Cost estimation and modeling for space missions at APL/JHU

Abstract

Cost estimation of space science missions at APL over the past two decades has been singularly successful in arriving at program costs that are within a few percent of the actual costs at program completion. The most recent example is the Near Earth Asteroid Rendezvous (NEAR) mission which was estimated at approximately 112 million FY-92 dollars and came in at approximately three percent under the estimated cost. This demonstrated performance has been achieved without the benefit of a formal cost model, such as those used in government and industry (GSFC, MSFC, SAIC, etc.). In light of this performance, it is important to understand the parameters that are used in the cost estimating process in an effort to quantify those elements in a program that are most important to the final cost. We have identified a number of areas which contribute to eventual cost performance; these include: (a) spacecraft and mission complexity; (b) use of already-developed (facility- class) instruments versus “to be developed” new instruments; (c) synergism among programs being implemented concurrently; (d) program implementation length; (e) design-to-cost practice for all major subsystems and instruments without contingency; (f) lead engineer responsibility throughout design, layout, fabrication, test, integration, and initial flight operations; (g) designed-in quality and testability to minimize rework; (h) incorporation of reliability and quality assurance engineering within the program structure; (i) minimization of documentation and encouragement of oral and electronic communication as required. We have found that gross parametrization of costs such as the traditional weight, power, and length of the program commonly included in typical models do not reliably predict actual costs. A methodology will be presented, whereby, the elements identified above plus others are used to describe the process implemented by APL in previous missions to generate cost estimates and to control costs. Actual data over several missions during the past two decades will also be presented that illustrate APL's cost performance while utilizing this methodology.