How Physical AI is Revolutionizing the Manufacturing Industry: The SUN Automation Success Story

In today’s rapidly evolving industrial landscape, artificial intelligence isn’t just for tech giants and autonomous vehicles. Traditional manufacturing sectors are experiencing their own AI revolution, with smart algorithms transforming operations from the factory floor up. One compelling example? In collaboration with SUN Automation Group, we implemented cutting-edge AI to solve critical challenges in corrugated manufacturing.

The Manufacturing Knowledge Gap (And How Physical AI Fills It)

If you’ve spent time in manufacturing environments, you’ve likely noticed a concerning trend: as veteran operators retire, decades of intuitive machine knowledge walks out the door with them. These seasoned professionals can often spot potential equipment issues just by listening to subtle changes in machine sounds or noticing minor variations in output quality.

SUN Automation Group, a global leader in providing corrugated box equipment, recognized this challenge. With Industry 4.0 transformations underway, they needed a solution that could not only replicate this human expertise but enhance it with capabilities only AI could provide.

Enter Helios: Manufacturing Intelligence Powered by Physical AI

Working alongside SUN’s team, we developed Helios – an intelligent system that combines edge computing with sophisticated machine learning algorithms to monitor equipment health, detect anomalies, and predict failures before they cause costly downtime.

What makes this AI implementation particularly powerful is how it bridges the gap between traditional manufacturing processes and next-generation technology:

• Real-time machine monitoring captures thousands of data points per second – far more than any human operator could process
• AI-driven pattern recognition identifies subtle anomalies that would be invisible to the naked eye
• Predictive maintenance algorithms forecast potential failures days or even weeks before they would occur
• Continuous learning capabilities mean the system gets smarter over time, adapting to each unique manufacturing environment

Behind the Scenes: Building Manufacturing Physical AI That Works

What does it take to bring AI capabilities to an industrial setting? Our team tackled this challenge using a comprehensive technology stack:

The edge computing devices run on Nerves and NervesHub, communicating with machine PLCs through industrial protocols. The collected data flows through a sophisticated pipeline built on AWS IoT, Lambda, and Kinesis Firehose, all orchestrated by the Serverless Framework.

On the backend, the AI models process incoming data, storing application information in AWS RDS with long-term archival in S3. Factory floor users interact with the system through an intuitive web interface built with Phoenix and React.

But the most critical component isn’t the technology itself – it’s understanding the users’ needs. As Matthew Miller, SUN’s Director of Technology, noted during our collaboration:

“What Very brings to the table are those soft skills and the ability to help you through the strategic pieces of your project. That’s something I’ve never found with any firm that I’ve dealt with before.”

This user-centered approach guided our AI development process from day one, ensuring the technology would genuinely solve real problems faced by corrugated manufacturers.

From Design Sprint to Deployment: The Physical AI Development Journey

Our process began with a Technical Design Sprint focused on a critical question: who would use this AI system, and what mattered most to them?

The research revealed that SUN’s customers needed:

1. Instant visibility into machine health with AI-powered alerts
2. Historical trend analysis to spot patterns in performance over time
3. Administrative control over the system, with customizable alerts and user management

With these insights, our team designed an AI solution that would continuously monitor machine performance, learn normal operating patterns, and predict potential issues before they cause disruption. We placed particular emphasis on the anomaly detection algorithms, ensuring they could identify subtle deviations that indicate developing problems.

Real Results: Physical AI Delivering Manufacturing Excellence

In just over five months from kickoff, SUN had a robust AI solution ready for deployment. The impact was immediate and significant. Factory operators who previously relied on experience and instinct now had AI-powered insights guiding their maintenance decisions.

The Helios system now enables corrugated manufacturers to:

• Monitor thousands of machine parameters in real-time
• Learn what constitutes normal operations for each unique machine
• Predict when maintenance will be needed before failures occur

For SUN, this AI implementation provided a crucial competitive edge in an industry undergoing rapid technological change. For their customers, it meant reduced downtime, optimized production, and a clearer understanding of their equipment performance.

What This Means for Your Manufacturing Operation

The SUN Automation story illustrates a broader truth about AI in manufacturing: the technology isn’t just for tomorrow’s factories – it’s delivering real value today in traditional industrial settings.

Whether you’re in corrugated manufacturing, metal fabrication, food processing, or any other production environment, similar AI capabilities can help you:

• Preserve and enhance institutional knowledge as experienced workers retire
• Identify efficiency improvements invisible to human observation
• Prevent costly downtime through predictive maintenance
• Build competitive advantage through technological leadership

The future of manufacturing isn’t just automated – it’s intelligent. And that intelligence is increasingly powered by AI algorithms that learn, adapt, and improve over time.

Ready to explore how AI could transform your manufacturing operation? Let’s start a conversation about your unique challenges and how intelligent technology might help you overcome them.