A New Learning Curve for Department of Defense Acquisition Programs: How to Account for the “Flattening Effect”

Abstract

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced; however, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, Boone’s Learning Curve (2018), was recently developed to model this phenomenon. This research confirmed that Boone’s Learning Curve is more accurate in modeling observed learning curves using production data of 169 Department of Defense (DoD) end-items. However, further empirical analysis revealed deficiencies in the theoretical justifications of why and under what conditions Boone’s Learning Curve more accurately models observations. This research also discovered that diminishing learning rates are present but not pervasive in the sampled observations. Additionally, this research explored the theoretical and empirical evidence that may cause learning curves to exhibit diminishing learning rates and be more accurately modeled by Boone’s Learning Curve. Only a limited number of theory-based variables were useful in explaining these phenomena. This research further justifies the necessity of a diminishing learning rate model and proposes a framework to investigate learning curves that exhibit diminishing learning rates.