MétaCan
Menu
Back to cohort
Record W4391096956 · doi:10.12819/2024.21.1.8

Os Impactos da Indústria 4.0 e da Inteligência Artificial nas Atividades Logísticas Empresariais

2024· article· pt· W4391096956 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRevista FSA · 2024
Typearticle
Languagept
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesContext (archaeology)Political scienceBusinessArtGeography

Abstract

fetched live from OpenAlex

Pretende-se com este artigo discutir e apresentar definições teóricas, benefícios e elementos provenientes da era da quarta Revolução industrial e da inteligência artificial, aplicados ao contexto logístico. Esses conceitos, em conjunto, visam potencializar as ações nas organizações, sejam essas operacionais ou mesmo gerenciais, sendo que por meio deste desenvolvimento as tomadas de decisões passam a ser cada vez mais assertivas e ágeis. Cada vez mais o processo de interação entre departamentos e até mesmo com o cliente final passa a ser de extrema relevância, tornando o nível de competitividade mais acirrado. Utiliza-se pesquisa exploratória e uma revisão bibliográfica, a fim de investigar na literatura os principais componentes da indústria 4.0 e da inteligência artificial, bem como as suas evoluções e os principais desafios para responder às exigências atuais, no intuito de preparar melhor as organizações para uma nova realidade necessária e irreversível. O objetivo principal deste trabalho é verificar como a junção desses importantes elementos tecnológicos pode causar efeitos positivos para as empresas que buscam crescimento organizacional. Palavras-chave : Evolução Logística, Indústria 4.0, Inteligência Artificial, Logística 4.0. ABSTRACT The aim of this article is to discuss and present theoretical definitions, benefits and elements from the era of the fourth industrial revolution and artificial intelligence, applied to the logistics context. These concepts together aim to enhance actions in organizations, whether operational or even managerial where, through this development, decision-making becomes increasingly assertive and agile. Increasingly, the process of interaction between departments and even with the end customer becomes extremely relevant, making the level of competitiveness fierce. Exploratory research and a bibliographic review are used in order to investigate in the literature the main components of industry 4.0, and artificial intelligence, as well as their evolutions and the main challenges to respond to current requirements, in order to better prepare organizations for a new necessary and irreversible reality. The main objective of the work is to verify how the combination of these important technological elements can cause positive effects for companies that seek organizational growth. Keywords : Logistics Evolution, Industry 4.0, Artificial Intelligence, Logistics 4.0

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.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.005

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 head0.168
GPT teacher head0.443
Teacher spread0.274 · 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