No abstract

Systems engineers are routinely tasked with facilitating the delicate balance between cost, schedule, and technical performance in acquisition programs that are continuously subjected to various outside influences. While there are several quantitative methods to estimate acquisition program cost and schedule performance as well as identify their risks (e.g., Earned Value Management), the estimation of technical performance and technical risk is generally heuristic in nature. In order to monitor the progress of the technical aspects of an acquisition program, the systems engineering discipline utilizes the process of tracking Technical Measures to gain insight into the design and development, to assess risks and issues, and to evaluate the likelihood of realizing objectives. However, with the diversity of so many technical programs, the estimation and risk analysis of technical performance in technology acquisition programs rely on the opinions of experts because the identification and application of relevant quantitative data for constructive modeling is not practical. The Expert-weighted Technical Risk Index methodology proposed in this article introduces a well-established method for mathematically combining expert judgment into the realm of systems engineering to develop predictive progress plans for technical performance estimation and risk analysis.

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.