Log-linear and non-log-linear learning curve models for production research and cost estimation

The empirical evidence supporting the use of learning curves for planning is well documented in the literature, although there still exist some misunderstandings on the use and accuracy of the various types of learning curve models currently used in production research and cost estimation. In this paper, we examine the continuous learning approach for log-linear learning curve models and its use in analysing productivity trends in manufacturing databases. In particular, we present the derivation of the mid-unit model, a continuous form of the log-linear learning curve, which can accurately provide production cost estimates from either cumulative average costs or unit costs. The formulation of the model requires negligible computational capabilities to accomplish even the most difficult learning curve projections, allowing for reasonable computation times when using regression analysis on large manufacturing databases. Further, we show that the ability to accurately project batch costs on a one or two slope learning curve with one equation allows complex production planning problems to be solved more easily than by the use of the other models. Finally, guidelines are provided for the use of both these learning curve models and more complicated non-log-linear models in production research and cost estimation.