Decision Support Systems

Chapter 9

Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin

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Learning Objectives

Identify the changes taking place in the form and use of decision support in business

Identify the role and reporting alternatives of management information systems

Describe how online analytical processing can meet key information needs of managers

Explain the decision support system concept and how it differs from traditional management information systems

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Learning Objectives

Explain how these information systems can support the information needs of executives, managers, and business professionals– Executive information systems– Enterprise information portals– Knowledge management systems

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Learning Objectives

Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business

Give examples of ways expert systems can be used in business decision-making situations

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Decision Support in Business

Changing marketing conditions

Customer needs

Companies invest in data-driven

decision support application

frameworks to help them respond to

Companies invest in data-driven

decision support application

frameworks to help them respond to

Management information

Decision support

Other information systems

Accomplished by several types of

Accomplished by several types of

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Levels of Managerial Decision Making

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Information Quality

Outdated, inaccurate, or hard to understand information has much less value

Information products are made more valuableby their attributes, characteristics, or qualities

Content FormTime

Information has three dimensions

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Attributes of Information Quality

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Decision Structure

The procedures to follow when a decision is needed can be

specified in advance

It is not possible to specify in advance most of the decision

procedures to follow

Decision procedures can be pre-specified, but not enough to

lead to the correct decision

Structured(operational)

Unstructured(strategic)

Semi-structured(tactical)

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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

Pre-specified, fixed format Ad hoc, flexible, andadaptable format

Information processing methodology

Information produced by extraction and manipulation

of business data

Information produced by analytical modeling

of business data

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Decision Support Trends

Personalizeddecisionsupport

ModelingInformation

retrieval

Datawarehousing

What-ifscenarios

Reporting

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Decision Support Trends

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Business Intelligence Applications

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Decision Support Systems

To support the making of semi-structured business decisions, DSS uses

– Analytical models– Specialized databases– Decision-maker’s own insights and judgments– Interactive, computer-based modeling process

DS systems

– Ad hoc, quick-response systems

– Initiated and controlled by decision makers

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DSS Components

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DSS Model Base

Model Base– A software component – Consists of models used in computational

and analytical routines– Mathematically expresses relationships

among variables

Spreadsheet Examples– Linear programming– Multiple regression forecasting– Capital budgeting present value

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Applications of Statistics and Modeling

Supply ChainSimulate & optimize supply chain

flows, reduce inventory & stock-outs

Pricing Identify the price that maximizes yield or profit

Product & Service Quality

Detect quality problems early in order to minimize them

Research & Development

Improve quality, efficacy, and safety of products and services

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Management Information Systems

The original type of information systemthat supported managerial decision making

Produces information products that supportmany day-to-day decision-making needs

Produces reports, displays, and responses

Satisfies needs of operational and tacticaldecision makers who face structured decisions

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Management Reporting Alternatives

Periodic Scheduled Reports

Pre-specified format, issued on a regular basis

Exception ReportsReports about exceptional

conditions, scheduled or on event

Demand Reports & Responses

Information is available on demand

Push Reporting Information is pushed to anetworked computer

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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

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Online Analytical Operations

Consolidation

Aggregation of data

Ex: sales office data, rolled up to the district level

Drill-Down

Display underlying detail data

Ex: sales figures by individual product

Slicing and Dicing

Viewing database from different viewpoints

Often performed along a time axis

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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 PositioningSystem (GPS) devices

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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

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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

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Using Decision Support Systems

Goal-seekingAnalysis

Goal-seekingAnalysis

What-IfAnalysisWhat-IfAnalysis

OptimizationAnalysis

OptimizationAnalysis

Basic analytical modeling activities

SensitivityAnalysisSensitivityAnalysis

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Data Mining

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

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Analysis of Customer Demographics

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Market Basket Analysis

One of the most common uses for data mining– Determines what products customers

purchase together with other products

Typical applications of MBA– Cross-selling– Product placement– Affinity promotion– Survey analysis– Fraud detection– Customer behavior identification

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Executive Information Systems (EIS)

Provides top executiveswith immediate, easyaccess to information

Identifies factorscritical to

accomplishingstrategic objectives

Combines manyfeatures of

MIS and DSS

So popular it wasexpanded to managers,

analysis, and otherknowledge workers

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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

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Web-Based Executive Information System

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Enterprise Information Portals

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

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Enterprise Information Portal Components

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Enterprise Knowledge Portal

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Artificial Intelligence (AI)

AI isa field of scienceand technology

based on…

Engineering Computerscience

Mathematics Biology

Linguistics Psychology

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Artificial Intelligence (AI)

Think

Feel

Talk

Walk

Hear

See

Ultimategoal for

computers

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Attributes of Intelligent Behavior

Learn or understand from

experienceThink and reason

Use reason to solve problems

Deal with complex or perplexing

situations

Acquire and apply knowledge

Exhibit creativity and imagination

Handle ambiguous, incomplete,

erroneous info

Respond quickly and successfully to new situations

Recognize relative

importance of situation elements

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Domains of Artificial Intelligence

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Expert Systems

An Expert System (ES)

Knowledge-based information system

Contains knowledge about aspecific, complex application area

Acts as an export consultant to end users

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Components of an Expert System

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Methods of Knowledge Representation

Case-based

Object-based

Frame-based

Rule-based

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Expert System Application Categories

Diagnostic/Troubleshooting

Design/Configuration

Decision Management

Selection/Classification

Process Monitoring/Control

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Benefits of Expert Systems

Captures expertise of expert(s) 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

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Limitations of Expert Systems

Major limitations of expert systems– Limited focus– Inability to learn– Maintenance problems– Development and maintenance costs– Can only solve specific types of problems

in a limited domain of knowledge

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Developing Expert Systems

Suitability Criteria for Expert Systems

DomainThe domain or subject area of the problem is small and well-

defined

DomainThe domain or subject area of the problem is small and well-

defined

ExpertiseSolutions to the problem require the efforts of an

expert

ExpertiseSolutions to the problem require the efforts of an

expert

ComplexityProblem solving is

complex, and requires logical

inference processing

ComplexityProblem solving is

complex, and requires logical

inference processing

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Developing Expert Systems

Structure… 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

Suitability Criteria for Expert Systems

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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

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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

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Neural Networks

Computing systems modeled after the brain’s mesh-like network of interconnected processing elements (neurons)– Interconnected processors operate in parallel

and interact with each other– Allows the network to learn from the data it processes

– Recognizes patterns and relationships in data

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Fuzzy Logic

Resembles human reasoning

Allows approximate values and inferences, and incomplete or ambiguous data

Uses terms like “very high” instead of precise measures

Allows processing of incomplete data

Results in quick, approximate solutions

Used in fuzzy process controllers(subway trains, elevators, cars)

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Example of Fuzzy Logic Rules and Query

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Genetic Algorithms

Stimulates an evolutionary process, yielding increasingly

better solutions

Stimulates an evolutionary process, yielding increasingly

better solutions

Being used to model a variety of scientific,

technical, and business processes

Being used to model a variety of scientific,

technical, and business processes

Especially useful for situations in which

thousands of solutions are possible

Especially useful for situations in which

thousands of solutions are possible

Uses Darwinian, randomizing, and

other mathematical functions

Uses Darwinian, randomizing, and

other mathematical functions

Genetic algorithm software

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Virtual Reality (VR)

Virtual reality is a computer-simulated reality– Fast-growing area of artificial intelligence– Originated from efforts to build natural,

realistic, multi-sensory human-computer interfaces

– Relies on multi-sensory input/output devices– Creates a three-dimensional world through

sight, sound, and touch– Also called telepresence

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Typical VR Applications

Computer-aideddesign

Entertainment

Employeetraining

Productdemonstrations

Flightsimulation

Scientificexperimentation

Medical diagnosticsand treatment

Current applications

of virtual reality

Current applications

of virtual reality

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Intelligent Agents

Software surrogate for an end user or a process that fulfills a stated need or activity

Uses built-in and learned knowledge base to makedecisions and accomplish tasks in a way

that fulfills the intentions of a user

Also called software robots or bots

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User Interface Agents

InterfaceTutors

Observe user computer operations, correctuser mistakes, provide hints/advice on

efficient software use

Presentation Agents

Show information in a variety of forms/media based on user preferences

Network Navigation

Agents

Discover paths to information, provide ways

to view it based on user preferences

RolePlaying

Play what-if games and other roles to helpusers understand information and make

better decisions

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Information Management Agents

SearchAgents

Help users find files and databases, search for information, and suggest and find new

types of information products, media, resources

InformationBrokers

Provide commercial services to discover and develop information resources that fit

business or personal needs

InformationFilters

Receive, find, filter, discard, save, forward, and notify users about products received or

desired, including e-mail, voice mail, and other information media