The use of decision tree analysis - Free Essay Example

Sample details Pages: 13 Words: 3843 Downloads: 3 Date added: 2017/06/26 Category Finance Essay Type Compare and contrast essay Did you like this example? The main focus of this seminar paper is on decision tree analysis and its applications in various industries. It also discusses about issues of decision making under uncertainty and risks involved in some of the projects. Statement of the problem For successful execution of a project, a project manager should be able to take right decisions under conditions of certainty, risk and uncertainty. As the situation progresses from certainty to risk to uncertainty, the expected potential damage to the project increases. Various methods of decision making are employed in order to take optimal decisions. The study of decision tree analysis is done to understand its significance in assisting project managers in taking decisions. Don’t waste time! Our writers will create an original "The use of decision tree analysis" essay for you Create order objectives of the study To gain understanding of method of decision tree analysis To grasp knowledge of role of decision tree analysis in decision making To learn about importance of decision tree analysis in assessment of projects Significance of the study The study of this seminar paper assists in learning about decision tree analysis role of decision tree analysis in decision making applications of decision tree analysis in various industries issues of decision making under uncertainty and risks involved in some of the projects Scope and Limitations of the study Scope The scope of the study is learning about decision tree analysis, its role in decision making, its applications in various industries and the issues of decision making under uncertainty and risks involved in some of the projects. limitations This study does not include techniques of decision making under certainty. It does not provide in-depth knowledge of all techniques of decision making under uncertainty and risk. Review of literature Basics of decision tree analysis Decision tree is a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. Its common application is in operations research, especially in decision analysis, for identifying a strategy to attain an objective. Other applications include its use as a descriptive means for calculating conditional probabilities. It acts as visual tool where the expected values (or expected utility) of competing alternatives are calculated and guides in choosing the best alternative. Decision trees depict the sequence of interrelated decisions and the expected results of making choice among alternatives. When a decision is to be made, there are generally more than one choices or options. The available choices are depicted in tree form starting at the left with the risk decision branching out to the right with possible outcomes. Decision trees are usually used for risk events associated with time or cost. A decision tree c onsists of 3 types of nodes:- 1. Decision nodes which are commonly represented by squares 2. Chance nodes which are represented by circles 3. End nodes which are represented by triangles Decision tree is drawn from left to right and has splitting paths but no converging paths. Therefore, in cases of elaborate future events, it is often difficult to draw manually. Nowadays various solutions and softwares are provided by many companies for drawing decision trees, analyzing the results and defining the best alternative amongst the available choices. Steps in decision tree analysis Main steps in decision tree analysis are as follows: 1. Identifying the problem and alternatives To understand the problem and develop alternatives, it is necessary to acquire information from different sources like marketing research, economic forecasting, financial analysis, etc. As the decision situation unfolds, various alternatives may arise which are to be identified. There would also be kinds of uncertainties in terms of market size, market share, prices, cost structure, availability of raw material and power, governmental regulation. Technological change, competition, etc. Recognising that risk and uncertainty are inherent characteristics of investment projects, persons involved in analyzing the situation must be encouraged to express freely their doubts, uncertainties, and reservation and motivated to suggest contingency plans and identify promising opportunities in the emerging environment. 2. Delineating the decision tree The decision tree represents the anat omy of decision situation. It illustrates decision points along with the alternative options available for experimentation and action at these decision points chance points where outcomes are dependent on a chance process and the likely outcomes at these points This decision tree diagrammatically reflects the nature of decision situation in terms of alternative courses of action and chance outcomes which have been identified in the first step of the analysis. If myriad possible future events and decisions are considered, it can become very complex and cumbersome. As a result, it would not be a useful tool of analysis. If many elaborate events are taken into account then it may obscure the critical issues. Hence it is necessary to simplify the decision tree so that focus can be given on major future alternatives. 3. Specifying probabilities and monetary outcomes After delineating the decision tree, probabilities corresponding with each of the possible outcomes at va rious chance points and monetary value of each combination of decision alternative and chance outcome have to be gathered. The probabilities of various outcomes can be defined objectively. For instance, based on objective historical data the probability of good monsoon can be defined. On the other hand, probabilities for real life outcomes are somewhat difficult and cannot be obtained. For example, one cannot determine the probabilities for success of a new automobile launch. These have to be defined subjectively and based on experience, judgment, understanding of informed executives and their intuition. Also, it is difficult to assess cash flows corresponding to these outcomes. So again judgment of experts helps in defining these cash flows. 4. Evaluating various decision alternatives The final step in decision tree analysis includes evaluation of various alternatives. This can be done as follows: Starting with the right- hand end of the tree and then we calculate the e xpected monetary value at various chance points that come first as we proceed leftward. Given the expected monetary values of chance points in step 1, evaluate the alternatives at the final stage decision points in terms of their expected monetary values. At each of the final stage decision points, select the alternative which has the highest expected monetary value and truncate the other alternatives. Each decision point is assigned a value equal to the expected monetary value of the alternative selected at that decision point. Proceed backward (leftward) in the same manner, calculating the expected monetary value at chance points, selecting the decision alternative which has the highest expected monetary value at various decision points, truncating inferior decision alternatives, and assigning values to decision points, till the first decision point is reached. Advantages Amongst decision support tools, decision trees have several advantages: Easy to interpret and understand With availability of little hard data this method helps in generating important insights Result provided by a model/ software can be easily explained Can be used in combination with other decision techniques Example Decision trees can be used to optimize an investment portfolio. The following example shows a portfolio of 7 investment options (projects). The organization has $10,000,000 available for the total investment. Bold lines mark the best selection 1, 3, 5, 6, and 7, which will cost $9,750,000 and create a payoff of 16,175,000. All other combinations would either exceed the budget or yield a lower payoff. Decision Making Tools: Decision Tree Analysis and EMV Decision Tree Analysis In decision tree analysis, a problem is depicted as a diagram which displays all possible acts, events, and payoffs (outcomes) needed to make choices at different poin ts over a period of time. Example of Decision Tree Analysis: A Manufacturing Proposal The company is assessing a new product development proposal. The cost of the development project is $500,000. The probability of successful development is projected to be 70%. If the development is unsuccessful, the project will be terminated. If it is successful, the manufacturer must then decide whether to begin manufacturing the product on a new production line or a modified production line. If the demand for the new product is high, the incremental revenue for a new production line is $1,200,000, and the incremental revenue for the modified production line is $850,000. If the demand is low, the incremental revenue for the new production line is $700,000, and the incremental revenue for the modified production line is $150,000. All of these incremental revenue values are gross figures, i.e., before subtracting the $500,000 development cost, $300,000 for the new production line and $100,000 for the modified production line. The probability of high demand is estimated as 40%, and of low demand as 60%. The development of a decision tree is a multi step process. The first step is to structure the problem using a method called decomposition, similar to the method used in the development of a work breakdown structure. This step enables the decision-maker to break a complex problem down into a series of simpler, more individually manageable problems, graphically displayed in a type of flow diagram called a decision tree. These are the symbols commonly used: The second step requires the payoff values to be developed for each end-position on the decision tree. These values will be in terms of the net gain or loss for each unique branch of the diagram. The net gain/loss will be revenue less expenditure. If the decision to not develop is made, the payoff is $0. If the product development is unsuccessful, the payoff is $500,000. If the development is successful, the decis ion is to build a new production line (NPL) or modify an existing production line (MPL). The payoff for the NPL high demand is ($ 1,200,000 $500,000 development cost -$300,000 build cost) or $400,000. For a low demand, the payoff is ($700,000 $500,000 development cost -$300,000 build cost) or -$100,000. The payoff for the MPL high demand is ($850,000 -$500,000 development cost $100,000 build cost) or $250,000. For a low demand, the payoff is ($720,000- $500,000 development cost $100,000 build cost) or $120,000. The third step is to assess the probability of occurrence for each outcome: Development Successful = 70% NPL High Demand = 40% MPL High Demand = 40% Development Unsuccessful = 30% NPL Low Demand = 60% MPL Low Demand = 60% Probability Totals* 100% 100% 100% *Probabilities must always equal 100%, of course. The fourth step is referred to as the roll-back and it involves calculating expected monetary values (EMV) for each alternative course of action payoff . The calculation is (probability X payoff) = EMV This is accomplished by working from the end points (right hand side) of the decision tree and folding it back towards the start (left hand side) choosing at each decision point the course of action with the highest expected monetary value (EMV). Decision D2: New Production Line vs. Modified Production Line high demand + low demand = EMV high demand + low demand = EMV (4 0% X $400,000) + (60%X -$100,000) (40% X $250,000)+(60% X $120,000) $100,000 $172,000 Decision Point 2 Decision: Modified Production Line with an EMV of $172,000 Decision 1: Develop or Do Not Develop Development Successful + Development Unsuccessful (70% X $172,000) (30% x (- $500,000)) $120,400 + (-$150,000) Decision Point 1 EMV=(-$29,600) Decision: DO NOT DEVELOP the product because the expected value is a negative number. When doing a decision tree analysis, any amount greater than zero signifies a positive decision. This too l is also very useful when there are multiple cases that need to be compared. The one with the highest payoff should be picked. Example The project manager can use decision tree analysis when a decision involves a series of several interrelated decisions. The project manager computes the Expected Monetary value (EMV) of all strategies and chooses the strategy with the highest EMV. Assume that the project manager has four alternative strategies, S1, S2, S3, and S4. The resultant values for each strategy at different probability levels are R1, R2, and R3. Assume that the probability of occurrence of these results is 0.5, 0.2 and 0.3. The payoff matrix for this problem is given in table 1. Table 1: Payoff Matrix R1 R2 R3 S1 13 10 9 S2 11 10 8 S3 10 12 11 S4 8 11 10 The project manager can also represent this problem as a decision tree. Figure: 1 depicts the decision tree for the given problem. The project manager finally selects strategy S1 as it has the highest expected value. Figure 1 EMV (A) = 0.5 (13) + 0.2(10) + 0.3(9) = 11.2 EMV (B) = 0.5 (11) + 0.2(10) + 0.3(8) = 9.9 EMV (C) = 0.5(10) + 0.2(12) + 0.3(11) = 9.8 EMV (D) = 0.5(8) +0.2(11) + 0.3(10) = 9.2 Case study R and D management, by its very nature, is characterized by uncertainty since effective R and D requires a complex interaction of variables. It is important to balance strategic management (allocate resources and do the right R and D) with operational management (execution of projects) and at the same time take into account issues of people management (leadership, motivation, organisation and teamwork) (Menke, 1994). The strategic aspect of R and D management alone requires the resolution of some very important questions, namely Whether we have the right budget of R and D? Whether allocation is done to right business and technology areas? Whether there is a right balance of risk and return; of incremental vs. innovation; of long- and short-term projects; of research vs. development? Are we working on the right projects and programmes with the right effort? It is clear that for success in R and D it is critical to determine what is right for the particular company. Th e normal process for doing this is through the development of a technology strategy. In practice, the approach used will be that which best fits the operating method of the company but, as Braunstein (1994) has pointed out, the approach is less important than the output, which has to link the corporate goals and strategy to the companys major functional units. Having defined what the business objectives should be for the R and D programme and the overall strategic framework that will define the technology plan, it is then possible to move on to what is probably one of the most problematic parts of technology management, the selection of individual R and D programmes. There is a comprehensive literature of potential methods which can be used (Baker and Pound, 1964; Gear et al., 1971; Souder, 1978). Many of these compare projects with different distributions of possible outcomes and risk, often using relatively complex quantitative methods. There are a number of interdependencies t hat have to come good before the project finally produces value for the company and it has been argued (Morris et al., 1991) that because many of the major decisions (and many sub-decisions at intermediate milestones) can be taken singly, the overall process is less risky that might initially be thought. Not surprisingly, therefore, Morris goes on to propose that, when choosing R and D projects, there is merit in going for long shots since this is effectively the purchase of options which can be dropped later if the project does not look like bearing fruit. Moreover, the higher risk projects (almost by definition) tend to be the ones that have the highest payback if they are successful (see also Kester, 1984). 2. Decision making under uncertainty Uncertainty in a business situation is often expressed verbally in terms such as it is likely, it is probable, the chances are, possibly, etc. This is not always very helpful because the words themselves are only useful when they convey the same meaning to all parties. It is clear that different people have different perceptions of the everyday expressions which are often used to describe uncertainty. Uncertainty exists if an action can lead to several possible outcomes and an essential, but, challenging aspect of R and D management is to identify the likelihood or probability that these outcomes or events will occur. There are two main interpretations of probability. The first is grounded in the estimation of the probability of an event in terms of relative frequency with which the event has occurred in the past and is usually referred to as objective probability. The second views probability as being the extent of an individuals or groups belief in the occurrence of an event a nd is usually termed subjective probability. Subjective probability estimates are often included in the models suggested as useful for project selection in R and D planning. Such probabilities might be derived from past experience with similar research projects plus any special features that make the current effort unique or different and alter the past up or down from this base line. A number of tools have been proposed to help in the process of generating probabilities, though they are by no means perfect. Schroder (1975) draws attention to some of the problems that occur in deriving probabilities of technical success and concludes that subjective probabilities are a rather unreliable predictor of the actual outcome of individual success. He proposes a number of reasons for this which he categorises as either intentional or unintentional (conscious biasing). To decrease the unintentional errors he suggests the following actions: ensure that risk assessors have sufficient exp ertise in their field and a comprehension of subjective probabilities. improve the availability of information and particularly documentation. fully exploit information systems and attempt to utilize incentive systems which reward accuracy and reliability. analyse past performance in assessing probabilities to provide valuable insight into potential improvements. utilise well-tried approaches to help in the subjective probability assessment. It is evident, however, that some confidence levels need to be established and perhaps the most obvious way of achieving this is by the collation over a period of time, of how prior assessments have compared with reality. For this to have genuine value will require a comparison of the assumptions that have been made at each assessment. 3. The use of financial methods for risk analysis Benefit/cost ratios have been popular for some time, since they are simple and are an attempt to understand the potential gain for the effort required. In performing even a simple benefit/cost analysis, it is necessary for the decision-maker to provide quantitative information in order to ascribe a value to a project. When this has been done, the project can be viewed as a relatively simple financial investment and therefore subject to more standard financial investment tools. The danger of this is that it gives no consideration to the fact that technical programmes are often aimed at a wide range of strategic objectives, a point made by Mitchell and Hamilton (1988) who made a separation into: exploratory/fundamental type work which is aimed primarily towards the concept of knowledge building. For this type of work, the business impact of which is often poorly defined and wide ranging and here R and D is often best considered as a necessary cost of business. well understoo d technical programmes usually associated with incremental improvements of existing products which can be clearly defined. Here the R and D can be seen as an investment and treated accordingly. As usual with two extremes, the difficult part is the mid-ground where neither approach is particularly suitable. Authors have attempted to use techniques borrowed from the financial community which often has to deal with uncertainty. Risk analysis is a key area in financial markets and several of the approaches used in financial analysis are also found in the R and D management area; for example, decision trees and Monte Carlo analysis. Decision trees have been discussed in many papers in terms of the principle and method of construction and use. They are relatively old technology in decision analysis terms, but have found wide use in both the literature and also in industry (Magee, 1964; Raiffa, 1968; Thomas, 1972). They can help in: Understanding the basic outline of the projec t path from inception to completion. The construction of the decision tree can help the project evaluator, project team and project reviewers to understand the likely events that that will have to be developed as the project progresses. It can also help to reduce/avoid management surprises in downstream activities. Understanding of probabilities of success along the project path. Once the tree has been constructed a normal process is then to roll back the tree and input probability success factors at event nodes. Developing project gates. Key project milestones can be readily seen such that project review groups can understand critical project phases, develop appropriate gates for the project for the project to pass through or be reconsidered. Facilitating the calculation of revised probabilities of success as the project passes through various development stages. The actual construction of a decision tree can be a time consuming process for each project but it can be re garded as a useful investment in time either by the project evaluator(s), the project team itself or perhaps a project review group. The time spent in consideration of the project is likely to be a worthwhile investment since it aids the above processes, helps in the resource decision and provides a potential communication route for the team, business management and others. In terms of the construction of a decision tree, Phillips (1980) suggests that the decision tree is most usefully used as part of a total decision analysis using a 10 step construction process as shown below: 1. Recognition that a decision problem exists. 2. Structure the decision problem. 3. Describe the consequences. 4. Specify the criteria. 5. Evaluate the consequences for each criterion. 6. Assess weights for the criteria. 7. Determine utilities of consequences. 8. Assess probabilities. 9. Apply expected utility principle. 10. Carry out sensitivity analysis. Being able to layo ut on a single sheet of paper the key elements of a project can prove useful in project selection, project management and even the termination decision. Some examples of potential utility can be seen from Fig. 1. This was generated from consultation with the portfolio manager in the company which we use as a case study later in this paper and a typical macro view of a generic research project for a new molecule. It highlights both the likely path, key decision points and also potential regrets along the way (primarily research expense). Decision trees do have some weaknesses associated with their use including the fact that they are quite difficult to draw on normal PC graphics programs (e.g., FreelanceTM and PowerpointTM ) which may not help the communication process. Also, they are not ideal for parallel events that happen over a period of time. An example of this would be middle stages of the tree developed in the previous diagram where toxicology, capability assessments/en gineering and economics are all events that would normally occur over a similar period of time and would often be run together. However, the thinking process that a decision tree naturally leads a reviewer through supports better project planning by increased understanding of the overall path that a project is likely to progress along. As well as the project approval phase, decision trees may prove even more useful in the project phase since they help in the process of: Ensuring that the project team understands the risks and challenges associated with the project. Provides a useful communication process for both internal team communication and also communication from the team to business and technology management. Helps the team to understand why a project might be nearing a termination decision and may in fact help in the process of reducing the emotion associated with a project termination decision Provides a logical layout for key decision points and project reviews. To some extent this is taken care of in the new project methodology by the stage-gate process. Provides a potential mechanism to enable recalculation of the probability of success, p(t), as any given project progresses along its development path. Potentially aids the easier overlay of time and may in fact be a useful project management tool working in partnership with GANTT charts.