Analyzing Cost Growth at Program Stages for DoD Aircraft
Capt Scott J. Kozlak, USAF, Edward D. White, Jonathan D. Ritschel, Lt Col Brandon Lucas, USAF, and Michael J. Seibel
This research examines cost growth factors from Milestone B to various program stages for 30 Department of Defense aircraft programs.
Kozlak, S. J., White, E. D., Ritschel, J. D. Lucas, B., & Seibel, M. J. (2017). Analyzing cost growth at program stages for DoD aircraft. Defense Acquisition Research Journal, 24(3), 386–407. https://doi.org/10.22594/dau.16-763.24.03
Estimating Firm-Anticipated Defense Acquisition Costs with a Value-Maximizing Framework
LTJG Sean Lavelle, USN
The author uses a value-maximizing framework to predict how firms will bid under varying levels of risk sharing, allowing the government to estimate future costs more accurately.
Lavelle, S. L. (2017). Estimating firm-anticipated defense acquisition costs with a value-maximizing framework. Defense Acquisition Research Journal, 24(3), 408–431. https://doi.org/10.22594/dau.16-762.24.03
Informing Policy through Quantification of the Intellectual Property Lock-in Associated with DoD Acquisition
Maj Christopher Berardi, USAF, Bruce Cameron, and Ed Crawley
This article introduces a quantitative analysis of intellectual property lock-in trends in DoD acquisition. The analysis ultimately quantifies the magnitude of the problem, illustrates trends across 8 fiscal years, and correlates lock-in to internal research and development funding.
Berardi, C., Cameron, B., & Crawley, E. (2017). Informing policy through quantification of the intellectual property lock-in associated with DoD acquisition. Defense Acquisition Research Journal, 24(3), 432–467. https://doi.org/10.22594/dau.16-767.24.03
The Impact of a Big Data Decision Support Tool on Military Logistics: Medical Analytics Meets the Mission
Felix K. Chang, Christopher J. Dente, and CAPT Eric A. Elster, USN
This study uses a combat simulation to demonstrate how military organizations not directly involved in logistics can use decision support tools to streamline their logistics operations. It further quantifies the benefits that one such tool designed for the military medical community might generate.
Chang F. K., Dente, C. J., & Elster, E. A. (2017). The impact of a big data decision support tool on military logistics: Medical analytics meets the mission. Defense Acquisition Research Journal, 24(3), 468–487. https://doi.org/10.22594/dau.16-769.24.03
Beyond Integration Readiness Level (IRL): A Multidimensional Framework to Facilitate the Integration of System of Systems
Maj Clarence Eder, USAF (Ret.),
Thomas A. Mazzuchi, and Shahram Sarkani
Data analyses of research aimed at understanding major integration issues of DoD Space Systems has been validated by experts, resulting in development of an integration assessment framework to help assess Integration Readiness Level of System of Systems.
Eder, C., Mazzuchi, T. A., & Sarkani, S. (2017).Beyond Integration Readiness Level (IRL): A multidimensional framework to facilitate the integration of system of systems. Defense Acquisition Research Journal, 24(3), 488–533. https://doi.org/10.22594/dau.16-766.24.03
Effectiveness Test and Evaluation of Non-lethal Weapons in Crowd Scenarios: Metrics, Measures, and Design
Elizabeth Mezzacappa, Gordon Cooke, Robert M. DeMarco, Gladstone V. Reid, Kevin Tevis, Charles Sheridan,
Kenneth R. Short, Nasir Jaffery, and John B. Riedener
This article describes methods for quantitative metrics and analyses for test and evaluation of non-lethal weapons. Results from human testing are also presented.
Mezzacappa, E., Cooke, G., DeMarco, R. M., Reid, G. V., Tevis, K., Sheridan, C., …Riedener, J. B. (2017). Effectiveness test and evaluation of non-lethal weapons in crowd scenarios: Metrics, measures, and design of experiments. Defense Acquisition Research Journal, 24(3), 534–573. https://doi.org/10.22594/dau.16-768.24.03
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