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Utility Value Correction Approach 1
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In the preceding example we started with good intentions to nd value-based decision factors via utility functions but have created another problem. How do we solve it There are a couple of solutions to correct this situation. One approach is to simply establish a utility value of 1.0 to represent the minimum speci cation requirement. This presents an issue. In the example Vehicle 1 has a minimum speed of 48 mph under speci ed operating conditions. If a utility value of 1.0 represents the minimum performance requirement, Vehicle 1 will have a utility value of -0.2. Simply applying this utility value infers acceptance as a viable option and allows it to continue to be evaluated in a trade study evaluation matrix. Our intention is to eliminate noncompliant solutions which is to remove Vehicle 1 from consideration. Thus, if a solution is unacceptable, it should have a utility value of 0.0. This brings us to Approach 2.
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Utility Value Correction Approach 2
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Another utility correction approach that overcomes the problems of Correction Approach 1 involves a hybrid digital and an analog solution. Rather than IMMERSING ourselves in the mathematical concepts, let s simply THINK about what we are attempting to accomplish. The reality is that either a candidate option satis es a minimum/maximum requirement or it doesn t. The result is digital: 1 = meets requirement, and 0 = does not meet requirement.
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Trade Study Analysis of Alternatives
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This then leads to the question: If an option meets the requirement, how well does it meet the requirement meaning an analog result We can state that more concisely as: Utility value = Digital utility + Analog utility where: Digital utility (DU) = 1 or 0 Analog utility (AU) = 0.0 to 1.0 (variable scale) In summary, should you use utility functions in your trade studies The decision depends on a case-by-case basis. In general, the preceding discussion simply provides a means of re ning and delineating the degree of utility for speci c capabilities relative to a User s mission application. (52.3)
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Although trade studies are intended to yield THE best answer that stands out for a given set of decision criteria, many times they do not. Often the data for competing alternatives are clustered together. How do you decluster the data to resolve this dilemma Theoretically, we perform a sensitivity analysis of the data. The sensitivity analysis enables us to vary any one decision factor weight by some quantity such as 10% to observe the effects on the decision. However, this may or may not decluster the data. So, let s take another approach.
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Better Sensitivity Analysis Approach
A better approach to differentiating clustered trade study data resides in selection of the decision factors and criteria. When we initially identi ed decision factors/criteria with the stakeholders, chances are there was a board range of factors. To keep things simple, assume we arbitrarily selected 6 criteria from a set of 10. Weight each criterion. As a result, the competing solutions became clustered. The next logical step is to factor in the n + 1 criterion and renormalize the weights based on earlier ranking. Then, you continue to factor in additional criteria until the data decluster. Recognize that if the n + 1 has a relative weight of 1%, it may not signi cantly in uence the results. This leaves two options: Option A: Make a judgmental decision and pick a solution. Option B: Establish a ground rule that the initial selection of decision criteria should not constitute more than 90% or 95% of the total points and scale the list to 100%. This effectively leaves 5% to 10% for the n + 1 or higher terms that may have a level of signi cance on the outcome. As each new item is added back, rescale the weights relative to their initial weights within the total set.
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On completion of the trade study analysis, the next challenge is being able to articulate the results in the Trade Study Report (TSR). The TSR serves as a quality record that documents accomplishment of the chartered or assigned task.
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