Aviation Fuel Forecasting at Base Level Using Programmed Air Force Flying Activities.

Abstract

The current Air Force base-level petroleum requirements determination and validation process involves manual computations and analysis at both base and Major Command levels in determining forecast quantities for procurement by the Defense Fuel Supply Center (DFSC). These forecasts often rely heavily on past consumption as the primary basis for future requirements and are often not as accurate as DFSC would like. This thesis sought to investigate an alternate forecast method based on programmed flying hours that may more accurately represent and predict future requirements. Past JP-4 fuel consumption data combined with past programmed and actual flying hours was collected from seven Air Training Command bases. This data was analyzed using statistical regression analysis which produced consumption coefficients associated with each type of aircraft assigned to each base studied. These prediction coefficients were assembled with mean transient and non-fly consumption and tested using past programmed flying hours multispeed times the prediction coefficients. Overall results indicated that the regression models forecast, compared against past forecasts by base and their actual consumption yielded more accurate forecasts in 17 out of 21 time periods.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1984
Accession Number
ADA148445

Entities

People

  • C. F. Stocky
  • J. D. Richardson Jr.

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Air Force Facilities
  • Aviation Fuels
  • Business Administration
  • Data Science
  • Databases
  • Delphi Method
  • Department Of Defense
  • Energy Management
  • Fuel Consumption
  • Fuels
  • Information Science
  • Materials
  • New York
  • Regression Analysis
  • Statistical Tests

Fields of Study

  • Environmental science

Readers

  • Aerospace logistics and air mobility.
  • Petroleum Engineering
  • Regression Analysis.