Transcript Ch 6 - Managerial Support Systems

MANAGING INFORMATION TECHNOLOGY
7th EDITION
CHAPTER 6
MANAGERIAL SUPPORT SYSTEMS
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PART II - APPLICATION AREAS
Intra-organizational systems:
• Enterprise systems: (Ch. 5)
support all or most of the organization
• Managerial Support systems (Ch. 6)
support a specific manager or group of managers
Inter-organizational systems:
• e-Business applications (Ch. 7)
- B2C – link businesses with end consumers
- B2B – link businesses with other businesses
- Intermediaries
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MANAGERIAL SUPPORT SYSTEMS
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Decision Support Systems
Data Mining
Group Support Systems
Geographic Information Systems
Executive Information Systems
Business Intelligence Systems
Knowledge Management Systems
Expert Systems
Neural Networks
Virtual Reality
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DECISION SUPPORT SYSTEMS
• Interactive decision support for complete or poorly structured
problems
• Data often comes from transaction processing systems or data
warehouse
• Incorporates data and models
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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 (ex. Spreadsheet)
used to build a specific DSS quickly and easily
used to create
DSS Generator
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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
• 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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ONLINE ANALYTICAL PROCESSING (OLAP)
• Human- driven analysis:
- Querying against a database
- Program extracts data from the database and structures it by
individual dimensions, such as region or dealer
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USES OF DATA MINING
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DATA MINING PRODUCT EXAMPLES
• Xerox installed Rapid Insight Analytics software to mine customer order,
sales prospects and supply chain data to develop monthly and quarterly
forecasts.
• Farmers Insurance Group uses IBM’s DecisionEdge software to mine
data.
• Vermont County store (VCS) a catalog retailer uses SAS’s Enterprise
miner software to segment its customers to create appropriate direct
marketing lists.
Data Mining software:
- Oracle 10g Data Mining
- SAS Enterprise Miner
- IBM Intelligent Miner Modeling
- Angoss Software’s Knowledge SEEKER,
Knowledge STUDIO, and Strategy BUILDER
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SAS Enterprise Miner
XL Miner
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DATA MINING
More Data Mining examples
• Data mining urban legend - beer and diapers
• Can data mining catch terrorists
• Data mining gamers
• Mayo builds toward customized medicines
• Data mining to locate Venusian Volcanoes
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GROUP SUPPORT SYSTEMS (GSS)
• Decision support for group meetings
Goal: more productive meetings
• Includes “different time, different place” mode = virtual teams
• Product example:
Group Systems (Purchased by IBM)
Group Systems
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GROUP SUPPORT SYSTEMS
• Traditional setup for “same-time, same-place” GSS
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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
• Current business uses:
- Determining site locations
- Market analysis and planning
- Logistics and routing
- Environmental engineering
- Geographic pattern analysis
• Applications for mobile users:
- Logistics (fastest route)
- Location intelligence
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GEOGRAPHIC INFORMATION SYSTEMS
• Representation of 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” data 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
“Coverage” data model
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GEOGRAPHIC INFORMATION SYSTEMS
• Organizations can buy off-the-shelf technologies and spatial
data:
- Base maps, zip code maps, street networks, and advertising
media market maps
• Other data sources may be spread throughout the organization
in different internal databases
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GEOGRAPHIC INFORMATION SYSTEMS
GIS Vendors
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Environmental Research Institute (ESRI)
Pitney Bowes ( with its MapInfo products)
Autodesk
Tactician Corp.
Intergraph Corp.
ESRI
MapInfo
Tactician
Intergraph
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Executive Information Systems (EIS)/
Business Intelligence Systems
• Hands-on tool that focuses, filters, and organizes information so
that an executive can make more effective use of it
• User base for EISs has expanded to encompass all levels of
management
Today also called performance management software
• Focus on competitive information…
today referred to as business intelligence systems
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Executive Information Systems/
Business Intelligence Systems
- Delivers online current information about business conditions in
aggregate form
- Filtered and summarized transaction data
- Competitive information, assessments and insights
- 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
Commercial EIS software
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Executive Dashboard from Qualitech Solutions
Oracle Enterprise performance Management Systems
SAP Business Objects Strategy Management
SAS/EIS
Symphony RPM from Symphony Metreo
IBM Cognos Business Intelligence
MicroStrategy Intelligence Server
Oracle Business Intelligence Suite
Executive Dashboard
SAP Business Objects
SAP Business Objects BI solutions
SAS/EIS
SAS Business Intelligence
Symphony Metreo
Infor PM
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Infor PM
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Executive Information Systems/
Business Intelligence Systems
• “Dashboard” layout for data representation:
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KNOWLEDGE MANAGEMENT SYSTEMS
What is Knowledge management (KM)?
• Practices to manage Organizational knowledge
• Strategies and processes for identifying, creating,
capturing, organizing, transferring, and leveraging
knowledge held by individuals and the firm
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KNOWLEDGE MANAGEMENT SYSTEMS
What is a Knowledge management system (KMS)?
• System to help manage organizational knowledge
• Technologies that facilitate the sharing and transferring of
knowledge so that it can be reused
• Enables people and organizations to learn from others to
improve performance of individuals, groups and the organization
as a whole
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KNOWLEDGE MANAGEMENT SYSTEMS
• Potential benefits of a corporate KMS:
• Operational improvements
- 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
Example: Corporate KMS in a Pharmaceutical Firm
- KM team formed to develop organization-wide KMS
- Coordinators within communities of practice (COP)
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
Example continued: Corporate KMS
• Field sales KMS
- 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
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KNOWLEDGE MANAGEMENT SYSTEMS
KMS Success Factors:
• Knowledge Contribution (Supply Side)
- Leadership commitment
- Manager and peer support for KM initiatives
- Knowledge quality control
• Knowledge Reuse (Demand Side)
- Incentives and reward systems
- Relevance of knowledge
- Ease of using the KMS
- Satisfaction with the use of the KMS
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ARTIFICIAL INTELLIGENCE
• The study of how to make computers do things that are
currently done better by people
• Natural languages: systems that translate ordinary human
instructions into a language that computers can understand and
execute
• Perceptive systems: machines possessing a visual and/or aural
perceptual ability that affects their physical behavior
• Genetic programming/ evolutionary design: problems are
divided into segments, and solutions to these segments are
linked together breeding new solutions
• Expert systems
Most relevant for
Managerial Support
• Neural networks
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EXPERT SYSTEMS
Expert Systems
• Captures the expertise of humans for a particular domain 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
Options for obtaining an Expert System:
• Buy a fully developed system created for a specific application
• Develop a system using a purchased expert system shell
(basic framework) and user-friendly special language
• Custom build system by knowledge engineers using a specialpurpose 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 longrange 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
Neural Networks
• Systems designed to tease out meaningful patterns from vast amounts of
data that humans would find difficult to analyze without computer support
• How it works:
1. Program given set of data
2. Program analyzes 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 identified, ready to be used to make
predictions for future cases
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NEURAL NETWORKS
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VIRTUAL REALITY (VR)
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
- Meetings
- Social Collaborations
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VIRTUAL REALITY (VR)
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 (VR)
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COPYRIGHT
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
system, or transmitted, in any form or by any means, electronic, mechanical,
photocopying, recording, or otherwise, without the prior written permission of the
publisher. Printed in the United States of America.
Copyright © 2012 Pearson Education, Inc.
Publishing as Prentice Hall
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