An integrated framework for the assessment of environmental sustainability in wood supply chains
Bibliographic record
Abstract
Nowadays, sustainability is one of the critical factors for the success of supply chains in organizations and firms that strive to maintain a competitive edge in the market. In wood industry, due to the need for integrating several business units such as forest entrepreneurs, carriers, pulp and paper mills and sawmills, such industries encounter various difficulties in maintaining effective supply chain collaborations. Literature on wood furniture industry seems to be lagging in terms of research on sustainable supply chain operations. To fill this gap, this study aims at identifying the critical factors that stand as a barrier between manufacturing and environmental sustainability in wood furniture industries. To achieve this aim, an integrated framework based on the triplet of Hierarchical Clustering, Analytical Hierarchy Process and Best-Worst Method has been proposed and implemented in a leading furniture manufacturer in UAE. The results show that waste management is the primary concern when an organization wants to pursue manufacturing environmental sustainability. Resources come in the second place where non-renewable resources should be substituted by renewable ones. The results were supported by a sensitivity analysis which confirms that higher attention should be directed to recycling of wood waste. Findings of this study provide recommendations to managers and decision makers on how to improve the manufacturing environmental sustainability in wood furniture industries to achieve the Triple Base Line (TBL) concept of sustainability and integrate sustainably in industry 4.0 context.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".