A generic decision support model for analysing the sustainable integration of new products : an application to the forest value chain
Bibliographic record
Abstract
By inspecting the industrial environment of a region, it is possible to distinguish the actors, the strategies employed regarding their product portfolio, their production-distribution network and how they improve their competitiveness using innovation. Inevitably, their actions affect the economic vectors of the region and create a distinctive network with particular characteristics. What is meant by economic vectors is all of the intangible synergies formed in a given region in regard to the strategic assets, commodities, and innovations used by the companies to generate value. When a company evaluates the possibility of introducing new products into a network, the assessment of their impact has to be conducted to ensure the best selection. Competition, synergies, and sustainability have to be taken into consideration to position the strategy of a company, where the product portfolio can be evaluated accordingly. \n \nConsequently, even though a good analysis can be conducted on a small network, it becomes almost infeasible at a regional scale to properly assess the introduction of new products into a complex network without a decision support tool. This situation results in hesitations as to which appropriate combination of products/technologies should be chosen, when to strategically implement them, and what would be the extent of their impact on the existing network. To help organizations with these challenges, the development of a strategic decision support tool to assess the impact of integrating new products into an existing network is brought forward. The generic tool allows a mathematical representation of a given network composed of manufacturing processes, bill of materials and distribution nodes. The model is applied to a realistic case study in the Mauricie region (Quebec) Canada, where the introduction of new products is evaluated for the forest value chain considering the concept of forest biorefinery. Accordingly, scenarios around the potential integration of four prospective processes (i.e. pressurized hot water extraction, fast pyrolysis, organosolv fractionation, and lignin recovery platform) are designed to evaluate the introduction of eight bioproducts. Kraft lignin and crude bio-oil are shown to have the best financial return in the Mauricie region.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".