Hello members,
We’ll be welcoming today Dr. Neda Parnian, VP of Innovation & Digital Transformation at Summit Interconnect (and a member of our World AI Council) at 7:00 PM Eastern Europe Time (EET) / 9:00 AM Pacific Time (PT).
Zoom link: https://us06web.zoom.us/j/87516901944
Dr. Neda Parnian is VP of Innovation & Digital Transformation at Summit Interconnect, where she leads digital twin, automation, and AI initiatives across multiple manufacturing sites. She also serves as fractional VP of AI/ML at Matt3r and previously held leadership roles at Amazon and Intel.
Today, Neda will present two real-life case studies:
PCB Manufacturing Digital Twin — Rebuilding a Manufacturing Network’s Nervous System
Amazon Yard Automation — Turning Chaos Into a Coordinated, AI-Driven Operation Across 100+ Sites
Below is a quick read before you join, so you come in with context and sharper questions.
Looking forward to seeing you all today,
-Sam,
Case Study 1 — PCB Manufacturing Digital Twin (Summit)
Summit is a multi-site PCB (Printed Circuit Board) manufacturer—they make the circuit boards that sit inside almost every electronic device. PCB manufacturing is not “one simple production line”; it’s a chain of tightly connected steps where work moves through multiple processes and handoffs, and where small differences in routing rules, setup assumptions, or cycle times can ripple into major schedule failures. Summit runs this at scale across multiple factories, which means the company is really operating as a network, not a single plant.
The problem wasn’t demand or effort—it was predictability. Each site had its own version of reality: different workflows, inconsistent data definitions, unclear constraints, and local optimization habits. The outcome was unstable schedules, recurring bottlenecks, firefighting, and on-time delivery under 50%. The breakthrough idea was to build a digital twin—not a dashboard—meaning a living model of how jobs, machines, people, materials, and constraints actually behave across the full network. Once the “system could see itself,” it became possible to simulate demand, predict bottlenecks, run what-if scenarios, and generate schedules that worked globally—not just locally.
Case Study 2 — Amazon Yard Automation (AI-driven orchestration)
At Amazon, the “yard” is the outdoor operating zone around a fulfillment/distribution site where trucks arrive, check in, park trailers, and move to dock doors for loading and unloading. It sounds simple—until you realize that during peak, thousands of decisions depend on yard flow: which trailer goes where, which dock is open, what’s late, what’s urgent, and what needs to move now. In many operations, this area is surprisingly under-instrumented: operators rely on radios, walking the yard, and manual logs, which creates delays and safety risk.
In this case, the hidden bottleneck was real-time visibility. Trucks could wait up to 60 minutes just to be processed; dock availability and trailer locations weren’t reliably known; and the yard system often didn’t match what was happening on the ground—so chaos cascaded into downstream failures when volume spiked. Neda reframed the yard as a computer vision + orchestration problem: if you can reliably detect which truck arrived, which trailer it’s paired with, where it is, and what docks are open, then you can coordinate the yard like air-traffic control. The real challenge wasn’t a single model—it was scaling across 100+ sites with different layouts, weather, lighting, behaviors, and integrations—so the system had to be built to handle variance from day one.
About the Authors
Sam Obeidat is a senior AI strategist, venture builder, and product leader with over 15 years of global experience. He has led AI transformations across 40+ organizations in 12+ sectors, including defense, aerospace, finance, healthcare, and government. As President of World AI X, a global corporate venture studio, Sam works with top executives and domain experts to co-develop high-impact AI use cases, validate them with host partners, and pilot them with investor backing—turning bold ideas into scalable ventures. Under his leadership, World AI X has launched ventures now valued at over $100 million, spanning sectors like defense tech, hedge funds, and education. Sam combines deep technical fluency with real-world execution. He’s built enterprise-grade AI systems from the ground up and developed proprietary frameworks that trigger KPIs, reduce costs, unlock revenue, and turn traditional organizations into AI-native leaders. He’s also the host of the Chief AI Officer (CAIO) Program, an executive training initiative empowering leaders to drive responsible AI transformation at scale.
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