The Strategic Design of Forest Industry Supply Chains
Bibliographic record
Abstract
This paper presents a market-driven approach to design production-distribution networks for the lumber industry. The approach is developed to tackle a vast array of issues, from the adaptation of an enterprise supply chain to its evolving environment, such as changing forest policies, to enterprise rationalizations through mergers or acquisitions. The methodology takes into account the specificity of the industry divergent manufacturing processes as well as the lumber market segmentation into contracts, vendor managed inventory (VMI) agreements and spot markets. The approach is based on a comprehensive two-stage stochastic programming with recourse model. A sample average approximation (SAA) method based on Monte Carlo sampling techniques is proposed to solve this stochastic program, and it is shown that this approach outperforms the use of a comparable deterministic model based on averages. Finally, the decision support system developed to implement the approach is used to show how it can contribute to dealing with strategic issues in the Eastern-Canadian lumber industry. Forest policy as well as acquisition and rationalization issues are analyzed through applications of the methodology to a virtual but realistic case called Virtu@l-Lumber.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".