1. Chapter 2 Decision-Making Systems, Models, and Support
Decision Support Systems
Chapter 2 Decision-Making Systems, Models, and Support
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2. Outline 1. Decision making 2. Systems 3. Models
4. A preview of the modeling process
5. Decision making: the Intelligence Phase
6. Decision making: the Design Phase
7. Decision making: the Choice Phase
8. Evaluation: Multiple goals, sentivity analysis, what-if and goal seeking
9.Decision making: the Implementation Phase
10. How decision are supported.
11. Human cognition and decision styles.
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3. 1. Decision Making
Decision making is a process of choosing among alternative courses of actions for the purpose of attaining a goal or goals.
The four phases of the decision process are:
Intelligence
Design
Choice
Implementation
Decision making = problem solving ?
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4. Team-Based Decision-Making
Increased information sharing
Daily feedback
Self-empowerment
Shifting responsibility towards teams
Elimination of middle management
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5. 2. Systems Structure of a system:
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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7. System Types Closed system Open 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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8. System effectiveness and efficiency
Two major performance measures:
Effectiveness is the degree to which goals are achieved. It is concerned with the outputs of a system.
Efficientcy is a measure of the use of inputs (or resources) to achieve outputs.
Effectiveness is doing the right thing
Efficiency is doing the thing right
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9. 3. Models Used for DSS
A model is a simplified representation or abstraction of reality.
Models are classified into 3 groups:
Iconic
Small physical replication of system
Analog
Behavioral representation of system
May not look like system
Quantitative (mathematical)
Demonstrates relationships between systems
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10. 4. A preview of the modeling process
There are some ways to solve a problem
Trial-and-error with the real system
Simulation
Optimization
Heuristics
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11. Phases of Decision-Making
Simon’s original three phases:
Intelligence
Design
Choice
He added fourth phase later:
Implementation
Book adds fifth stage:
Monitoring
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12. Phases of decision-making process
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13. 5. 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
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14. 6. 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
Criteria and constraints
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15. Components of quantitative models
Decision variables: describe alternative courses of actions.
Result variables: indicates how well the system performs or attains its goals. Result variables are considered dependent variables.
Uncontrollable variables or parameters. There are factors that affect the result variables but not under the control of the decision maker.
Intermediate result variables
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16. An example of modeling
MBI Corporation makes special-purpose computers. A decision must be made: How many computers should be produced next month at the Boston plant? Two types of computers are considered: the CC-7 which requires 300 days of labor and $10000 in materials, and the CC-8, which requires 500 days of labor and $12000.
The profit of each CC-7 is $8000, whereas that of each CC-8 is $12000.
The plant has a capacity of working days per month, and the material budget is $8 million per month. Marketing requires that at least 100 units of CC-7 and at least 200 units of the CC-8 be produced each month.
The problem is to maximize the company’s profits by determining how many units of CC-7 and how many units of CC-8 should be produced each month.
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17. Modeling by Linear programming
Decision variables:
X1 = units of CC-7 to be produced
X2 = units of CC-8 to be produced
Result variable:
Total profit = Z. The objective is the maximize total profit: Z = 8000X X2
Uncontrollable variables (constraints):
Labor constraint: 300X X2 
Budget constraint: 10000X X2 
Marketing requirements: X1  100
Marketing requirements: X2  200
Optimal solution: X1 = , X2 = 200, Profit= $
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18. Selection of a principle of choice
A criterion that describes acceptability of a solution approach.
There are two main principles of choice: normative and descriptive
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
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19. 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
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20. Developing (generating) Alternatives
Generation of alternatives
May be automatic or manual
Can be a lengthy process
Take time and cost money
Alternatives can be generated with heuristics
Outcome measured by goal attainment
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21. Good enough or satisficing
Satisficing is the willingness to settle for less than ideal.
Form of suboptimization
“Bounded rationality” (Simon’s idea)
Limited human capacity
Limited by individual differences and biases
Bounded rationality is also why many models are descriptive rather than normative.
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22. 7.Decision-Making: Choice Phase
Decision making with commitment to act
Determine courses of action
Analytical techniques
Algorithms
Heuristics
Blind searches
Analyze for robustness
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23. 8. Evaluation: Multiple goals, sentivity analysis, what-if and goal seeking
Multiple goals. Managers want to attain simultaneous goals, where some of them are conflicts.  goal programming.
Sensitivity analysis. Sensitivity analysis attempts to assess the impact of a change in the input data or parameters on the proposed solution.
What-if analysis. “What will happen to the solution if an input variable, an assumption, or a parameter value is changed?”
EX: “What will be the market share if the advertisement budget increases by 5 percent?”
Goal seeking. Goal seeking analysis calculates the values of inputs necessary to achieve a desired level of an output (goal). It is a backward solution approach.
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24. 9. Decision-Making: Implementation Phase
Putting solution to work
Vague boundaries which include:
Dealing with resistance to change
User training
Upper management support
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25. 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.
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26. 10. How Decisions are Supported
Support for Intelligence Phase
Automatic
Data Mining
Expert systems, CRM, neural networks
Manual
OLAP
KMS
Reporting
Routine and ad hoc
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27. Decision Support Systems
Support for 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, RMS, ERP, and SCM
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28. Decision Support Systems
Support for Choice Phase
Identification of best alternative
Identification of good enough alternative
What-if analysis
Goal-seeking analysis
May use KMS, GSS, CRM, ERP, and SCM systems
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29. Decision Support Systems
Support for Implementation Phase
Improved communications
Collaboration
Training
Supported by KMS, expert systems, GSS
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30. 11. Decision-Making In Humans
Temperament
Hippocrates’ personality types
Myers-Briggs’ Type Indicator
Kiersey and Bates’ Types and Motivations
Birkman’s True Colours
Gender
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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
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