10
Decision Support SystemsI. CHAPTER OVERVIEW
This chapter shows how management information systems, decision
support systems, executive information systems, expert systems, and
artificial intelligence technologies can be applied to
decision-making situations faced by business managers and
professionals in todays dynamic business environment. Section I:
Section II: Decision Support in Business Artificial Intelligence
Technologies in Business
II. LEARNING OBJECTIVESLearning Objectives 1. Identify the
changes taking place in the form and use of decision support in
business. 2. Identify the role and reporting alternatives of
management information systems. 3. Describe how online analytical
processing can meet key information needs of managers. 4. Explain
the decision support system concept and how it differs from
traditional management information systems. 5. Explain how the
following information systems can support the information needs of
executives, managers, and business professionals: a. Executive
information systems b. Enterprise information portals c. Knowledge
management systems 6. Identify how neural networks, fuzzy logic,
genetic algorithms, virtual reality, and intelligent agents can be
used in business. 7. Give examples of several ways expert systems
can be used in business decision-making situations.
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
1
III. TEACHING SUGGESTIONSInstructors can use Figure 10.2 to
discuss the different levels of management and the structure of
decision situations they face. It can also be used to discuss the
different types of information that is required. The instructor
should discuss the three information-reporting alternatives as
outlined in the text (periodic, exception, and demand). While using
this figure be sure to stress that unstructured, semistructured,
and structured decisions are information products that are produced
by the three levels of management (operational, tactical, and
strategic). Figure 10.11 illustrates the concept of online
analytical processing. OLAP may involve the use of specialized
servers and multidimensional databases. It also provides fast
answers to complex queries posed by managers and analysts using
management, decision support, and executive information systems.
Figure 10.15 illustrates four basic types of analytical modelling
activities (1) what-if analysis, (2) sensitivity analysis, (3)
goal-seeking analysis, and (4) optimisation analysis. Figure 10.20
outlines the components of an enterprise information portal. It can
be used to identify how e-business decision support systems can be
personalized for executives, managers, employees, suppliers,
customers, and other business partners. Figure 10.23 illustrates
some of the attributes of intelligent behavior. AI is attempting to
duplicate these capabilities in the design of computer-based
systems. The major application areas of artificial intelligence can
be explained using Figure 10.24. This figure groups AI applications
into four major areas - cognitive science, computer science,
robotics, and natural interfaces. Figure 10.25 summarizes a few of
the many types of intelligent agents that are in use or currently
being developed. Figure 10.26 outlines some of the major categories
and examples of typical expert systems. Figure 10.29 gives an
excellent example of many major application categories and examples
of typical expert systems. Figure 10.36 details the components of
an expert system. This figure emphasizes that the software modules
perform inferences on a knowledge base built by an expert and/or
knowledge engineer. This model provides expert answers to an end
users questions in an interactive process.
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
2
IV. LECTURE NOTES Section I: Decision Support in
BusinessIntroductionTo succeed in business today, companies need
information systems that can support the diverse information and
decision-making needs of their managers and business professionals.
This is accomplished by several types of management information,
decision support, and other information systems. The Internet,
intranets, and other Web-enabled information technologies have
significantly strengthened the role that information systems play
in supporting the decision-making activities of every manager and
knowledge worker in business. Analyzing Allstate Insurance, Aviva
Canada, and Others: We can learn a lot from this case about the
value of business intelligence. Take a few minutes to read the
case, and we will discuss it (See Allstate Insurance, Aviva Canada,
and Others: Centralized Business Intelligence at Work in section
IX). Information, Decisions, and Management: [Figure 10.2] The type
of information required by decision-makers in a company is directly
related to the level of management decision-making and the amount
of structure in the decision situations they face. The framework of
the classic managerial pyramid applies even in todays downsized
organizations and flattened or non-hierarchical organizational
structures. Levels of management decision making still exist, but
their size, shape, and participants continue to change as todays
fluid organizational structures evolve. Thus, the levels of
managerial decision-making that must be supported by information
technology in a successful organization are: Strategic Management:
- Typically, a board of directors and an executive committee of the
CEO and top executives develop overall organizational goals,
strategies, policies, and objectives as part of a strategic
planning process. They monitor the strategic performance of the
organization and its overall direction in the political, economic,
and competitive business environment. Unstructured Decisions -
Involve decision situations where it is not possible to specify in
advance most of the decision procedures to follow. Strategic
Decision Makers - Require more summarized, ad hoc, unscheduled
reports, forecasts, and external intelligence to support their more
unstructured planning and policy-making responsibilities. Tactical
Management - Increasingly self-directed teams as well as middle
managers develop short- and medium-range plans, schedules, and
budgets and specify the policies, procedures, and business
objectives for their subunits of the organization. They also
allocate resources and monitor the performance of their
organizational subunits, including departments, divisions, process
teams, and other workgroups. Semistructured Decisions - Some
decision procedures can be prespecified, but not enough to lead to
a definite recommended decision. Tactical Decision-Makers - Require
information from both the operational level and the strategic level
to support their semistructured decision making
responsibilities.
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
3
Operational Management - The members of self-directed teams or
supervisory managers develop short-range plans such as weekly
production schedules. They direct the use of resources and the
performance of tasks according to procedures and within budgets and
schedules they establish for the teams and other workgroups of the
organization. Structured Decisions - Involve situations where the
procedures to follow when a decision is needed can be specified in
advance. Operational Decision Makers - Require more prespecified
internal reports emphasizing detailed current and historical data
comparisons that support their more structured responsibilities in
day-to-day operations.
Decision Structure: Providing information and support for all
levels of management decision-making is no easy task. Therefore,
information systems must be designed to produce a variety of
information products to meet the changing needs of decision-makers
throughout an organization.
Decision Support TrendsUsing information systems to support
business decision making has been on of the primary thrusts of the
business use of information technology. The fast pace of new
information technologies like PC hardware and software suites,
client/server networks, and networked PC versions of DSS/EIS
software made decision support available to lower levels of
management, as well as to non-managerial individuals and
self-directed team of business professionals. This trend has
accelerated with the dramatic growth of the Internet and intranets
and extranets that internetwork companies and their stakeholder.
The e-commerce initiatives that are being implemented by many
companies are also expanding the information and decision support
uses and expectations of a company and its business partners.
Todays businesses are responding to with a variety of personalized
and proactive Web-based analytical techniques to support the
decision-making requirements of all of their constituents. The
dramatic expansion of DSS growth has opened the door to the user of
business intelligence (BI) tools by the suppliers, customers, and
other business stakeholders of a company for customer relationship
management, supply chain management, and other e-business
applications.
Decision Support Systems:Decision support systems are
computer-based information systems that provide interactive
information support to managers and business professionals during
the decision-making process. Decision support systems use:
Analytical models Specialized databases Decision makers own
insights and judgments Interactive, computer-based modeling process
to support the making of semistructured and unstructured business
decisions DSS Components: Decision support systems rely on model
bases as well as databases as vital system resources. A DSS model
base is a software component that consists of models used in
computational and analytical routines that mathematically express
relationships among variables.OBrien, Management Information
Systems, 7/e IM - Chapter 10 pg. 4
Examples include: Spreadsheet models Linear programming models
Multiple regression forecasting models Capital budgeting present
value models
Management Information Systems:Management information systems
were the original type of information systems developed to support
managerial decision-making. A management information system
produces information products that support many of the day-to-day
decision-making needs of managers and business professionals.
Reports, displays, and responses produced by information systems
provide information that managers have specified in advance as
adequately meeting their information needs. Such predefined
information products satisfy the information needs of managers at
the operational and tactical levels of the organization who are
faced with more structured types of decision situations. Management
Reporting Alternatives: MIS provide a variety of information
products to managers. Three major reporting alternatives are
provided by such systems as: Periodic scheduled reports -
Traditional form of providing information to managers. Uses a
prespecified format designed to provide managers with information
on a regular basis. Exception Reports - Reports that are produced
only when exceptional conditions occur. Demand Reports and
Responses - Information is provided whenever a manager demands it.
Push Reporting - Information is pushed to a managers networked
workstation.
Online Analytical Processing: [Figure 10.10]Online analytical
processing is a capability of management, decision support, and
executive information systems that enables managers and analysts to
interactively examine and manipulate large amounts of detailed and
consolidated data from many perspectives (analytical databases,
data marts, data warehouses, data mining techniques, and
multidimensional database structures, specialized servers and
web-enabled software products). Online analytical processing
involves several basic analytical operations: Consolidation -
Involves the aggregation of data. This can involve simple roll-ups
or complex groupings involving interrelated data. Drill-Down - OLAP
can go in the reverse direction and automatically display detailed
data that comprises consolidated data. Slicing and Dicing - Refers
to the ability to look at the database from different viewpoints.
Slicing and dicing is often performed along a time axis in order to
analyze trends and find patterns.
OLAP applications: Access very large amounts of data to discover
patterns, trends, and exception conditions Analyze the techniques
between many types of business elements
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
5
Involve aggregated data Compare aggregated data over
hierarchical time periods Present data in different perspectives
Involve complex calculations between data elements Are able to
respond quickly to user requests so that managers or analysts can
pursue an analytical or decision thought process without being
hindered by the system
Geographic Information and Data Visualization Systems Geographic
information systems (GIS) and data visualization systems (DVS) are
special categories of DSS that integrate computer graphics with
other DSS features. Geographic Information System is a DSS that
uses geographic databases to construct and display maps and other
graphics displays that support decisions affecting the geographic
distribution of people and other resources. Data Visualization
Systems DVS systems represent complex data using interactive
three-dimensional graphical forms such as charts, graphs, and maps.
DVS tools help users to interactively sort, subdivide, combine, and
organize data while it is in its graphical form.
Using Decision Support Systems: [Figure 10.15]Using a decision
support system involves an interactive analytical modelling
process. Typically, a manager uses a DSS software package at his
workstation to make inquiries, responses and to issue commands.
This differs from the demand responses of information reporting
systems, since managers are not demanding prespecified information.
Rather, they are exploring possible alternatives. They do not have
to specify their information needs in advance. Instead they use the
DSS to find the information they need to help them make a decision.
Using a DSS involves four basic types of analytical modelling
activities: What-If Analysis: - In what-if analysis, an end user
makes changes to variables, or relationships among variables, and
observes the resulting changes in the values of other variables.
Sensitivity Analysis: - Is a special case of what-if analysis.
Typically, the value of only one variable is changed repeatedly,
and the resulting changes on other variables are observed. So
sensitivity analysis is really a case of what-if analysis involving
repeated changes to only one variable at a time. Typically,
sensitivity analysis is used when decision-makers are uncertain
about the assumptions made in estimating the value of certain key
variables. Goal-Seeking Analysis: - Reverses the direction of the
analysis done in what-if and sensitivity analysis. Instead of
observing how changes in a variable affect other variables,
goal-seeking analysis sets a target value for a variable and then
repeatedly changes other variables until the target value is
achieved. Optimization Analysis: - Is a more complex extension of
goal-seeking analysis. Instead of setting a specific target value
for a variable, the goal is to find the optimum value for one or
more target variables, given certain constraints. Then one or more
other variables are changed repeatedly, subject to the specified
constraints, until the best values for the target variables are
discovered.
Data Mining for Decision Support: The main purpose of data
mining is knowledge discovery, which will lead to decision
support.OBrien, Management Information Systems, 7/e IM - Chapter 10
pg. 6
Characteristics of data mining include: Data mining software
analyzes the vast stores of historical business data that have been
prepared for analysis in corporate data warehouses. Data mining
attempts to discover patterns, trends, and correlations hidden in
the data that can give a company a strategic business advantage.
Data mining software may perform regression, decision-tree, neural
network, cluster detection, or market basket analysis for a
business. Data mining can highlight buying patterns, reveal
customer tendencies, cut redundant costs, or uncover unseen
profitable relationships and opportunities.
Executive Information Systems:Executive information systems
(EIS) are information systems that combine many of the features of
management information systems and decision support systems. EIS
focus on meeting the strategic information needs of top management.
The goal of EIS is to provide top executives with immediate and
easy access to information about a firm's critical success factors
(CSFs), that is, key factors that are critical to accomplishing the
organizations strategic objectives. Capabilities of EIS include:
More features such as Web browsing, electronic mail, groupware
tools, and DSS and expert system capabilities are being added.
Information is presented in forms tailored to the preferences of
the executives using the system. Heavy use of graphical user
interface and graphics displays. Information presentation methods
used by an EIS include exception reporting and trend analysis. The
ability to drill down allows executives to quickly retrieve
displays of related information at lower levels of detail. Internet
and intranet technologies have added capabilities to EIS systems.
EISs have spread into the ranks of middle management and business
professionals as they have recognized their feasibility and
benefits, and as less-expensive systems for client/server and
corporate intranets become available.
Enterprise Portals and Decision Support:Major changes and
expansion are taking place in traditional MIS, DSS, and EIS tools
for providing the information and modeling that managers need to
support their decision making. Some of these changes include:
Decision support in business is changing, driven by rapid
developments in end user computing and networking; Internet, Web
browser, and related technologies, and the explosion of e-commerce
activity. Growth of corporate intranets, extranets, as well as the
Web, has accelerated the development and use of executive class
information delivery and decision support software tools by lower
levels of management and by individuals and teams of business
professionals. Dramatic expansion of e-commerce has opened the door
to the use of such e-business DSS tools by the suppliers,
customers, and other business stakeholders of a company for
customer relationship management, supply chain management, and
other e-business applications. Enterprise Information Portals:
[Figure 10.20] Enterprise information portals (EIP) are being
developed by companies as a way to provide web-enabled information,
knowledge, and decision support to executives, managers, employees,
suppliers, customers, and other business partners. Enterprise
information portals are described as a customized and personalized
web-based interface for corporate intranets, that give users easy
access to a variety of internal and external business applications,
databases, andOBrien, Management Information Systems, 7/e IM -
Chapter 10 pg. 7
services.
Enterprise Knowledge Portals: Enterprise information portal is
the entry to corporate intranets that serve as the primary
knowledge management systems for many companies. They are often
called enterprise knowledge portals by some vendors. Knowledge
management systems are defined as the use of information technology
to help gather, organize, and share business knowledge within an
organization. Enterprise information portals can play a major role
in helping a company use its intranets as knowledge management
systems to share and disseminate knowledge in support of its
business decision-making.
Knowledge Management SystemsIn many organizations, hypermedia
databases at corporate intranet websites have become the knowledge
bases for storage and dissemination of business knowledge. This
knowledge frequently takes the form of best practices, policies,
and business solutions at the project, team, business unit, and
enterprise levels of the company. For many companies, enterprise
information portals are the entry to corporate intranets that serve
as their knowledge management systems. Enterprise information
portals play an essential role in helping companies use their
intranets as knowledge management systems to share and disseminate
knowledge in support of business decision making by managers and
business professionals.
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
8
IV. LECTURE NOTES (cont) Section II: Artificial Intelligence
Technologies in BusinessBusiness and AIBusiness and other
organizations are significantly increasing their attempts to assist
the human intelligence and productivity of their knowledge workers
with artificial intelligence tools and techniques. AI includes:
Natural languages Industrial robots Expert systems Intelligent
agents
Analyzing Wal-Mart, BankFinancial, and HP We can learn a lot
about the business vale of artificial intelligence technologies
from this case. Take a few minutes to read it, and we will discuss
it (See Wal-Mart, BankFinancial, and HP: The Business Value of AI
in Section IX).
An Overview of Artificial Intelligence [Figure 10.23]:Artificial
intelligence (AI) is a science and technology based on disciplines
such as computer science, biology, psychology, linguistics,
mathematics, and engineering. The goal of AI is to develop
computers that can think, as well as see, hear, walk, talk, and
feel. A major thrust of AI is the development of computer functions
normally associated with human intelligence, such as reasoning,
learning, and problem solving. The Domains of Artificial
Intelligence: [Figure 10.24 & Figure 10.25] AI applications can
be grouped into three major areas: Cognitive Science - This area of
artificial intelligence is based on research in biology, neurology,
psychology, mathematics, and many allied disciplines. It focuses on
researching how the human brain works and how humans think and
learn. The results of such research in human information processing
are the basis for the development of a variety of computer-based
applications in artificial intelligence. Applications in the
cognitive science area of AI include: Expert Systems - A
computer-based information system that uses its knowledge about a
specific complex application area to act as an expert consultant to
users. The system consists of knowledge base and software modules
that perform inferences on the knowledge, and communicate answers
to a users questions. Knowledge-Based Systems - An information
system, which adds a knowledge base and some, reasoning capability
to the database and other components, found in other types of
computer-based information systems. Adaptive Learning Systems - An
information system that can modify its behavior based on
information acquired as it operates. Fuzzy Logic Systems -
Computer-based systems that can process data that are incomplete or
only partially correct. Such systems can solve unstructured
problems with incomplete knowledge by developing approximate
inferences and answers.OBrien, Management Information Systems, 7/e
IM - Chapter 10 pg. 9
Neural Network - software can learn by processing sample
problems and their solutions. As neural nets start to recognize
patterns, they can begin to program themselves to solve such
problems on their own. Genetic Algorithm - software uses Darwinian
(survival of the fittest), randomizing, and other mathematical
functions to simulate evolutionary processes that can generate
increasingly better solutions to problems. Intelligent Agents - Use
expert system and other AI technologies to serve as software
surrogates for a variety of end user applications. Robotics: - AI,
engineering, and physiology are the basic disciplines of robotics.
This technology produces robot machines with computer intelligence
and computer-controlled, humanlike physical capabilities. Robotics
applications include: 1. Visual perception (sight) 2. Tactility
(touch) 3. Dexterity (skill in handling and manipulation) 4.
Locomotion (ability to move over any terrain) 5. Navigation
(properly find ones way to a destination) Natural Interface: - The
development of natural interfaces is considered a major area of AI
applications and is essential to the natural use of computers by
humans. For example, the developments of natural languages and
speech recognition are major thrusts of this area. Being able to
talk to computers and robots in conversational human languages and
have them understand us is the goal of AI researchers. This
application area involves research and development in linguistics,
psychology, computer science, and other disciplines. Efforts in
this area include: Natural Language - A programming language that
is very close to human language. Also, called very high-level
language. Multisensory Interfaces - The ability of computer systems
to recognize a variety of human body movements, which allows them
to operate. Speech Recognition - The ability of a computer system
to recognize speech patterns, and to operate using these patterns.
Virtual Reality - The use of multisensory human/computer interfaces
that enables human users to experience computer-simulated objects,
entities, spaces, and worlds as if they actually existed.
Expert SystemsOne of the most practical and widely implemented
application of artificial intelligence in business is the
development of expert systems and other knowledge-based information
systems. Knowledge-based information system - adds a knowledge base
to the major components found in other types of computer-based
information systems. Expert System - A computer-based information
system that uses its knowledge about a specific complex application
area to act as an expert consultant to users. ES provide answers to
questions in a very specific problem area by making humanlike
inferences about knowledge contained in a specialized knowledge
base. They must also be able to explain their reasoning process and
conclusions to a user.
Components of Expert Systems: [Figure 10.26]OBrien, Management
Information Systems, 7/e IM - Chapter 10 pg. 10
The components of an expert system include a knowledge base and
software modules that perform inferences on the knowledge and
communicate answers to a users question. The interrelated
components of an expert system include: Knowledge base: - the
knowledge base of an ES contains: 1. Facts about a specific subject
area 2. Heuristics (rule of thumb) that express the reasoning
procedures of an expert on the subject Software resources: - An ES
software package contains: 1. Inference engine that processes the
knowledge related to a specific problem 2. User interface program
that communicates with end users 3. Explanation program to explain
the reasoning process to the user 4. Software tools for developing
expert systems include knowledge acquisition programs and expert
system shells Hardware resources: - These include: 1. Stand alone
microcomputer systems 2. Microcomputer workstations and terminals
connected to minicomputers or mainframes in a telecommunications
network 3. Special-purpose computers People resources: - People
resources include: 1. Knowledge engineers 2. End-users
Expert System Applications: [Figure 10.29] Using an expert
system involves an interactive computer-based session, in which:
The solution to a problem is explored with the expert system acting
as a consultant. Expert system asks questions of the user, searches
its knowledge base for facts and rules or other knowledge. Explains
its reasoning process when asked. Gives expert advice to the user
in the subject area being explored. Examples include: credit
management, customer service, and productivity management.
Expert System Applications: [Figure 9.34] Expert systems
typically accomplish one or more generic uses. Six activities
include: Decision management Diagnostic/troubleshooting Maintenance
scheduling Design/configuration Selection/classification Process
monitoring/control
Developing Expert SystemsThe easiest way to develop an expert
system is to use an expert system shell as a developmental tool. An
expert system
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
11
shell is a software package consisting of an expert system
without a kernel, that is, its knowledge base. This leaves a shell
of software (the inference engine and user interface programs) with
generic inferencing and user interface capabilities. Other
development tools (such as rule editors and user interface
generators) are added in making the shell a powerful expert system
development tool.
Knowledge Engineering A knowledge engineer is a professional who
works with experts to capture the knowledge (facts and rules of
thumb) they possess. The knowledge engineer then builds the
knowledge base using an interactive, prototyping process until the
expert system is acceptable. Thus, knowledge engineers perform a
role similar to that of systems analysts in conventional
information systems development. Obviously, knowledge engineers
must be able to understand and work with experts in many subject
areas. Therefore, this information systems speciality requires good
people skills, as well as a background in artificial intelligence
and information systems.
Neural Networks:Neural networks are computing systems modelled
on the human brain's mesh-like network of interconnected processing
elements, called neurons. Of course, neural networks are much
simpler than the human brain (estimated to have more than 100
billion neuron brain cells). Like the brain, however, such networks
can process many pieces of information simultaneously and can learn
to recognize patterns and program themselves to solve related
problems on their own. Neural networks can be implemented on
microcomputers and other computer systems via software packages,
which simulate the activities of a neural network of many
processing elements. Specialized neural network coprocessor circuit
boards are also available. Special-purpose neural net
microprocessor chips are used in some application areas. Uses
include: Military weapons systems Voice recognition Check signature
verification Manufacturing quality control Image processing Credit
risk assessment Investment forecasting Data mining
Fuzzy Logic SystemsFuzzy Logic is a method of reasoning that
resembles human reasoning since it allows for approximate values
and inferences (fuzzy logic) and incomplete or ambiguous data
(fuzzy data) instead of relying only on crisp data, such as binary
(yes/no) choices.
Fuzzy Logic in Business: Examples of applications of fuzzy logic
are numerous in Japan, but rate in the United States. The United
States has tended to prefer using AI solutions like expert systems
or neural networks. Japan has implemented many fuzzy logic
applications, especially the use of special-purpose fuzzy logic
microprocessors chips, called fuzzy process controllers.
Examples of fuzzy logic applications in Japan include:OBrien,
Management Information Systems, 7/e IM - Chapter 10 pg. 12
Riding in subway trains and elevators Riding in cars that are
guided or supported by fuzzy process controllers Trading shares on
the Tokyo Stock Exchange using a stock-trading program based on
fuzzy logic Japanese-made products that use fuzzy logic
microprocessors include auto-focus cameras, auto-stabilizing,
camcorders, energy-efficient air conditioners, self-adjusting
washing machines, and automatic transmissions.
Genetic Algorithms:The use of genetic algorithms is a growing
application of artificial intelligence. Genetic algorithm software
uses Darwinian (survival of the fittest); randomizing, and other
mathematical functions to simulate an evolutionary process that can
yield increasingly better solutions to a problem. Genetic
algorithms were first used to simulate millions of years in
biological, geological, and ecosystem evolution in just a few
minutes on a computer. Now genetic algorithm software is being used
to model a variety of scientific, technical, and business
processes. Genetic algorithms are especially useful for situations
in which thousands of solutions are possible and must be evaluated
to produce an optimal solution. Genetic algorithm software uses
sets of mathematical process rules (algorithms) that specify how
combinations of process components or steps are to be formed. This
may involve: Trying random process combinations (mutation)
Combining parts of several good processes (crossover) Selecting
good sets of processes and discarding poor ones (selection)
Virtual Reality (VR)Virtual reality (VR) is computer-simulated
reality. VR is the use of multisensory human/computer interfaces
that enable human users to experience computer-simulated objects,
entities, spaces, and "worlds" as if they actually existed (also
called cyberspace and artificial reality). VR Applications:
Computer-aided design (CAD) Medical diagnostics and treatment
Scientific experimentation in many physical and biological sciences
Flight simulation for training pilots and astronauts Product
demonstrations Employee training Entertainment (3-D video
games)
VR Limitations: The use of virtual reality seems limited only by
the performance and cost of its technology. For example, some VR
users develop: Cybersickness - eye strain, motion sickness,
performance problems Cost of VR is quite expensive
Intelligent Agents [Figure 10.36]:An intelligent agent (also
called intelligent assistants/wizards) is a software surrogate for
an end user or a process that fulfils a stated need or activity. An
intelligent agent uses a built-in and learned knowledge base about
a person or process to make decisions and accomplish tasks in a way
that fulfils the intentions of a user. One of the most wellOBrien,
Management Information Systems, 7/e IM - Chapter 10 pg. 13
known uses of intelligent agents is the wizards found in
Microsoft Office and other software suites. The use of intelligent
agents is expected to grow rapidly as a way for users to: Simplify
software use Search websites on the Internet and corporate
intranets Help customers do comparison-shopping among the many
e-commerce sites on the Web.
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
14
IV. LECTURE NOTES (cont)Summary Information, Decisions, and
Management. Information systems can support a variety of management
decision-making levels and decision. These include the three levels
of management activity (strategic, tactical, and operational
decision making) and three types of decision structures
(structures, semistructured, and unstructured). Information systems
provide a wide range of information products to support these types
of decisions at all levels of the organization. Decision Support
Trends. Major changes are taking place in traditional MIS, DSS, and
EIS tools for providing the information and modelling managers need
to support their decision making. Decision support in business is
changing, driven by rapid developments in end user computing and
networking; Internet and Web technologies; and Web-enables business
applications. The growth of corporate intranets, extranets, as well
as the Web, has accelerated the development of executive class
interfaces like enterprise information portals and Web-enabled
business professionals. In addition, the growth of e-commerce and
e-business applications has expanded the use of enterprise portals
and DSS tools by the suppliers, customers, and other business
stakeholders of a company. Management Information Systems.
Management information systems provide pre-specified reports and
responses to managers of a periodic, exception, demand, or push
reporting basis, to meet their need for information to support
decision making. OLAP and Data Mining. Online analytical processing
interactively analyzes complex relationships among large amounts of
data stored in multidimensional databases. Data mining analyzes the
vast amounts of historical data that have been prepared for
analysis in data warehouses. Both technologies discover patterns,
trends, and exceptional conditions in a companys data that support
their business analysis and decision making. Decision Support
Systems. Decision support systems are interactive, computer-bases
information systems that use DSS software and a model base and
database to provide information tailored to support semistructured
and unstructured decision faced by individual managers. They are
designed to use a decision makers own insights and judgements in an
ad hoc, interactive, analytical modelling process leading to a
specific decision. Executive Information Systems. Executive
information systems are information systems originally designed to
support the strategic information needs of top management. However,
their use is spreading to lower levels of management and business
professionals. EIS are easy to use and enable executives to
retrieve information tailored to their needs and preferences. Thus,
EIS can provide information about a companys critical success
factors to executives to support their planning and control
responsibilities. Enterprise Information and Knowledge Portals.
Enterprise information portals provide a customized and
personalized Web-based interface for corporate intranets to give
their users easy access to a variety of internal and external
business applications, databases, and information services that are
tailored to their individual preferences and information needs.
Thus, an EIP can supply personalized Web-enabled information,
knowledge, and decision support to executives, managers, and
business professionals, as well as customers, suppliers, and other
business partners. As enterprise knowledge portal is a corporate
intranet portal that extends the use of an EIP to include knowledge
management functions and knowledge base resources so that it
becomes a major form of knowledge management system for a company.
Artificial Intelligence. The major application domains of
artificial intelligence (AI) include a variety of applications in
cognitive science, robotics, and natural interfaces. The goal of AI
is the development of computer functions normally associated with
human physical and mental capabilities, such as robots that see,
hear, talk, feel, and move, and software capable of reasoning,
learning, and problem solving. Thus, AI is being applied to many
applications in business operations and managerial decision making,
as well as in many other fields. AI Technologies. The many
application areas of AI are summarized in Figure 10.24, including
neural networks,
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
15
fuzzy logic, genetic algorithms, virtual reality, and
intelligent agents. Neural nets are hardware or software systems
based on simple models of the brains neuron structure that can
learn to recognize patterns in data. Fuzzy logic systems use rules
of approximate reasoning to solve problems where data are
incomplete or ambiguous. Genetic algorithms use selection,
randomizing, and other mathematics functions to simulated an
evolutionary process that can yield increasingly better solutions
to problems. Virtual reality systems are multisensory systems that
enable human users to experience computer-simulated environments as
if they actually existed. Intelligent agents are knowledge-bases
software surrogates for a user of a process in the accomplishment
of selected tasks. Expert Systems. Expert systems are
knowledge-based information systems that use software and a
knowledge base about a specific, complex application area to act as
expert consultants to users in many business and technical
applications. Software includes an inference engine program that
makes inferences based on the facts and rules stored in the
knowledge base. A knowledge base consists of facts about a specific
subject area and heuristics (rules of thumb) that express the
reasoning procedures of an expert. The benefits of expert systems
(such as preservation and replication of expertise) must be balance
with their limited applicability in many problem situations.
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
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V. KEY TERMS AND CONCEPTS - DEFINEDAnalytical Modeling (333):
Interactive use of computer-based mathematical models to explore
decision alternatives using what-if analysis, sensitivity analysis,
goal-seeking analysis, and optimization analysis. Analytical
Modeling Goal-Seeking Analysis (335): Making repeated changes to
selected variables until a chosen variable reaches a target value.
Analytical Modeling - Optimization Analysis (335): Finding an
optimum value for selected variables in a mathematical model, given
certain constraints. Analytical Modeling - Sensitivity Analysis
(334): Observing how repeated changes to a single variable affects
other variables in a mathematical model. Analytical Modeling -
What-if Analysis (333): Observing how changes to selected variables
affect other variables in a mathematical model. Artificial
Intelligence (343): A science and technology, whose goal is to
develop computers that can think, as well as see, hear, walk, talk,
and feel. Artificial Intelligence - Application Areas (345): Major
areas of AI research and development include cognitive science,
computer science, robotics, and natural interface applications.
Artificial Intelligence Domains (345): The major domains of AI
intelligence are grouped under three major areas: Cognitive science
applications, robotics applications, and natural interface
applications. Business Intelligence (325): A term primarily used in
industry that incorporates a range of analytical and decision
support applications in business including data mining, decision
support systems, knowledge management systems, and online
analytical processing. Data Mining (336): Using special-purpose
software to analyze data from a data warehouse to find hidden
patterns and trends. Data Visualization Systems (331): DVS systems
represent complex data using interactive three-dimensional
graphical forms such as charts, graphs, and maps. DVS tools help
users to interactively sort, subdivide, combine, and organize data
while it is in its graphical form. Decision Structure (323):
Information systems can support a variety of management levels and
decisions. These include the three levels of management activity
(strategic, tactical, and operational), and three types of decision
structures (structured, semistructured, and unstructured). Decision
Support System (326): An information system that utilizes decision
models, a database, and a decision makers own insights in an ad
hoc, interactive analytical modelling process to reach a specific
decision by a specific decision maker.
Decision Support Trends (324): Major changes are taking place in
traditional MIS, DSS, and EIS tools for providing the information
and modelingOBrien, Management Information Systems, 7/e IM -
Chapter 10 pg. 17
managers need to support their decision-making. DSS Components
(326): Decision support systems rely on model bases as well as
databases as vital system resources. Enterprise Information Portal
(339): Enterprise information portals are being developed by
companies as a way to provide web-enabled information, knowledge,
and decision support to executives, managers, employees, suppliers,
customers, and other business partners. Enterprise Knowledge Portal
(341): An enterprise information portal that serves as a knowledge
management system by providing users with access to enterprise
knowledge bases. Executive Information System (338): An information
system that provides strategic information tailored to the needs of
top management. Expert System (348): A computer-based information
system that uses its knowledge about a specific complex application
area to act as an expert consultant to users. Expert System
Applications (349): Includes applications such as diagnosis,
design, prediction, interpretation, and repair. Expert System -
Benefits and Limitations (350): Benefits include the preservation
and replication of expertise. They have limited applicability in
many problem situations. Expert System Components (348): The system
consists of a knowledge base and software modules that perform
inferences on the knowledge, and communicate answers to a users
questions. Expert System System Development (352): Expert systems
can be purchased or developed if a problem situation exists that is
suitable for solution by expert systems rather than by conventional
experts and information processing. Expert System Shell (353): An
expert system without its knowledge base. Fuzzy Logic (355): A
computer-based system that can process data that are incomplete or
only partially correct, i.e., fuzzy data. Such systems can solve
unstructured problems with incomplete knowledge as humans do.
Genetic Algorithms (356): Genetic algorithms use sets of
mathematical process rules (algorithms) that specify how
combinations of process components or steps are to be formed.
Geographic Information System (331): A GIS is a DSS that constructs
and displays maps and other graphics displays that support
decisions affecting the geographic distribution of people and other
resources.
Inference Engine (348): The software component of an expert
system, which processes the rules and facts, related to a specific
problem and
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
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makes associations and inferences resulting in recommended
sources of action. Intelligent Agent (359): A knowledge base
software surrogate for a user or process in the accomplishment of
selected tasks. Knowledge Base (348): A computer-accessible
collection of knowledge about a subject in a variety of forms, such
as facts and rules of inference, frames, and objects. Knowledge
Engineer (353): A specialist who works with experts to capture the
knowledge they possess in order to develop a knowledge base for
expert systems and other knowledge-based systems. Knowledge
Management System (341): Knowledge management systems are defined
as the use of information technology to help gather, organize, and
share business knowledge within an organization. Level of
Management Decision Making (320): Information systems can support a
variety of management levels and decisions. These include the three
levels of management activity (strategic, tactical, and
operational), and three types of decision structures (structured,
semistructured, and unstructured). Management Information System
(328): A management support system that produces prespecified
reports, displays, and responses on a periodic, exception, or
demand basis. Model Base (326): An organized collection of
conceptual, mathematical, and logical models that express business
relationships, computational routines, or analytical techniques.
Such models are stored in the form of programs and program
subroutines, command files, and spreadsheets. Neural Network (354):
Massively parallel neurocomputer systems whose architecture is
based on the human brains mesh-like neuron structure. Such networks
can process many pieces of information simultaneously and can learn
to recognize patterns and programs themselves to solve related
problems on their own. Online Analytical Processing (329):
Management, decision support, and executive information systems can
be enhanced with an online analytical processing capability.
Through OLAP, managers are able to analyze complex relationships in
order to discover patterns, trends, and exception conditions in an
online, realtime process that supports their business analysis and
decision-making. Reporting Alternatives (328): Three major
reporting alternatives include periodic scheduled reports,
exception reports, and demand reports and responses. Robotics
(347): The technology of building machines (robots) with computer
intelligence and human like physical capabilities. Virtual Reality
(356): The use of multisensory human/computer interfaces that
enable human users to experience computer-simulated objects,
entities, spaces, and worlds as if they actually existed.
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
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VI. REVIEW QUIZ - Match one of the key terms and concepts1 2 3 4
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 8 9 23 6 12 24 28 7 3 25 1
1d 1c 1a 1b 27 4 5 10 Decision support trends DSS components Level
of management decision making Decision structure Executive
information system Management information system Reporting
alternatives Decision support system Business intelligence Model
base Analytical modelling What-if analysis Sensitivity analysis
Goal-seeking analysis Optimization analysis Online analytical
processing Data mining Data visualization system Enterprise
information portal 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
36 37 38 22 11 2 2a 29 30 17 13 13a 13b 20 18 14 13d 21 26 15 19 16
Knowledge management system Enterprise knowledge portal Artificial
intelligence AI Application areas Robotics Virtual reality
Geographic information system Expert system Expert system
Applications Expert system Benefits & limitations Knowledge
base Inference engine Expert system shell Expert system System
development Knowledge engineer Neural network Fuzzy logic
Intelligent agent Genetic algorithms
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
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VII. ANSWERS TO DISCUSSION QUESTIONS1. Is the form and use of
information and decision support systems for managers and business
professionals changing and expanding? Why or why not?
Yes, the form and use of information and decision support in
e-business is changing and expanding. Certainly changes are taking
place in traditional MIS, DSS, and EIS tools, and these changes are
being driven by the rapid developments in end user computing and
networking. Internet, web browser, and related technologies, and
the explosion of e-commerce activities are also causing rapid
change. The growth of corporate intranets, extranets, as well as
the Web, has accelerated the development of executive class
interfaces like enterprise information portals, and Web enabled
decision support software tools and their use by lower of
management and by individuals and teams of business professionals.
The expansion of e-commerce has increased the use of enterprise
portals and DSS tools by the suppliers, customers, and other
business stakeholders of a company. 2. Has the growth of
self-directed teams to manage work in organizations changed the
need for strategic, tactical, and operational decision making in
business?
Although there has been tremendous growth in the use of
self-directed teams in organizations in order to manage the work,
the basics for decision making have not changed that much.
Strategic, tactical, and operational decision making continue to be
carried out in organizations regardless of how the work is
completed. What has changed is the way in which the work is being
completed. Through technology, self-directed teams now have new and
creative ways of completing their duties. 3. What is the difference
between the ability of a manager to retrieve information instantly
on demand using an MIS, and the capabilities provided by a DSS?
Managers have traditionally relied on the capabilities of MIS to
obtain the data that they required. However, the information for
these requests had traditionally been structured in advance, and
was of the structured type of request. In a DSS support system, the
capabilities are much broader. Now managers can query the
information in a number of ways, and these systems can handle the
ad hoc queries that come about. DSS provide the capabilities for a
manager to participate in interactive analytical modeling in order
to make more informed decision. DSS software is capable of
supporting semistructured and unstructured decisions faced by
individual managers. They are designed to use decision makers own
insights and judgments in an ad hoc, interactive, analytical
modeling process which will lead them to a specific decision. 4.
Refer to the Real World Case on Allstate Insurance, Aviva Canada,
and others in the chapter. Companies appear to believe that
business intelligence is a business issue and not a technology
issue. If this is the case, why does it appear that companies are
placing more and more responsibility for BI in the hands of the IT
department? In what ways does using an electronic spreadsheet
package provide you with the capabilities of a decision support
system?
5.
An electronic spreadsheet package can be thought of as one of
the earlier forms of decision support systems. Spreadsheets allow
users to complete what-if, sensitivity, goal seeking, and
optimization analysis. They also provide some features of database
management and dialog management support. 6. Are enterprise
information portals making executive information systems
unnecessary? Explain your reasoning.
First of all, in answering the question students should explain
what an EIP system is versus an EIS system. As such,
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
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EIPs are developed by companies as a way to provide web-enabled
information, knowledge, and decision support to executives,
managers, employees, suppliers, customers, and other business
partners. EISs on the other hand, are designed to provide strategic
information that are tailored to the needs of top management.
Whether or not EIPs will eventually make EIS systems unnecessary is
a matter of debate. Students may agree that as more and more
enriched features are added to EIP systems that their importance
will be heightened. On the other hand, EIS systems are also being
developed with enriched features such as Web browsing, electronic
mail, groupware tools, and DSS and expert systems capabilities to
make them even more useful to managers and business professionals.
7. Refer to the Real World Case on Wal-Mart, BankFinancial, and HP
in the chapter. Why are neural network and expert system
technologies used in many data-mining applications? Reasons could
include: Neural networks can learn from the data it processes,
thereby learning to recognize patterns and relationships in the
data it processes. Thus neural networks can change the strengths of
the interconnections between the elements in response to changing
patterns in the data it receives and the results that occur. The
neural network technology can be used to evaluate or make decisions
on its own. An example is that of BankFinancial using neural
networks to more accurately target promotions to customers and
prospects. Expert system technologies act as a consultant to end
users in very specific problem areas by making humanlike inferences
about knowledge contained in a specialized knowledge base. Expert
systems must be able to explain their reasoning process and
conclusions to a user. An example would be the If-Ten analysis used
by Wal-Mart in managing its inventory.
8.
Can computers think? Will they ever be able to? Explain why or
why not.
Computers will probably never be able to reason in the same way
that humans do. However, computers are likely to be able to perform
more and more tasks that up until now could only be performed by
humans. Experimentation continues to develop in the field of
artificial intelligence, and improvements are ongoing. Will a
computer ever pass the Turing test is questionable. 9. What are
some of the most important applications of AI in business? Defend
your choices.
In business, expert systems are probably the most important
application of artificial intelligence, though the use of such
systems is still quite limited. In other areas, robotics is widely
used in manufacturing, and natural interface applications are
becoming more and more a part of information systems for many
different applications. Major areas of AI research and development
include cognitive science, computer science, robotics, and natural
interface applications. 10. What are some of the limitations or
dangers you see in the use of AI technologies such as expert
systems, virtual reality, and intelligent agents? What could be
done to minimize such effects?
Students will suggest a number of answers to this question.
However, one possible solution could deal with the ethical issues
of these systems. Are they being used for the good of society or is
the potential for their misuse growing increasingly with the more
complex developments taking place. The design of these systems is
both complex and powerful. We must begin to ask ourselves what is
the harmful potential of these systems, and how far will we be
willing to go to use them to supplement the human reasoning process
that we are born with.
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VIII. ANSWERS TO ANALYSIS EXERCISES1. BizRate.com: eCommerce
Website Reviews a. Use BizRate.com to check out retailers for a
product you want to buy. How thorough, valid, and valuable were the
reviews to you? Explain. Many of the sites had received high
ratings. Ratings in the 4 4.5 category are common, and gives the
indication that they are relatively good sites to shop from. b. How
could other businesses use a similar web-enabled review system?
Give an example. Similar web-enabled reporting systems could be
used in a large number of business situations. This could include
reporting systems on automotive dealerships, hotels, restaurants,
airlines, amusement parks, and car rental agencies. c. How is
BizRate.com similar to a web enabled decision support system (DSS)?
Decision support systems provide summaries of critical information,
real-time monitoring, and exception reporting to decision makers.
They also allow decision makers to drill down into the information
in order to receive more detailed information on specific topics.
BizRate.com is similar to a DSS in those regards. One could easily
imagine a similarly designed system that provided information about
authorized vendors, products, bids, availability, performance,
order tracking, and account status to an organization's purchasing
agents.
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2.
Enterprise Application Integration a. Using Figure 10.22,
indicate whether or not each of the attributes of artificially
intelligent behavior applies to Amazon.com's case based reasoning
system. Students' answers will vary, however the results should
differ meaningfully from the portal's default settings. b. For
those attributes that apply as indicated by your answers above,
explain how Amazon.com's system creates that behavior. For example,
Amazon.com handles ambiguous, incomplete, or erroneous information
by linking its recommendations to a specific book rather than to
the user's search terms. In short, Amazon.com's system works to
reduce ambiguity by forcing a user to select a specific book first.
Students' answers will vary depending on the product and the
review. One business EAI provider, www.sunopsis.com, allows not
only access to disparate systems but also allows analysis between
data elements across these systems. For example, an executive may
choose to see if a relationship exists between overdue accounts
(accounts receivable) and shipping delays (shipping).
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
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3.
Case Based System Sells Books on Amazon a. Using Figure 10.22,
indicate whether or not each of the attributes of artificially
intelligent behavior applies to Amazon.com's case based reasoning
system. Attribute Applies to Amazon Think and reason no Use reason
to solve problems no or very rudimentary Learn from experience yes
Acquire and apply knowledge yes Exhibit creativity nothing outside
the bounds of its rules Deal with complex issues no Respond quickly
to new situations no (but does OK for almost new) Recognize
relative importance no (simple tallies) Handle ambiguous
information yes (sort of) b. For those attributes that apply as
indicated by your answers above, explain how Amazon.com's system
creates that behavior. For example, Amazon.com handles ambiguous,
incomplete, or erroneous information by linking its recommendations
to a specific book rather than to the user's search terms. In
short, Amazon.com's system works to reduce ambiguity by forcing a
user to select a specific book first. Attribute Applies to Amazon
Think and reason Does not apply. Case-based systems typically use
very simple rules for evaluating cases with modestly sophisticated
systems for interpolating between near misses when no case matches
exactly. Use reason to solve Does not apply. At best, a case-base
system might do a bit of problems interpolation. Learn from
experience Yes! Case-based systems learn from experience by storing
examples of past behavior and then matching these examples to the
current situation. The more examples, the better the results. Yes!
Amazon.com acquires knowledge through sales and applies this
Acquire and apply knowledge through its case based reasoning
engine. knowledge Exhibit creativity Does not apply. The system is
no more creative than the evaluation rules provided by the
programmer. However, if customers have shown creativity in the past
(purchasing paper making books along with origami books), then the
systems results will also reflect this case driven bit of
creativity. Respond quickly to Does not apply. The first time
Amazon.com offers a title for sale, the new situations case-based
system has no prior cases to use and so produces no results.
However, as soon as Amazon.com completes a few sales, the system
can begin offering recommendations. Does not apply. Amazon's system
uses simple tallies to determine relative Recognize relative
importance. importance Handle ambiguous Rather than relying on a
natural language search, Amazon simply information provides results
for a very specific book title. The system does not permit
ambiguous information. However, if the user has selected the wrong
book, Amazon's system will provide results appropriate to the
incorrect selection. That is, Amazon's results probably won't help
much.
4.
Palm City Police Department
OBrien, Management Information Systems, 7/e IM - Chapter 10 pg.
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a. b.
Build a spreadsheet to perform the analysis described above and
print it out. Currently, no funds are available to hire additional
officers. Based on the citywide ratios, the department has decided
to develop a plan to shift resources as needed in order to ensure
that no precinct has more than 1,100 residents per police officer
and no precinct has more than seven violent crimes per police
officer. The department will transfer officers from precincts that
easily meet these goals to precincts that violate one or both of
these ratios. Use "goal seeking" on your spreadsheet to move police
officers between precincts until the goals are met. You can use the
goal seek function to see how many officers would be required to
bring each precinct into compliance and then judgmentally reduce
officers in precincts that are substantially within the criteria.
Print out a set of results that allow the departments to comply
with these ratios and a memorandum to your instructor summarizing
your results and the process you used to develop them.
[See Data/Solutions File Ch 10 Exercise 4.xls]
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IX. ANSWERS TO REAL WORLD CASESRWC 1: KeyCorp, Allstate
Insurance, Aviva Canada, and Others: Centralized Business
Intelligence At Work 1. What is business intelligence? Why are
business intelligence systems such a popular business application
of IT? Business intelligence (BI) is a broad category of
applications and technologies for gathering, storing, analyzing,
and providing access to data to help enterprise users make better
business decisions. One reason for its popularity is the high
visibility of the data it makes available to business units to be
used in decision making. Data from BI centers supports sales
forecasting, financial projections, CRM solutions, new product
development, etc. 2. What is the business value of the various BI
applications discussed in the case? Examples in the case include:
Information integration across business units or applications
Support of new initiatives and customer relationship management
Support for integration in an M&A environment Reuse
data-mappings (links between data and its source) Provide a
comprehensive picture of the competitive environment, with
information from both company and competitors 3. Is a business
intelligence system an MIS or a DSS? To the extent that Decision
Support Systems provide one or mode logical models embedded within
them to support decision making, business intelligence is an MIS.
BI applications support the processes of organizing, categorizing
and accessing data; however, all decision making capabilities rest
within the user.
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RWC 2: Wal-Mart, BankFinancial, and HP: The Business Value of AI
1. What is the business value of AI technologies in business today?
Use several examples from the case to illustrate your answer.
Business values of AI technologies would be illustrated by these
examples: 2. AI software helps engineers create better jet engines.
AI technology boosts productivity by monitoring equipment and
signaling when preventive maintenance is needed. It is used to gain
new insights into the tremendous amount of data on the human
genome. Use of neural networks for detecting credit-card fraud.
Used to qualify for debit card insurance. Shifts through a deluge
of data to uncover patterns and relationships that would elude an
army of human searches. Predicting customer behavior for companies
such as banks.
What are some of the benefits and limitations of data mining for
business intelligence? Use BankFinancials experience to illustrate
your answer. Benefits would include: Potential for mining
cost-savings and revenue-boosting ideas. More accurately target
promotions to customers and prospects. Helping users set up
predictive models. Reduce the time it takes the bank to develop a
model by 50% to 70%. Developing applications such as a model to
predict customer churn, the rate at which customers come and
go.
3.
Why have banks and other financial institutions been leading
users of AI technologies like neural networks? What are the
benefits and limitations of this technology? Why banks and other
financial institutions have used AI (benefits) would include:
Detecting credit-card fraud. Use of predictive models to understand
customer behavior. Revenue enhancing. Cost reduction.
Limitations would include: Biggest stumbling block is getting
the data. Accessing the correct data needed for predictive models
(limited to only account data prepared weekly and monthly when
daily customer activity data is needed). Dealing with disparate
data sources. Systems integration and interface work is needed.
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RWC 3: Proctor & Gamble and Others: Using Agent-Based
Modeling for Supply Chain Management 1. Do you agree with Proctor
& Gamble that a supply chain should be called a supply network?
Why or why not? Discussion points that students should develop
would include: 2. A supply network is more complex than what was
intended when supply chain was coined to describe the activities of
a company with its customers, suppliers, and other business
partners. Agent-based modeling is more sophisticated and involves
more advanced applications of IT that chain no longer adequately
describes the supply management for a company such as P&G.
Computer modeling adds an additional dimension to a traditional
supply chain management system.
What is the business value of agent-based modeling? Use P&G
and other companies in this case as examples. Discussion points
would include: P&G performs what-if simulations to test the
impact of new logistics rules on three key metrics: inventory
levels, transportation costs and in-store stock-outs. The model
convinced P&G to relax rigid rules in order to improve overall
performance of the supply network. The model convinced P&G that
cultural changes, such as convincing freight managers that its
sometimes OK to let a truck go half full, is good. There is a need
for more flexibility in the manufacturing operations to reduce
stock-outs and keep customers happier. There is a need for more
flexibility in distribution. Southwest Airlines used agent-based
modeling to optimize cargo routing. Air Liquide America LP reduced
production and distribution costs with agent-based modeling. Merck
& Co. used it to help find more efficient ways to distribute
anti-HIV drugs in Zimbabwe. Ford Motor Co. used it to simulate
buyer preferences to optimize the trade-offs between production
costs and customer demands. Edison Chouest Offshore LLC optimized
its deployment of service and supply vessels in the Gulf of
Mexico.
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3.
Visit the website of NuTech Solutions. How does NuTech use AI
techniques to help companies gain adaptive business intelligence?
Give several examples from the website case studies. Examples from
site could include: Branch Banking and Trust ARROW. ChevronTexaco
scheduling optimization.. DaimlerChrysler Aerospace engineering
design optimization. Dutch Ministry of Traffic scheduling
optimization. F. E. Bording supply network optimization. General
Motors vehicle distribution system. U. S. Internal Revenue Service
expert system development. Kraftwerksunion (KWU) scheduling
optimization. Major Automaker marketing diffusion. Major Producer
resource allocation optimization. Major Telecom Company engineering
design optimization. Major U. S. Automaker data mining. Major U. S.
Bank vehicle distribution system. NASA modeling and simulation.
Nasdaq modeling and simulation.
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RWC 4: Boehringer Ingelheim: Using Web-Based Tools for Financial
Analysis and Reporting 1. What are the business benefits and
limitations of Boehringers Web-based financial analysis and
reporting systems? Discussion points would include: Rapidly
consolidate and present key financial data on a daily, weekly or
monthly basis which allows it to drill down and draw conclusions
based on the latest available financial and operational data.
Boehringer is now able to close its books for most of its divisions
just two hours after the close of business at the end of each month
vs. a three day requirement in the past. The accounting department
can spot product sales trends and track expenses quickly.
Boehringer can create multidimensional views of profit and loss
data. 2. Which of Boehringers financial analysis and reporting
systems are MIS tools? DSS tools? Why? Students should present
discussion that would include consideration of: Decision support
provided o MIS provide information about the performance of
Boehringer. o DSS provide information and decision support
techniques to analyze specific problems or opportunities.
Information form and frequency o MIS periodic, exception, demand,
and push reports and responses. o DSS interactive inquiries and
responses. Information format o MIS - Prespecified, fixed format o
DSS ad hoc, flexible, and adaptable format Information processing
methodology o MIS information produced by extraction and
manipulation of business data. o DSS information produced by
analytical modeling of business data. 3. How could the Cognos tools
used by Boehringer be used for marketing and other business
analysis and reporting applications? Visit the Cognos website to
help you answer.
Discussion points would include: Use of the Cognos tools by the
marketing staff to increase Boehringers competitive position. Use
of the Cognos tools by the marketing staff to improve their
customer relationship management system. Training of the marketing
staff to use the Cognos tools. Ability of Boehringers IT staff to
implement the system in all divisions.
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