2. Decision Support in Business
• Companies are investing in data-driven
decision support application frameworks
to help them respond to
– Changing market conditions
– Customer needs
• This is accomplished by several types of
– Management information
– Decision support
– Other information systems
10-2
4. Information Quality
• Information products made more valuable
by their attributes, characteristics, or
qualities
– Information that is outdated, inaccurate, or
hard to understand has much less value
• Information has three dimensions
– Time
– Content
– Form
10-4
6. Decision Structure
• Structured (operational)
– The procedures to follow when decision
is needed can be specified in advance
• Unstructured (strategic)
– It is not possible to specify in advance
most of the decision procedures to follow
• Semi-structured (tactical)
– Decision procedures can be pre-specified,
but not enough to lead to the correct decision
10-6
7. Decision Support Systems
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 produced by
extraction and manipulation
of business data
Information produced by
analytical modeling of
business data
Information
processing
methodology
10-7
8. Decision Support Trends
• The emerging class of applications
focuses on
– Personalized decision support
– Modeling
– Information retrieval
– Data warehousing
– What-if scenarios
– Reporting
10-8
10. Decision Support Systems
• Decision support systems use the
following to support the making of semistructured business decisions
– Analytical models
– Specialized databases
– A decision-maker’s own insights and
judgments
– An interactive, computer-based modeling
process
• DSS systems are designed to be ad hoc,
quick-response systems that are initiated
and controlled by decision makers
10-10
12. DSS Model Base
• Model Base
– A software component that consists of
models used in computational and analytical
routines that mathematically express
relations among variables
• Spreadsheet Examples
– Linear programming
– Multiple regression forecasting
– Capital budgeting present value
10-12
13. Applications of Statistics and
Modeling
– Supply Chain: simulate and optimize supply
chain flows, reduce inventory, reduce stock-outs
– Pricing: identify the price that maximizes
yield or profit
– Product and Service Quality: detect quality
problems early in order to minimize them
– Research and Development: improve quality,
efficacy, and safety of products and services
10-13
14. Management Information Systems
• The original type of information system
that supported managerial decision
making
– Produces information products that support
many day-to-day decision-making needs
– Produces reports, display, and responses
– Satisfies needs of operational and tactical
decision makers who face structured
decisions
10-14
15. Management Reporting Alternatives
• Periodic Scheduled Reports
– Prespecified format on a regular basis
• Exception Reports
– Reports about exceptional conditions
– May be produced regularly or when an
exception occurs
• Demand Reports and Responses
– Information is available on demand
• Push Reporting
– Information is pushed to a networked
computer
10-15
16. Online Analytical Processing
(OLAP)
• Enables managers and analysts to
examine and manipulate large amounts
of detailed and consolidated data from
many perspectives
• Done interactively, in real time, with rapid
response to queries
10-16
17. Online Analytical Operations
• Consolidation
– Aggregation of data
– Example: data about sales offices rolled up
to the district level
• Drill-Down
– Display underlying detail data
– Example: sales figures by individual product
• Slicing and Dicing
– Viewing database from different viewpoints
– Often performed along a time axis
10-17
18. Geographic Information Systems
(GIS)
• DSS uses geographic databases to construct
and display maps and other graphic displays
• Supports decisions affecting the geographic
distribution of people and other resources
• Often used with Global Positioning Systems
(GPS) devices
10-18
19. Data Visualization Systems
(DVS)
• Represents complex data using
interactive, three-dimensional graphical
forms (charts, graphs, maps)
• Helps users interactively sort, subdivide,
combine, and organize data while it is in
its graphical form
10-19
20. Using Decision Support Systems
• Using a decision support system involves an
interactive analytical modeling process
– Decision makers are not demanding
pre-specified information
– They are exploring possible alternatives
• What-If Analysis
– Observing how changes to selected variables
affect other variables
10-20
21. Using Decision Support Systems
• Sensitivity Analysis
– Observing how repeated changes to a single
variable affect other variables
• Goal-seeking Analysis
– Making repeated changes to selected variables
until a chosen variable reaches a target value
• Optimization Analysis
– Finding an optimum value for selected
variables, given certain constraints
10-21
22. Data Mining
• Provides decision support through
knowledge discovery
– Analyzes vast stores of historical business data
– Looks for patterns, trends, and correlations
– Goal is to improve business performance
• Types of analysis
–
–
–
–
–
Regression
Decision tree
Neural network
Cluster detection
Market basket analysis
10-22
24. Market Basket Analysis
• One of the most common uses for data
mining
– Determines what products customers purchase
together with other products
• Results affect how companies
–
–
–
–
–
Market products
Place merchandise in the store
Lay out catalogs and order forms
Determine what new products to offer
Customize solicitation phone calls
10-24
25. Executive Information Systems
(EIS)
– Combines many features of MIS and DSS
– Provide top executives with immediate and
easy access to information
– Identify factors that are critical to
accomplishing strategic objectives (critical
success factors)
– So popular that it has been expanded to
managers, analysis, and other knowledge
workers
10-25
26. Features of an EIS
• Information presented in forms tailored to
the preferences of the executives using
the system
– Customizable graphical user interfaces
– Exception reports
– Trend analysis
– Drill down capability
10-26
27. Enterprise Information Portals
• An EIP is a Web-based interface and
integration of MIS, DSS, EIS, and other
technologies
– Available to all intranet users and select
extranet users
– Provides access to a variety of internal and
external business applications and services
– Typically tailored or personalized to the user
or groups of users
– Often has a digital dashboard
– Also called enterprise knowledge portals
10-27
29. Artificial Intelligence (AI)
• AI is a field of science and technology
based on
–
–
–
–
–
–
Computer science
Biology
Psychology
Linguistics
Mathematics
Engineering
• The goal is to develop computers than
can simulate the ability to think
– And see, hear, walk, talk, and feel as well
10-29
30. 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 situations
Respond quickly and successfully to new situations
Recognize the relative importance of
elements in a situation
– Handle ambiguous, incomplete, or
erroneous information
10-30
32. Cognitive Science
• Applications in the cognitive science of AI
–
–
–
–
–
–
–
Expert systems
Knowledge-based systems
Adaptive learning systems
Fuzzy logic systems
Neural networks
Genetic algorithm software
Intelligent agents
• Focuses on how the human brain works
and how humans think and learn
10-32
33. Latest Commercial Applications of AI
•
Decision Support
– Helps capture the why as well as the what of engineered design and
decision making
•
Information Retrieval
– Distills tidal waves of information into simple presentations
– Natural language technology
– Database mining
•
Virtual Reality
– X-ray-like vision enabled by enhanced-reality visualization helps
surgeons
– Automated animation and haptic interfaces allow users to interact with
virtual objects
•
Robotics
– Machine-vision inspections systems
– Cutting-edge robotics systems
• From micro robots and hands and legs, to cognitive and trainable
modular vision systems
10-33
34. Expert Systems
• An Expert System (ES)
– A knowledge-based information
system
– Contain knowledge about a specific,
complex application area
– Acts as an expert consultant to end
users
10-34
35. Components of an Expert System
• Knowledge Base
– Facts about a specific subject area
– Heuristics that express the reasoning
procedures of an expert (rules of thumb)
• Software Resources
– An inference engine processes the
knowledge
and recommends a course of action
– User interface programs communicate with
the end user
– Explanation programs explain the
reasoning process to the end user
10-35
37. Methods of Knowledge Representation
• Case-Based
– Knowledge organized in the form of cases
– Cases are examples of past performance,
occurrences, and experiences
• Frame-Based
– Knowledge organized in a hierarchy or
network of frames
– A frame is a collection of knowledge about
an entity, consisting of a complex package
of data values describing its attributes
10-37
38. Methods of Knowledge Representation
• Object-Based
– Knowledge represented as a network of objects
– An object is a data element that includes both
data and the methods or processes that act on
those data
• Rule-Based
– Knowledge represented in the form of rules
and statements of fact
– Rules are statements that typically take the
form of a premise and a conclusion (If, Then)
10-38
40. Expert System Application
Categories (cont’d)
• Selection/Classification
–
–
–
–
Material selection
Delinquent account identification
Information classification
Suspect identification
• Process Monitoring/Control
–
–
–
–
Machine control (including robotics)
Inventory control
Production monitoring
Chemical testing
10-40
41. Benefits of Expert Systems
• Captures the expertise of an expert or
group of experts in a computer-based
information system
– Faster and more consistent than an expert
– Can contain knowledge of multiple experts
– Does not get tired or distracted
– Cannot be overworked or stressed
– Helps preserve and reproduce the
knowledge of human experts
10-41
42. Limitations of Expert Systems
• Limited focus
• Inability to learn
• Maintenance problems
• Development cost
• Can only solve specific types of problems in
a limited domain of knowledge
10-42
43. Developing Expert Systems
• Suitability Criteria for Expert Systems
– Domain: the domain or subject area of the problem
is small and well-defined
– Expertise: a body of knowledge, techniques, and
intuition is needed that only a few people possess
– Complexity: solving the problem is a complex task
that requires logical inference processing
– Structure: the solution process must be able
to cope with ill-structured, uncertain, missing, and
conflicting data and a changing problem situation
– Availability: an expert exists who is articulate,
cooperative, and supported by the management
and end users involved in the development process
10-43
44. Development Tool
• Expert System Shell
– The easiest way to develop an expert system
– A software package consisting of an expert
system without its knowledge base
– Has an inference engine and user interface
programs
10-44
45. Knowledge Engineering
• A knowledge engineer
– Works with experts to capture the knowledge
(facts and rules of thumb) they possess
– Builds the knowledge base, and if necessary,
the rest of the expert system
– Performs a role similar to that of systems
analysts in conventional information systems
development
10-45
