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Technology Maturity Assessments and Confidence Intervals
Author(s) -
Peters Wanda,
Doskey Steven,
Moreland James
Publication year - 2017
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.21389
Subject(s) - maturity (psychological) , confidence interval , schedule , margin (machine learning) , population , key (lock) , statistics , point (geometry) , reliability engineering , capability maturity model , computer science , engineering , mathematics , machine learning , demography , psychology , computer security , geometry , sociology , operating system , software , programming language , developmental psychology
The research presented in this paper examines the accuracy of technology maturity assessments at a key decision point in the acquisition life cycle. This study utilizes statistical means to quantify the confidence interval of technology maturity determinations and established confidence intervals for various population sizes using a Monte Carlo simulation. The study identified with a 95% confidence that there are no significant differences in the standard deviation or the confidence interval ranges of heritage developments and new developments at a key decision point. The significance of this finding is the margin of error, which is derived from the standard deviation and used to compute confidence intervals, measures accuracy. One challenge facing the field of system engineering is the ability to accurately measure technology maturity when transitioning from formulation to implementation. Correctly assessing the technology maturity of a development is crucial for the organization's ability to manage performance, cost, and schedule. The findings from this research have the potential for minimizing inaccurate maturity determinations, which could lead to reductions in unsatisfactory technical performance and programmatic overruns.