Decision Support Systems
Chapter 2
Decision-Making Systems, Models, and Support
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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• 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.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• 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 ?
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Team-Based Decision-Making
• Team-based decision making
– Increased information sharing
– Daily feedback
– Self-empowerment
• Shifting responsibility towards teams
• Elimination of middle management
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• 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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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• Closed system
– Independent
– Takes no inputs
– Delivers no outputs to the environment
– Black Box
• Open system
– Accepts inputs
– Delivers outputs to environment
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• 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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• 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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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– Trial-and-error with the real system
– Simulation
– Optimization
– Heuristics
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• Simon’s original three phases:
– Intelligence
– Design
– Choice
• He added fourth phase later:
– Implementation
• Book adds fifth stage:
– Monitoring
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Phases of decision-making process
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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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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• 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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• 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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• 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 200000 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.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• 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
1
+12000X
2
• Uncontrollable variables (constraints):
– Labor constraint: 300X
1
+ 500X
2
– Budget constraint: 10000X
1
– Marketing requirements: X
– Marketing requirements: X
1
2

200000
+ 15000X

100

200
2

8000000
• Optimal solution: X
1
$5066667.
= 333.33, X
2
= 200, Profit=
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• 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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• 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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• 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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• 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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• Decision making with commitment to act
• Determine courses of action
– Analytical techniques
– Algorithms
– Heuristics
– Blind searches
• Analyze for robustness
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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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• Putting solution to work
• Vague boundaries which include:
– Dealing with resistance to change
– User training
– Upper management support
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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.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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– Automatic
• Data Mining
– Expert systems, CRM, neural networks
– Manual
• OLAP
• KMS
– Reporting
• Routine and ad hoc
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– 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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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– 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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– Improved communications
– Collaboration
– Training
– Supported by KMS, expert systems,
GSS
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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– Hippocrates’ personality types
– Myers-Briggs’ Type Indicator
– Kiersey and Bates’ Types and
Motivations
– Birkman’s True Colours
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– What is perceived?
– How is it organized?
– Subjective
– How do people think?
– How do they react?
– Heuristic, analytical, autocratic, democratic, consultative
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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