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Record W4210585062 · doi:10.5267/j.uscm.2021.12.005

The role of supply chain visibility, supply chain flexibility, supplier development on business performance of logistics companies

2022· article· en· W4210585062 on OpenAlexvenueno aff
Faurani Santi Singagerda, Achmad Tito Fauzan, Andi Desfiandi

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainFlexibility (engineering)VisibilitySample (material)Supply chain managementBusinessData collectionStructural equation modelingMarketingProcess managementOperations managementComputer scienceStatistics

Abstract

fetched live from OpenAlex

This study aims to determine the effect of supply chain visibility on business performance, to determine the effect of supply chain flexibility on business performance and to determine the effect of supplier development on business performance. The sample of respondents in this study were 120 respondents from logistics companies in Tangerang. Type of research is quantitative research. The data analysis technique used structural equation modeling and SmartPLS for analyzing data. The data collection method in this study was carried out using a survey method, namely by distributing online questionnaires to respondents in the form of questions. The data collection method in this study was an online questionnaire by google form. The sample selection method in this study is simple random sampling. The results of the analysis that have been carried out concluded that Inventory Control, Supply Chain Flexibility, Supply Chain Visibility, Supplier Development had positive and significant influence on business performance.

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)0.007
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.234
Teacher spread0.217 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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