Transcript Information systemn voor eindgebruiker en kantoor

Chapter 9
Information systems for
Managerial Decision
Form of the Reports
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Line chart
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Bar chart
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Pie chart
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Image
Management tasks
Planning:
goal seeking and strategy design
Organisation: develop organisational structures
Personnel:
hiring , training , nominating
Management: motivation and communication
Guiding:
evaluation of performance and draw
conclusions
Management roles
The management has several roles to play:
 concerning persons
 manage
 contact person
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concerning information
 control
 distribute
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concerning decisions
 take
 problem solving
 distribution of the resources
 negotiate
Management levels
Strategic:
strategic planning and management
support for the direction committee
Tactic:
tactic planning and support of the
departments by the middle management
Operational: planning and support of the operations
by the operations management
O’Brien p. 352
Information Requirements
Decision structure
Unstructured
Semistructured
Structured
O’Brien p. 353
Information properties
Strategic
Management
Tactic
Management
Operational
Management
Ad hoc
Irregular
Compact
Not frequent
Future Oriented
External
Broad
Pre-defined
Regular
Detailed
Frequent
Historical
Internal
Model for the decision making process
Research
activities
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Design
activities
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Choice
activities
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Try to find and recognise situations that
require a decision
Opportunities need to be identified and
notified
Design and evaluate differences in
behaviour
An information system has to contribute to
create and evaluate opportunities
Decide on actions and control the
implementation
The information system has to contribute
to the decision making on the priorities of
alternative decisions and has to provide
feed-back for the execution
Decision making structure
Operations
Management
Unstructured
Cash management
Partially
structured
Credit
management
Production
scheduling
Daily work
distribution
Structured
O’Brien p. 354
Stock
management
Billing
Tactical
Management
Strategic
Management
Work organisation
Performance
Analysis
Planning new
business
Company
reorganisation
Personnel evaluation
Budgeting
Project budgets
Production planning
Plant location
Co-operation
Program
management
Decision Support Systems (DSS)
Computer supported Information systems designed to provide
interactive and informative support for the managers during the
decision making process.
DSS use :
analytical models
specialised databases
input and expertise of the person that has to take the decision
interactive , automated modelling process to support the usage
of partially structured and unstructured decisions by individual
managers
Ad-hoc quick response systems directed by mangers
Architecture
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Hardware : workstation and communication system
Software
 DSS software packages (DSS generators)
• database management
• model base management
• generate and manage dialogue windows
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Data
 company database
 external databases
 personal databases for the manager
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Models : libraries of mathematical models and
analytical techniques
People
What is a decision support system?
More precise goal than a standard MIS system.
The aim is to deliver capabilities and not only to provide information .
Corporate
MIS
TPS
Statistical
model
Finance
Marketing
Strategic
plan
Production
Operational
model
Model
Database
DBMS database management system
MBMS Model base management system
DGMS Dialogue generation and
management software
DBMS
MBMS
DGMS
USER
Flowchart analysis of investment decision
A
Portfolio
data
Retrieve
Portfolio
No
display
performance
each industry
Yes
project
Future
Performance
pick
strategy
research
data
Retrieve
Research
reports
Stock
OK ?
No
Still
OK ?
Yes
Pick
stock
Purchase
Stock
data
graph
stock
performance
A
Decision process is
frozen as system
is developed
Client
DSS approach to same problem
Set of 4 capabilities
Representation
Portfolio
lists
Graphs
Research
reports
Simulation
outputs
Interface
language
Graph
operations
Report
operations
Simulation
operations
Procedure
operations
Storage
Databases
Operations
List
operations
Memory aids
Work space
representations
Control aids
Menus
Training
documents
A DSS is a decision-making scratch path , backed up by a database , that
decision makers can use to support many decision making processes.
Differences
Dimension
Microcomputing
DSS
MIS
Philosophy
Provide computing
power to end users
and simple models
Provide integrated
tools, data models
and language to users
Provide information
to end users
Objectives
Increase productivity
of knowledge and
office workers
Directly impact key
decisions and enhance
effectiveness of
decision making
Enhance control
and monitoring
power of middle
managers
Systems Analysis Identify what software
packages suitable for
task at hand
Establish what tools
are used in the
decision process
Identify information
requirements
Design
Iterative process
never frozen
Deliver system
based on frozen
requirements
Customise package
to task
Three levels of DSS technology
Specific DSS
software to guide decision making ( spreadsheets , ... )
DSS generators
package of hardware and software , providing tools to build specific
DDS
examples :
IFPS ( Interactive Financial Planning System )
EIS ( Executive Information System )
DSS tools
building blocks of generators
special purpose languages ( APL )
permit rapid development of applications , screens , menus , ...
graphics routines , graphics hardware , supporting
telecommunications
Roles to play
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Manager or end user
responsible for making key organisational decisions
a DSS must provide information on how things are going
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The Intermediary
skilled staffer who helps to schedule manager’s or task force work
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The DSS builder
must be familiar with the business problem
must have good understanding of how to make the technology work
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The technical supporter
member of the data processing group
develops and installs DSS generators and tools
DSS requires links to databases , graphic software , ...
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The Toolsmith
develops new technology , new software
works often for private vendors
Type of Analytical Model
What-if analysis
Examine how changes in selected variables
influence other variables
e.g.: what is the impact on sales if we spent 10%
less on publicity?
Sensitivity analysis
Examine how repeated changes in a variable
can influence other variables
e.g.: Lower the budget for publicity several years
with € 5.000 to discover the relationship between
publicity budget and sales
Goal-seeking analysis Modify selected variables until a specific
variable reaches a pre-defined value
e.g.: Increase the publicity budget until sales
reaches € 10M
Optimise
Determine optimal value for variables
Executive information systems (EIS)
Information systems where the characteristics of modern
information reporting systems are combined with
characteristics of DSS’s .
Provide direct and easy access to information on CSF’s.
Factors for good EIS:
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Involvement and support of top-management
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Knowledge of information sources
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Concentrate on crucial factors
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Response times
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Insight in the level of computer knowledge of managers
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Learning time for the development team
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Flexibility
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Ongoing support
Artificial Intelligence
Characteristics of intelligent behaviour
 think and logical reasoning
 problem solving via logical reasoning
 learn and getting insight based on experience
 gather knowledge and apply this
 creativity and imagination
 handle complex and chaotic situations
 react successfully on new situations
 estimate the relative importance of different factors
 ability to work with ambiguous or incorrect information
AI tries to build computer systems that show
this type of behaviour
Artificial Intelligence Family Tree
Natural
Language
Expert systems
Intelligent
machines:
AI hardware
Robotics
Perceptive
Systems
(vision, hearing)
Human and Artificial Intelligence
Successful AI systems are neither artificial nor intelligent
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based on : human expertise
knowledge
selected reasoning patterns
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act like textbooks
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cannot learn without being rewritten
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existing systems extend the powers of experts
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they do not substitute experts
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they have no common sense
Knowledge-based Expert Systems
An expert system is a knowledge-intensive program
that normally requires human expertise.
An expert system can assist decision-making by asking
relevant questions and explaining the reasons for
adopting certain actions.
Characteristics:
they perform some of the problem-solving work of humans
they use knowledge in the form of rules or frames
they interact with humans
they can consider multiple hypotheses simultaneously
Today’s expert systems are quite narrow , they do not
think , do not resort to reasoning , do not draw
analogies , lack common sense .
Three levels
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assistant
helps doing routine analysis
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Colleague
user discusses the problem until a joint decision is reached
when system is wrong , user adds additional information
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Complete expert automaton
makes the decisions for the user without questions
operates remotely beyond human intervention
not yet applied in practical areas
Components of an expert system
Development
team
Corpus of
Knowledge
Expert(s)
Knowledge Engineers
Shell or
Development
environment
Development Interface
Production Rules
Semantic nets
Frames
User Interfaces
suggested
solutions
Answers data
Questions
Commands
Users
Expert systems vs.
Decision Support Systems
DSS
Goals
ES
Support of human decision
maker
Who makes decisions the man or the system
or recommendations?
Direction of the
man inquiries system
inquiry
Type of support
individuals , groups and
Copy or replace human
advisor
the system
type of data
manipulation
Characteristics of the
problem domain
Type of problems
Database contents
Deduction capacity
Explain capacity
Numeric
symbolic
complex, broad
limited , specialised
ad hoc, unique
facts
no
repeating
procedures and facts
yes , limited
limited
yes
system inquiries man
individuals and groups
ES applications
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Decision making management
 evaluate performances, insurance's , ...
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Diagnostics / problem solving
 help desk, error detection in software, ...
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Maintenance / planning
 maintenance planning , production planning , training , ...
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Intelligent text / documentation
 regulations , security standards , taxation , ...
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Design / configuration
 feasibility studies , assembly schema’s, ...
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Selection / classification
 material selection, information classification, person identification
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Process management / Steering
 machine steering, production control, stock management, ...