A SCOR-based process modelling approach for green performance evaluation of forestry systems
Bibliographic record
Abstract
The development of a modelling approach that allows forest industries to assess wood use efficiency along the forest value chain is a practical strategy that can reduce the gap between harvested volume and the proportion that is effectively transformed.While a changing paradigm to circumvent wood loss along the value chain is sweeping across the world, it is important for the forest products industry to make a transition in which value chain management options that stimulate green technologies are introduced into its traditional business model.This study is grounded in the view that there is lack of a reliable modelling framework for visibility and green performance evaluation and reporting of forest value chain activities.We argue that the introduction of green practices to maximize wood utilization is a plausible commitment to environmental accounting and reporting.A number of recent papers on the well-known Supply Chain Operations Reference (SCOR) model, the cross-industry de facto standard diagnostic tool for Supply Chain Management were analyzed to identify research gaps in relation to environmental criteria.Based on the results of the analysis, we concluded that SCOR model is not applied in natural resource management.We employed the design science research procedure to develop a conceptual framework known as Forest Supply Chain Operations Reference (f-SCOR) model, as a decision support tool for green performance and reporting along the forest value chain.The tool creates opportunities for management visibility of the supply chain spectrum.It also offers prospects for informed decisionmaking process, as well as organizational processes.We contextualized the processes and functionalities of the original SCOR model and extended its performance measurement component to Level 5, which is made-up of decompose tasks that are defined by the company.Key performance indicators and green metrics were developed for two plywood supply chain strategies, both in the forest and mill settings, to assess wood utilization efficiency along the chain.The framework was subjected to a theoretical validation by domain experts from Nigeria and Australia, as well as a practical validation by plywood Make agents, based on a proof-ofconcept procedure in two typical plywood industries in Cameroon.In the theoretical module, data on the individual opinions of the experts was subjected to Friedman's Test, using XLSTAT software, to determine their degree of commonality regarding the quality of the model, based on three assessment constructs.The test revealed a high degree of correlation (p>0.448);α = 0.01 between the opinions.They agreed that f-SCOR model possesses a satisfactory degree of accuracy that is consistent with empirical realities of the forest value chain.The practical application component was conducted by Make agents of the plywood value chain.They concluded that the f-SCOR is user friendly and has an acceptable degree of representation of a real-world plywood value chain.This model should be tested using other forest industry products and in different regions to increase its robustness, scope and application domain.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".