1. LECTURE 5 Amare Michael Desta
Decision Support & Executive Information Systems:
LECTURE 5 Amare Michael Desta

2. 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

3. Role of Systems in DSS - Inputs - Processes - Outputs
Structure
- Inputs
- Processes
- Outputs
- Feedback from output to decision maker
- Separated from environment by boundary
- Surrounded by environment
Input
Processes
Output
boundary
Environment

5. System Types - Closed system - Independent - Takes no inputs
- Delivers no outputs to the environment
- Black Box
Open system
- Accepts inputs
- Delivers outputs to environment

6. Decision-Making (Certainty)
- Assume complete knowledge
- All potential outcomes known
- Easy to develop
- Resolution determined easily
- Can be very complex

7. Decision-Making (Uncertainty)
Several outcomes for each decision
Probability of occurrence of each outcome unknown
Insufficient information
Assess risk and willingness to take it
Pessimistic/optimistic approaches

8. Decision-Making (Probabilistic)
Decision under risk
Probability of each of several possible outcomes occurring
Risk analysis
Calculate value of each alternative
Select best expected value

9. Influence Diagram (Presenting the model)
Graphical representation of model
Provides relationship framework
Examines dependencies of variables
Any level of detail
Shows impact of change
Shows what-if analysis

11. Modeling with Spreadsheets
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

13. Simulations - Allows for experimentation and time compression
- 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

14. 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

16. 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)

18. Decision-Making Intelligence Phase
- 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

19. Intelligence Phase( Contd…)
- Automatic
- Data Mining
- Expert systems, CRM, neural networks
- Manual
- OLAP
- KMS
- Reporting
- Routine and ad hoc

20. Decision-Making Design Phase
Develop alternative courses of action
Analyze potential solutions
Create model
Test for feasibility
Validate results
Select a principle of choice
Establish objectives
Incorporate into models
Risk assessment and acceptance
Criteria and constraints

21. Design Phase (Contd…) 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…

22. Decision-Making Choice Phase
Principle of choice
- Describes acceptability of a solution approach
Normative Models
- Optimization
Effect of each alternative
Rationalization
More of good things, less of bad things
Courses of action are known quantity
Options ranked from best to worse
Suboptimization
Decisions made in separate parts of organization without consideration of whole

23. Choice Phase (Contd...) Decision making with commitment to act
Determine courses of action
Analytical techniques
Algorithms
Heuristics
Blind searches
Analyze for robustness

24. Choice Phase (Contd…) Choice Phase Identification of best alternative
Identification of good enough alternative
What-if analysis
Goal-seeking analysis
May use KMS, GSS, CRM, and ERP systems

25. Decision-Making Implementation Phase
Putting solution to work
Vague boundaries which include:
Dealing with resistance to change
User training
Upper management support

26. Implementation Phase (Contd…)
Improved communications
Collaboration
Training
Supported by KMS, expert systems, GSS

27. Developing Alternatives
Generation of alternatives
May be automatic or manual
May be legion, leading to information overload
Scenarios
Evaluate with heuristics
Outcome measured by goal attainment

28. Descriptive Models Describe how things are believed to be
Typically, mathematically based
Applies single set of alternatives
Examples:
Simulations
What-if scenarios
Cognitive map
Narratives

29. Problems Satisfying is the willingness to settle for less than ideal.
Form of sub optimization
Bounded rationality
Limited human capacity
Limited by individual differences and biases
Too many choices

30. Source: Based on Sprague, R. H. , Jr
Source: Based on Sprague, R.H., Jr., “A Framework for the Development of DSS.” MIS Quarterly, Dec. 1980, Fig. 5, p. 13.

31. Decision-Making in humans
Cognitive styles
What is perceived?
How is it organized?
Subjective
Decision styles
How do people think?
How do they react?
Heuristic, analytical, autocratic, democratic, consultative

33. DSS as a methodology
A DSS is a methodology that supports decision-making.
It is:
Flexible;
Adaptive;
Interactive;
GUI-based;
Iterative; and
Employs modeling.

34. `

35. Business Intelligence
Proactive
Accelerates decision-making
Increases information flows
Components of proactive BI:
Real-time warehousing
Exception and anomaly detection
Proactive alerting with automatic recipient determination
Seamless follow-through workflow
Automatic learning and refinement

36. Components of DSS Subsystems: Data management Model management
Managed by DBMS
Model management
Managed by MBMS
User interface
Knowledge Management and organizational knowledge base

38. Data Management Subsystem
Components:
Database
Database management system
Data directory
Query facility

40. Levels of decision making
Strategic
Supports top management decisions
Tactical
Used primarily by middle management to allocate resources
Operational
Supports daily activities
Analytical
Used to perform analysis of data

41. (ad hoc analysis)

42. DSS Classifications GSS v. Individual DSS
Decisions made by entire group or by lone decision maker
Custom made v. vendor ready made
Generic DSS may be modified for use
Database, models, interface, support are built in
Addresses repeatable industry problems
Reduces costs