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Record W7081978717 · doi:10.1016/j.aej.2025.08.040

Industry 6.0: Vision, technical landscape, and opportunities

2025· article· en· W7081978717 on OpenAlexaff

Bibliographic record

VenueAlexandria Engineering Journal · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsBrandon University
Fundersnot available
KeywordsIndustry 4.0WorkforceSustainabilityProcess (computing)Production (economics)Manufacturing

Abstract

fetched live from OpenAlex

Industry 5.0 is designed with the objective of leveraging collaboration between human intelligence and cyber-driven processes. It aims to present customized manufacturing solutions to the end users as per demand. Despite its promising benefits in the current production landscape, Industry 5.0 faces critical challenges in scalability, workforce transition to collaborate with advanced technologies, high production costs, and privacy and security challenges in the post-quantum era. Thus, necessitates a shift towards more advanced Industrial paradigm that modernize and reinvent operations to synergize with high end sustainable and scalable machineries, products and processes. Industry 6.0 is defined as ubiquitous, hyper-customer driven, virtualized, and sustainable manufacturing, where focus is towards hyper-connected factories and dynamic supply chains. Industry 6.0 is expected to connect cross-vertical applications, and in this paper, we present a tutorial-based survey on the vision, technical landscape, and advancements which would drive the Industry 6.0. New concepts are introduced over Industry 5.0 processes to support industrial applications like supply-chain based productions, human–robotic industrial pipelines, green computing, and generative artificial intelligence (GAI) induction in control processes. We highlight the key enablers to support the 6.0 vision-automated digital twins, metaverse-assisted virtual production, 6G, dew computing, GAI Cobots Networks (GOBOTs), Internet-of-Anything (IoX), quantum-assisted nano production, and other technologies. We highlight the reference architecture, Industry 6.0 vision, features, components, and the threats surrounding Industry 6.0, and solutions. We also present the sustainability aspects of Industry 6.0, and finally discuss future challenges and directions. The article is presented to assist researchers, industry practitioners, and allied stakeholders to design cost-effective, customized, and process driven Industrial operations.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.003
Scholarly communication0.0080.016
Open science0.0010.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0050.004

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations13
Published2025
Admission routes1
Has abstractyes

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