Planning systems, agility and customisation in wood supply chains – results from six international case studies
Bibliographic record
Abstract
This paper is based on wood supply chain (WSC) data collected in six countries (Canada, Chile, France, Poland, Sweden, and USA) where a total of 94 local actors and experts were consulted. For each WSC studied, processes for operational plan-ning and execution of the procurement activities were mapped. Descriptions of the information, material and financial flows were also completed. Three basic designs of planning systems were identified, and for each design, a decision matrix was de-vised. WSC agility capabilities were assessed according to a four dimensions refer-ence model and compared with those theoretically required by the environment's uncertainties. When comparing location of the decoupling point, agility capabilities, and average order fulfilment cycle time of each WSC, it was possible to reinforce results found in the literature which state that supply chain agility is linked to shorter order fulfilment cycle time. Finally, the personalisation capabilities of each WSC were assessed and two key processes were identified where most of the product differen-tiation activities along a WSC occur.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".