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May 10, 2026Automation1 citationsOpen Access

RAMI 4.0 Architecture for Industrial Traceability with Artificial Intelligence and Integrated Security

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CVCarlos VillafuerteMMMelissa MoncayoWOWilliam Oñate

Key Points

  • The aim is to present an architecture that ensures industrial traceability using AI and integrated security measures.
  • Designed a distributed architecture based on RAMI 4.0 for product traceability in industrial settings.
  • Integrated automation tools, IIoT communication, and encrypted data transmission.
  • Validated the architecture in a simulated environment to assess performance and security.
  • Achieved average end-to-end communication latency of less than 200 ms.
  • Demonstrated a packet loss rate of 2.67% and 100% reliability in report verification.
  • Identified significant risk reduction in overall asset vulnerability, shifting most assets to low or moderate risk.

Abstract

The demands of competitiveness in global markets require the integration of Industry 4.0 (I4.0) digital technologies for any manufacturing company, regardless of size. Industrial operations require complete supply chain visibility to ensure data protection and authenticity throughout the process. This document presents a distributed architecture based on RAMI 4.0, designed for product traceability in industrial environments. It integrates automation tools, IIoT communication, cloud storage, artificial intelligence, and secure data transmission using encrypted communication protocols. The system consists of a hybrid architecture; only the first, lower-level layer corresponds to a simulated manufacturing plant with deterministic and stochastic dynamics within the production line. In the second part, the middle and upper layers are implemented, where plant data is transmitted to a cloud instance, stored in a PostgreSQL database, and subsequently analyzed using automated scripts. Reporting capabilities are incorporated with ChatGPT-3.5 Turbo, and visualization is provided through Odoo. Experimental tests demonstrated an average end-to-end communication latency of less than 200 ms, a packet loss rate of 2.67%, and 100% reliability in verifying requested reports when using the cognitive computing service. Furthermore, the results of the systematic vulnerability identification process for the architecture show a significant reduction in overall risk for most assets, with a predominant shift from high or moderate to low or moderate. The proposed architecture is validated in a simulated industrial environment under controlled conditions, demonstrating its viability as a prototype rather than as a fully implemented industrial solution.

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Villafuerte et al. (2026) studied this question.

synapsesocial.com/papers/6a002162c8f74e3340f9c37chttps://doi.org/10.3390/automation7030072
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