Agility capabilities in wood procurement systems: a literature synthesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it