Agility capabilities in wood procurement systems: a literature synthesis
Bibliographic record
Abstract
The ability of a firm to detect changing demands and efficiently respond to them can be described as agility. The past decade has seen a significant rise in the literature on the concept of agility. It has been identified as a requirement for growth and competitiveness. However, a review of the related literature reveals that the concept has scarcely been studied in the forest industry context. This study contributes to filling this gap. More specifically, we contextualize agility in wood procurement systems (WPSs). A WPS includes upstream processes and actors in the forest-products supply chain, responsible for procuring and delivering raw materials from the forest to the mill. We first identify the capabilities a WPS needs to possess in order to enable agility. Next, we review the literature in the WPS domain to search for evidence of these capabilities. It was found that aspects of the practices embodied in agility capabilities have already been proposed in the WPS literature but without explicit reference to agility. However, opportunities to further improve the agility of WPSs were also identified. It is suggested that future research focus on determining optimal levels of investments in agility in order to maximize supply-chain profits.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".