Optimization Helps Shermag Gain Competitive Edge
Bibliographic record
Abstract
Shermag Inc. is a vertically integrated furniture company with business units across the supply chain from the forest to the final customer. During the last decade, Shermag has been losing market share to low-cost Asian manufacturers. To reduce the procurement and other significant costs of Shermag's raw material (wood), which constitute a major component of its total furniture cost, we developed a tool to optimize the tactical planning of the company's wood supply chain. We propose an optimization-based approach for coordinating operations at each echelon of the wood supply chain. However, the problem size caused computer-related issues, such as long processing times and computer crashes. In our proposed solution approach, we use decomposition to overcome these issues. Our implementation uses C++, CPLEX (optimization software), and Microsoft Access. In this paper, we present a comparative study of traditional decision making versus optimal decision making. Using Shermag data for 2004 and 2005, we show that our solution reduces total operations costs by more than 22 percent. For any set of parameters, the tool can generate a good, feasible solution. These results convinced Shermag to use our tool for future configurations of its supply chain network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".