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Managerial Support Systems
MIS 503
Management Information Systems
MBA Program
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Structured vs. Semi-Structured
For each decision you make, the
decision will fall into one of thefollowing categories:
Structured Decisions
Unstructured Semi-Structured
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Structured Decisions
Often called programmed decisions
because they are routine and there areusually specific policies, procedures, or
actions that can be identified to help make
the decision
This is how we usually solve this type of
problem
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Unstructured Decisions
Decision scenarios that often involve new
or unique problems and the individual haslittle or no programmatic or routine
procedure for addressing the problem or
making a decision
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Semi-structured Decisions
Decision scenarios that have some
structured components and someunstructured components.
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The Role of the Decision Maker Decision makers can be
Individuals
Teams Groups
Organizations
All of these types of decision makers will
differ in their knowledge and experience;
therefore, there will be differences in how
they will react to a given problem scenario
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The Decision Making Process
Regardless of the type of decision
maker, all decisions involve thefollowing steps
Intelligence
Design
Choice
Decision
Implementation
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Strategies for Making Decisions
Optimization
Satisficing Elimination by Aspects
Incrementalism
Mixed Scanning Analytic Hierarchy Process
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How can IT be used tosupport decision makers?
By supporting various individual and team
activities and roles:
Communication and team interaction
The assimilation and filtering of data
Assist with problem recognition Assist with problem solving
Putting together the results into a cohesive
package
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Types of Managerial SupportSystems and Applications
Decision Support Systems
Geographic Information Systems (GIS) Data Mining
Group Support Systems
Business Intelligence Systems
Knowledge Management Systems
Artificial Intelligence
Expert Systems
Neural Networks
Virtual Reality
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DECISION SUPPORT SYSTEMS
Designed to assist decision
makers with unstructuredproblems
Usually interactive
Incorporates data and
models Data often comes from
transaction processingsystems or data warehouse
Page 212
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15Page 213Figure 7.1 Decision Support Systems Components
DECISION SUPPORT SYSTEMS
Three major components
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Decision Support Systems (DSS)DSS can be classified as
data-oriented
provide tools for the manipulation and analysis of data
model-based
generally have some kind of mathematical model of the decision
being supported
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So, how do decision supportsystems benefit decision makers? Supplements the decision maker
Allows improved intelligence, decision,and choice activities
Facilitates problem solving
Provides assistance with non-structures
decisions
Assists with knowledge management
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Spatial DSS: A GeographicInformation System
A geographic information system (GIS) is
a computer-based information system that
provides tools to collect, integrate,
manage, analyze, model, and display data
that is referenced to an accuratecartographic representation of objects in
space.
(Mennecke, Dangermond, Santoro, Darling, & Crossland, 1995).
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Location Based Services Location-based services incorporate
information about the user's location into the
provision of products or services. Theseinclude
Locator services (e.g., wheres the closest ATM?)
Navigation systems (e.g., in the car or on your PC)
M-commerce applications (e.g., proximity alerts,
closest service, mobile advertizing)
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GIS Examples
Online:
www.MapQuest.com
Maps.google.com
Desktop
ArcGIS by ESRI
MS MapPoint 2004
http://www.mapquest.com/http://maps.google.com/http://www.esri.com/http://msdn07.e-academy.com/elms/Storefront/Storefront.aspx?campus=iastate_buslomishttp://msdn07.e-academy.com/elms/Storefront/Storefront.aspx?campus=iastate_buslomishttp://www.esri.com/http://maps.google.com/http://www.mapquest.com/ -
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GISssystems based on manipulation of
relationships in space that use geographic
data
GEOGRAPHIC INFORMATIONSYSTEMS
Early GIS users:
Natural resource management
Public administration NASA and the military
Urban planning
Forestry
Map makersPage 219
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Business Adopts Geographic Technologies
GEOGRAPHIC INFORMATIONSYSTEMS
Business uses:
Determining site locations
Market analysis and planning
Logistics and routing
Environmental engineering
Geographic pattern analysis
Page 219
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23Figure 7.3 Department Store Analysis Page 219
(Reprinted courtesy of Environmental Systems Research Institute, Inc. Copyright 2003 Environmental Systems Research
Institute, Inc. All rights reserved.)
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Approaches to representing spatial data:
Raster-basedrely on dividing space into small,uniform cells (rasters) in a grid
Vector-based GISsassociate features in thelandscape with a point, line, or polygon
Geodatabase modeluses object-oriented data
concepts
Whats Behind Geographic Technologies
GEOGRAPHIC INFORMATIONSYSTEMS
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25Page 221Figure 7.4 Map Layers in a GIS
GEOGRAPHIC INFORMATIONSYSTEMS
Coverage modeluses different layers
to represent similar
types of geographic
features in the
same area
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Questions geographic analysis can answer:
What is adjacent to this feature?
Which site is the nearest one?
What is contained within this area?
Which features does this element cross?
How many features are within a certain distance of a site?
Whats Behind Geographic Technologies
GEOGRAPHIC INFORMATIONSYSTEMS
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DATA MINING
Data mining software:
Oracle 9i Data Mining and Oracle Data Mining Suite
SAS Enterprise Miner IBM Intelligent Miner Modeling
Angoss Softwares KnowledgeSEEKER, Knowledge Studio,
and KnowledgeExcelerator
Datamations Data Mining and Business Intelligence Product
Data Mininguses different technologies to search for (mine) nuggets ofinformation from data stored in a data warehouse
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Decision techniques used:
Decision trees
Linear and logistic regression
Clustering for market segmentation
Rule induction
Nearest neighbor Genetic algorithms
DATA MINING
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29Page 216see Table 7.1 Uses of Data Mining
Uses: Cross-selling
Customer churn Customer retention
Direct marketing
Fraud detection
Interactive marketing
Market basket analysis Market segmentation
Payment or default analysis
Trend analysis
DATA MINING
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Type of DSS 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 Page 217-218
GROUP SUPPORT SYSTEMS
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GROUP SUPPORT SYSTEMS
Figure 7.2 Group Support System LayoutPage 217
Traditional same time, same place meeting layout
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EXECUTIVE INFORMATIONSYSTEMS/BUSINESSINTELLIGENCE SYSTEMS
Page 222-223
Where does EIS data come from?
Filtered and summarized transaction data (internal)
Collected competitive information (internal and external)
EISsa hands-on tool that focuses, filters, and organizesan executives information so he or she can make more
effective use of it
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EXECUTIVE INFORMATIONSYSTEMS/BUSINESSINTELLIGENCE SYSTEMS
Page 222-223
Executive information system (EIS): 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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34Page 225Figure 7.5 Example Geac PerformanceManagement Displays
(Courtesy of Geac Computer Corporation Limited. Copyright 2003 Geac Computer Corporation Limited.)
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Figure 7.5 Example Geac Performance
Management Displays
(Courtesy of Geac Computer Corporation Limited. Copyright 2003 Geac Computer Corporation Limited.)
Page 225
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Artificial Intelligence
Artificial intelligence systems include
the people, procedures, hardware,
software, data and knowledge to develop
computer systems and machines that
demonstrate characteristics of intelligence.
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Intelligent Systems Turings test for Artificial Intelligence (AI)
place a computer and a human in two
separate rooms an interviewer in a third room, who cannot see
the human or the computer user, asksquestions that are passed to the computer
and to the human if the interviewer cannot tell the difference
between the answers from the computer andthe human, the machine is said to exhibit
intelligent behavior
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AI Versus TraditionalPrograms
AI programs manipulate symbols or
rules rather than numbers
AI programs are generally non-
algorithmic often employing heuristics or
rules of thumb
Many AI programs are concerned with
pattern recognition
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Six areas:
Natural languages
Robotics Perceptive systems
Genetic programming
Expert systems
Neural networks
AIthe study of how to make computers
do things that are currently done better by
people
ARTIFICIAL INTELLIGENCE
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Six areas: Natural languages
Robotics
Perceptive systems
Genetic programming Expert systems
Neural networks
Most relevant fo r
managerial sup po rt
ARTIFICIAL INTELLIGENCE
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Expert systemsattempt to capture the
expertise of humans in a computer program
EXPERT SYSTEMS
Knowledge engineer:
A specially trained systems analyst who works
closely with one or more experts in the area ofstudy
Tries to learn about how experts make decisions
Loads information (what learned) into module
called knowledge base
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EXPERT SYSTEMS
Figure 7.6 Architecture of an Expert System
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Page 230
EXPERT SYSTEMS
Approaches:
Buy a fully developed system createdfor 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)
Obtaining an Expert System
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Standford Universitys MYCIN to diagnose and
prescribe treatment for meningitis and blood diseases
General Electrics CATS-1 to diagnose mechanical
problems in diesel locomotives
AT&Ts ACE to locate faults in telephone cables
Market Surveillance softwareto detect insider trading FAST softwarefor credit analysis, used by banking
industry
Nestle Foods developed system to provide employees
information on pension fund status
EXPERT SYSTEMSExamples of Expert Systems
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Online Expert Systems
Whats wrong with your car?
http://www.expertise2go.com/webesie/car/
Buying the right PDA
http://www.expertise2go.com/shop/pda.htm
Choosing a Desktop PChttp://www.expertise2go.com/shop/desktop.htm
http://www.expertise2go.com/webesie/car/http://www.expertise2go.com/shop/pda.htmhttp://www.expertise2go.com/shop/desktop.htmhttp://www.expertise2go.com/shop/desktop.htmhttp://www.expertise2go.com/shop/pda.htmhttp://www.expertise2go.com/webesie/car/ -
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Neural networksattempt to tease out meaningful patterns
from vast amounts of data
Process:1. Program given set of data
2. Program analyzed data, works out correlations, selectsvariables to create patterns
3. Pattern used to predict outcomes, then results compared to
known results4. 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
NEURAL NETWORKS
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Page 232
NEURAL NETWORKS
Table 7.2 Uses of Neural Networks
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Neural Networks Two Types:
Biological neural networks
Artificial neural networks
The most popular type of artificial NN are
used to classify input into different
categories
A neural network has to be first trained
by presenting it with past cases
After training the network can be used for
classification
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Intelligent Agents
An agent is a piece of software that
performs a task for its owner
involves AI combined with networks
applications for intelligent agents have
been for consumer tasks like shopping andproviding recommendations based on
profile matches (check out botspot.com)
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Data is turned into information, butthe decision maker also needs
Knowledge to make decisions
Types of knowledge: Descriptive Knowledge
Procedural Knowledge
Reasoning Knowledge Forms of Knowledge
Tacit Knowledge
Explicit Knowledge
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Examples of technologies that can support or
enhance the transformation of knowledge
(IBM Systems Journal)
Tacit to Tacit Tacit to Explicit
E-meetings Answering questions
Synchronous collaboration (chat) Annotation
Explicit to Tacit Explicit to Explicit
Visualization Text search
Browsable video/audio of
presentations
Document
categorization
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Knowledge Management Tools Text and Forms management
Database and Reporting management Spreadsheet, Solvers and Charts
management
Programming management. Rules management
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Page 233
VIRTUAL REALITYVirtual realityuse of a computer-based system to create
an environment that seems real to one or more senses of
users
Non-entertainment categories: Training
Design
Marketing
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Page 234-235
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
VIRTUAL REALITY
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Page 235
VIRTUAL REALITY
Figure 7.7 Hometour 360o Virtual Tour
of Living Room
(Courtesy of Homestore, Inc. Copyright 2004 Homestore, Inc.)
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Page 188-189
Also include transaction processing systems Set of integrated business applications (modules)
that carry out common business functions:
General ledger, accounts payable, accounts receivable,
material requirements planning, order management,inventory control, human resources management
Usually purchased from software vendor
ENTERPRISE RESOURCEPLANNING SYSTEMS
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Page 189
How they differ:
1. ERP modules are integrated
2. ERP modules reflect a particular way of
doing business
ENTERPRISE RESOURCEPLANNING SYSTEMS
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Page 190
Choosing right software and implementation
difficult and expensive Requires large investment of money and
people resources
Leading ERP software vendors: SAP PeopleSoft, Inc. (bought J.D. Edwards)
Oracle
Baan
ENTERPRISE RESOURCEPLANNING SYSTEMS
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And now what really needs tohappen to be an innovator!
Entrepreneurship and creativity are really
represented by a process! Identify an Opportunity
Develop a Concept
Determine the Required Resources
Acquire the Necessary Resources
Implement and Manage
Harvest the Venture
Source: Morris et al. Entrepreneurship & Innovation
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Entrepreneurship andBusiness Models
Frameworks
Source: Morris et al. Entrepreneurship & Innovation
Entrepreneurial
Process
The Environment
The Entrepreneur
The ResourcesThe Concept
The Organizational
Context
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Entrepreneurship andBusiness Models How to find opportunities
Source: Morris et al. Entrepreneurship & Innovation
Types Methods Sources Detractors
Perennial Deliberate
Search vs. Discovery
The Rules Change
DemographicsChange
No Need Present
Window is not yetopen
Occasional Market Pull vs.
Resource or Capacity
Push
Underserved Markets
Social Trends
Strong Loyalties
High Switching Costs
Multiple Causes New customers to the
market
Satisfied customers
Multiple Effects Increase in usagerates
Shortages
Easy for others toenter with
alternatives
Intense competition
New Knowledge Customers hard to
reach
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Entrepreneurship andBusiness Models Types of Innovations
New to the world products or services
New to the market products or services New product or service line that at least one
competitor is offering
Addition to existing products or service lines
Product/service improvement, revision, includingaddition of new features or options
New application of existing products or services,including application to a new market segment
Repositioning of an existing product or serviceSource: Morris et al. Entrepreneurship & Innovation
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Entrepreneurship andBusiness Models Entry Wedges
Source: Morris et al. Entrepreneurship & Innovation
Other Entry Wedges New Product orService
ParallelCompetition
Franchising Acquisition
Exploiting Parallel Momentum Geographic Transfer Supply Shortages Tapping Utilized Resources XX
X
Customer Sponsorship
Customer Contract Becoming a 2ndSource XX
Parent Co. Sponsorship Joint Venture Licensing Market Relinquishment Selloff Division
X
XX
XGovernmental Sponsorship
Favored Purchasing Rule Changes XX
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What is a Business Model? Six key questions
How do we create value?
For whom do we create value? What is our source of competence/ advantage?
How do we differentiate ourselves?
How do we make money?
What are our time, scope, and size ambitions?
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Porters Competitive Forces Model: Howthe Internet Influences Industry Structure