Transcript Chapter Slides

Lecture
2
Organisational Information
Systems
(Unit 2)
Different ways in which information can create value for
organisations: Add value Customers and markets
Organisation A
Manage
risks
Market,
financial,
legal,
operational
Organisation B
Reduce
cost
Transactions
and processes
Organisation C
New products, new services, Create new reality
new business ideas
(Chaffey and
Wood, 2005)
Information Systems
Support of
business
operations
Operations Support
Systems
Management Support
Systems
Transaction
Processing
Systems
Process
Control
Systems
Enterprise
Collaboration
Systems
Processing
business
transactions
Control of
industrial
processes
Team and work
group collaboration
Management
Information
Systems
Pre-specified reporting for
managers
Support of
managerial
decision
making
Decision
Support
Systems
Executive
Information
Systems
Interactive
decision
support
Information
tailored for
executives
Operations and management classification of information systems
(James A O’Brien (2004), ‘Management Information Systems,
Managing information technology in the business enterprise’, 6th Edition, McGraw-Hill Irwin).
Advances in IT and
telecommunications
Globalisation
Virtual enterprise
Digital firms
Globalisation
“..the increasing integration of
economies around the world,
particularly through trade and financial
flows. .. the movement of people
(labour) and knowledge (technology)
across international borders.”
(The IMF Staff (2002) at
www,imf.org/external/np/exr/ib/2000/041200.htm)
Virtual enterprise
A company that: joins with another
company operationally, but not physically, to
design and manufacture a product; distributed
geographically and whose work is coordinated
through electronic communications; share
skills, costs, and access to one another’s
markets
Digital firms
A firm in which nearly all organisation’s
significant business relationships with
customers, suppliers, and employees are
digitally enabled and mediated. Core
business processes are accomplished
through digital networks
Digital Firms
• sense and respond to their environments
more rapidly than traditional firms
• offer extraordinary opportunities for more
flexible global organisation and
management.
• time shifting and space shifting are the
norms
Factories
The Emerging
Digital Firm
•Just-in-time production
•Continuous inventory
replenishment
•Production planning
Customers
•Online marketing
•Online sales
•Built-to-order
products
•Customer service
•Sales force
automation
Remote offices
and work groups
•Communicate plans and
policies
•Group collaboration
•Electronic communication
•Scheduling
(Laudon & Laudon, 9th Edition, 2006:12)
Suppliers
Business
partners
•Joint design
•outsourcing
•Procurement
•Supply chain
management
Exercise
Laudon and Laudon, 10th Edition: Read the
case study on Accenture in Chapter 1, page
9 and do the exercises at the end.
OR
Laudon and Laudon, 9th Edition: Read the
case study on CEMEX in Chapter 1, page
14, and do the exercises at the end.
Characteristics of organisational problems
and solutions
Bounded-rationality
Satisficing
The rational model
Optimising
Solution
Problem
structured
unstructured
Semistructured
Decision Dimensions in an Organisation
Stair and Reynolds
High
Strategic
management
Decision
making
authority
Tactical
management
Impact on
reaching
corporate
goals
Problem
uniqueness
Number of
people and
functions
affected by
decision
Planning
horizon
Operational
management
Low
Need
for
external
data
Decision Support Systems
• A set of interactive software
programs that provide managers
with data, tools, and models to make
semistructured and unstructured
decisions.
DSS support management decision
making by integrating:
• Company performance data
• Business rules based on decision tables
• Analytical tools and models for forecasting
and planning
The structure of DSS
Model
Management
Dialog
Management
Knowledge
Management
User
Data Management
DSS
(Information Systems, Zwass, p57)
Internal and External databases
Decision Models
Summary statistics,
trend projections,
hypothesis testing, etc.
• Statistical Models
• Financial and Accounting
Cash flow,
Models
internal rate of return,
other investment analysis
• Production Models
• Marketing Models
• Human Resource Models
Examples of Model driven DSS
• Voyage estimating system (Laudon &
Laudon, Chapter 2, pages 54-57
• More examples in Laudon & Laudon,
Chapter 12,
1
request
Cargo booking
agent
Confirm/reject
Cargo
reservation
system
Availability/
minimum price
CargoProf
revenue
management
system
Passenger
booking agent
(Laudon & Laudon, 8th ed., page 351)
Cargo size,
rate data
Cargo availability forecast
Passenger
reservation
system
2
Flight
schedule
server
Passenger
forecast data
Data driven DSS
• Make use of OLAP and data mining to
extract useful information.
• With OLAP uses need to have a good idea
of what information they are looking for.
• OLAP allows data to be viewed from
different perspectives, i.e. the same data is
viewed in different ways using multiple
dimensions.
Data driven DSS
• Data mining is more discovery driven.
• Finds hidden patterns and relationships.
• Data mining can yield associations,
sequences, classifications, clusters, and
forecasts.
Types of Analytical Modelling
• What-if Analysis
– Change selected variables and observe its effect
on other variables
• Sensitivity Analysis
– Observe how repeated changes to one variable
affect other variables
• Goal-seeking Analysis (how-can)
– Make repeated changes to selected variables
until a chosen variable reach a target value
• Optimisation Analysis
– Finding an optimum value for selected
variables, under a set of given constraints
Group Decision Support
Systems (GDSS)
• Computer-based systems that
enhance group decision making and
improve the flow of information
among group members.
GDSS Alternatives
[Figure 10.14]
Stair & Raynolds
Decision Room
Decision room
alternative
– Decision makers are
located in the same
building or geographic
area.
– Decision makers are
occasional users of the
GDSS approach.
Stair & Raynolds
Local Decision network
Schultheis & Sumner
GDSS Alternatives
l Teleconferencing alternative
-Location of group members is
distant.
-Decision frequency is low.
-Group meetings at different
locations are tied together
Teleconferencing
chairs
terminals
table
video
cameras
public
screen
Schultheis & Sumner
Robert Schulthesis and Mary Sumner
Wide area decision network
Wide area decision
network
– Location of group
members is
geographically remote.
– Decision frequency is
high.
– Virtual workgroups
• Groups of workers located
around the world working
on common problems via a
GDSS
Stair & Raynolds
The Executive
Support System
The Executive
Support System (ESS)
• An IS that is focused on meeting the
strategic needs of the organisation
• Designed explicitly for the purposes of
senior management
• Used by senior management without
technical intermediaries
Easy to use, easy to learn
•
Use state-of-the-art integrated
graphics, text, and communication
technology
Web browsing, e-mail, groupware tools, DSS
and Expert System capabilities
•
Also known as an Executive
Information System (EIS)
The Executive
Support System (ESS)
•
Require a greater proportion of
information from outside the
business
Competitors, government, trade associations,
consultants, etc.
•
Are linked with value added
business processes
ESS Support:
•
•
•
•
•
defining an overall vision
strategic planning
strategic organising and staffing
strategic control
crisis management
Expert Systems
uKnowledge
Based Information System
(KBIS)
uExpert
System (ES):
–A KBIS that uses its knowledge about a
specific area to act as an expert consultant
to the end user
Expert System
Expert System Software
Inference Engine
QUERY
USER
INPUT
IF…
and IF …
and IF …
and IF …
THEN
User Interface
Programs
EXPERT
ADVICE
User Interface
Programs
Knowledge Base
OUTPUT
Fact…
Fact…
Realtionship …
Fact …
Realtionship …
Realtionship …
Expert System Development
THE EXPERT
and/or THE
KNOWLEDGE
ENGINEER
Knowledge
Acquisition
programme
Knowledge Engineering
Components of an Expert System, and the components involved in
building the knowledge base.
(Adapted from O’Brien (2004:293) and Oz(2006:333))
Whale Watcher
http://www.aiinc.ca/demos/whale.
html
Expert Systems Applications in
Business
Chapter 11, Minicase 2, Page 501-502 of
Turban etal.
Pages 438-439, Laudon and Laudon
http://www.exsys.com/exsys.html Case Studies
Expert Systems Applications in
Business
CLUES (Countrywide’s Loan
Underwriting Expert Systems)
Intelligent help desk IBM, Microsoft, Compaq
CADS (Consumer Appliance Diagnostic
System) - Whirlpool
Web-based Expert Systems
u
Disseminating knowledge and expertise
u
Transferring ESs over the Net to human
users and other computerised systems
u
Also supports the spread of multimediabased ES (intellimedia systems)
Laudon & Laudon, p47
Executive
support systems
(ESS)
Management
Information
systems (MIS)
Decision
support systems
(DSS)
Knowledge
systems (ES
and office
systems)
Transaction
processing
systems (TPS)
Artificial Intelligence
Cognitive Science
Applications
Expert systems
Learning systems
Fuzzy Logic
Genetic Algorithms
Neural Networks
Intelligent Agents
Robotics
Applications
Visual perception
Tactility
Dexterity
Locomotion
Navigation
Natural Interface
Applications
Natural languages
Speech recognition
Multisensory
interfaces
Virtual reality
The major application areas of AI
(O’Brien, 2002:223)
Intelligent Support Systems
•
Systems that augment a manager’s
intelligence and expertise
– Expert Systems (ES)
– Artificial intelligence
•
•
•
•
Natural Language processing
Neural networks
Fuzzy Logic
Intelligent agents