Premium
A Performance‐based Statistical Expert Judgment Model to Assess Technical Performance and Risk
Author(s) -
Eggstaff Justin W.,
Mazzuchi Thomas A.,
Sarkani Shahram
Publication year - 2012
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2012.tb01460.x
Subject(s) - schedule , computer science , identification (biology) , constructive , earned value management , risk analysis (engineering) , operations research , accountability , government (linguistics) , estimation , cost estimate , project management , engineering , business , systems engineering , law , program management , biology , operating system , linguistics , philosophy , botany , process (computing) , project charter , political science
The rapidly changing environment and asymmetric threats currently encountered on the modern battlefield requires the timely delivery of effective weapons systems. Unfortunately in fiscal year 2008, according to the US Government Accountability Office, research and development costs of the United States Department of Defense major weapons acquisition programs increased 42 percent above original estimates, and delays in initial operational capability deliveries slipped to 22 months. While there are several quantitative methods to estimate acquisition program cost and schedule performance and to identify their risks (e.g., Earned Value Management), the estimation of technical performance and technical risk identification is generally heuristic in nature and based on expert judgment because of limited quantitative data for constructive modeling. The proposed research in this paper expands upon the Technical Risk Index Distribution method developed by Lewis, Mazzuchi and Sarkani by incorporating a performance‐based method of mathematically combining quantified expert opinion for technical performance estimation and risk analysis.