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Record W2607195755 · doi:10.1504/ijpd.2016.10004479

The relevance of configuration management in supply chain management in the aerospace industry

2016· article· en· W2607195755 on OpenAlexaff
Darli Rodrigues Vieira, Marcela Pereira

Bibliographic record

VenueInternational Journal of Product Development · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsAerospaceSupply chainProcess managementSupply chain managementRelevance (law)BusinessProduct (mathematics)Quality (philosophy)Reliability (semiconductor)Systems engineeringManufacturing engineeringEngineeringEngineering managementMarketingAerospace engineering

Abstract

fetched live from OpenAlex

In the aerospace industry, the management of complex projects and interfaces with suppliers from all over the world are a daily challenge. Therefore, it is imperative that aerospace companies reinforce their configuration and supply chain management processes to better control product evolution and integrate all players to deliver products and services with a high level of quality as well as to enhance their levels of reliability and competitiveness in the marketplace. Thus, this study examines the relevance of Configuration Management (CM) in Supply Chain Management (SCM) in the aerospace industry. It also identifies some important issues when the relationship between these processes is not well established, and it proposes good practices to integrate such processes and provide benefits to stakeholders.

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)0.006
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0030.005
Scholarly communication0.0100.009
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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