The use of quantitative risk analysis of cost and schedule is an imperative for complex aerospace projects. NASA policy is to analyze cost and schedule risks jointly. This talk presents a high-level statistical approach to the estimate of joint cost and schedule risk, including the use of machine learning techniques.
This approach provides advantages over a detailed, bottom-up method that is often used. The application of this approach to a NASA mission that resulted in median cost and schedule estimates within 1% of the actuals is presented.
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