Planning and Forecasting Level of Effort Contracts with Monte Carlo Simulation
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//github.com/fullejr/GWU_Praxis_Forecasting_LOE_Contracts_with_MC_Simulation
Greater than 65% of large projects suffer cost overruns due to poor project planning and forecasting even when deterministic management systems such as Earned Value Management are used for assessing project progress management effectiveness. Historically, U.S. Government projects have exhibited 40-60% cost overruns during the acquisition phase, which impacts material readiness and effectiveness of the U.S. Government and associated civilian and military functions. This Praxis developed a Monte Carlo (MC) simulation approach to forecasting, tracking, and performing probabilistic risk assessment for level of effort (LOE) type government contracts.While Monte Carlo techniques have been considered by other researchers, those efforts focused on statistical representations of total cost and schedule based on total execution of similar projects. The model developed in this Praxis evaluates errors in staff labor planning to develop a project agnostic statistical model of the variability in performance across a wide variety of projects. This approach allows the project manager to not only forecast project performance on any project using a single model, but also to augment traditional project management approaches like the Earned Value Management System with statistical metrics to dynamically update project plans and assess probabilistic risk relative to cost and schedule goals. An example code base for the simulation can be accessed via GitHub
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Fullerton_gwu_0075A_17414.pdf | 2025-12-12 | Open Access |
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