A RISKY BUSINESS
M
uch like a high-stakes gambler deciding whether to raise, check or fold, the acquisition process for military systems is a series of high-stakes decisions in which we commit more resources for further
development (raise) or stop funding an effort (fold). Tere is seldom an option to delay a decision with no cost (check). Ulti- mately, Soldiers’ lives are at stake. Successful gamblers understand the risks they are taking and know how to consider these risks when making decisions.
Acquisition leaders emphasize that the Army needs to accept more risk when making decisions, and, for acquisition professionals, comfort with risk comes from understanding it. Understand- ing risk allows us to make appropriately risk-aware decisions as individuals and as an organization. We must make decisions the way successful gamblers do—with a clear understanding of risk. Troughout the acquisition process, how can decision-makers best understand the risks they are taking and make risk-aware decisions? What kind of information must decision-makers insist on being provided?
To manage risk when making a decision, Army acquisition profes- sionals often collect data through testing or experimentation (for the rest of the article, simply called testing). We then analyze the data to create the information needed to support the decision. Te cost of collecting data often limits the data we can collect; there- fore, there are limitations in what we can learn from the data. For example, when testing $500,000 missiles, the costs of missiles, test range time and personnel can quickly become too great, so the number of missiles that can be tested is limited. When data is analyzed appropriately, an important way the data limitations will show up is in the form of uncertainty in our conclusions. Understanding this helps us make risk-aware decisions.
We collect data for many purposes and from many sources to inform decisions throughout the acquisition process. Examples include performing basic science experiments to identify technol- ogies worthy of further investment, running computer models to determine the best design characteristics, using flight simula- tors to assess pilot-vehicle interfaces and range testing to support vendor selection or a fielding decision. In many cases, we test to understand how changes in the inputs to a process affect some response, the end result that is being measured. Tough the exam- ple below is of a fielding decision, the points made in this article apply any time we attempt to understand how inputs affect a response.
A RISKY BET? Consider a system that warns aircraft pilots of incoming missiles. A program manager (PM) must decide whether to field a new version of software or continue with the current version.
In this simple case there is only one input, the software version (current or new), but there could be many more. To determine whether the new software is “better” and should be fielded, the response being measured is whether the system successfully detects a simulated missile (success or failure). Te PM will consider the new software worthy of fielding if its probability of successfully detecting a missile is at least 5 percent greater than for the current software. Te PM has budgeted 20 test events for each software version.
Using the design of experiments to plan tests allows us to use expert assumptions to determine a sufficient but not excessive number of test events to achieve acceptable levels of risk.
94 Army AL&T Magazine Fall 2020
During the test, the new software is successful in 18 of 20 events (90 percent success rate), and the current software is successful in 16 of 20 events (80 percent success rate). How should the PM decide whether to field the new software? One common approach is to select the software with the greater success rate—in this case the new software. But is this a good decision? Although the new software performed 10 percent better, do test results provide suffi- cient evidence that the new software really is better? Is it possible to get these results even if the new software is equivalent or even worse? If so, how likely is that to happen? To make a risk-aware decision, the PM must be able to answer these questions. Other- wise, the PM is acting like an unskilled gambler. Tese questions and others can be answered using appropriate statistical analy- sis methods.
Te plot in Figure 1 is a result of statistical analysis of the test data described above (more specifically, logistic regression was used for analysis). Understanding what the plot tells us is critical to making a risk-aware decision.
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