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Cost growth in major DoD acquisition programs has been commonplace
for the last 35 years, and shows no signs of improvement despite
the adoption of new business practices legislation. In the current
environment where taxpayer dollars are heavily competed for, and
the expenditure of those dollars is highly scrutinized, it has
become a high priority in Department of Defense leadership to build
accurate cost estimates that reduce overruns and restore
credibility to the defense acquisition process. Previous research
has validated the use of two-pronged logistic and multiple
regression approach that offers better predictive ability than the
traditional multiple regression approach alone. This research
further validates the use of this two-pronged approach by applying
it to the engineering and schedule cost growth categories. We
update and augment previously collected programmatic data from the
Selected Acquisition Reports (SARs) between 1990 and 2001 for
programs covering all defense departments, with the latest SAR
database (1990-2002). We start the analysis by building logistic
regression models to predict whether cost growth will occur. Then
we build multiple regression models to predict the extent to which
a program will experience cost growth. The response variables for
our models are the respective cost growth on procurement-funded
efforts in the engineering and schedule cost categories, during the
Engineering and Management Development phase of the acquisition
process.
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