Estimating the effects of parameter variability on learning curve model predictions

Abstract

The learning curve concept has proven to be a valuable management tool. However, regardless of which learning curve model is used, uncertainty is inherent in the forecast due to the empirical nature of learning curve theory and complications with establishing model parameters. Such variability is often ignored but can greatly affect the reliability of the model's predictions. Thus, as a means of approximating the effects of such uncertainty on model predictions, this paper proposes an analytical stochastic approach to estimating the precision of learning curve forecasts and provides an illustration of the technique with actual product cost data. The example shows that this analytical stochastic approach can provide accurate cost predictions with reliable prediction interval estimates.