Ramen for your Manufacturing Plant upgrade

A Leading Plasma-Cutting ManufacturerTransforms Factory Operations with Ramen Autonomous Network Operations (RANO)

When a leading plasma-cutting manufacturer set out to modernize its North American factories, leadership recognized that digitizing production required more than upgrading machines — it required reinventing the operations network. The company partnered with Ramen to deploy the Ramen Autonomous Network Operations (RANO) platform, enabling automated workflows and unlocking machine data previously trapped inside legacy factory equipment.

Executive Summary

The manufacturer, a leader in industrial metal‑cutting products, operates complex machining, assembly, and testing stations that generate massive volumes of data. Much of this data was historically stranded at the edge — isolated within individual machines and unavailable for real‑time analytics. RANO delivered automated configuration, predictive analytics, and unified control across wired, Wi‑Fi, private cellular, and mmWave networks, all without requiring forklift upgrades.

Challenge

Legacy vendor systems lacked reliability, automation, and real‑time intelligence. Machine data was siloed, and manual configuration slowed the onboarding of new equipment. In short, the legacy network could not keep up with the data intensity of modern manufacturing.

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Solution

Ramen layered RANO on top of the existing infrastructure, providing:

• Automated provisioning and patching across Cisco, HPE, and white‑box gear
• Digital‑twin analytics to predict failures and optimize throughput  
• Chat‑based workflows for operations teams to manage VLANs, QoS, and device onboarding
• Zero‑trust security with CVE‑enriched scanning and anomaly detection
• A plug‑and‑play EdgeAI stack for predictive maintenance and machine vision

Each legacy machine connects through a Ramen Machine Gateway (RMG), which automatically selects the optimal connection path —Ethernet, Wi‑Fi, private cellular, or mmWave — based on application performance and reliability requirements.

Impact

By deploying RANO on top of its existing network, the manufacturer achieved:

• Real‑time visibility into machine data across the production floor
• A 30% reduction in IT workload
• Autonomous incident detection and remediation
• Rapid onboarding of new machines and self‑service production workflows

This co‑deployment model preserved the manufacturer infrastructure investment while extending it with AI‑driven autonomous operations.

Conclusion

The manufacturer now operates a modern, AI‑driven manufacturing network that coexists with legacy hardware while enabling smarter operations, better visibility, and continuous adaptation to production needs.

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