management information systems, sixth edition · –design phase: design the method for considering...

Management Information Systems,

Sixth Edition

Chapter 10:

Decision Support and Expert Systems

Management Information Systems, Sixth Edition 2

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


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

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



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


(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




(continued)



(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


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



(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


(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




(continued)


Decision Support Systems

• Decision support system (DSS): a computer-based 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

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



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


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



(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



(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


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


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

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



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



(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



(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


(continued)



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


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


• 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



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


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



• 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


• 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


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



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


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


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


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


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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