Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 6-1 MANAGING INFORMATION...

Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 6-1

MANAGING INFORMATION TECHNOLOGY7th EDITION

CHAPTER 6

MANAGERIAL SUPPORT SYSTEMS


PART II - APPLICATION AREAS

Inter-organizational systems:• e-Business applications (Ch. 7)

- B2C – link businesses with end consumers- B2B – link businesses with other businesses- Intermediaries

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


MANAGERIAL SUPPORT SYSTEMS

• 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


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



• 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



• 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

DSS GeneratorDSS Model 1DSS Model 2DSS Model 3

used to create


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


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


USES OF DATA MINING


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

SAS Enterprise MinerXL Miner

http://www.sas.com/technologies/analytics/datamining/miner/

http://www.resample.com/xlminer/


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


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

http://www.groupsystems.com/


GROUP SUPPORT SYSTEMS

• Traditional setup for “same-time, same-place” GSS


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



• 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



• 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



“Coverage” data model



• 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



• Environmental Research Institute (ESRI) • Pitney Bowes ( with its MapInfo products)• Autodesk• Tactician Corp.• Intergraph Corp.

GIS Vendors

ESRIMapInfo

TacticianIntergraph

http://www.esri.com/

http://www.pbinsight.com/welcome/mapinfo/

http://www.tactician.com/

http://www.intergraph.com/


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


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



• 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• SAP Business Objects BI solutions• SAS Business Intelligence• Infor PM

Commercial EIS software

Executive DashboardSAP Business ObjectsSAS/EIS

Symphony MetreoInfor PM

http://www.iexecutivedashboard.com/

http://www.sap.com/solutions/sapbusinessobjects/large/enterprise-performance-management/strategy/index.epx

http://www.sas.com/technologies/bi/appdev/eis/

http://www.symphony-metreo.com/products/rpm_performance_management.asp

http://www.infor.com/solutions/pm/



• “Dashboard” layout for data representation:


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



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



• 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



- 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

Example: Corporate KMS in a Pharmaceutical Firm



• 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

Example continued: Corporate KMS



• 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

KMS Success Factors:


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• Neural networks

Most relevant for Managerial Support


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


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


EXPERT SYSTEMS

• 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 special-purpose language (such as Prolog or Lisp)

Options for obtaining an Expert System:


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


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 data2. Program analyzes data, works out correlations, selects variables to

create patterns3. Pattern used to predict outcomes, then results compared to known

results4. Program changes pattern by adjusting variable weights or variables

themselves5. Repeats process over and over to adjust pattern6. When no further adjustment identified, ready to be used to make

predictions for future cases


NEURAL NETWORKS


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



Example Uses of VRTraining 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




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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