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Record W2173745940 · doi:10.5539/ass.v11n28p37

The Moderating Role of Advance Manufacturing Technology (AMT) on the Relationship between LARG- Supply Chain and Supply Chain Performance

2015· article· en· W2173745940 on OpenAlexvenueno aff
Raghed Ibrahim Esmaeel, Inda Sukati, Noriza Mohd Jamal

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsnot available
FundersUniversiti Teknologi Malaysia
KeywordsAgile software developmentModerationSupply chainBusinessSupply chain managementAgile manufacturingProcess managementIndustrial organizationOperations managementMarketingComputer scienceManagementEngineeringEconomics

Abstract

fetched live from OpenAlex

Supply-chain management (SCM) considers one of the essential parts in international marketplaces. Supply-chain management comprises a number of paradigms such as Lean, Agile, Resilient, and Green (LARG). This research explains that advanced manufacturing technology (AMT) has a significant effect on the relationship between LARG-supply chain, which comprise (lean, agile, resilient, and green) with supply chain performance.The following study illustrates the correlation between each variable like (lean, agile, resilient, and green) LARG-supply chain with supply chain performance, next to an investigated suitable theory.This study utilizes several library entrances toward assembly knowledge.This study suggests the framework of research during its determination each variable this study,which comprise independent variable, moderator, and dependent variables.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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 designObservational
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

Citations6
Published2015
Admission routes1
Has abstractyes

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