Learning Curve Analysis in Department of Defense Acquisition Programs

Abstract

Learning curves are used to describe and estimate the cost performance of a serial production process. There are numerous models and methods, however it is not precisely known which model and method is preferred depending on the situation. The primary objective of this research is to compare performance of the more common learning curve models. The research goal is improved understanding of the systemic cost drivers of a production process, their relationship to cost, and present modeling methods. We use qualitative analysis combined with statistical regression modeling to assess fit. The research identified that preference for one function or another depended upon the shape of the data and how well a model formulation could be made to fit that shape. This was reliant upon the model's basic shape and the available parameters to alter its appearance. The typical learning curve model assumes that cost is a function of time but commonly omits factors such as production process resources changes (capital and labor) and its effect on cost. A learning curve model that includes the effects of resource changes would likely provide higher estimative utility given that it establishes a systemic relationship to the underlying production process.