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Record W2440724966 · doi:10.3329/jme.v45i2.28977

Mathematical Modeling for Measures of Supply Chain Flexibility

2016· article· en· W2440724966 on OpenAlexaff
Gazi Farok

Bibliographic record

VenueJournal of Mechanical Engineering · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsYork University
Fundersnot available
KeywordsSupply chainFlexibility (engineering)Service managementSupply chain risk managementLinkage (software)Industrial organizationProduction (economics)BusinessDemand chainCommoditySupply chain managementCompetitive advantageProduct (mathematics)Measure (data warehouse)Operations managementRisk analysis (engineering)MicroeconomicsComputer scienceMarketingEconomics

Abstract

fetched live from OpenAlex

Supply chain flexibility is a competitive measure of supply chain aspects. It is a linkage medium between supply chain players and business performance. It absorbs the change in market conditions, demand volume and mix, product differentiations, commodity prices, availability of technology, labor costs, and exchange rates, utilization of equipment, production and logistics environment, cultural issues or any disruption in supply chain elements. This conception can be used as flexible guide that develop very well for multistage supply chains and it is established a model to meet the balancing situation of supply chain flexibilities by adjusting weight and individual probabilities. These results would lead to support and accommodate the managerial decisions as well as global supply chain management strategy.

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)0.003
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.036
GPT teacher head0.244
Teacher spread0.208 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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