Chapter 2 Decision-Making Systems, Models, and Support 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

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. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 ? © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Team-Based Decision-Making Increased information sharing Daily feedback Self-empowerment Shifting responsibility towards teams Elimination of middle management © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Phases of Decision-Making Simon’s original three phases: Intelligence Design Choice He added fourth phase later: Implementation Book adds fifth stage: Monitoring © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Phases of decision-making process © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 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, Turban, Aronson, and Liang

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 = 8000X1+12000X2 Uncontrollable variables (constraints): Labor constraint: 300X1 + 500X2  200000 Budget constraint: 10000X1 + 15000X2  8000000 Marketing requirements: X1  100 Marketing requirements: X2  200 Optimal solution: X1 = 333.33, X2 = 200, Profit= $5066667. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

7.Decision-Making: Choice Phase Decision making with commitment to act Determine courses of action Analytical techniques Algorithms Heuristics Blind searches Analyze for robustness © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

9. Decision-Making: Implementation Phase Putting solution to work Vague boundaries which include: Dealing with resistance to change User training Upper management support © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision Support Systems Support for Implementation Phase Improved communications Collaboration Training Supported by KMS, expert systems, GSS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

11. Decision-Making In Humans Temperament Hippocrates’ personality types Myers-Briggs’ Type Indicator Kiersey and Bates’ Types and Motivations Birkman’s True Colours Gender © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang