Kyrie M. Rojo

Date of Award

3-2023

Document Type

Thesis

Degree Name

Master of Science in Cost Analysis

Department

Department of Systems Engineering and Management

First Advisor

Edward D. White III, PhD

Abstract

This research investigates a dataset of over 80 Air Force and Navy aircraft and applies regression techniques to create two cost estimating relationships (CERs) for predicting recurring T100 flyaway costs, depending on where in the acquisition lifecycle the estimate takes place. The first CER explains 89 percent of the variation in the dataset and can be applied prior to Milestone B (MS B). The second CER explains 88 percent of the variation in the dataset and can be applied between MS B and MS C. Significant cost drivers identified include stealth, cohort, empty weight, the natural log of speed, legacy aircraft, fighter aircraft, and Engineering and Manufacturing Development costs. This research is the largest aircraft regression study to date for recurring T100 flyaway costs and can be used by cost analysts as a reliable cross-check in early estimates.

AFIT Designator

AFIT-ENV-MS-23-M-228

Comments

A 12-month embargo was observed.

Approved for public release: 88ABW-2023-0142

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