Modeling Unit Learning Curves from Lot Data with @RISK

Estimating the cost of a project or manufacturing operation where the effect of learning is anticipated can pose a calculational challenge, especially when extrapolating from lot data. After a brief introduction covering the basics of unit learning/experience curves, this webinar will: demonstrate how to make the appropriate transformations for modeling a power curve from lot data; use Excel’s Goal Seek to solve for the unknown learning curve “slope;” and with the resulting regression equation’s error term as the input variable run an @RISK simulation for the cost of a succeeding lot. It may be helpful for those who’ve never worked with power models or regression error terms to first watch the “Modeling Regression Errors with @RISK” webinar. It can be found at https://go.palisade.com/Modeling-Regression-Errors-with-RISK.html .