Utilizing product design complexity to predict production cycle time learning curves

Predicting learning curves can give organizations a competitive advantage. This study synthesizes learning curve and design structure matrix (DSM) methodologies to quantify product change and complexity in order to estimate the performance of future generations of products. Based on a small-scale experiment and in-depth case studies, we propose a model that forecasts the disruption and subsequent improvement in manufacturing cycle time due to changes in a product. The study contributes to research on how technological complexity impacts organizational learning curves.