Using flow simulation as a decision tool for improvements in sawmill productivity
Bibliographic record
Abstract
We developed a sawmill fl ow simulation model to identify production bottlenecks and determine where productivity improvements could be made. Sawmills often invest in a new machine center and then find out that the processing bottleneck just moves somewhere else. Our approach was specifically designed to investigate the effects of such changes on the entire system. We determined that the trimmer was the system bottleneck when both the small log and large log lines were running concomitantly. Under base case conditions, the model predicted an average board output of 13,147 boards. An increase in the processing capability of the trimmer resulted in a shift of the bottleneck from the small log line to the large log line (at the edger). This bottleneck shift was further investigated and, by allowing the simulation model to manipulate machine settings for the trimmer and edger, it was able to maximize the modeled average board output to 17,996 boards per shift (when edger set up times were not considered) and 16,708 boards per shift (with edger setup times included). These findings were presented to the sawmill management and subsequently implemented as specific improvements at the trimmer machine center, which in turn resulted in an actual increase of 10% in their sawmill’s lumber volume output.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".