of BI—one
of its analytical tools
others think that BI is a special case of DSS that
deals mostly with reporting, communication, and
collaboration (a form of data-oriented DSS)
BI is a result of a continuous revolution and, as
such, DSS is one of BI's original elements
In this book, we separate DSS from BI
MSS = BI and/or DSS
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A Work System View of Decision
Support (Alter, 2004)
1-43
drop the word “systems” from DSS
focus on “decision support”
“use of any plausible computerized or
noncomputerized means for improving decision
making in a particular repetitive or nonrepetitive
business situation in a particular organization”
Work system: a system in which human participants
and/or machines perform a business process, using
information, technology, and other resources, to
produce products and/or services for internal or
external customers
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Elements of a Work System
1.
2.
3.
4.
1-44
Business process. Variations in the process rationale,
sequence of steps, or methods used for performing
particular steps
Participants. Better training, better skills, higher
levels of commitment, or better real-time or delayed
feedback
Information. Better information quality, information
availability, or information presentation
Technology. Better data storage and retrieval,
models, algorithms, statistical or graphical
capabilities, or computer interaction
-->
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Elements of a Work System – cont.
5.
6.
7.
8.
9.
1-45
Product and services. Better ways to evaluate
potential decisions
Customers. Better ways to involve customers in the
decision process and to obtain greater clarity about
their needs
Infrastructure. More effective use of shared
infrastructure, which might lead to improvements
Environment. Better methods for incorporating
concerns from the surrounding environment
Strategy. A fundamentally different operational
strategy for the work system
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Major Tool Categories for MSS
TOOL CATEGORY
TOOLS AND THEIR ACRONYMS
Data management
1-46
Databases and database management system (DBMS)
Extraction, transformation, and load (ETL) systems
Data warehouses (DW), real-time DW, and data marts
Reporting status tracking
Online analytical processing (OLAP)
Executive information systems (EIS)
Visualization
Geographical information systems (GIS)
Dashboards, Information portals
Multidimensional presentations
Business analytics
Optimization, Web analytics
Data mining, Web mining, and text mining
Strategy and performance
Business performance management (BPM)/
management
Corporate performance management (CPM)
Business activity management (BAM)
Dashboards and Scorecards
Communication and
Group decision support systems (GDSS)
collaboration
Group support systems (GSS)
Collaborative information portals and systems
Social networking
Web 2.0, Expert locating systems
Knowledge management
Knowledge management systems (KMS)
Intelligent systems
Expert systems (ES)
Artificial neural networks (ANN)
Fuzzy logic, Genetic algorithms, Intelligent agents
Enterprise systems
Enterprise resource planning (ERP),
Customer Relationship Management (CRM), and
Supply-Chain Management (SCM)
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Source: Table 1.4
Hybrid (Integrated) Support Systems
1-47
The objective of computerized decision support,
regardless of its name or nature, is to assist
management in solving managerial or organizational
problems (and assess opportunities and strategies)
faster and better than possible without computers
Every type of tool has certain capabilities and
limitations. By integrating several tools, we can
improve decision support because one tool can provide
advantages where another is weak
The trend is therefore towards developing
hybrid (integrated) support system
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Hybrid (Integrated) Support Systems
Type of integration
1-48
Use each tool independently to solve different
aspects of the problem
Use several loosely integrated tools. This mainly
involves transferring data from one tool to another
for further processing
Use several tightly integrated tools. From the user's
standpoint, the tool appears as a unified system
In addition to performing different tasks in the
problem-solving process, tools can support
each other
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End of the Chapter
1-49
Questions / Comments…
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retrieval system, or transmitted, in any form or by any means, electronic,
mechanical, photocopying, recording, or otherwise, without the prior written
permission of the publisher. Printed in the United States of America.
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Publishing as Prentice Hall
1-50
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Decision Support and
Business Intelligence
Systems
(9th Ed., Prentice Hall)
Chapter 2:
Decision Making, Systems,
Modeling, and Support
Learning Objectives
Understand the conceptual foundations of
decision making
Understand the need for and the nature of
models in decision making
Understand Simon's four phases of decision
making:
2-2
intelligence,
design,
choice, and
implementation
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Learning Objectives
2-3
Recognize the concepts of rationality and
bounded rationality and how they relate to
decision making
Differentiate between the concepts of making
a choice and establishing a principle of choice
Learn how DSS provide support for decision
making in practice
Understand the systems approach
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Opening Vignette:
“Decision Modeling at HP Using
Spreadsheets”
Company background
Problem
Proposed solution
Results
Answer and discuss the case questions
2-4
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Decision Support Systems (DSS)
Dissecting DSS into
its main concepts
Building successful DSS
requires a through
understanding of these
concepts
2-5
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Characteristics of Decision Making
2-6
Groupthink
Evaluating what-if scenarios
Experimentation with a real system!
Changes in the decision-making
environment may occur continuously
Time pressure on the decision maker
Analyzing a problem takes time/money
Insufficient or too much information
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Characteristics of Decision Making
Better decisions
Fast decision may be detrimental
Areas suffering most from fast decisions
2-7
Tradeoff: accuracy versus speed
personnel/human resources (27%)
budgeting/finance (24%)
organizational structuring (22%)
quality/productivity (20%)
IT selection and installation (17%)
process improvement (17%)
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Decision Making
A process of choosing among two or
more alternative courses of action for
the purpose of attaining a goal(s)
Managerial decision making is
synonymous with the entire
management process
- Simon (1977)
e.g., Planning
2-8
What should be done? When? Where?
Why? How? By whom?
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Decision Making and Problem Solving
A problem occurs when a system
2-9
does not meet its established goals
does not yield the predicted results, or
does not work as planned
Problem is the difference between the
desired and actual outcome
Problem solving also involves
identification of new opportunities
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Decision Making and Problem Solving
Are problem solving and decision making
different? Or, are they the same thing?
Consider phases of the decision process
Phase (1) Intelligence
Phase (2) Design
Phase (3) Choice, and
Phase (4) Implementation
(1)-(4): problem solving; (3): decision making
(1)-(3): decision making; (4): problem solving
2-10
This book: decision making problem solving
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Decision-Making Disciplines
2-11
Behavioral: anthropology, law, philosophy,
political science, psychology, social
psychology, and sociology
Scientific: computer science, decision
analysis, economics, engineering, the hard
sciences (e.g., biology, chemistry, physics),
management science/operations research,
mathematics, and statistics
Each discipline has its own set of assumptions
and each contributes a unique, valid view of
how people make decisions
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Decision Style
The manner by which decision makers
think and react to problems
When making decisions, people…
2-12
perceive a problem
cognitive response
values and beliefs
follow different steps/sequence
give different emphasis, time allotment,
and priority to each steps
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Decision Style
Personality temperament tests are often
used to determine decision styles
There are many such tests
2-13
Meyers/Briggs,
True Colors (Birkman),
Keirsey Temperament Theory, …
Various tests measure somewhat
different aspects of personality
They cannot be equated!
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Decision Style
Decision-making styles
A successful computerized system
should fit the decision style and the
decision situation
2-14
Heuristic versus Analytic
Autocratic versus Democratic
Consultative (with individuals or groups)
Should be flexible and adaptable to
different users (individuals vs. groups)
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Decision Makers
Small organizations
Medium-to-large organizations
2-15
Individuals
Conflicting objectives
Groups
Different styles, backgrounds, expectations
Conflicting objectives
Consensus is often difficult to reach
Help: Computer support, GSS, …
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Model
2-16
A significant part of many DSS and BI
systems
A model is a simplified representation
(or abstraction) of reality
Often, reality is too complex to describe
Much of the complexity is actually
irrelevant in solving a specific problem
Models can represent systems/problems
at various degrees of abstraction
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Types of Models
Models can be classified based on their
degree of abstraction
Degree of abstraction
Less
More
2-17
Iconic models (scale models)
Analog models
Mental Models
Mathematical (quantitative) models
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The Benefits of Models
2-18
Ease of manipulation
Compression of time
Lower cost of analysis on models
Cost of making mistakes on experiments
Inclusion of risk/uncertainty
Evaluation of many alternatives
Reinforce learning and training
Web is source and a destination for it
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Phases of Decision-Making Process
Humans consciously or sub consciously
follow a systematic decision-making
process
- Simon (1977)
1)
2)
3)
4)
5)
2-19
Intelligence
Design
Choice
Implementation
(?) Monitoring (a part of intelligence?)
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Simon’s Decision-Making Process
2-20
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Decision-Making: Intelligence Phase
Scan the environment, either intermittently or
continuously
Identify problem situations or opportunities
Monitor the results of the implementation
Problem is the difference between what
people desire (or expect) and what is actually
occurring
2-21
Symptom versus Problem
Timely identification of opportunities is as
important as identification of problems
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Decision-Making: Intelligence Phase
Potential issues in data/information
collection and estimation
2-22
Lack of data
Cost of data collection
Inaccurate and/or imprecise data
Data estimation is often subjective
Data may be insecure
Key data may be qualitative
Data change over time (time-dependence)
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Decision-Making: Intelligence Phase
Problem Classification
Problem Decomposition
2-23
Classification of problems according to the degree
of structuredness
Often solving the simpler subproblems may help
in solving a complex problem
Information/data can improve the structuredness
of a problem situation
Problem Ownership
A Formal
Outcome of intelligence phase: Problem
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Statement
Decision-Making: The Design Phase
Finding/developing and analyzing possible
courses of actions
A model of the decision-making problem is
constructed, tested, and validated
Modeling: conceptualizing a problem and
abstracting it into a quantitative and/or
qualitative form (i.e., using symbols/variables)
2-24
Abstraction: making assumptions for simplification
Tradeoff (cost/benefit): more or less abstraction
Modeling: both an art and a science
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Decision-Making: The Design Phase
Selection of a Principle of Choice
It is a criterion that describes the
acceptability of a solution approach
Reflection of decision-making objective(s)
In a model, it is the result variable
Choosing and validating against
2-25
High-risk versus low-risk
Optimize versus satisfice
Criterion is not a constraint
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Decision-Making: The Design Phase
Normative models (= optimization)
the chosen alternative is demonstrably the
best of all possible alternatives
Assumptions of rational decision makers
2-26
Humans are economic beings whose objective is
to maximize the attainment of goals
For a decision-making situation, all alternative
courses of action and consequences are known
Decision makers have an order or preference
that enables them to rank the desirability of all
consequences
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Decision-Making: The Design Phase
Heuristic models (= suboptimization)
2-27
the chosen alternative is the best of only a
subset of possible alternatives
Often, it is not feasible to optimize realistic
(size/complexity) problems
Suboptimization may also help relax
unrealistic assumptions in models
Help reach a good enough solution faster
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Decision-Making: The Design Phase
Descriptive models
2-28
describe things as they are or as they are
believed to be (mathematically based)
They do not provide a solution but
information that may lead to a solution
Simulation - most common descriptive
modeling method (mathematical depiction
of systems in a computer environment)
Allows experimentation with the descriptive
model of a system
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Decision-Making: The Design Phase
Good Enough, or Satisficing
“something less than the best”
A form of suboptimization
Seeking to achieving a desired level of
performance as opposed to the “best”
Benefit: time saving
2-29
Simon’s idea of bounded rationality
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Decision-Making: The Design Phase
Developing (Generating) Alternatives
Measuring/ranking the outcomes
2-30
In optimization models (such as linear
programming), the alternatives may be
generated automatically
In most MSS situations, however, it is
necessary to generate alternatives manually
Use of GSS helps generating alternatives
Using the principle of choice
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Decision-Making: The Design Phase
Risk
Scenario (what-if case)
2-31
Lack of precise knowledge (uncertainty)
Risk can be measured with probability
A statement of assumptions about the
operating environment (variables) of a
particular system at a given time
Possible scenarios: best, worst, most likely,
average (and custom intervals)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Decision-Making: The Choice Phase
The actual decision and the commitment to
follow a certain course of action are made here
The boundary between the design and choice
is often unclear (partially overlapping phases)
2-32
Generate alternatives while performing evaluations
Includes the search, evaluation, and
recommendation of an appropriate solution to
the model
Solving the model versus solving the problem!
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Decision-Making: The Choice Phase
Search approaches
Additional activities
2-33
Analytic techniques (solving with a formula)
Algorithms (step-by-step procedures)
Heuristics (rule of thumb)
Blind search (truly random search)
Sensitivity analysis
What-if analysis
Goal seeking
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Decision-Making:
The Implementation Phase
“Nothing more difficult to carry out, nor
more doubtful of success, nor more
dangerous to handle, than to initiate a
new order of things.”
- The Prince, Machiavelli 1500s
2-34
Solution to a problem = Change
Change management?
Implementation: putting a recommended
solution to work
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How Decisions Are Supported
2-35
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How Decisions Are Supported
Support for the Intelligence Phase
2-36
Enabling continuous scanning of external
and internal information sources to identify
problems and/or opportunities
Resources/technologies: Web; ES, OLAP,
data warehousing, data/text/Web mining,
EIS/Dashboards, KMS, GSS, GIS,…
Business activity monitoring (BAM)
Business process management (BPM)
Product life-cycle management (PLM)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
How Decisions Are Supported
Support for the Design Phase
Enabling generating alternative courses of
action, determining the criteria for choice
Generating alternatives
2-37
Structured/simple problems: standard and/or
special models
Unstructured/complex problems: human
experts, ES, KMS, brainstorming/GSS, OLAP,
data/text mining
A good “criteria for choice” is critical!
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
How Decisions Are Supported
Support for the Choice Phase
Enabling selection of the best alternative
given a complex constraint structure
Use sensitivity analyses, what-if analyses,
goal seeking
Resources
2-38
KMS
CRM, ERP, and SCM
Simulation and other descriptive models
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
How Decisions Are Supported
Support for the Implementation Phase
Enabling implementation/deployment of
the selected solution to the system
Decision communication, explanation and
justification to reduce resistance to change
Resources
2-39
Corporate portals, Web 2.0/Wikis
Brainstorming/GSS
KMS , ES
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
New Technologies
to Support Decision Making
2-40
Web-based systems
m-Commerce
PDA, Cell phones, Tablet PCs
GSS with visual/immersive presence
RFID and other wireless technologies
Faster computers, better algorithms, to
process “huge” amounts of
heterogeneous/distributed data
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End of the Chapter
2-41
Questions / Comments…
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All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted, in any form or by any means, electronic,
mechanical, photocopying, recording, or otherwise, without the prior written
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Publishing as Prentice Hall
2-42
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Decision Support and
Business Intelligence
Systems
(9th Ed., Prentice Hall)
Chapter 3:
Decision Support Systems
Concepts, Methodologies, and
Technologies: An Overview
Learning Objectives
3-2
Understand possible decision support system
(DSS) configurations
Understand the key differences and
similarities between DSS and BI systems
Describe DSS characteristics and capabilities
Understand the essential definition of DSS
Understand important DSS classifications
Understand DSS components and how they
integrate
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Learning Objectives
3-3
Describe the components and structure of
each DSS component
Explain Internet impacts on DSS (and vice
versa)
Explain the unique role of the user in DSS
versus management information systems
Describe DSS hardware and software platforms
Become familiar with a DSS development
language
Understand current DSS issues
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Opening Vignette:
“Decision Support System Cures for
Health Care”
Company background
Problem
Proposed solution
Results
Answer and discuss the case questions
3-4
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Opening Vignette:
“Decision Support System Cures for Health Care”
- Projected Vacancy Rate versus Desired Vacancy Rate
3-5
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Opening Vignette:
- Projected Vacancy Rate vs. Desired Vacancy Rate
"What-if" scenario with 6 additional RN recruiters
3-6
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Opening Vignette:
- Demanded Hours versus Total Actual Hours versus
Total Actual Hours with New Hires
3-7
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Configurations
Many configurations exist; based on
management-decision situation
specific technologies used for support
DSS have three basic components
Data
2. Model
3. User interface
4. (+ optional) Knowledge
1.
3-8
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Configurations
Each component
Typical types:
3-9
has several
variations; are
typically deployed
online
Managed by a
commercial of
custom software
Model-oriented DSS
Data-oriented DSS
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Description
An early definition of DSS
3-10
A system intended to support managerial decision
makers in semistructured and unstructured
decision situations
meant to be adjuncts to decision makers
(extending their capabilities but not replacing their
judgment)
aimed at decisions that required judgment or at
decisions that could not be completely supported
by algorithms
would be computer based; operate interactively;
and would have graphical output capabilities…
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Description
A DSS is typically built to support the solution
of a certain problem (or to evaluate a specific
opportunity). This is a key difference between
DSS and BI applications
3-11
BI systems monitor situations and identify
problems and/or opportunities, using variety of
analytic methods
The user generally must identify whether a
particular situation warrants attention
Reporting/data warehouse plays a major role in BI
DSS often has its own database and models
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Description
DSS is an approach (or methodology) for
supporting decision making
3-12
uses an interactive, flexible, adaptable computerbased information system (CBIS)
developed (by end user) for supporting the solution
to a specific nonstructured management problem
uses data, model and knowledge along with a
friendly (often graphical; Web-based) user interface
incorporate the decision maker's own insights
supports all phases of decision making
can be used by a single user or by many people
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A Web-Based DSS Architecture
3-13
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DSS Characteristics and Capabilities
DSS is not quite synonymous with BI
3-14
DSS are generally built to solve a specific
problem and include their own database(s)
BI applications focus on reporting and
identifying problems by scanning data
stored in data warehouses
Both systems generally include analytical
tools (BI called business analytics systems)
Although some may run locally as a
spreadsheet, both DSS and BI uses Web
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Characteristics and Capabilities
3-15
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Characteristics and Capabilities
3-16
Business analytics implies the use of models
and data to improve an organization's
performance and/or competitive posture
Web analytics implies using business analytics
on real-time Web information to assist in
decision making; often related to e-Commerce
Predictive analytics describes the business
analytics method of forecasting problems and
opportunities rather than simply reporting
them as they occur
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Classifications
Other DSS Categories
3-17
Institutional and ad-hoc DSS
Personal, group, and organizational
support
Individual support system versus group
support system (GSS)
Custom-made systems versus ready-made
systems
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Classifications
Holsapple and Whinston's Classification
1.
2.
3.
4.
5.
6.
3-18
The text-oriented DSS
The database-oriented DSS.
The spreadsheet-oriented DSS
The solver-oriented DSS
The rule-oriented DSS (include most
knowledge-driven DSS, data mining,
management, and ES applications)
The compound DSS
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Classifications
Alter's Output Classification
Orientation Category
Type of Operation
Data
Access data items
File drawer systems
Data analysis systems Ad hoc analysis of data files
3-19
Data or
models
Analysis information
systems
Ad hoc analysis involving
multiple databases and small
models
Models
Accounting models
Standard calculations that
estimate future results on the
basis of accounting definitions
Optimization models
Calculating an optimal solution to
a combinatorial problem
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Classifications
Holsapple and Whinston's Classification
1.
2.
3.
4.
5.
6.
3-20
The text-oriented DSS
The database-oriented DSS
The spreadsheet-oriented DSS
The solver-oriented DSS
The rule-oriented DSS (include most
knowledge-driven DSS, data mining,
management, and ES applications)
The compound DSS
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Components of DSS
3-21
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Components of DSS
Data Management Subsystem
Model Management Subsystem
Model base management system (MBMS)
User Interface Subsystem
Knowledgebase Management Subsystem
3-22
Includes the database that contains the data
Database management system (DBMS)
Can be connected to a data warehouse
Organizational knowledge base
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Overall Capabilities of DSS
Easy access to data/models/knowledge
Proper management of organizational
experiences and knowledge
Easy to use, adaptive and flexible GUI
Timely, correct, concise, consistent
support for decision making
Support for all who needs it, where
and when he/she needs it
- See Table 3.2 for a complete list...
3-23
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Components and Web Impacts
Impacts of Web to DSS
DSS impact on Web
3-24
Data management via Web servers
Easy access to variety of models, tools
Consistent user interface (browsers)
Deployment to PDAs, cell phones, etc. …
Intelligent e-Business/e-Commerce
Better management of Web resources and
security, …
(see Table 3.3 for more…)
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DSS Components
Data Management Subsystem
3-25
DSS database
DBMS
Data directory
Query facility
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Database Management Subsystem
Key Data Issues
Data quality
Data integration
3-26
“Garbage in/garbage out" (GIGO)
“Creating a single version of the truth”
Scalability
Data Security
Timeliness
Completeness, …
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
10 Key Ingredients of Data
(Information) Quality Management
1.
2.
3.
4.
5.
3-27
Data quality is a business problem, not only
a systems problem
Focus on information about customers and
suppliers, not just data
Focus on all components of data: definition,
content, and presentation
Implement data/information quality
management processes, not just software to
handle them
Measure data accuracy as well as validity
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
10 Key Ingredients of Data
(Information) Quality Management
6.
7.
8.
9.
10.
3-28
Measure real costs (not just the percentage)
of poor quality data/information
Emphasize process improvement/preventive
maintenance, not just data cleansing
Improve processes (and hence data quality)
at the source
Educate managers about the impacts of
poor data quality and how to improve it
Actively transform the culture to one that
values data quality
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Components
Model Management Subsystem
3-29
Model base
MBMS
Modeling
language
Model directory
Model execution,
integration, and
command
processor
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DSS Components
Model Management Subsystem
Model base (= database ?)
Model Types
3-30
Strategic models
Tactical models
Operational models
Analytic models
Model building blocks
Modeling tools
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DSS Components
Model Management Subsystem
The four (4) functions
Model creation, using programming
languages, DSS tools and/or subroutines,
and other building blocks
2. Generation of new routines and reports
3. Model updating and changing
4. Model data manipulation
1.
3-31
Model directory
Model execution, integration and command
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Components
User Interface (Dialog) Subsystem
Interface
Application interface
User Interface
DSS User Interface
Portal
Graphical icons
3-32
Graphical User Interface
(GUI)
Dashboard
Color coding
Interfacing with PDAs,
cell phones, etc.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Components
Knowledgebase Management System
Incorporation of intelligence and expertise
Knowledge components:
3-33
Expert systems,
Knowledge management systems,
Neural networks,
Intelligent agents,
Fuzzy logic,
Case-based reasoning systems, and so on
Often used to better manage the other DSS
components
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Components
Future/current DSS Developments
Hardware enhancements
Software/hardware advancements
3-34
Smaller, faster, cheaper, …
data warehousing, data mining, OLAP,
Web technologies, integration and
dissemination technologies (XML, Web
services, SOA, grid computing, cloud
computing, …)
Integration of AI -> smart systems
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS User
One faced with a decision that an MSS is
designed to support
The users differ greatly from each other
3-35
Manager, decision maker, problem solver, …
Different organizational positions they occupy;
cognitive preferences/abilities; the ways of
arriving at a decision (i.e., decision styles)
User = Individual versus Group
Managers versus Staff Specialists [staff
assistants, expert tool users, business
(system) analysts, facilitators (in a GSS)]
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
DSS Hardware
Typically, MSS run on standard hardware
Can be composed of mainframe computers
with legacy DBMS, workstations, personal
computers, or client/server systems
Nowadays, usually implemented as a
distributed/integrated, loosely-coupled
Web-based systems
Can be acquired from
3-36
A single vendor
Many vendors (best-of-breed)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
Generating
Assumptions
3-37
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
Creating a
new model
3-38
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-39
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-40
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-41
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-42
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-43
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-44
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-45
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-46
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-47
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-48
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-49
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
A DSS Modeling Language
Planners Lab (plannerslab.com)
3-50
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
End of the Chapter
3-51
Questions / Comments…
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All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted, in any form or by any means, electronic,
mechanical, photocopying, recording, or otherwise, without the prior written
permission of the publisher. Printed in the United States of America.
Copyright © 2011 Pearson Education, Inc.
Publishing as Prentice Hall
3-52
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
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