We explore the transformative world of industrial AI with the insightful Bryan DeBois, a seasoned expert in applying AI to manufacturing systems. This conversation unpacks the critical role of system integrators, the distinctions between IT and OT, and the groundbreaking impact of AI on processes like quality control and production scheduling.
You will gain practical insights from real-world examples demonstrating how companies have optimized operations, reduced waste, and achieved unprecedented efficiencies. This is a great one if you’re curious about the future of smart manufacturing that implements AI!
Watch the interview here:
Here’s a summary of some main points, but remember to listen for the full episode…
00:00: Introduction and guest background
Bryan DeBois, Director of Industrial AI at Rovisys, joins us. Bryan shares his expertise in digital transformation, Industry 4.0, and the critical role of system integrators in solving complex problems by integrating multiple technologies to deliver value. (00:00)
01:39: What is System Integration?
Bryan explains the role of system integrators as the linchpin in connecting multiple systems and technologies for holistic solutions. He emphasizes their focus on operational technology (OT) over information technology (IT), describing the unique challenges and interdisciplinary skills involved. (01:39)
IT vs. OT in Manufacturing
The discussion delves into the distinction between IT (information systems) and OT (operational systems on the plant floor). Bryan highlights the unique aspects of OT, including its focus on physical processes, real-time data, and control systems crucial for manufacturing. (03:34)
Metrics in Manufacturing
Bryan elaborates on the importance of historians—databases optimized for time-series data—in enabling AI-driven insights. He shares examples of AI applications, such as quality predictions for drywall manufacturing, which dramatically reduced waste and improved real-time decision-making. (08:54)
Case Study 1: AI in Quality Control for Drywall Manufacturing
A detailed case study on using predictive AI models to replace destructive testing for drywall quality. By deploying vision-based machine learning, manufacturers gained instant feedback on product quality, cutting waste and enabling faster adjustments. (13:08)
Case Study 2: Autonomous AI in Glass Bottle Production
Bryan describes applying autonomous AI to glass bottle manufacturing. By embedding expert knowledge into AI systems, manufacturers achieved superhuman decision-making capabilities, reducing process drift and enhancing productivity. (18:42)
And more…
- Case Study 3: Optimizing Production Scheduling with AI for a Paint Manufacturer (25:27)
- Becoming ‘Data Ready’ (30:53)
- Key Takeaways and Contact Information (33:59)
Conclusion
This episode highlights the transformative power of AI in manufacturing. These advancements, from enhancing quality control to optimizing schedules and embedding expert-level decision-making, illustrate how AI can drive efficiency and innovation. Now, you might consider exploring AI to maintain competitiveness as the manufacturing industry evolves.
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