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CHAPTER 6
Managerial Support
Systems
6.1
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MANAGERIAL SUPPORT
SYSTEMS
DECISION SUPPORT SYSTEMS
DATA MINING
GROUP SUPPORT SYSTEMS
GEOGRAPHIC INFO SYSTEMS
EXECUTIVE INFO SYSTEMS
EXPERT SYSTEMS
NEURAL NETWORKS
VIRTUAL REALITY
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6.2
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DECISION SUPPORT SYSTEMS
• COMPUTER-BASED SYSTEM,
USUALLY INTERACTIVE, DESIGNED
TO ASSIST MANAGERS IN MAKING
DECISIONS
• INCORPORATES BOTH DATA AND
MODELS, INTENDED TO ASSIST IN
THE SOLUTION OF SEMI- OR
UNSTRUCTURED PROBLEMS
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6.3
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DSS COMPONENTS
• MODEL MANAGEMENT: Helps user
determine appropriate analytic tools
• DATA MANAGEMENT: Provides access
to select, handle data
• USER INTERFACE: Allows user to
interact with system
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6.4
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TYPICAL DSS APPLICATIONS
• PROFIT & LOSS MODEL
• MACHINE LOADING OF MACHINES
IN A JOB SHOP
• COST/BENEFIT ANALYSIS
• PRO FORMA FINANCIAL
STATEMENT
• “WHAT-IF” ANALYSIS
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6.5
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DATA MINING
EMPLOYS TECHNIQUES (SUCH AS
DECISION TREES OR NEURAL
NETWORKS) TO SEARCH OR “MINE” FOR
SMALL “NUGGETS” OF INFORMATION
FROM VAST QUANTITIES OF DATA
STORED IN AN ORGANIZATION’S DATA
WAREHOUSE
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6.6
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DATA MINING TECHNIQUES
• ONLINE ANALYTICAL PROCESSING:
Human-driven analysis querying a database
with specific criteria
• DECISION TREES
• NEURAL NETWORKS
• MATHEMATICAL PROGRAMMING
• STATISTICAL ANALYSIS
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6.7
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USES OF DATAMINING
APPLICATION
DESCRIPTION
CROSS-SELLING
TAILOR SALES TO CUSTOMER SEGMENTS
CUSTOMER CHURN
PREDICT RISK OF LOSING CUSTOMERS
CUSTOMER RETENTION
DETERMINE LONG TERM CUSTOMERS
DIRECT MARKETING
IDENTIFY, TARGET MOST LIKELY PROSPECTS
FRAUD DETECTION
IDENTIFY FRAUDULENT TRANSACTIONS
6.8
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USES OF DATAMINING
APPLICATION
DESCRIPTION
INTERACTIVE MARKETING
PREDICT CUSTOMER'S WEB DESIRES
MARKET BASKET ANALYSIS
WHAT ITEMS COMMONLY PURCHASED TOGETHER?
MARKET SEGMENTATION
SEGMENT CUSTOMERS INTO APPROPRIATE GROUPS
PAYMENT OR DEFAULT ANALYSIS WHY DO CUSTOMERS DEFAULT ON PAYMENTS?
TREND ANALYSIS
6.9
DETECT CHANGE IN SALES PATTERNS OVER TIME
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GROUP SUPPORT SYSTEMS
(GPS)
• SYSTEM DESIGNED TO MAKE GROUP
SESSIONS MORE PRODUCTIVE:
Brainstorming, issue structuring, voting,
conflict resolution
• A VARIANT OF DSS IN WHICH THE
SYSTEM IS DESIGNED TO SUPPORT A
GROUP
• A SPECIALIZED TYPE OF GROUPWARE
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6.10
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GSS CHARACTERISTICS
• PARALLEL HUMAN PROCESSING
• EQUAL OPPORTUNITY FOR
PARTICIPATION
• ANONYMITY
• COMPLETE RECORD OF MEETING
• OUTPUT OF ONE PHASE LEADS TO NEXT
• CAN MORE EASILY APPLY STRUCTURE
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6.11
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GEOGRAPHIC INFORMATION
SYSTEMS (GIS)
• A COMPUTER-BASED SYSTEM
DESIGNED TO COLLECT, STORE,
RETRIEVE, MANIPULATE, AND
DISPLAY SPATIAL DATA
• A SPATIALLY BASED DSS
• TYPICALLY A DIGITIZED MAP WITH
OTHER DATA LINKED TO THE MAP
COORDINATES
6.12
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TWO TYPES OF GIS
• RASTER
– Grids of equal-sized cells grouped
or linked to make lines and shapes
– Values of cells vary
– Example: Satellite images, pixels on screen
• VECTOR
– Points, Lines, and Polygons
– Approximates curves, can link into networks
– Example: Property boundaries, sales
territories
6.13
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GIS COVERAGE MODEL
• WHAT IS ADJACENT TO FEATURE?
• WHICH IS NEAREST SITE?
• WHAT DOES AREA CONTAIN?
• WHICH FEATURES DOES THIS
ELEMENT CROSS?
• HOW MANY FEATURES ARE A
CERTAIN DISTANCE FROM SITE?
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6.14
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NEW DIRECTIONS FOR GIS
• 3-D, DYNAMIC SIMULATION
• MAP-ENABLED INTERNET SITES
• GIS EMBEDDED IN APPLICATIONS
• REAL-TIME TRACKING OF ASSETSIN-MOTION
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6.15
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EXECUTIVE INFORMATION
SYSTEMS (EIS)
COMPUTER APPLICATION USED
DIRECTLY BY TOP MANAGERS,
WITHOUT THE ASSISTANCE OF
INTERMEDIARIES, TO PROVIDE
THEM ON-LINE ACCESS TO
CURRENT INFORMATION ABOUT
STATUS OF ORGANIZATION AND
ITS ENVIRONMENT
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6.16
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CHARACTERISTICS OF EIS
• PRIMARILY USED FOR TRACKING AND
CONTROL
• CUSTOMIZED TO THE INDIVIDUAL
EXECUTIVE
• GRAPHICAL
• EASY TO USE
• INCORPORATES BOTH HARD AND SOFT
DATA
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6.17
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ARTIFICIAL INTELLIGENCE
(AI)
USING THE COMPUTER TO PERFORM
TASKS DONE BY HUMANS IN
SELECTED AREAS:
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NATURAL LANGUAGES
ROBOTICS
PERCEPTIVE SYSTEMS
EXPERT SYSTEMS
NEURAL NETWORKS
6.18
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EXPERT SYSTEMS
• ONE BRANCH OF ARTIFICIAL
INTELLIGENCE (AI)
• CONCERNED WITH BUILDING
SYSTEMS THAT INCORPORATE
DECISION-MAKING LOGIC OF A
HUMAN EXPERT IN A SPECIFIC
SKILL
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6.19
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EXPERT SYSTEMS
• KNOWLEDGE BASE: Model of Human
Knowledge
• RULE - BASED EXPERT SYSTEM: AI system
based on IF - THEN statements (Bifurcation); Rule
Base: Collection of IF - THEN knowledge
• KNOWLEDGE FRAMES: Knowledge organizes
in chunks based on shared relationships
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6.20
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EXPERT SYSTEMS
• AI SHELL: Programming environment of expert
system
• INFERENCE ENGINE: Search through rule base
– FORWARD CHAINING: Uses input, searches
rules for answer
– BACKWARD CHAINING: Begins with
hypothesis, seeks information until hypothesis
accepted or rejected
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6.21
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EXAMPLES OF EXPERT SYSTEMS
• MYCIN: Diagnose, treat blood diseases
• CATS-1: Diagnose locomotive problems
• MARKET SURVEILLANCE: Detects
insider trading on stock market
• FINANCIAL ANALYSIS SUPPORT
TECHNIQUE: Credit analysis in banks
• INDIVIDUAL DEVELOPMENT PLAN
GOAL ADVISOR: Helps set career goals
6.22
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NEURAL NETWORKS
• BASED ON HOW HUMAN NERVOUS
SYSTEM WORKS
• USE STATISTICAL ANALYSIS TO
RECOGNIZE PATTERNS FROM VAST
AMOUNTS OF DATA BY A PROCESS OF
ADAPTIVE LEARNING
• CONSIST OF SOFTWARE THAT
ATTEMPTS TO EMULATE PROCESSING
PATTERNS OF BIOLOGICAL BRAIN
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6.23
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EXAMPLES OF NEURAL NETWORKS
• BANKAMERICA: Neural network
evaluates commercial loan applications
• AMERICAN EXPRESS: System reads
handwriting on credit card slips
• STATE OF WYOMING: System reads
hand-printed numbers on tax forms
• ARCO AND TEXACO: Neural network
helps pinpoint oil and gas deposits
6.24
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EXAMPLES OF NEURAL NETWORKS
• SPIEGEL: Prune mailing list to eliminate
those unlikely to order again
• DEERE & COMPANY: Pension fund
management
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6.25
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VIRTUAL REALITY (VR)
• USE OF COMPUTER-BASED SYSTEMS TO
CREATE AN ENVIRONMENT THAT SEEMS
REAL TO ONE OR MORE SENSE (USUALLY
INCLUDING SIGHT)
• USED IN VIDEO GAMES, TRAINING &
EDUCATION, PROVIDING SERVICE AT A
DISTANCE, PRODUCT DESIGN,
INTERACTIVE WORLD WIDE WEB
APPLICATIONS
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6.26
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CHAPTER 6
Managerial Support
Systems
6.27
© Prentice Hall 2002