managerial support systems - lecture fall 2008

8/13/2019 Managerial Support Systems - Lecture Fall 2008

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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


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



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25Page 221Figure 7.4 Map Layers in a GIS


Coverage modeluses different layers

to represent similar

types of geographic

features in the

same area


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26Page 221

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



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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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39Page 229

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)
http://www.botspot.com/http://www.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



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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



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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


Entrepreneurial

Process

The Environment

The Entrepreneur

The ResourcesThe Concept

The Organizational

Context


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Entrepreneurship andBusiness Models How to find opportunities


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


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

managerial support systems - lecture fall 2008

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