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zenodo.6984944

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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-25425-4_5openalex-extracted

Requirement Analysis for the Design of Smart Logistics in SMEs

inferredcites →https://doi.org/10.1007/s00170-017-0233-1openalex-extracted

Digital twin-driven product design, manufacturing and service with big data

inferredcites →https://doi.org/10.1016/j.cie.2017.11.017openalex-extracted

Big data analytics in supply chain management between 2010 and 2016: Insights to industries

inferredcites →https://doi.org/10.1016/j.promfg.2019.03.048openalex-extracted

Using an interdisciplinary demonstration platform for teaching Industry 4.0

inferredcites →https://doi.org/10.1109/etfa.2019.8869509openalex-extracted

Active Learning based Laboratory towards Engineering Education 4.0

inferredcites →https://doi.org/10.1109/iceed.2017.8251157openalex-extracted

Multidisciplinary curriculum design approaches towards balanced and holistic graduates

inferredcites →https://doi.org/10.1109/ieem44572.2019.8978634openalex-extracted

Challenges in Implementing Industry 4 Laboratories and Learning Factories in Academia

inferredcites →https://doi.org/10.13140/rg.2.2.31506.17607openalex-extracted

Comparison of Industry 4.0 Requirements between Central- European and South-East-Asian Enterprises

inferredcites →https://doi.org/10.24867/ijiem-2015-3-115openalex-extracted

Sharpening the Industrial Engineering and Management Qualification Profile: Research Findings from Austria

What points at this work

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inferred← citeshttps://doi.org/10.1007/s00501-021-01119-wopenalex-snapshot

MUL 4.0 – Digitalisierung der Wertschöpfungskette vom Rohmaterial bis hin zum Recycling

inferred← citeshttps://doi.org/10.37957/rfd.v9i1.145openalex-snapshot

Most commonly used imaging methods for the detection of COVID-19. Literature review

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.