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Record W3034948897 · doi:10.5267/j.uscm.2019.1.001

Critical evaluation of policies in supply chain performance: Quality assurance, continuous process improvement and environmental regulation and policies

2019· article· en· W3034948897 on OpenAlexvenueno aff
Andriansyah Andriansyah, Taufiqurokhman Taufiqurokhman, Ismail Suardi Wekke

Bibliographic record

VenueUncertain Supply Chain Management · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsQuality assuranceProcess (computing)Supply chainBusinessQuality (philosophy)Process managementEnvironmental economicsIndustrial organizationOperations managementComputer scienceMarketingEconomicsService (business)

Abstract

fetched live from OpenAlex

The primary objective of this study is to find the role of different policies on supply chain performance (SCP). To achieve this objective, quality assurance policies (QAP) and continuous process improvement (CPI) are selected as independent variables. Additionally, the moderating effects of environmental regulations and policy (ERP) are examined between various policies and SCP. ERP is taken as moderating variable because the role of ERP in logistics is crucial. Logistics transport has serious effect on environment due to the emission of CO2. Primary data are collected from supply chain companies of Indonesia. Three hundred questionnaires are used in this study and they are analyzed through statistical tests. Conclusion of the study shows that QAP and CPI had major role in SCP. Better implementation of QAP and CPI increase the SCP among Indonesian supply chain companies. Moreover, ERP is a moderating variable between the relationship of QAP and CPI and SCP. Therefore, Indonesian supply chain companies should enhance the policies related to quality assurance, process improvement and environmental policies to enhance SCP.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.278
Teacher spread0.260 · 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 teacher head, not a consensus.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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