Applied Decision Analysis


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The technology has been enhanced to capture more considerations relevant to sound decision making, notably through multiattribute utility analysis and improved interaction with the user. Decision analysis has emerged as a complement to older decision-making techniques such as systems modeling and operations research. In addition to statistical decision theory, the new technology draws on psychology, economics, and social science.

What is retained and is distinctive about the approach is that the quantitative models incorporate personal judgments. Analysts have learned to use the data and expertise that are immediately available to the decision maker and to play back conclusions to the manager in close to real time. In , an analysis was rarely completed within three months. Now, meaningful analysis of a problem can be generated in an afternoon, and a succession of analyses can be presented in intervals of one or two days.

Without greatly disturbing their schedule of meetings or reflective process, managers can now respond to these analyses and provide input for other rounds. Although such decision analysis has not become the dominant analytic discipline that some people expected, its use has grown dramatically since Personalized decision analysis has become an accepted part of the staff services that major corporations draw on routinely, much as they do industrial psychology, cost analysis, marketing research, and economic analysis.

And virtually all the major areas of government have adopted decision analysis in one form or another. The case studies that we present in this article illustrate how managers use the three major variants of decision analysis currently in use—decision tree analysis, probabilistic forecasting, and multiattribute utility analysis. Decision tree analysis is the oldest and most widely used form of decision analysis. Managers have used it in making business decisions in uncertain conditions since the late s, and its techniques are familiar.

The following case illustrates some of the components of a successful decision tree analysis. They include the use of simple displays, sensitivity analysis to guide refinements, and subsidiary models to ensure completeness. Also important are the direction and integration the analysis gives to the contributions of experts as well as the involvement of top managers. The inventor claimed he had a strong patent position as well as technical superiority, but the market for the product was very uncertain, mostly because of pending legislative action. Because the inventor wished to make an offer to other companies if AIL was not interested, he asked AIL to make the decision in a few weeks, a period of time clearly inadequate to resolve any uncertainties it was aware of.

AIL had not used formal decision analysis before. Top managers were, however, familiar with the theory and its typical applications through the literature and interested in trying these techniques on an actual decision to evaluate their worth. The patent decision appeared to be a good candidate for such a trial.

A team of AIL personnel and outside analysts spent two weeks developing an analysis of the patent idea. All the while, the team stayed in continual contact with top management.

Multi-criteria decision analysis applied to harmful algal bloom management: A case study.

The analysts used standard decision tree techniques. Exhibit I shows the immediate choice, to purchase a six-month option on the patent rights or not, and the main uncertainties that affected the decision. The attractiveness of each outcome, or path through the tree, is represented by its present value earnings. The expected value at each node in the tree is calculated by taking a probability-weighted average of its branches.

The mechanics of the analysis—specifying the tree, assigning values, and calculating results—are straightforward. The usefulness of the analysis, however, depends more on how the analysis process is managed than on the mechanics. Five features that are often absent in unsuccessful attempts to apply decision analysis marked this implementation as a success.


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The focus of the analysis was the simple tree shown in Exhibit I. The most common mistake that a beginner at decision analysis makes is to include everything the choice involves in the tree. This is a sure way to end up with a mess, which only the analyst, if anyone, can understand.

The trick is to design a simple tree that captures the essence of the problem by including its most important elements. In this case, the most important elements affecting earnings were the probabilities of exercising the option, receiving an initial contract, and continuing on a second contract. By means of an interactive computer program, the analysts determined how sensitive the results were to changes in the inputs to the tree.

The analysis was then expanded in a way that would be most responsive to what the decision maker needed to know. The analysts at AIL developed models to refine estimates of the most sensitive inputs. Using a simple tree does not make the analysis coarse or incomplete; subsidiary models can ensure any desired level of detail and sophistication. In this case, the analysts used three subsidiary models. They used one to determine yearly earnings and calculate present values and another to assess the probability of receiving a contract.

The latter model reflected important factors such as the timing and terms of a possible legislative mandate for the system, the strength of possible competitive systems, and the likelihood of a crash or near crash of a plane within the next several months. The analysts used a third model to assess probability distributions of earnings from the contracts that reflected uncertainties in the number of units, the price per unit, and the profit margin.

At AIL various people were involved with each aspect of the model, so that each expert could focus on the area of his expertise. Those most knowledgeable about the chances of winning the second contract, for example, addressed this aspect of the problem but did not consider other aspects.

The combined contributions of all the experts formed a unified picture for top management. The most important factor was the close work of top management with the analysis team throughout the analysis. This interaction ensured that:. The choices modeled were in fact the choices under consideration. In this case, as a result of the modeling, the analysts identified a new choice—waiting and seeking a sublicense.

All important concerns were addressed. For AIL, the issues included the impact that decision factors other than direct earnings have in this case, return on capital. The level of modeling was right. That is, some aspects were modeled formally, but others were left to informal consideration.

In this case, the uncertainties of the decision could be sufficiently characterized in simple risk profiles. This picture corresponds to a more detailed analysis of the earnings from a defense contract.

Decision Theory Under Uncertainity Practically Solved Example IN HINDI By JOLLY COACHING

This display facilitated a unanimous decision by the decision-making group the president and his vice presidents for business development and operations to go with the less risky strategy even though it offered a slightly lower expected value. Companies in a wide range of industries are using decision tree analysis to make a variety of decisions. For example:. The previous section illustrates how personalized decision analysis can capture all the thinking that goes into a particular decision. The technique has other uses as well. Analysts can develop certain aspects of decision analysis into analytic tools that can be used in a variety of contexts.

Analysts can use the probabilistic modeling aspects of decision analysis to develop forecasts of, for instance, future sales and profits, which in turn can be used to support decisions about planning, investment, and marketing. Developing a single aspect of personalized decision analysis to support decisions in a variety of contexts is likely to become very popular. Because its cost can be spread over many uses, a company can afford to use enough computerization and staff time to do this type of analysis properly. The following example illustrates how personalized decision analysis can be used for forecasting.

Of course, this kind of analysis is not the only way to carry out quantitative forecasting. In late , the manager of planning for the defense systems division of Honeywell, Inc. In addition, the chances for success of some of the products were interrelated, and several products offered the chance of significant collateral business. The approach the analysts took was to build a composite forecast for the division by combining decision tree analyses of individual products. The analysis team worked closely with each project manager and his staff to build the decision tree, assess probabilities and values, and discuss results and sensitivities.

The two analyses differed significantly, however, in a number of ways. This use meant that the analysts would need to model additional factors and would have to make the form the outputs took suitable for forecasting. Second, because the success of some products was related to the success of others, the analysts had to include in the analysis such factors as common investments, collateral business opportunities, and marketing interactions. Honeywell considered several financial criteria such as internal rate of return, net present value, and yearly streams of profits, investments, and return on investment.

Exhibit III shows the probabilistic sales forecast for one group of interrelated products. This forecast is based on decision tree analyses of three main products and two collateral business opportunities. The analysts first developed decision trees for each product to determine the distributions of sales in the event that a market sufficient to support full production both did and did not emerge. Then they developed a second level of analysis to model the key interdependencies among the products; specifically, the probability of any particular product being in full production depended on which other products were also in full production.

The forecast shows that low sales are expected from the products for the first seven years. This amount is not certain, however. This analysis helped Honeywell to assess the chances that these products would meet sales goals, the uncertainties in the assessment, and the reasons for the uncertainties. By detailing the chain of events that would produce different levels of sales, it also identified points of leverage—places where Honeywell could take action to change probabilities and improve sales.

The analysts also used the decision trees to forecast yearly profits, fund flows, assets, research and development investments, and the related financial quantities of net present value, internal rate of return, and annual return on investment. Their forecasts indicated that these products could be expected to exceed requirements on all factors and that, unless Honeywell was very risk averse, they were attractive.

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These comparisons provided an additional screen since some products were clearly worse than others on all factors. In trying to determine where to place its European subsidiary, the top management of one company completely ignored a decision tree analysis that showed careful consideration of the financial implications of possible locations. When pressed for an explanation, top management confided that the choice was dominated by the fact that key personnel wanted to be near the International School in Geneva. Somehow, that consideration seemed too non-economical and nonrational to be included in the analysis—yet it did dominate the decision.

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At the time the decision was made, the technology of decision analysis was ill equipped to handle the tradeoffs between financial effects and intangibles. A new technique, multiattribute utility analysis, makes such modeling possible by precisely specifying the factors that affect the choice, making trade-offs among the factors, and choosing the alternative that offers the best balance. Multiattribute utility analysis evolved out of decision analysis that supported government decisions, in which the need to balance multiple objectives is most obvious.

As the case of the company deciding where to put its plant shows, however, its usefulness for business decision making is evident. For example, plant sites usually differ in such intangibles as the skill of the local work force, the ability of local management, and the management problems of operating plants in the locations under consideration.

These are important factors to consider in the decision of where to locate the plant, yet it is virtually impossible to specify their impact on profit with any precision. Many factors also arise in strategic decisions. For example, Michael E. Porter has argued that, when considering the strategic decisions of vertical integration, major capacity expansion, and entry into new businesses, managers should go beyond cost and investment analyses to consider broad strategic issues and perplexing administrative problems and that these are very hard to quantify.

Over the past few years, the Federal Aviation Administration has been supporting research and development on a system to detect bombs in airplane baggage. In early , the program manager for this project had to decide which of several candidate systems to continue funding. The candidates differed greatly in their potential performance and technical characteristics.

Decision Analysis and Resolution (DAR) (CMMI-DEV)

Since none was clearly superior in all respects, the program manager sought a method for weighing the various characteristics to arrive at a measure of overall value. A team of outside analysts worked closely with the program manager and other FAA personnel to develop a comprehensive model of the value of each system.

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Applied Decision Analysis Applied Decision Analysis
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Applied Decision Analysis Applied Decision Analysis
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