Module Summary: Cost Estimating
{"Image DocumentName": "Module Summary: Cost Estimating", "Image ImageDescription": "Methods for estimating mission costs include parametric cost models, analogy, and grassroots (or bottoms-up). Certain methods are appropriate based on where the project is in its life cycle. Parametric cost models rely on databases of historical mission and spacecraft data. Model inputs, such as mass, are used to construct cost estimating relationships (CERs). Complexity factors are used as an adjustment to a CER to compensate for a project\u2019s unique features, not accounted for in the CER historical data. Learning curve is based on the concept that resources required to produce each additional unit decline as the total number of units produced increases. Uncertainty in parametric cost models can be estimated using probability distributions that are summed via Monte Carlo simulation. The S curve is the cumulative probability distribution coming out of the statistical summing process. Cost phasing (or spreading) takes the point-estimate derived from a parametric cost model and spreads it over the project\u2019s schedule, resulting in the project\u2019s annual phasing requirements. Most cost phasing tools use a beta curve.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}