A SCOR-based process modelling approach for green performance evaluation of forestry systems
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».