A Regression-Based Method for Estimating Rip-First Rough-Mill Lumber Yield

Estimating yield from lumber cut-up in rip-first rough mills for material management and job costing purposes is uncertain unless simulation models are used. To augment the toolbox for industry practitioners, a novel yield estimation model was derived using linear least squares techniques and data derived from an orthogonal, 220–11 fractional factorial design of resolution V. The model estimated 450 of 512 cutting bills tested within 1 percent absolute yield. However, cutting bills that do not adhere to the model’s framework suffer a larger estimation error. The least squares estimation model thus is a helpful tool in ranking cutting bills that adhere to the model’s framework for their expected yield levels and facilitates the selection of part sizes to be included in cutting bills. Further research is needed to make the model useful for a wider range of cutting bills.