Transcript Chapter 10 - cda college

Management Information Systems,
Sixth Edition
Chapter 10:
Decision Support and Expert Systems
Objectives
• List and explain the phases in decision making
• Articulate the difference between structured and
unstructured decision making
• Describe the typical software components that
decision support systems and expert systems
comprise
• Give examples of how decision support systems
and expert systems are used in various domains
• Describe the typical elements and uses of
geographic information systems
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Decision Support
• Success of an organization largely depends on
the quality of decisions made by employees
• Computer-based systems can help when:
– There are large amounts of information
– There is a lot of processing involved
• Two types of decision support aids:
– Decision support systems (DSSs)
– Expert systems (ESs)
• Applications today may combine both types
– Provide single optimal solution or set of solutions
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Decision Support (continued)
• Decision support modules today may be part of
larger enterprise applications
• Are also called business analysis tools or
business intelligence applications
• Are designed to streamline the decision-making
process
• Data warehouses and online processing (OLAP)
technologies have enhanced the ability to use
data for decision making
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The Decision-Making Process
• A decision must be made whenever more than
one possible action is available
• It can be difficult to make decisions when many
reasonable alternatives are present
– In business, there may be dozens, hundreds, or
even millions of different courses of actions
available to achieve a desired result
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The Decision-Making Process
(continued)
• Decision making is a three-phase process:
– Intelligence phase: collect facts, beliefs, and
ideas
– Design phase: design the method for
considering the collected data, to reduce the
alternatives to a manageable number
– Choice phase: select an alternative from the
remaining choices
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The Decision-Making Process
(continued)
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The Decision-Making Process
(continued)
• Businesses collect data internally within the
organization and externally from outside sources
• Model: a representation of reality, such as:
– Map: represents a geographical area
– Tabletop representation of a building
– Mathematical equations representing
relationships among variables
• Managers either choose universal models or
design their own models
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Structured and Unstructured Problems
• Structured problem: one in which an optimal
solution can be reached through a single set of
steps
• Algorithm: a sequence of steps to complete a
task
• Parameters: categories of data that are
considered in an algorithm
• Most mathematical and physical problems are
structured, but many business problems are
not
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Structured and Unstructured Problems
(continued)
• Unstructured problem: one for which there is
no algorithm that leads to an optimal solution
– May not be enough information
– May be a large number of potential factors
• Unstructuredness is closely related to
uncertainty
• Examples of unstructured problems include:
– Weather prediction
– Stock market prediction
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Structured and Unstructured Problems
(continued)
• Semistructured problem: one that is neither
fully structured nor totally unstructured
• Professionals encounter semistructured
problems almost daily in many different
industries
• The goal is to choose the one alternative that will
bring about the best outcome
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Structured and Unstructured Problems
(continued)
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Structured and Unstructured Problems
(continued)
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Decision Support Systems
• Decision support system (DSS): a computerbased information system designed to help
knowledge workers select one of many
alternative solutions to a problem
• Advantages of DSSs include:
–
–
–
–
Help increase market share
Help reduce costs
Help increase profitability
Help enhance product quality
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Decision Support Systems (continued)
• Most DSSs consist of three components:
– Data management module
– Model management module
– Dialog module
• These components help users:
–
–
–
–
Enter a request in a convenient manner
Search vast amounts of data
Process the data through desired models
View the results in a desired format
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The Data Management Module
• Data management module: a database or data
warehouse that provides data for the intelligence
phase
– Accesses the data
– Provides a means to select data by specified
criteria
• Many DSSs are intertwined with other
organizational systems, including data
warehouses, data marts, and ERP systems
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The Model Management Module
• Model management module: turns data into
useful information
• May offer a fixed model, a dynamically modified
model, or a collection of models
– Dynamically modified model: one that is
automatically adjusted based on changing
relationships among variables
• A sequence of events or a pattern of behavior
can become a useful model
• Models are often based on mathematical
research
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The Model Management Module
(continued)
• Patterns or models may be unique to a certain
industry, such as:
–
–
–
–
ATM placement
Truck route planning
Airline ticket pricing
Car rental pricing
• A linear regression model is a general
statistical model that is often used
– Gives a best-fit linear relationship between two
variables
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The Model Management Module
(continued)
• A linear relationship can be translated into a
program in a DSS
• The actual data points rarely lie directly on the
regression line, illustrating the uncertainty
• Regression models are not necessarily always
straight lines; they may be curves
• Models often describe relationships between
more than two variables
• Some DSSs simulate physical environments
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The Dialog Module
• Dialog module: part of a DSS that allows user
interaction with the program
– Prompts the user to select a model and data to
process
– Allows the user to change parameters and view
the results of the changes (“what if” analysis)
– Displays the results of the analysis in textual,
tabular, or graphical format
• Many DSSs are available through the Internet
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Sensitivity Analysis
• An outcome is often affected by more than one
parameter, but changes to parameter values
usually affect outcomes differently
• It is important to determine which parameters
have the most effect on the outcome
• Sensitivity analysis: tests the degree to which
the outcome goal grows with each factor
– Indicates the relative sensitivity of the outcome to
changes in a parameter
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Sensitivity Analysis (continued)
• If a small change in a parameter causes a
significant change to the outcome, the sensitivity
of the outcome to the parameter is high
• If the outcome is affected very little by a large
change in a parameter, the sensitivity of the
outcome to the parameter is low
• Sensitivity analysis is also called what if
analysis
• Can perform sensitivity analysis on multiple
parameters simultaneously
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Decision Support Systems in Action
• DSSs can be used on demand or integrated into
a scheme that enforces corporate policy
• DSSs help maintain standard criteria in decision
making throughout the organization
• Automated decision production is becoming very
popular
– The only labor required is for data entry
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Decision Support Systems in Action
(continued)
• DSSs are used in many industries:
– Food production and retailing: to forecast the
number of patrons, the amount of ingredients to
purchase, etc.
– Agriculture: allows farmers to make decisions
about how to control specific pests, and for
picking farm locations
– Tax planning: tax helper applications such as
TurboTax and TaxCut
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Decision Support Systems in Action
(continued)
• DSSs are used in many industries (continued):
– Web site planning and adjustment: to analyze
shopper behavior, and to design Web sites based
on page usage
– Yield management: to maximize revenue from
airline trips or lodging
– Financial services: to determine loan amounts,
and to qualify customers based on credit history
– Benefits selection: to allow employees to make
decisions about their benefits
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Decision Support Systems in Action
(continued)
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Decision Support Systems in Action
(continued)
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Expert Systems
• Expert system (ES): emulates the knowledge of
a human expert
– Solves problems
– Makes decisions in a relatively narrow domain
• Domain: a specific area of knowledge
• Purpose is to replicate the unstructured and
undocumented knowledge of experts, and make
that expertise available to novices
• Neural network: a program that emulates how
the human brain works
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Expert Systems (continued)
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Expert Systems (continued)
• ESs are part of artificial intelligence (AI)
research
• AI focuses on methods and technologies that
emulate how humans learn and solve problems
• Knowledge base: used by an ES
– A collection of facts and the relationships among
them
– Built as a series of IF-THEN rules
– Uses an inference engine
• Inference engine: software that combines data
input by the user with the data relationships
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Expert Systems (continued)
• Neural networks: used by more sophisticated
ESs to mimic the way a human brain learns
– Constructed with a set of rules, but then it refines
itself based on its decision success rate
– Very effective for detecting fraud
• Intelligent agent: software that is dormant until
it detects a certain event, and then performs a
prescribed action
• There are also case-based ESs
– Especially useful in medical decision making
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Expert Systems in Action
• ESs have been implemented in many industries:
– Medical diagnosis:
• Help doctors with the diagnosis of symptoms and
treatment advice
• Can help enhance the accuracy of Alzheimer’s
disease diagnosis
– Medical management:
• Help discern which treatments patient should
receive
• Help with administrative decisions
– Telephone network maintenance:
• Used to help diagnose and fix network failures
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Expert Systems in Action (continued)
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Expert Systems in Action (continued)
• ESs have been implemented in many industries
(continued):
– Credit evaluation:
• Used to approve credit card charges
• Used to analyze financial reports submitted with
credit applications
• Local loan officers may periodically update the
knowledge base to customize it for current loan
policy
– Detection of insider securities trading:
• Help prevent trading of stocks based on private
information by analyzing the stock’s history
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Expert Systems in Action (continued)
• ESs have been implemented in many industries
(continued):
– Detection of common metals:
• Help nonexperts identify common metals and
alloys outside laboratories
• Based on results of simple chemical tests and
other information available at the scene
– Irrigation and pest management:
• Provide recommendations on irrigation, application
of fungicides, and likelihood of pest conditions
• Can significantly improve crop yields
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Expert Systems in Action (continued)
• ESs have been implemented in many industries
(continued):
– Diagnosis and prediction of mechanical failure:
• Diagnose cause of component failure
• Can provide a set of instructions for fixing the
problem
• Help companies know when to replace
components before a failure occurs
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Group Decision Support Systems
• Group decision support system (GDSS):
– Also called a group intelligence system,
collaborative system, or simply a group system
– Facilitates the contribution of ideas,
brainstorming, and choosing promising solutions
• Typically allows participants to define a problem,
contribute ideas, then vote on the decision
• GDSSs help structure the decision-making
process while allowing participants to remain
anonymous
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Geographic Information Systems
• Geographic information system (GIS): a
decision aid for map-related decisions
– Processes location data to aid in decision making
• GISs are used to help:
– Find shortest paths for deliveries or school bus
routes
– City planning for police coverage and health care
resources
– Find oil drilling locations
– Locate suitable outdoor recreation sites
– Businesses determine locations for service kiosks
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Geographic Information Systems
(continued)
• A typical GIS consists of three components:
– A database of quantitative and qualitative data
– A database of maps
– A program that displays information on maps
• Web technology helps promote the use of GISs:
– Examples: Google Earth, Mapquest, Yahoo Maps
• HTML and XML support the presentation of
marked maps
• Used to aid sales and government work
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Summary
• Decision aids include decision support systems,
expert systems, group decision support systems,
and geographic information systems
• Three major phases of decision-making process:
intelligence, design, and choice
• Two types of problems: unstructured and
structured
• Most DSSs have three components: data
management module, model management
module, and dialog module
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Summary (continued)
• Sensitivity analysis measures how parameters
affect results, and allow “what if” analysis
• Spreadsheets allow users to create DSSs
without expertise
• Expert systems are designed to emulate the
knowledge of an expert, using artificial
intelligence techniques
• Neural network software may be integrated into
an expert system to emulate learning
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Summary (continued)
• Expert systems are used in narrow domains
where decisions are unstructured
• Geographic information systems are used when
decisions involve locations and routes
• Computerized decision aids may overlook
important circumstances, leading to inaccuracies
or unfairness to individuals
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