Formulation of a Model to Predict Second Destination Transportation Tonnage Estimates for Future Budget Requirements.

Abstract

The purpose of the research was to develop a better method for computing SDT tonnage estimates which are used to derive future budget requirements. The objectives were to determine whether flying hours, manpower, or both were significantly reliable predictors of tonnage estimates and to develop a computerized model for computation of SDT tonnage and budget estimates. Obtaining both actual and programmed data with MAC as a data source, discontinuous linear regression was used to derive twelve equations for the five overseas geographic areas serviced by MAC. The equations were synthesized into four models which were tested by various statistical methods to determine their overall validity. The optimal model chosen verified that programmed flying hours and manpower were significantly reliable predictors of tonnage estimates. A comparison between the optimal model and the method currently used to estimate tonnage revealed that the model provided a significantly more accurate estimation of tonnage to be moved. Computerization of the model was developed utilizing FORTRAN and the Statistical Package for the Social Sciences. After reviewing the results, the authors concluded that tonnage can be estimated using programmed flying hours and manpower as variables to derive future SDT budget requirements. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1978
Accession Number
ADA060490

Entities

People

  • Christopher J. Lamb
  • Joseph B. Sarnacki

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Budget Estimates
  • Cargo Aircraft
  • Computer Programs
  • Data Acquisition
  • Databases
  • Geographic Regions
  • Information Science
  • Linear Regression Analysis
  • Logistics Management
  • Military Personnel
  • New York
  • Regression Analysis
  • Social Sciences
  • Statistical Analysis
  • Transportation

Readers

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  • Logistics and Supply Chain Management.