A Parametric Cost Model for Estimating Operating and Support Costs of U.S. Navy Aircraft

Abstract

This study provides parametric O&S cost models for future US Navy aircraft acquisition programs based on physical and performance parameters. The proposed parametric cost models provide decision makers with a tool for developing rough-order-of-magnitude annual O&S cost estimates for future US Navy aircraft acquisition programs. The historic aircraft cost data was provided by the Naval Center for Cost Analysis (NCCA) in a spreadsheet format and the data were extracted from the Navy Visibility and Maintenance of Operating and Support Cost (VAMOSC) data warehouse. After validating the assumption that the average annual O&S cost for any aircraft type/model/series is constant from year to year, cost estimating relationships are developed. The first model developed is based on multivariate regression. In this case, forward stepwise regression was used to find the model with the best fit. Since the multivariate regression model turns out to be impractical, having more than 30 variables in the equation, a tree-based model is presented as an alternative. Additionally, single variable cost estimating relationships are formulated based on the physical and performance parameters length, weight, and thrust.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2000
Accession Number
ADA389448

Entities

People

  • Mustafa Donmez

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Ground and Sea Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Air Force
  • Aircraft Equipment
  • Aircrafts
  • Airframes
  • Cost Analysis
  • Cost Estimates
  • Cost Models
  • Data Analysis
  • Databases
  • Fighter Aircraft
  • Fixed Wing Aircraft
  • Information Science
  • Navy Aircraft
  • Operations Research
  • Regression Analysis
  • Rotary Wing Aircraft

Fields of Study

  • Business

Readers

  • Computational Modeling and Simulation
  • Life Cycle Cost Analysis
  • Regression Analysis.