Core services

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Create defensible cost estimates for government programs, based on historical data and subject matter expert (SME) expertise in the realm of Satellite Ground Systems and other government custom software/hardware intensive systems.  

Evaluate cost and technical risk for future government ground systems based on historical analysis of software effort productivities, labor rates, and software/hardware sizing metrics.  

Create cost and technical data models to evaluate and compare several potential architectures side by side, allowing leaders to make intelligent decisions about the architecture of the future.  

Utilize EVM data and metrics to predict upcoming issues with cost and schedule on a wide range of government programs.  

Create excel-based, traceable, and user-friendly models that provide instant visuals of large, complex datasets.  Model inputs and outputs are straightforward, allowing for quick results when underlying assumptions are changed.  

Use regression analysis (ANOVA) to create parametric cost estimating relationships (CERs) from historical cost and technical data on government/commercial programs.  Use linear ordinary least squares (LOLS) and zero minimum percent errors (ZMPE) regression techniques to create useful models for predicting future costs of hardware, software, and systems engineering, integration & test, and program management (SEIT/PM).  

Collect and normalize cost and technical data.  Map data to standard work break down structure (WBS).  Inflate data to constant year dollars.  Allocate costs to end items based on SME judgment and communication with program engineers.  Calculate cost metrics from resulting data to be used in parametric model development and analogy cost estimates.  

Determine new ways of collecting and normalizing data by observing trends in previously collected data/metrics, and by working closely with technical personal to understand how future changes in technology and processes impact the future costs of projects.  

Evaluate proposal submissions against analogous historical data.  Create graphics to communicate areas of interest for source selection decision makers, allowing for data driven selections (competitive) and contract negotiations (sole source).