Transcript Slide 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
used to create
DSS Generator
DSS Model 1
DSS Model 2
DSS Model 3
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DATA MINING
• Employs different technologies to search for (mine)
“nuggets” of information from data stored in a data
warehouse
• Data mining decision techniques:
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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 Software’s 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
Data Mining example
• American Honda Motor Co.
– Uses SAS Data Mining to analyze warranty claims, call
center data, customer feedback, parts sales, and
vehicle sales
– Early warning system to find and forestall problems
– Allows analysts to zero in on a single performance
issue
– During development, analysts identified issues with
three different vehicle models and were able to
resolve the problems quickly
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GROUP SUPPORT SYSTEMS
• Type of DSS to support a group rather than an
individual
• Specialized type of groupware
• Attempt to make group meetings more
productive
• Now focus on supporting team in all its
endeavors, including “different time, different
place” 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:
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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
What’s behind geographic technologies
• 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 represent
similar types of geographic features in the same
area and are stacked on top of one another
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GEOGRAPHIC INFORMATION SYSTEMS
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GEOGRAPHIC INFORMATION SYSTEMS
What’s behind geographic technologies (cont’d)
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 paths
are available?
• What could be seen from this location?
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GEOGRAPHIC INFORMATION SYSTEMS
Issues for information systems organizations
• 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
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GEOGRAPHIC INFORMATION SYSTEMS
GIS vendors
• 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/)
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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 more
effective 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) (cont’d)
– 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 and
data 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
Commercial EIS software
• 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)
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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 knowledge
can be reused
– Enables people and organizations to enhance
learning, improve performance, and produce longterm competitive advantage
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KNOWLEDGE MANAGEMENT SYSTEMS
• Tangible benefits of KMS
– Operational improvements
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Faster and better dissemination of knowledge
Efficient processes
Change management processes
Knowledge reuse
– Market improvements
• Increased sales
• Lower cost of products and services
• Customer satisfaction
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KNOWLEDGE MANAGEMENT SYSTEMS
• May have little formal management and control
– Communities of practice (COP): individuals with similar
interests
– COP KMS provides members with vehicle to exchange
ideas, tips, and other knowledge
– Members are responsible for validating and structuring
knowledge
• May have extensive management and control
– KM team to oversee process of validating knowledge
– Team provides structure, organization, and packaging for
how knowledge is presented to users
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KNOWLEDGE MANAGEMENT SYSTEMS
KMS Initiatives Within a Pharmaceutical Firm
• Corporate KMS
– KM team formed to develop organization-wide KMS
– Coordinators within communities of practice
responsible for overseeing knowledge in the
community
– Portal software provides tools, including discussion
forums
– Any member of the community can post a question or
tip
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KNOWLEDGE MANAGEMENT SYSTEMS
KMS Initiatives Within a Pharmaceutical Firm
• 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
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KNOWLEDGE MANAGEMENT SYSTEMS
KMS success
• Supply-side (i.e., knowledge contribution)
– Leadership commitment
– Manager and peer support for KM initiatives
– Knowledge quality control
• Demand-side (i.e., knowledge reuse)
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Incentives and reward systems
Relevance of knowledge
Ease of using the KMS
Satisfaction with the use of the KMS
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KNOWLEDGE MANAGEMENT SYSTEMS
KMS success (cont’d)
• Social capital
– Motivation to participate
– Cognitive capability to understand and apply the
knowledge
– Strong relationships among individuals
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ARTIFICIAL INTELLIGENCE
• The study of how to make computers do things
that are currently done better by people
• Six areas of AI research:
– Natural languages: systems that translate ordinary
human instructions into a language that computers
can 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 their
physical behavior
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ARTIFICIAL INTELLIGENCE
• Six areas of AI research (cont’d):
– Genetic programming: problems are divided into
segments, and solutions to these segments are linked
together to breed new solutions
– Expert systems
Most relevant for
managerial support
– Neural networks
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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 with
one 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
Obtaining an expert system
• Buy a fully developed system created for a
specific application
• Develop using a purchased expert system shell
(basic framework) and user-friendly special
language
• Have knowledge engineers custom build using
special-purpose language (such as Prolog or
Lisp)
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EXPERT SYSTEMS
Examples of Expert Systems
• Stanford University’s MYCIN
Diagnoses and prescribes treatment for
meningitis and blood diseases
• General Electric’s CATS-1
Diagnoses mechanical problems in diesel
locomotives
• AT&T’s 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
• Nestlé Foods
Provides employees information on pension
fund status
• USDA’s EXNUT
Helps peanut farmers manage irrigated peanut
production
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NEURAL NETWORKS
• Designed to tease out meaningful patterns from vast
amounts of data that humans would find difficult to
analyze without computer support
• Process:
1. Program given set of data
2. Program analyzed data, works out correlations, selects variables
to create patterns
3. Pattern used to predict outcomes, then results compared to
known results
4. Program changes pattern by adjusting variable weights or
variables themselves
5. Repeats process over and over to adjust pattern
6. When no further adjustment possible, ready to be used to
make 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 of VR:
– Training
– Design
– Marketing
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VIRTUAL REALITY
Example Uses of VR
Training
U.S. Army to train tank crews
Amoco for training its drivers
Duracell for training factory workers on using new
equipment
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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Copyright © 2009 Pearson Education, Inc.
Publishing as Prentice Hall
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