Download Choice Phase

Decision Support &
Executive Information
Systems:
LECTURE 5
Amare Michael Desta
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Decision Support Systems
- Systems designed to support managerial
decision-making in unstructured problems
- More recently, emphasis has shifted to inputs
from outputs
- Mechanism for interaction between user and
components
- Usually built to support solution or evaluate
opportunities
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Role of Systems in DSS
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Structure
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Inputs
Processes
Outputs
Feedback from output to decision maker
- Separated from environment by boundary
- Surrounded by environment
Input
Processes
Output
boundary
Environment
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System Types
- Closed system
- Independent
- Takes no inputs
- Delivers no outputs to the environment
- Black Box
- Open system
- Accepts inputs
- Delivers outputs to environment
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Decision-Making (Certainty)
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Assume complete knowledge
All potential outcomes known
Easy to develop
Resolution determined easily
Can be very complex
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Decision-Making (Uncertainty)
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Several outcomes for each decision
Probability of occurrence of each outcome
unknown
Insufficient information
Assess risk and willingness to take it
Pessimistic/optimistic approaches
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Decision-Making (Probabilistic)
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Decision under risk
Probability of each of several possible
outcomes occurring
Risk analysis
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Calculate value of each alternative
Select best expected value
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Influence Diagram
(Presenting the model)
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Graphical representation of model
Provides relationship framework
Examines dependencies of variables
Any level of detail
Shows impact of change
Shows what-if analysis
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Modeling with Spreadsheets
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Flexible and easy to use
End-user modeling tool
Allows linear programming and regression
analysis
Features what-if analysis, data management,
macros
Seamless and transparent
Incorporates both static and dynamic models
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Simulations
Imitation of reality
- Allows for experimentation and time compression
- Descriptive, not normative
- Can include complexities, but requires special skills
- Handles unstructured problems
- Optimal solution not guaranteed
- Methodology
- Problem definition
- Construction of model
- Testing and validation
- Design of experiment
- Experimentation & Evaluation
- Implementation
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Simulations
Probabilistic independent variables
- Discrete or continuous distributions
- Time-dependent or time-independent
- Visual interactive modeling
- Graphical
- Decision-makers interact with model
- may be used with artificial intelligence
- Can be objected oriented
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Decision Making
- Process of choosing amongst alternative
courses of action for the purpose of attaining
a goal or goals.
- The four phases of the decision process are:
(Simon’s)
- Intelligence
- Design
- Choice
- Implementation
- Monitoring (added recently)
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Decision-Making Intelligence
Phase
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Scan the environment
Analyze organizational goals
Collect data
Identify problem
Categorize problem
- Programmed and non-programmed
- Decomposed into smaller parts
- Assess ownership and responsibility for
problem resolution
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Intelligence Phase(
Contd…)
- Intelligence Phase
- Automatic
- Data Mining
- Expert systems, CRM, neural networks
- Manual
- OLAP
- KMS
- Reporting
- Routine and ad hoc
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Decision-Making Design Phase
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Develop alternative courses of action
Analyze potential solutions
Create model
Test for feasibility
Validate results
Select a principle of choice
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Establish objectives
Incorporate into models
Risk assessment and acceptance
Criteria and constraints
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Design Phase (Contd…)
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Design Phase
- Financial and forecasting models
- Generation of alternatives by expert system
- Relationship identification through OLAP and
data mining
- Recognition through KMS
- Business process models from CRM and ERP
etc…
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Decision-Making
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Choice Phase
Principle of choice
- Describes acceptability of a solution
approach
Normative Models
- Optimization
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Rationalization
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Effect of each alternative
More of good things, less of bad things
Courses of action are known quantity
Options ranked from best to worse
Suboptimization
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Decisions made in separate parts of organization without
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Choice Phase (Contd...)
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Decision making with commitment to
act
Determine courses of action
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Analytical techniques
Algorithms
Heuristics
Blind searches
Analyze for robustness
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Choice Phase (Contd…)
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Choice Phase
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Identification of best alternative
Identification of good enough alternative
What-if analysis
Goal-seeking analysis
May use KMS, GSS, CRM, and ERP systems
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Decision-Making
Implementation Phase
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Putting solution to work
Vague boundaries which include:
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Dealing with resistance to change
User training
Upper management support
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Implementation Phase
(Contd…)
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Implementation Phase
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Improved communications
Collaboration
Training
Supported by KMS, expert systems, GSS
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Developing Alternatives
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Generation of alternatives
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May be automatic or manual
May be legion, leading to information
overload
Scenarios
Evaluate with heuristics
Outcome measured by goal attainment
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Descriptive Models
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Describe how things are believed to be
Typically, mathematically based
Applies single set of alternatives
Examples:
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Simulations
What-if scenarios
Cognitive map
Narratives
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Problems
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Satisfying is the willingness to settle for
less than ideal.
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Bounded rationality
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Form of sub optimization
Limited human capacity
Limited by individual differences and biases
Too many choices
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Source: Based on Sprague, R.H., Jr., “A Framework for the Development of DSS.” MIS Quarterly, Dec. 1980, Fig. 5, p. 13.
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Decision-Making in humans
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Cognitive styles
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What is perceived?
How is it organized?
Subjective
Decision styles
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How do people think?
How do they react?
Heuristic, analytical, autocratic, democratic,
consultative
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DSS as a methodology
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A DSS is a methodology that supports
decision-making.
It is:
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Flexible;
Adaptive;
Interactive;
GUI-based;
Iterative; and
Employs modeling.
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Business Intelligence
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Proactive
Accelerates decision-making
Increases information flows
Components of proactive BI:
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Real-time warehousing
Exception and anomaly detection
Proactive alerting with automatic recipient
determination
Seamless follow-through workflow
Automatic learning and refinement
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Components of DSS
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Subsystems:
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Data management
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Model management
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Managed by DBMS
Managed by MBMS
User interface
Knowledge Management and
organizational knowledge base
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Data Management Subsystem
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Components:
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Database
Database management system
Data directory
Query facility
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Levels of decision making
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Strategic
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Tactical
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Used primarily by middle management to allocate
resources
Operational
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Supports top management decisions
Supports daily activities
Analytical
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Used to perform analysis of data
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(ad hoc analysis)
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DSS Classifications
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GSS v. Individual DSS
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Decisions made by entire group or by lone
decision maker
Custom made v. vendor ready made
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Generic DSS may be modified for use
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Database, models, interface, support are built
in
Addresses repeatable industry problems
Reduces costs
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