mit6e ch07 by firli
TRANSCRIPT
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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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DECISION SUPPORT SYSTEMS
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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DECISION SUPPORT SYSTEMS
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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GEOGRAPHIC INFORMATION SYSTEMS
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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GEOGRAPHIC INFORMATION SYSTEMS
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GEOGRAPHIC INFORMATION SYSTEMS
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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GEOGRAPHIC INFORMATION SYSTEMS
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GEOGRAPHIC INFORMATION SYSTEMS
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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GEOGRAPHIC INFORMATION SYSTEMS
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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GEOGRAPHIC INFORMATION SYSTEMS
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 Systems/
Business Intelligence Systems
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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Executive Information Systems/
Business Intelligence Systems
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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Executive Information Systems/
Business Intelligence Systems
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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Executive Information Systems/
Business Intelligence Systems The term dashboard is used by many vendors for
this type of layout:
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Executive Information Systems/
Business Intelligence Systems
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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 SYSTEMS
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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KNOWLEDGE MANAGEMENT SYSTEMS
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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KNOWLEDGE MANAGEMENT SYSTEMS
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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KNOWLEDGE MANAGEMENT SYSTEMS
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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KNOWLEDGE MANAGEMENT SYSTEMS
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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KNOWLEDGE MANAGEMENT SYSTEMS
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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Copyright 2009 Pearson Education, Inc.Publishing as Prentice Hall