Domestic secondary wood products manufacturers are losing their competitive edge in the global economy. Foreign competition is steadily gaining market-share due to decreased labor costs. While domestic operations can not compete with labor costs available to foreign manufacturers, they may be able to remain competitive through product lead time reduction and on-time delivery to the final customer. Pull based manufacturing is one technique to reduce lead time increase on-time delivery. Value stream mapping was used in this project to evaluate a furniture rough mill located in Virginia to assess the current state, as well as develop 2 future state value streams. The current state evaluation found the system to be yield driven and production was based on a forecast. The lead time for internal nightstand components in the current state was found to be 15.1 hours. Using pull production and supermarket methodology in proposed future states, it was found that the lead time could be reduced to 7.5 hours. Lead times could be reduced by eliminating yield increasing non-value added activities currently in place which not only increase lead time, but also manufacturing waste as defined by lean manufacturing concepts. A cost analysis found that the labor and overhead costs associated with yield increasing activities in the current state outweighed the costs of a decreased yield measurement in the future state. While this project was limited to one rough mill and one product family of a lesser valued wood species it represents what is possible for assisting secondary manufacturers to remain competitive. The once successful traditional yield driven rough mill does not guarantee internal customer satisfaction and in this project is not cost effective. Future research should focus on the implications of the furniture rough mill's inability to meet downstream demand to internal customers. / Master of Science

Up to 70 percent of rough mill manufacturing expenses stem from raw material (lumber) cost. Rough mill costs can be reduced by optimizing the lumber grade or grades that are purchased. This solution is known as the least-cost lumber grade-mix solution. The least-cost lumber grade-mix solutions has been a topic of great interest to both the secondary hardwood industry and to academia since even small changes in raw material cost can contribute to substantial reduction in rough mill expenses. A statistical model was developed for finding the least-cost lumber grade-mix which uses the rough mill simulator, ROMI-RIP 2.0, and the statistical package, SAS 8.2. The SAS 8.2-based least-cost lumber grade-mix model was validated by comparing SAS 8.2-based least-cost grade-mix solutions to OPTIGRAMI 2.0, a least-cost lumber grade-mix solver that relies on linear modeling. The SAS 8.2-based least-cost lumber grade-mix solver found lower cost solutions in 9 of 10 cutting bills that were tested. The SAS 8.2-based least-cost lumber grade-mix solver was packaged with ROMI 3.0, an updated version of ROMI-RIP, and provided to industry free of charge by the USDA Forest Service. The USDA Forest Service also purchased a SAS server license to allow least-cost lumber grade-mix solver users free access to SAS 8.2. However, industry users were reluctant to use the USDA Forest Service SAS server since it requires the user to enter individual cost and yield data to a government computer. This solution also required the user to have internet access and limited access to one user at any time. Thus, the goal of this research was to incorporate the least-cost lumber grade-mix solver into ROMI using the free, open source statistical package R 2.7.2. An R 2.7.2-based least-cost lumber grade-mix solver was developed and validated by comparing the R 2.7.2-based least-cost lumber grade-mix solutions to the updated SAS 9.2-based least-cost lumber grade-mix solutions. No differences were found in the least-cost lumber grade-mix solutions from either solver. Thus, a new least-cost lumber grade-mix solver using the R 2.7.2 open source statistical package was created. R 2.7.2 is installed on each personal computer on which the USDA Forest Service's ROMI rough mill simulation software is installed and, thus, no external computing resources are needed when solving the least-cost lumber grade-mix problem. / Master of Science

Secondary hardwood products manufacturers have been placing heavy emphasis on lumber yield improvements in recent years. More attention has been on lumber grade and cutting technology rather than cutting bill design. However, understanding the underlying physical phenomena of cutting bill requirements and yield is essential to improve lumber yield in rough mills. This understanding could also be helpful in constructing a novel lumber yield estimation model. The purpose of this study was to advance the understanding of the phenomena relating cutting bill requirements and yield. The scientific knowledge gained was used to describe and quantify the effect of part length, width, and quantity on yield. Based on this knowledge, a statistics based approach to the lumber yield estimation problem was undertaken. Rip-first rough mill simulation techniques and statistical methods were used to attain the study's goals. To facilitate the statistical analysis of the relationship of cutting bill requirements and lumber yield, a theoretical concept, called cutting bill part groups, was developed. Part groups are a standardized way to describe cutting bill requirements. All parts required by a cutting bill are clustered within 20 individual groups according to their size. Each group's midpoint is the representative part size for all parts falling within an individual group. These groups are made such that the error from clustering is minimized. This concept allowed a decrease in the number of possible factors to account for in the analysis of the cutting bill requirements - lumber yield relationship. Validation of the concept revealed that the average error due to clustering parts is 1.82 percent absolute yield. An orthogonal, 220-11 fractional factorial design of resolution V was then used to determine the contribution of different part sizes to lumber yield. All 20 part sizes and 113 of a total of 190 unique secondary interactions were found to be significant (a = 0.05) in explaining the variability in yield observed. Parameter estimates of the part sizes and the secondary interactions were then used to specify the average yield contribution of each variable. Parts with size 17.50 inches in length and 2.50 inches in width were found to contribute the most to higher yield. The positive effect on yield due to parts smaller than 17.50 by 2.50 inches is less pronounced because their quantity is relatively small in an average cutting bill. Parts with size 72.50 by 4.25 inches, on the other hand, had the most negative influence on high yield. However, as further analysis showed, not only the individual parts required by a cutting bill, but also their interaction determines yield. By adding a sufficiently large number of smaller parts to a cutting bill that requires large parts to be cut, high levels of yield can be achieved. A novel yield estimation model using linear least squares techniques was derived based on the data from the fractional factorial design. This model estimates expected yield based on part quantities required by a standardized cutting bill. The final model contained all 20 part groups and their 190 unique secondary interactions. The adjusted R2 for this model was found to be 0.94. The model estimated 450 of the 512 standardized cutting bills used for its derivation to within one percent absolute yield. Standardized cutting bills, whose yield level differs by more than two percent can thus be classified correctly in 88 percent of the cases. Standardized cutting bills whose part quantities were tested beyond the established framework, i.e. the settings used for the data derivation, were estimated with an average error of 2.19 percent absolute yield. Despite the error observed, the model ranked the cutting bills as to their yield level quite accurately. However, cutting bills from actual rough mill operations, which were well beyond the framework of the model, were found to have an average estimation error of 7.62 percent. Nonetheless, the model classified four out of five cutting bills correctly as to their ranking of the yield level achieved. The least squares estimation model thus is a helpful tool in ranking cutting bills for their expected yield level. Overall, the model performs well for standardized cutting bills, but more work is needed to make the model generally applicable for cutting bills whose requirements are beyond the framework established in this study. / Ph. D.

The percentage of low-grade material composing the annual hardwood lumber production in the U.S. is on the rise. As a result, finding markets for low-grade and low-value lumber has been identified as a top priority by researchers and industry associations. Computer simulation has been used by the manufacturing industry for several decades as a decision support tool. Simulation programs are commonly used and relied on by researchers and the industry alike to conduct research on various aspects of the rough mill from processing to recovery efficiency. This research used the ROMI-RIP and ROMI-CROSS simulation programs to determine specific conditions that led to optimal part yield when processing No. 3A Common, 4/4-thickness, kiln-dried, red oak lumber in rip-first and crosscut-first operations. Results of the simulations indicated that cutting bills with narrow part widths and short part lengths are conducive to obtaining optimal part yield while processing No. 3A Common lumber. Furthermore, it was found that as the percent of No. 3A Common lumber in a grade mix increases, part yields and sawing efficiencies decrease. The results also indicated that higher part yields will be obtained when processing short-length No. 3A Common lumber between 6 and 8 feet in length. / Master of Science