Chapter 2: Decision Making, Systems, Modeling, and Support Flashcards
Algorithm
A step-by-step search in which improvement is made at every step until the best solution is found.
Analog Model
An abstract, symbolic model of a system that behaves like the system but looks different.
Analytical Techniques
Methods that use mathematical formulas to derive an optimal solution directly or to predict a certain result, mainly in solving structured problems.
Choice Phase
The third phase in decision making, in which an alternative is selected.
Decision Making
The action of selecting among alternatives.
Decision Style
The manner in which a decision maker thinks and reacts to problems. It includes perceptions, cognitive responses, values, and beliefs.
Decision Variable
A variable in a model that can be changed and manipulated by the decision maker. Decision variables correspond to the decisions to be made, such as quantity to produce, amounts of resources to allocate, etc.
Descriptive model
A model that describes things as they are.
Design Phase
The second decision-making phase, which involves finding possible alternatives in decision making and assessing their contributions.
Effectiveness
The degree of goal attainment. Doing the right things.
Efficiency
The ratio of output to input. Appropriate use of resources. Doing things right.
Iconic Model
A scaled physical replica.
Implementation Phase
The fourth decision-making phase, involving actually putting a recommended solution to work.
Intelligence Phase
The initial phase of problem definition in decision making.
Mental Model
The mechanisms or images through which a human mind performs sense-making in decision making.
Normative Model
A model that prescribes how a system should operate.
Optimization
The process of identifying the best possible solution to a problem.
Principle of Choice
The criterion for making a choice among alternatives.
Problem Ownership
The jurisdiction (authority) to solve a problem.
Problem Solving
A process in which one starts from an initial state and proceeds to search through a problem space to identify a goal.
Satisficing
A process by which one seeks a solution that will satisfy a set of constraints. In contrast to optimization, which seeks the best possible solution, satisficing simply seeks a solution that will work well enough.
Scenario
A statement of assumptions and configurations concerning the operating environment of a particular system at a particular time.
Sensitivity Analysis
A study of the effect of a change in one or more input variables on a proposed solution.
Simulation
An imitation of reality in computers.
Suboptimization
An optimization-based procedure that does not consider all the alternatives for or impacts on an organization.
What-If Analysis
A process that involves asking a computer what the effect of changing some of the input data or parameters would be.
Groupthink
group members accept the solution without thinking for themselves.
Characteristics of decision making
- Groupthink (i.e. group members accept the solution without thinking for themselves) can lead to bad decisions.
- Decision makers are interested in evaluating what-if scenarios.
- Experimentation with a real system (e.g develop a schedule, try it, and see how well it works) may result in failure
- Experimentation with a real system is possible only for one set of conditions as a time and can be disastrous.
- Changes in the decision-making environment may occur continuously, leading to invalidating assumptions about a situation (e.g. deliveries around holiday times may increase, requiring a different view of the problem).
- Changes in the decision-making environment may affect decision quality by imposing time pressure on the decision maker.
- Collecting information and analyzing a problem takes time and can be expensive. It is difficult to determine when to stop and make a decision.
- There may not be sufficient information to make an intelligent decision.
- Too much information may be available (i.e. information overload)
Models can be used to support decisions for the following reasons
- Manipulating a model (changing decision variables or the environment) is much easier than manipulating a real system. Experimentation is easier and does not interfere with the organization’s daily operations.
- Models enable the compression of time. Years of operations can be simulated in minutes or seconds of computer time.
- The cost of modeling analysis is much lower than the cost of a similar experiment conducted on a real system.
- The cost of making mistakes during trial-and-error experiment is much lower when models are used than with real systems.
- The business environment involves considerable uncertainty. With modeling, a manger can estimate the risks resulting from specific actions.
- Mathematical models enable the analysis of a very large, sometimes infinite, number of possible solutions. Even in simple problems, managers often have a large number of alternatives from which to choose.
- Models enhance and reinforce learning and training.
- Models and solution methods are readily available on the Web.
- Many Java applets (and other Web programs) are available to readily solve models.
Decision-Making Process
The three phases intelligence, design, and choice represent the steps required to create a solution to a problem.
• The intelligence phase represents the identification of the problem at hand.
• In the design phase a model that represents the problem is constructed
• In the choice phase a solution to the problem is selected for implementation.
-The implementation phase could be considered a fourth phase in the process during which the solution is put into action in the real world.
The Intelligence Phase
Depending on the nature of the decision to be made a problem or opportunity must be correctly identified for solution creation.
Collecting data to aid in the intelligence phase can be difficult, here is a list of some problems with data collection:
• Data are not available. As a result, the model is made with, and relies on, potentially inaccurate estimates.
• Obtaining data may be expensive.
• Data may not be accurate of precise enough.
• Data estimation is often subjective.
• Data may be insecure.
• Important data that influence the results may be qualitative (soft)
• There may be too many data (i.e., information overload)
• Outcomes (or results) may occur over an extended period. As a result, revenues, expenses, and profits will be recorded at different points in time. To overcome this difficulty, a present-value approach can be used if the results are quantifiable.
• It is assumed that future data will be similar to historical data. If this is not the case, the nature of the change has to be predicted and included in the analysis.
The Design Phase
Designing and testing a model is informed by what types of decision variables to include in the models testing as well as what result variables, or outcomes, are desired.
Descriptive Models
- Complex inventory decisions
- Financial planning
- Waiting-line (queueing) management
The Choice Phase
Choosing the correct solution to a problem is a difficult process and is aided by many tools including analytical techniques, algorithms, heuristics, and sometimes shooting in the dark.
The Implementation Phase
Once an optimal solution to a problem has been chosen using the first three steps of the decision making process the solution must be implemented in the real world. Issues may arise during the process such as resistance from employees, training new users, and unforeseen problems with the new system.
