Date of Award
3-23-2018
Document Type
Thesis
Degree Name
Master of Science in Cost Analysis
Department
Department of Systems Engineering and Management
First Advisor
John J. Elshaw, PhD.
Abstract
The premise of this research is to identify and model modifications to the prescribed learning curve model, provided by the Air Force Cost Analysis Handbook, such that the estimated learning rate is modeled as a decreasing learning rate function over time as opposed to the constant learning rate that is currently in use. The current learning curve model mathematically states that for every doubling of units there will be a constant gain in efficiency. The purpose of this thesis was to determine if a new learning curve model could be implemented to reduce the error in the cost estimates for weapon systems across the DoD. To do this, a new model was created that mathematically allowed for a “flattening effect” later in the production process. This model was then compared to Wright’s learning curve, which is the prescribed method to use throughout the Air Force. The results showed a statistically significant reduction in error through the measurement the two error terms, Sum of Squared Errors and Mean Absolute Percent Error. This paper will explain in detail how the new learning curve was formulated as well as how the testing was conducted to compare the different learning curve methodologies
AFIT Designator
AFIT-ENV-MS-18-M-181
DTIC Accession Number
AD1056447
Recommended Citation
Boone, Evan R., "An Analysis of Learning Curve Theory and the Flattening Effect at the End of the Production Cycle" (2018). Theses and Dissertations. 1877.
https://scholar.afit.edu/etd/1877