Final-Cost Estimates for Research and Development Programs Conditioned on Realized Costs.

Abstract

We apply multiple model adaptive estimation (MMAE), a proven method of system identification widely used in engineering applications, to the problem of determining Bayesian probability distributions of the final cost and completion time of on-going research and development (R&D) programs, conditioned on actual cost of w9rk performed (ACWP) data. Modeling cumulative expenditures with Rayleigh distributions, we produce graphs of the results that give useful assessments of cost and schedule risks. The procedure is implemented in a convenient computer program. We give three examples of its application to actual data, and results of a Monte Carlo analysis verify the method. (KAR) P. 1

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Document Details

Document Type
Technical Report
Publication Date
Jun 06, 1995
Accession Number
ADA297298

Entities

People

  • David A. Lee
  • Mark Gallagher

Organizations

  • Office of the Secretary of Defense

Tags

Communities of Interest

  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Airframes
  • Algorithms
  • Artificial Satellites
  • Basic Programming Language
  • Computers
  • Contracts
  • Cost Estimates
  • Costs
  • Distribution Functions
  • Global Positioning Systems
  • Logistics Management
  • Probability
  • Probability Distributions
  • Random Variables
  • Software Development
  • Statistics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Life Cycle Cost Analysis
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference