Mit6e Ch07 by Firli

8/6/2019 Mit6e Ch07 by Firli

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Copyright 2009 Pearson Education, Inc. Publishing as Prentice Hall 1

Managing Information Technology

6th Edition

CHAPTER 7

MANAGERIAL SUPPORT SYSTEMS


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DECISION SUPPORT SYSTEMS

Designed to assist decision makers with

unstructured problems

Usually interactive

Incorporates data and models

Data often comes from transaction processing

systems or data warehouse


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Three major components:

1. Data management: select

and handle appropriate

data

2. Model management:

apply the appropriate

model

3. Dialog management:

facilitate user interface to

the DSS


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Specific DSS actual DSS applications that

directly assist in decision making

DSS generator a software package used to build

a specific DSS quickly and easily

Example: Microsoft Excel

DSS GeneratorDSS Model 1

DSS Model 2

DSS Model 3

used to create


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DATA MINING

Employs different technologies to search for (mine)nuggets of information from data stored in a datawarehouse

Data mining decision techniques: Decision trees

Linear and logistic regression

Association rules for finding patterns

Clustering for market segmentation

Rule induction Statistical extraction of if-then rules

Nearest neighbor

Genetic algorithms


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DATA MINING

Online analytical processing (OLAP)

Essentially querying against a database

Program extracts data from the database and

structures it by individual dimensions, such as

region or dealer

OLAP described as human-driven, whereas data

mining is technique-driven


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DATA MINING

Data mining software:

Oracle 10g Data Mining(http://www.oracle.com/technology/products/bi/odm/index.html)

SAS Enterprise Miner(http://www.sas.com/technologies/analytics/datamining/miner/)

XLMiner(http://www.xlminer.com/)

IBM Intelligent Miner Modeling(http://www-306.ibm.com/software/data/iminer/)

Angoss Softwares KnowledgeSEEKER,KnowledgeSTUDIO, and StrategyBUILDER(http://www.angoss.com/analytics_software/)


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DATA MINING


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DATA MINING


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DATA MINING

American Honda Motor Co.

Uses SAS Data Mining to analyze warranty claims, call

center data, customer feedback, parts sales, andvehicle sales

Early warning system to find and forestall problems

Allows analysts to zero in on a single performanceissue

During development, analysts identified issues withthree different vehicle models and were able toresolve the problems quickly

Data Mining example


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GROUP SUPPORT SYSTEMS

Type ofDSS to support a group rather than anindividual

Specialized type of groupware

Attempt to make group meetings moreproductive

Now focus on supporting team in all itsendeavors, including different time, differentplace mode virtual teams

Example of GSS software: GroupSystems(http://www.groupsystems.com/)


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GROUP SUPPORT SYSTEMS

Traditional same-time, same-place meeting layout


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GEOGRAPHIC INFORMATION SYSTEMS

Systems based on manipulation of relationships

in space that use geographic data

Early GIS users:

Natural resource management

Public administration

NASA and the military

Urban planning Forestry

Map makers


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Businesses are increasing their usage of

geographic technologies

Business uses: Determining site locations

Market analysis and planning

Logistics and routing

Environmental engineering

Geographic pattern analysis


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Approaches to representing spatial data:

Raster-based GISs rely on dividing space into

small, uniform cells (rasters) in a grid

Vector-based GISs associate features in the

landscape with a point, line, or polygon

Coverage model different layers representsimilar types of geographic features in the same

area and are stacked on top of one another

Whats behind geographic technologies


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Whats behind geographic technologies (contd)

Questions Answered by Geographic Analysis

What is adjacent to this feature?

Which site is the nearest one, or how many are within a

certain distance?

What is contained within this area, or how many are

contained within this area?

Which features does this element cross, or how many pathsare available?

What could be seen from this location?


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Thanks to maturity of GIS tools, organizations

can acquire off-the-shelf technologies

Managing technology options now less of a

challenge than managing spatial data

Base maps, zip code maps, street networks, and

advertising media market maps should be bought Other data are spread throughout the

organization in internal databases

Issues for information systems organizations


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Environmental Systems Research Institute (ESRI)(http://www.esri.com/)

MapInfo(http://www.mapinfo.com/)

Autodesk(http://www.autodesk.com/geospatial )

Tactician(http://www.tactician.com/)

Intergraph Corp.(http://www.intergraph.com/)

GISvendors


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Executive Information Systems/

Business Intelligence Systems

Executive information system (EIS)

Hands-on tool that focuses, filters, and organizes

information so that an executive can make moreeffective use of it

Data come from:

Filtered and summarized transaction data

Competitive information, assessments and insights


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Executive information system (EIS) (contd)

Delivers online current information about business

conditions in aggregate form Easily accessible to senior executives and other

managers

Designed to be used without intermediary assistance

Uses state-of-the-art graphics, communications anddata storage methods


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User base for EISs has expanded to encompass all

levels of management new label is performance

management (PM) software Focus on competitive information has also lead to

the term business intelligence system


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InforPM(http://www.infor.com/solutions/pm/)

Qualitech Solutions Executive Dashboard(http://www.iexecutivedashboard.com/)

SAP Strategy Management(http://www.sap.com/solutions/performancemanagement/strategy/ )

SAS/EIS(http://www.sas.com/products/eis/)

Symphony Metreo SymphonyRPM(http://www.symphony-metreo.com/products/rpm_performance_management.asp )

Commercial EIS software


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Business Intelligence Systems The term dashboard is used by many vendors for

this type of layout:


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KNOWLEDGE MANAGEMENT SYSTEMS

Knowledge management (KM):

Set of practical and action-oriented management

practices

Involves strategies and processes of identifying,creating, capturing, organizing, transferring, and

leveraging knowledge to help compete

Relies on recognizing knowledge held by individuals

and the firm


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Knowledge management system (KMS):

System for managing organizational knowledge

Technology or vehicle that facilitates the sharing and

transferring of knowledge so that valuable knowledgecan be reused

Enables people and organizations to enhance

learning, improve performance, and produce long-

term competitive advantage


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May have little formal management and control Communities ofpractice (COP):individuals with similar

interests

COP KMS provides members with vehicle to exchangeideas, tips, and other knowledge

Members are responsible for validating and structuringknowledge

May have extensive management and control

KM team to oversee process of validating knowledge

Team provides structure, organization, and packaging forhow knowledge is presented to users


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Corporate KMS

KM team formed to develop organization-wide KMS

Coordinators within communities of practiceresponsible for overseeing knowledge in the

community

Portal software provides tools, including discussion

forums

Any member of the community can post a question or

tip

KMS Initiatives Within a Pharmaceutical Firm


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Field sales KMS

Another KM team formed to build both content

and structure of KMS for field sales

Taxonomy developed so that knowledge would be

organized separately

KM team formats documents and enters into KMS Tips and advice required to go through validation

and approval process first

KMS Initiatives Within a Pharmaceutical Firm


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Supply-side (i.e., knowledge contribution)

Leadership commitment

Manager and peer support for KM initiatives Knowledge quality control

Demand-side (i.e., knowledge reuse)

Incentives and reward systems

Relevance of knowledge

Ease of using the KMS

Satisfaction with the use of the KMS

KMS success


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Social capital

Motivation to participate

Cognitive capability to understand and apply the

knowledge

Strong relationships among individuals

KMS success (contd)


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ARTIFICIAL INTELLIGENCE

The study of how to make computers do thingsthat are currently done better by people

Six areas of AI research:

Natural languages: systems that translate ordinaryhuman instructions into a language that computerscan understand and execute

Robotics: machines that accomplish coordinated

physical tasks like humans do (see Ch.6) Perceptive systems: machines possessing a visual

and/or aural perceptual ability that affects theirphysical behavior


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ARTIFICIAL INTELLIGENCE

Six areas of AI research (contd):

Genetic programming: problems are divided into

segments, and solutions to these segments are linked

together to breed new solutions Expert systems

Neural networksMost relevant for

managerial support


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EXPERT SYSTEMS

Attempt to capture the expertise of humans in a

computer program

Knowledge engineer:

A specially trained systems analyst who works closely withone or more experts in the area of study

Learns from experts how they make decisions

Loads decision information from experts (rules) into

module called knowledge base


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EXPERT SYSTEMS

Major components of an expert system: Knowledge base: contains the inference rules that are followed in

decision making and the parameters, or facts, relevant to the decision

Inference engine: a logical framework that automatically executes a

line of reasoning when supplied with the inference rules and

parameters involved in the decision

User interface: the module used by the end user


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EXPERT SYSTEMS

Buy a fully developed system created for aspecific application

Develop using a purchased expert system shell(basic framework) and user-friendly speciallanguage

Have knowledge engineers custom build usingspecial-purpose language (such as Prolog orLisp)

Obtaining an expert system


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EXPERT SYSTEMS

Examples ofExpert Systems

Stanford Universitys MYCIN Diagnoses and prescribes treatment for

meningitis and blood diseases

General Electrics CATS-1 Diagnoses mechanical problems in diesel

locomotives

AT&Ts ACE Locates faults in telephone cables

Market Surveillance Detects insider trading

FAST Used by banking industry for credit analysis

IDP Goal Advisor Assists in setting short- and long-range

employee career goals Nestl Foods Provides employees information on pension

fund status

USDAs EXNUT Helps peanut farmers manage irrigated peanut

production


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NEURAL NETWORKS

Designed to tease out meaningful patterns from vastamounts of data that humans would find difficult toanalyze without computer support

Process:

1. Program given set of data2. Program analyzed data, works out correlations, selects variables

to create patterns

3. Pattern used to predict outcomes, then results compared toknown results

4. Program changes pattern by adjusting variable weights orvariables themselves

5. Repeats process over and over to adjust pattern

6. When no further adjustment possible, ready to be used tomake predictions for future cases


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NEURAL NETWORKS


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VIRTUAL REALITY

Use of a computer-based system to create an

environment that seems real to one or more of the

human senses

Non-entertainment uses ofVR:

Training

Design

Marketing


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VIRTUAL REALITY

Example Uses ofVR

Training U.S. Army to train tank crews

Amoco for training its drivers

Duracell for training factory workers on using newequipment

Design Design of automobiles

Walk-throughs of air conditioning/ furnace units

Marketing Interactive 3-D images of products (used on the Web)

Virtual tours used by real estate companies or resort

hotels


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VIRTUAL REALITY


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Holland-America

http://www.hollandamerica.com/virtual-tours-

videos/Main.action


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permission of the publisher. Printed in the United States of America.

Copyright 2009 Pearson Education, Inc.Publishing as Prentice Hall

Mit6e Ch07 by Firli

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