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A Human Missions Cost Model

Over approximately the past year, the author has worked on a database for a prospective Human Missions Cost model which, when used as the basis for a cost model, would be capable of estimating the life cycle cost of new human space missions such as the NASA Lunar Orbital Platform-Gateway, accelerated human lunar missions and/or human missions to other destinations such as Mars.  The premise of this work is that a parametric cost model can be developed from historical human space projects.  To that end, a database comprised of past human rated projects has begun to be assembled by this effort.[1]

Approximately 70 past human space “projects” have been identified and significant initial effort has been expended to compile cost, programmatic and technical data which will be shown in this presentation.  The “projects” include those listed in Exhibit 1.

Exhibit 1

For each of the projects in Exhibit 1, a number of data fields have already been compiled.  These data fields are:

  • Project description
  • Heritage notes
  • Dimensional data (length, diameter, computed volume)
  • Mass
  • Cost including DDT&E and TFU

Presently, most of the data is at the total project (i.e. total system level).  It is the intent of this effort to drive down the cost and technical parameters to the subsystem level (from which then, subsystem CERs can be developed using regression analysis).  For a number of the projects listed in Exhibit 1, subsystem level cost and technical data exists from a combination of sources in the author’s possession.  This includes U.S. projects as well as projects funded by ESA, JAXA, CSA, ASI, RKA and commercial projects.  The symposium briefing will explain how cost for non-U.S. projects was researched.

A key issue in continuing to deepen this database to the subsystem level is that of obtaining or estimating subsystem level mass and cost information for some of the newer projects such as ISS for which such data has not been collected and archived.  The author has devised a methodology to “fill the holes” in the database using a nearest neighbor estimating approach.  As an example, consider the ISS Laboratory Module.  The total cost and mass of the Lab Module is available from sources which will be discussed in the symposium presentation.  But this data is at the total project level.  However, the ISS Lab Module is very similar functionally and in scale to certain historical projects for which subsystem level insight is available—in the case of the ISS Lab Module the historical analogs are Skylab and Spacelab.  The paper will demonstrate how the Skylab and Spacelab subsystem cost and mass data can be used as a nearest neighbor analogy to fracture the known total ISS Lab Module down to the subsystem level taking into account key differences in the ISS Lab Module and the Skylab and Spacelab analogs[2].  The paper will demonstrate how other datapoints in the database for which only total project level data is available, can be broken down to the subsystem level.

The major tasks in the development of a Human Missions cost model (indeed with most cost models) is the assembly of the database.  Thus, the primary focus of this paper will be on the development of the database (as opposed to the regression analysis or final structure of a Human Missions Cost model).  One of the major things to be gained from this presentation is the solicitation of advice and suggestions to the author regarding how to continue with this effort.

[1] This effort has not been funded by any customer nor has it been funded by Galorath overhead.  The work to date has been accomplished by the author as a “labor of love” on personal time.

[2] For example, Spacelab flew as a Space Shuttle payload and lacked certain subsystems that the ISS Lab Module contains. The paper will discuss how such differences can be taken into account when breaking the ISS Lab Module down to the subsystem level.

View the presentation.

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