2. 9- 2
Learning 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.
3. 9- 3
Learning Objectives
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
4. 9- 4
Learning Objectives
5. Identify how neural networks, fuzzy logic,
genetic algorithms, virtual reality, and
intelligent agents can be used in business.
6. Give examples of several ways expert systems
can be used in business decision-making
situations.
5. 9- 5
Case 1: Oracle Corporation and Others:
Dashboards for Executives and Business
Professionals: The Power and the Challenge
• The dashboard has become the CEO’s killer app.
• Dashboards provide key business information to
executives, managers, and business professionals.
• At GE executives use dashboard to follow the
production of everything from light bulbs to
dishwashers, making sure production lines are
running smoothly.
• Dashboards have some challenges. These tools can
raise pressure on employees, create divisions in the
office, and lead workers to hoard information.
• Dashboards can hurt the morale of employees.
6. 9- 6
Case Study Questions
1. What is the attraction of dashboards to CEOs and
other executives? What real business value do
they provide to executives?
2. The case emphasizes that managers of small
businesses and many business professionals now
rely on dashboards. What business benefits do
dashboards provide to this business audience?
3. What are several reasons for criticism of the use of
dashboards by executives? Do you agree with any
of this criticism? Why or why not?
7. 9- 7
Real World Internet Activity
1. Use the Internet to research makers of
dashboards for large and small business. For
example, try NetSuite, Hyperion Solutions, and
Salesforce.com for relatively inexpensive
versions and Microsoft, Oracle, and SAP for
more costly corporate dashboards. Evaluate
the dashboard examples and demos you
experience. Pick your favorites and explain
your reasons for doing so to the class.
8. 9- 8
Real World Group Activity
2. How would you like to work for an executive
whose dashboard provides the level of
information about company and employee
performance described in this case? Would you
want that level of information when you enter
the executive ranks?
– Discuss this issue, and formulate suggestions
on any changes or safeguards you would
propose for the business use of dashboards.
10. 9- 10
Levels of Management Decision
Making
• Strategic management
– Executives develop organizational goals, strategies,
policies, and objectives
– As part of a strategic planning process
• Tactical management
– Managers and business professionals in self-directed
teams
– Develop short- and medium-range plans, schedules
and budgets
– Specify the policies, procedures and business
objectives for their subunits
11. 9- 11
Levels of Management Decision
Making
• Operational management
– Managers or members of self-directed teams
– Develop short-range plans such as weekly production
schedules
12. 9- 12
Information Quality
• Information products whose characteristics,
attributes, or qualities make the information more
value
• Information has 3 dimensions:
– Time
– Content
– Form
14. 9- 14
Decision Structure
• Structured – situations where the procedures to
follow when a decision is needed can be
specified in advance
• Unstructured – decision situations where it is not
possible to specify in advance most of the
decision procedures to follow
• Semistructured - decision procedures that can
be prespecified, but not enough to lead to a
definite recommended decision
15. 9- 15
Information Systems to support
decisions
Management
Information Systems
Decision Support
Systems
Decision
support
provided
Provide information about
the performance of the
organization
Provide information and
techniques to analyze
specific problems
Information
form and
frequency
Periodic, exception,
demand, and push reports
and responses
Interactive inquiries and
responses
Information
format
Prespecified, fixed format Ad hoc, flexible, and
adaptable format
Information
processing
methodology
Information produced by
extraction and manipulation
of business data
Information produced by
analytical modeling of
business data
16. 9- 16
Decision Support Trends
• Personalized proactive decision analytics
• Web-Based applications
• Decisions at lower levels of management and by
teams and individuals
• Business intelligence applications
18. 9- 18
Decision Support Systems
• DSS
• Provide interactive information support to
managers and business professionals during the
decision-making process
• Use:
– Analytical models
– Specialized databases
– A decision maker’s own insights and judgments
– Interactive computer-based modeling
• To support semistructured business decisions
20. 9- 20
DSS Model base
• Model base
– A software component that consists of models used in
computational and analytical routines that
mathematically express relations among variables
• Examples:
– Linear programming models,
– Multiple regression forecasting models
– Capital budgeting present value models
21. 9- 21
Management Information
Systems
• MIS
• Produces information products that support
many of the day-to-day decision-making needs
of managers and business professionals
• Prespecified reports, displays and responses
• Support more structured decisions
22. 9- 22
MIS Reporting Alternatives
• Periodic Scheduled Reports
– Prespecified format on a regular basis
• Exception Reports
– Reports about exceptional conditions
– May be produced regularly or when exception occurs
• Demand Reports and Responses
– Information available when demanded
• Push Reporting
– Information pushed to manager
23. 9- 23
Online Analytical Processing
• OLAP
– Enables mangers and analysts to examine and
manipulate large amounts of detailed and consolidated
data from many perspectives
– Done interactively in real time with rapid response
24. 9- 24
OLAP Analytical Operations
• Consolidation
– Aggregation of data
• Drill-down
– Display detail data that comprise consolidated data
• Slicing and Dicing
– Ability to look at the database from different viewpoints
26. 9- 26
Geographic Information Systems
• GIS
– 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
– Often used with Global Position Systems (GPS)
devices
27. 9- 27
Data Visualization Systems
• DVS
– DSS that represents 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.
28. 9- 28
Using DSS
• What-if Analysis
– End user makes changes to variables, or relationships
among variables, and observes the resulting changes in
the values of other variables
• Sensitivity Analysis
– Value of only one variable is changed repeatedly and
the resulting changes in other variables are observed
29. 9- 29
Using DSS
• Goal-Seeking
– Set a target value for a variable and then repeatedly
change other variables until the target value is achieved
– How can analysis
• Optimization
– Goal is to find the optimum value for one or more target
variables given certain constraints
– One or more other variables are changed repeatedly
until the best values for the target variables are
discovered
30. 9- 30
Data Mining
• Main purpose is to provide decision support to
managers and business professionals through
knowledge discovery
• Analyzes vast store of historical business data
• Tries to discover patterns, trends, and
correlations hidden in the data that can help a
company improve its business performance
• Use regression, decision tree, neural network,
cluster analysis, or market basket analysis
31. 9- 31
Market Basket Analysis
• One of most common data mining for marketing
• The purpose is to determine what products
customers purchase together with other products
32. 9- 32
Executive Information Systems
• EIS
– Combine many features of MIS and DSS
– Provide top executives with immediate and easy access
to information
– About the factors that are critical to accomplishing an
organization’s strategic objectives (Critical success
factors)
– So popular, expanded to managers, analysts and other
knowledge workers
33. 9- 33
Features of an EIS
• Information presented in forms tailored to the
preferences of the executives using the system
– Customizable graphical user interfaces
– Exception reporting
– Trend analysis
– Drill down capability
34. 9- 34
Enterprise Interface Portals
• EIP
– Web-based interface
– Integration of MIS, DSS, EIS, and other technologies
– Gives all intranet users and selected extranet users
access
– To a variety of internal and external business
applications and services
• Typically tailored to the user giving them a
personalized digital dashboard
36. 9- 36
Knowledge Management
Systems
• The use of information technology to help
gather, organize, and share business knowledge
within an organization
• Enterprise Knowledge Portals
– EIPs that are the entry to corporate intranets that serve
as knowledge management systems
38. 9- 38
Case 2: Harrah’s Entertainment,
LendingTree, DeepGreen Financial, and
Cisco Systems:
• The promise of AI of automating decision making has been
very slow to materialize.
• The new generation AI applications are easier to create and
manage, do not require anyone to identify the problems or to
initiate the analysis, decision-making capabilities are
embedded into the normal flow of work, and are triggered
without human intervention.
• They sense online data or conditions, apply codified
knowledge or logic and make decisions with minimal human
intervention.
• But they rely on experts and managers to create and maintain
rules and monitor the results.
• Also, managers in charge of automated decision systems
must develop processes for managing exceptions.
39. 9- 39
Case Study Questions
1. Why did some previous attempts to use artificial
intelligence technologies fail? What key
differences of the new AI-based applications
versus the old cause the authors to declare that
automated decision making is finally coming of
age?
2. What types of decisions are best suited for
automated decision making? Provide several
examples of successful applications from the
companies in this case to illustrate your answer.
40. 9- 40
Case Study Questions
3. What role do humans play in automated
decision making applications? What are some
of the challenges faced by managers where
automated decision-making systems are being
used? What solutions are needed to meet
such challenges?
41. 9- 41
Real World Internet Activity
1. Use the Internet to find examples of companies
that are using automated decision making or
other business applications of artificial
intelligence. You might begin by looking for
such information on the companies mentioned
in this case and their main competitors, and
then widen your search to encompass other
companies. What business benefits or
challenges do you discover?
42. 9- 42
Real World Group Activity
2. Artificial intelligence applications in business
such as automated decision making pose
potential business risks, as evidenced by the
Cisco Systems experience, and have the
potential for other risks to business and human
security and safety, for example.
– Discuss such risks and propose controls and
safeguards to lessen the possibility of such
occurrences.
43. 9- 43
Artificial Intelligence (AI)
• A field of science and technology based on
disciplines such as computer science, biology,
psychology, linguistics, mathematics, and
engineering
• Goal is to develop computers that can simulate
the ability to think, as well as see, hear, walk,
talk, and feel
44. 9- 44
Attributes of Intelligent Behavior
• Think and reason
• Use reason to solve problems
• Learn or understand from experience
• Acquire and apply knowledge
• Exhibit creativity and imagination
• Deal with complex or perplexing situations
• Respond quickly and successfully to new
situations
• Recognize the relative importance of elements in
a situation
• Handle ambiguous, incomplete, or erroneous
information
46. 9- 46
Cognitive Science
• Based in biology, neurology, psychology, etc.
• Focuses on researching how the human brain
works and how humans think and learn
47. 9- 47
Robotics
• Based in AI, engineering and physiology
• Robot machines with computer intelligence and
computer controlled, humanlike physical
capabilities
48. 9- 48
Natural Interfaces
• Based in linguistics, psychology, computer
science, etc.
• Includes natural language and speech
recognition
• Development of multisensory devices that use a
variety of body movements to operate computers
• Virtual reality
– Using multisensory human-computer interfaces that
enable human users to experience computer-simulated
objects, spaces and “worlds” as if they actually exist
49. 9- 49
Expert Systems
• ES
• A knowledge-based information system (KBIS)
that uses its knowledge about a specific,
complex application to act as an expert
consultant to end users
• KBIS is a system that adds a knowledge base to
the other components on an IS
50. 9- 50
Expert System Components
• Knowledge Base
– Facts about specific subject area
– Heuristics that express the reasoning procedures of an
expert (rules of thumb)
• Software Resources
– Inference engine processes the knowledge and makes
inferences to make recommend course of action
– User interface programs to communicate with end user
– Explanation programs to explain the reasoning process
to end user
52. 9- 52
Methods of Knowledge
Representation
• Case-Based – knowledge organized in form of
cases
– Cases: examples of past performance, occurrences
and experiences
• Frame-Based – knowledge organized in a
hierarchy or network of frames
– Frames: entities consisting of a complex package of
data values
53. 9- 53
Methods of Knowledge
Representation
• Object-Based – knowledge organized in network
of objects
– Objects: data elements and the methods or processes
that act on those data
• Rule-Based – knowledge represented in rules
and statements of fact
– Rules: statements that typically take the form of a
premise and a conclusion
– Such as, If (condition) then (conclusion)
54. 9- 54
Expert System Benefits
• Faster and more consistent than an expert
• Can have the knowledge of several experts
• Does not get tired or distracted by overwork or
stress
• Helps preserve and reproduce the knowledge of
experts
55. 9- 55
Expert System Limitations
• Limited focus
• Inability to learn
• Maintenance problems
• Developmental costs
• Can only solve specific types of problems in a
limited domain of knowledge
56. 9- 56
Suitability Criteria for Expert
Systems
• Domain: subject area relatively small and limited to well-
defined area
• Expertise: solutions require the efforts of an expert
• Complexity: solution of the problem is a complex task that
requires logical inference processing (not possible in
conventional information processing)
• Structure: solution process must be able to cope with ill-
structured, uncertain, missing and conflicting data
• Availability: an expert exists who is articulate and
cooperative
57. 9- 57
Development Tool
• Expert System Shell
– Software package consisting of an expert system
without its knowledge base
– Has inference engine and user interface programs
58. 9- 58
Knowledge Engineer
• A professional who works with experts to capture
the knowledge they possess
• Builds the knowledge base using an iterative,
prototyping process
59. 9- 59
Neural Networks
• Computing systems modeled after the brain’s
mesh-like network of interconnected processing
elements, called neurons
• Interconnected processors operate in parallel
and interact with each other
• Allows network to learn from data it processes
60. 9- 60
Fuzzy Logic
• Method of reasoning that resembles human
reasoning
• Allows for approximate values and inferences
and incomplete or ambiguous data instead of
relying only on crisp data
• Uses terms such as “very high” rather than
precise measures
61. 9- 61
Genetic Algorithms
• Software that 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
62. 9- 62
Virtual Reality (VR)
• Computer-simulated reality
• Relies on multisensory input/output devices such
as
– a tracking headset with video goggles and stereo
earphones,
– a data glove or jumpsuit with fiber-optic sensors that
track your body movements, and
– a walker that monitors the movement of your feet
63. 9- 63
Intelligent Agents
• A software surrogate for an end user or a
process that fulfills a stated need or activity
• Uses its built-in and learned knowledge base
• To make decisions and accomplish tasks in a
way that fulfills the intentions of a user
• Also called software robots or bots
64. 9- 64
User Interface Agents
• Interface Tutors – observe user computer
operations, correct user mistakes, and provide
hints and advice on efficient software use
• Presentation – show information in a variety of
forms and media based on user preferences
• Network Navigation – discover paths to
information and provide ways to view information
based on user preferences
• Role-Playing – play what-if games and other
roles to help users understand information and
make better decisions
65. 9- 65
Information Management Agents
• Search Agents – help users find files and
databases, search for desired information, and
suggest and find new types of information
products, media, and resources
• Information Brokers – provide commercial
services to discover and develop information
resources that fit the business or personal needs
of a user
• Information Filters – receive, find, filter, discard,
save, forward, and notify users about products
received or desired
66. 9- 66
Case 3: IBM, Linden Labs, and Others: The
Business Case for Virtual Worlds in a 3D
Internet
• Second Life is a 3-D virtual world entirely built and owned by
its Residents.
• Since opening to the public in 2003, it has grown explosively
and today it is inhabited by more than eight million residents
from around the globe.
• It is catching the attention of many companies because of
it’s ability to use as a platform for a whole new Net with huge
opportunities to sell products and services.
• It is also possible to exchange Second Life’s currency,
called Linden dollars, for the real currency for a fee.
• Residents could thus build, own, or sell their digital
creations.
• Second Life has become a real economy.
67. 9- 67
Case Study Questions
1. What are the most important business benefits
and limitations of 3D virtual worlds like Second
Life to real-world companies such as those
mentioned in this case?
2. Why do you think IBM is taking a leadership
role in promoting and using 3D metaverses like
Second Life? What business benefits might it
expect to gain from its involvement in
developing a 3D Internet? Explain your
reasoning.
68. 9- 68
Case Study Questions
3. Are 3D virtual worlds like Second Life
“solutions in search of a problem” at this stage
of their development, in that do not satisfy any
vital business need? Why or why not?
69. 9- 69
Real World Internet Activity
1. Search the Internet to determine how Second
Life, Linden Labs, IBM, and other companies
mentioned in this case are doing in terms of
the growth and business success of their
development or use of 3D virtual worlds. Have
new competitors successfully entered the 3D
Internet market? If so, how do they differ in the
products and services they offer?
70. 9- 70
Real World Group Activity
2. Visit the Second Life Web site and evaluate
the experience in terms of level of difficulty,
response times, operation of basic functions,
realism, and so forth. Are 3D virtual worlds like
Second Life ready for widespread use as an
important form of social networking? How
could they improve what they offer to make it
more appealing and successful? Debate these
issues.
Editor's Notes
Need information systems to support decisions of all types. Decision support systems support the unstructured with unscheduled ad hoc reports Management information systems support the more structured with prespecified reports
Example is a Web-enabled marketing DSS. Note the hardware, software, model, data and network resources involved.
Unlike an MIS, DSS contains a model base as well as a database
