Pick any two topics covered in the course and apply real world examples and appl
ID: 455039 • Letter: P
Question
Pick any two topics covered in the course and apply real world examples and applications of these topics. You do not need to show the calculations as much as discuss how that models are used in the real world similar to the discussion forum assignments. List one pro and one con of each of the two topics you choose. Some sample topics that you can discuss but feel free to discuss others such as Linear Programming, Sensitivity Analysis, Simulation, Inventory, Decision Analysis, Multi-criteria Decisions, Forecasting & Time Series.
The paper as a guide should be about 5 pages give or take a page, typed, single or double spaced in 12 pt. font. IN YOUR OWN WORDS.
The importance to this paper analysis is the use of the decision analysis topics and how they are used in the real world beyond calculations and definitions so use your OWN words! Please note that this is NOT intended to be a term paper just more along the lines of a long essay.
Please a lots of words
Explanation / Answer
Decision-making is an integral part of modern management. Essentially, Rational or sound decision making is taken as primary function of management.
In our life day to day we come across the decision making, time management theories to solve no of situation. Output of any theory is fruitful when it is apply to situation & get the result. Not 100 % success but 99% it will work. Again it is varying man to man how to take decision/in which situation/whether it good or bad but needs to take decision.
Now what is mean by Decision Making??-
“Decision-making involves the selection of a course of action from among two or more possible alternatives in order to arrive at a solution for a given problem”. Decision-making is one of the hard things in life. True decision-making occurs not when you already know exactly what to do, but when you do not know what to do. When you have to balance conflicting values, sort through complex situations, and deal with real uncertainty, you have reached the point of true decision-making. And to make things more difficult, the most important decisions in corporate or personal life are often those that put you in situations where you least know what to do.
Imagine a decision-maker struggling with a difficult decision problem. The decision analysis approach provides a normative approach that can support the decision-maker. Decision analysis functions at four different levels as a philosophy, as a decision framework, as a decision-making process, and as a decision- making methodology and each level focuses on different aspects of the problem of making decisions.
Philosophy
The first insight is that uncertainty is a consequence of our incomplete knowledge of the world. In some cases, uncertainty can be partially or completely resolved before decisions are made and resources committed. However, in many important cases, complete information is simply not available or is too expensive (in time, money, or other resources) to obtain.
The second basic insight is that there is a distinction between good decisions and good outcomes.
A Decision Framework
By using decision analysis, the decision-maker is aware of the adequacy or inadequacy of the decision basis: the set of knowledge (including uncertainty), alternatives, and values brought to the decision. There is also a clear distinction between decision factors (factors completely under the decision-makers control) and chance factors (uncertain factors completely outside the decision-makers control)
A Decision-Making Process
As a decision-making process, decision analysis provides a step-by-step procedure that has proved practical in tackling even the most complex problems in an efficient and orderly way. The decision analysis cycle provides an iterative approach that keeps the focus on the decision and that enable the decision facilitator* to efficiently compare the decision alternatives
Methodology
As a methodology, decision analysis provides a number of specific tools that are sometimes indispensable in analyzing a decision problem. These tools include procedures for eliciting and constructing influence diagrams, probability trees, and decision trees; procedures for encoding probability functions and utility curves; and a methodology for evaluating these trees and obtaining information useful to further refine the analysis.
1. Dealing with Complex Problems
Imagine a decision-maker struggling with a decision problem that involves a complex set of interactions. A decision may affect several products in several different markets. There may be many different alternatives which should be under consideration. Information may be difficult to obtain and there is a temptation in problems of this type to go to either of two extremes in using decision analysis. Either the analysis is done at a superficial and often simplistic level, resulting in inadequate insight for the decision-maker and perhaps in incorrect conclusions
1.1Discovering the Real Problem
Finding the real problem is often the most crucial task facing the decision maker and the decision facilitator. Problems worth extended analysis often come to the surface because many people see only parts of a problem or opportunity. The decision analysis cycle then refines the decision basis through a series of approximations. Start with a simple analysis and use the tools of sensitivity analysis to discover what is important and what is irrelevant. With one or two iterations, the problem is almost always clearly identified.
1.2Keeping the Analysis Manageable
People involved in the decision-making process will usually keep the facilitator from falling into the trap of making the analysis too simple. But what will keep the facilitator from making the analysis too complex? The decision analysis methodology provides guidance. The decision analysis cycle not only guides the direction in which the analysis grows, but also contains the rules for judging when the analysis should stop and the decision made. In iteration of the cycle, various forms of sensitivity analysis determine what information is important and why one alternative is better than another. This guides the next iteration of the analysis, and helps the facilitator avoid the addition of irrelevant detail and complexity
1.3Finding the Insights
The purpose of the analysis is not to obtain a set of numbers describing decision alternatives. It is to provide the decision-maker the insight needed to choose between alternatives. These insights typically have three elements: What is important to making the decision? Why is it important? How important is it? The various forms of sensitivity analysis and probabilistic analysis readily identify which factors are important in making a choice and which are not.
2.Dealing with Complex Organization
Imagine a set of decision-makers trying to identify a set of alternatives, choose between them, and create the conditions required for successful implementation in a multi-organizational environment.
2.1The Team Approach
The most effective means of dealing with cross-organizational problems and opportunities appears to be the cross-organizational team. The team normally has someone from each organization to present the information and concerns of that organization. Team members are ordinarily detached from their ordinary duties (either part- or full-time) for a fixed length of time to achieve some well defined goal. The Dialog Decision Process (DDP) has been developed to combine the decision analysis approach with the team approach.
2.2Structured Dialog
The DDP is based on a structured dialog between the decision team and the project team. At several points during the project, the two teams will meet for a specific purpose. At these meetings, the project team members present the results they have developed up to that point and request input from the decision team members.
2.3Decision Quality
A single decision-maker can decide when the time has come to stop the analysis and make the decision; decision analysis can provide some guidance, but it is really up to the decision-maker to decide when the decision is “good “logically consistent with the decision-maker's decision basis (alternatives, information, values)
Dealing with Uncertainty Clarity of discussion through the language of probability is one of the hallmarks of decision analysis. We must confront the reality of uncertainty and be able to describe it, and probability is the natural language to describe uncertainty. This section develops the concepts and language that facilitate discussion of uncertainty and the linkage between uncertainty and probability. Some of the more important rules for calculating with probabilities are reviewed. The most used representations of probability are defined and motivated. Finally, some hard-to-find results on cumulates are recorded for the expert.
Dealing with Complex Informational Relationships
Influence diagrams are an intuitively clear way of representing this knowledge, even when states of information are related in a complex fashion. Influence diagrams are mathematical constructs that obey strict mathematical rules.
Obtaining Reliable Information
One task that faces every decision facilitator is obtaining information about uncertainty. And experience has shown that expressing our state of knowledge about uncertainty is not something that we do well.
Decision maker also have the quality of decision. In figure include points of quality decision.
The Complexity of Real-World Problems
Up to now, we have dealt with problems that are relatively simple and easy to structure and analyze. Actual decision problems, however, are usually complicated and thorny. Furthermore, they never come to the decision facilitator in simple form. The professional decision facilitator sees only the problems that are complicated and often poorly described; frequently, it is unclear what the problem is and what decisions need to be made. Fortunately, the techniques for dealing with simple problems are virtually the same as those for dealing with complex problems. The most important rule for dealing with both kinds of problems is “Keep it simple!”Unnecessary complexity makes the analysis more difficult without offering additional insight.
A Cyclical Approach
To start with, we bring some initial knowledge to a problem. Then, in the basis development phase, we gather data, generate alternatives, and identify values for making the decision. In the deterministic structuring phase, we build a model of the decision, develop base-case input to the model, and perform deterministic sensitivity analysis to find the crucial uncertainties in the problem. Using the cyclical approach minimizes wasted time and effort. For instance, while the initial pass does not produce definitive results, it does give us a sense of how much additional information will be required and of how to further structure the model.
Using Decision Hierarchy
The Decision Hierarchy Figure helps people sort through the different types of decisions. This hierarchy distinguishes between three types of decisions. Policy decisions are those decisions made at a “higher” level within the organization; more loosely, they are the “givens” of the problem. Strategy is the name given to the decisions that are under consideration in the analysis; the symbol in the middle section of the decision hierarchy stands for the strategy table dealt with below. Tactics are the decisions to be taken later, after the strategy has been chosen.
Using Strategy Tables
In the decision hierarchy, the policy decisions and the strategy decision areas were identified. Now the decision facilitator needs to develop several strategic alternatives that are significantly different.
The first problem that often arises in finding strategy alternatives is tunnel vision, where companies evaluate only a few fundamentally similar alternatives. By using processes ranging from simple heuristics to extensive group exercises, companies can stimulate the creativity needed to generate these alternatives. One simple exercise that often elicits creative responses is to imagine we are looking back from some future point (perhaps retirement) and critically examining this portion of our life.
Using Influence Diagrams
As soon as possible, the decision facilitator should develop a list of the uncertainties that will probably be important. Although this list will be revised during the analysis, it lays the ground work for developing a deterministic model. The model will need to contain as explicit variables the major uncertainties identified and should be suitable for analyzing the alternatives that have been developed
To understand the things of decision making no of theories/ factors base on that take decision like Uncertainty& probability, Probabilistic Dependence, Attitudes towards risk taking & last but not least Decision Quality.