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Record W7047343881

A generic decision support model for analysing the sustainable integration of new products : an application to the forest value chain

2018· other· en· W7047343881 on OpenAlexaboutno aff

Bibliographic record

VenueEspace École de technologie supérieure (École de technologie supérieure) · 2018
Typeother
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsDecision support systemProduct (mathematics)SustainabilityPortfolioPosition (finance)Identification (biology)Supply chainScale (ratio)Sustainable developmentSWOT analysis
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.014
GPT teacher head0.270
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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