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Record W2099220599 · doi:10.1109/iccie.2010.5668213

Enhancing network efficiency lead time reduction in a three-level supply chain

2010· article· en· W2099220599 on OpenAlexaffabout
Ludovick Valéra, Denis Lagacé, L. Bergeron

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsSupply chainStandardizationSupply chain managementService managementBusinessProcess managementProcess (computing)Lead timeStrengths and weaknessesSupply chain networkIndustrial organizationComputer scienceMarketing

Abstract

fetched live from OpenAlex

The study illustrates the supply chain management (SCM) of Canadian companies in the furniture industry, including a reduction of lead times, communication, logistics, and supply chain integration. This paper uses a 2-year case study of a supply chain network composed of one large firm and several small- and medium-sized firms (SMEs). It also uses the Supply Chain Operations Reference (SCOR) model to analyze and evaluate the performance of the supply chain. The results, based on an evaluation of strengths and weaknesses, for the basis for a number of recommendations, which may subsequently be used by the companies concerned to increase their potential of becoming world-class performers. Process integration and standardization, inter-organizational information system (IOIS) implementation, manufacturing process improvement, and logistics are found to be the most critical factors for supporting the supply chain network. In light of these results, the study discusses the implications of the findings and suggests several avenues for future research.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.019
GPT teacher head0.227
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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations2
Published2010
Admission routes2
Has abstractyes

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