1. Chapter 2 Decision-Making Systems, Models, and Support
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition
Chapter 2 Decision-Making Systems, Models, and Support
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

2. Learning Objectives Learn the basic concepts of decision making.
Understand systems approach.
Learn Simon’s four phases of decision making.
Understand the concepts of rationality and bounded rationality.
Differentiate betwixt making a choice and establishing a principle of choice.
Learn which factors affect decision making.
Learn how DSS supports decision making in practice.
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3. Team-based decision making
Standard Motor Products Shifts Gears Into Team-Based Decision-Making Vignette
Team-based decision making
Increased information sharing
Daily feedback
Self-empowerment
Idea generation
Time/Quality aspect of the decision
Shifting responsibility towards teams
Elimination of middle management
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4. Shifting responsibility towards teams-based decisions.
Standard Motor Products Shifts Gears Into Team-Based Decision-Making Vignette
Shifting responsibility towards teams-based decisions.
Produce Quality Decisions.
Sharing Responsibility, experiences.
Self-Empowerment.
Improve implementation.
Because of higher risks, org. volume.
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5. Decision Making 1-3 Problem Solving 1-4
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:
Intelligence
Design
Choice
implementation
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6. Systems Structure Separated from environment by boundary
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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8. 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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9. Models Used for DSS Iconic Analog Quantitative (mathematical)
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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11. Phases of Decision-Making
Simon’s original three phases:
Intelligence
Design
Choice
He added fourth phase later:
Implementation (putting words into action)
Book adds fifth stage:
Monitoring (Evaluation, Control)
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12. Decision-Making Intelligence Phase
Scan the environment (Identify Problem, Situation, Opportunity)
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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13. 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
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14. Decision-Making Choice Phase
Principle of choice
Describes acceptability of a solution approach. (Willingness to assume high or low risk, optimization .
Normative Models
Optimization
Effect of each alternative
Rationalization (only for human ????!!!!!!)
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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15. Descriptive Models Describe how things are believed to be
Typically, mathematically based
Applies single set of alternatives
Examples:
Simulations
What-if scenarios
Scenario analysis
Financial planning.
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16. 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
And Achievements.
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17. Alternative Evaluation
To evaluate and compare each proposed alternatives it is necessary to predict the future outcome of each alternative. Decision Situation:
Uncertainty
Ignorance
Certainty Complete Knowledge
RISK
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18. Decision Situation Decision Making under certainty.
Complete knowledge is available
Decision making under risk.
Probabilistic or stochastic, Calculated risk (Risk Analysis)
Decision making under Uncertainty
No knowledge, estimate or probability
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19. Measuring Outcomes and goals
Outcome: Profit.
Goal: Max. Profit.
Outcome: Customer Satisfaction can be measured by complaints
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20. Scenarios Possible scenarios: The worst possible. The best possible.
The most likely.
The Average.
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21. Errors in D.M. DM is based on a model.
DM errors are possible due to structural errors in the model.
It is critical to evaluate the model.
Gather the correct amount of info. With the right level of precision and accuracy.
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22. DM 7 Deadly sins. Sawyer ‘99
Common pitfalls decision makers often unwittingly encounter.
Believing that you know all the answer (no attempt to seek outside information or expertise).
Asking the wrong Questions. You need the right info.
Ego, refuse to back down from bad policy or decision.
Saving money by not seeking out info. IS or Expert.
Copying. If it works for them it’ll works for us.
Ignore negative advice. Kill the messenger with the bad news.
Making no decision can be the same as making a bad decision. Procrastination is not necessary a good management techniques.
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23. Problems
Satisficing is the willingness to settle for less than ideal or perfect or the best (Satisfactory).
Form of suboptimization
Bounded rationality
Limited human capacity
Limited by individual differences and biases
Too many choices
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24. 2.8 DM Choice Phase
Decision making with commitment to act. Choice is the critical act of DM
Search Approaches Determine the appropriate courses of action
Analytical techniques (mathematically derive optimal solution)
Algorithms (Step by step search process)
Heuristics (With rules to guide the search)
Blind search (Arbitrary, not guided search)
Analyze for robustness
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25. Analytical Techniques
Mathematically deriving the optimal solution.
Used mainly in structured problems.
At tactical or operational level.
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26. Algorithms
Algorithm: is a step-by-step search process for optimal solution.
May be used by Analytical Techniques to increase efficiency.
Algorithm process continue to improve reached solution until objective value is found.
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27. Blind search
A two arbitrary search approach that is not guided. 1- complete search of all possible alternative until optimal solution is found. 2- Partial search which continue until good enough solution is found.
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28. Heuristic search Heurism Discovery
Are decision rules governing how a problem should be solved.
Example of Heuristics
Sequence jobs through a machine.
Do the jobs that require the least time first.
Purchase stocks
If a price-to-earning ratio exceeds 10, do not buy.
Travel
Do not use the freeway between 9 and 10
Capital investment
Consider projects with less than 2yearspay back period
Purchase of a house
In good neighborhood, but only in lower price range.
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29. 2.9 Evaluation
The search process discribed earlier is coupled with evaluation. Evaluation is the final step that leads to a recommended solution.
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30. Multiple Goals
Management decision aims at evaluating how far each alternative advances mgmt towards its GOALS. Today’s mgmt systems are much more complex and managers want to attain CONFLICTING MULTIPLE GOALS. GOALS: 1- Profit-making. 2- Growth 3- Development ( Market and product and employees) 4- Provide job security. 5- Serve the local community. 6- Satisfying the stackeholders 7- Enjoy high salaries
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31. Sensitivity Analysis
Attempts to assess the impact of in the input data or parameters on the proposed solution.
The impact of changes in external (uncontrollable) variables and parameters on outcome variable(s).
The impact of changes in decision variables on outcome variable(s).
The effect of uncertainty in estimating external variables.
The robustness of decisions under changing conditions.
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32. What-If Analysis
What will happen to the solution if an input variable, an assumption, or parameter value is changed???
What will happen to the total inventory cost if the cost of carrying inventory increases by 10%.
What will be the market share if the advertising budget increases by 5%.
See figures 2.9 pp.66
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33. Goal Seeking
Backward solution approach, to calculate the value of input necessary to achieve a desired level of an output GOAL.
What annual R&D budget is needed for an annual growth rate of 15%.
How many nurses are needed to reduce the average waiting time of patients in the emergency room to less than 10 minutes.
See figures PP. 66
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34. 2.10 Decision-Making Implementation Phase
Putting recomm. solution to work.
A process with vague, ambiguous, uncertain and unclear boundaries which include:
Dealing with resistance to change
User training (Empowerment)
Upper management support.
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35. Individual Experience Realizing CHANGE
Clear vision and objectives.
Strategic plan.
Top management support.
Awareness through empowerment.
Participation.
Involvement.
Strong, good will and Persistence, Diligence
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36. 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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37. Decision Support Systems
Intelligence Phase
Automatic
Data Mining
Expert systems, CRM, neural networks
Manual
OLAP
KMS
Reporting
Routine and ad hoc
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38. Decision Support Systems
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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39. Decision Support Systems
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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40. Decision Support Systems
Implementation Phase
Improved communications
Collaboration
Training
Supported by KMS, expert systems, GSS
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41. 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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42. 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 highhanded, democratic, consultative
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