Managing information systems 7th edition brow ch06

publishing as Prentice Hall 6-1MANAGEMENT INFORMATION SYSTEMS CHAPTER 6 MANAGERIAL SUPPORT SYSTEMS... publishing as Prentice Hall 6-7DATA MINING • Employs different technologies to searc

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

MANAGEMENT INFORMATION SYSTEMS

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-3

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-5

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

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

DSS Model 3 used to create

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-7

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-9

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 Miner

XL Miner

SAS Enterprise Miner

XL Miner

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-11

DATA MINING

More Data Mining examples

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-13

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-15

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

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-17

GEOGRAPHIC INFORMATION SYSTEMS

“Coverage” data model

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-19

GEOGRAPHIC INFORMATION SYSTEMS

• Environmental Research Institute (ESRI)

• Pitney Bowes ( with its MapInfo products)

Tactician Intergraph

ESRI MapInfo Tactician Intergraph

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-21

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 Information Systems/

Business Intelligence Systems

• 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 Dashboard SAP Business Objects SAS/EIS

Symphony Metreo Infor PM

Executive Dashboard SAP Business Objects SAS/EIS

Symphony Metreo Infor PM

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-23

Executive Information Systems/

Business Intelligence Systems

• “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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-25

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

KNOWLEDGE MANAGEMENT SYSTEMS

• Potential benefits of a corporate KMS:

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-27

KNOWLEDGE MANAGEMENT SYSTEMS

- 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

KNOWLEDGE MANAGEMENT SYSTEMS

- KM team formats documents and enters into KMS

- Tips and advice required to go through validation and approval process

Example continued: Corporate KMS

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-29

KNOWLEDGE MANAGEMENT SYSTEMS

• 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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-31

- 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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-33

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-35

1 Program given set of data

2 Program analyzes data, works out correlations, selects variables to

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

NEURAL NETWORKS

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-37

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

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 6-39

VIRTUAL REALITY (VR)