Noise and learning in semiconductor manufacturing
Management Science
| Hi-index | 0.00 |
Learning is a decrease in the time to perform an operation due to repetition and is an important consideration when forecasting process times or product costs. This paper presents a new method for calculating the learning rate for a family of parts using a matrix-based approach to organize historical data on production times. By calculating a single learning rate for the entire family, the data on individual parts is pooled, creating a larger sample size and reducing the variance of the estimate. Applying this method to forecasting costs of a family of jet engine parts shows that it provides much more accurate estimates than the previously available method of taking a weighted average of individual parts' learning rates. The matrix-based method also allows for calculation of first-unit costs more reliably (since the estimates are less affected by outliers in a larger sample) and for calculation of confidence limits on the estimates, to provide users with information on the reliability of the estimates.