From Supply Chains to Strategic Impact: How Dmytro Verner Helped Build a Predictive Logistics Platform For the U.S. Economy

Against this backdrop, predictive logistics platforms — technologies that combine real-time data with anticipatory analytics — are emerging as a cornerstone of operational resilience. To understand how these systems work in practice, we spoke with Dmytro Verner, a Senior Software Engineer with a Master’s in Computer Science. A Senior Member of IEEE, he has spent the past several years shaping the architecture behind one of the sector’s most advanced platforms.

Dmytro was a core engineering force at TransVoyant, a company serving major clients such as McKesson, Merck, Bridgestone, and ConAir. His work strengthened the technological backbone of one of the industry’s most advanced logistics platforms, improving its efficiency, stability, and ability to operate seamlessly on a global scale.

In this conversation, we explore how predictive logistics systems are built, what makes them resilient, and how they’re already impacting the U.S. economy.

Dmytro, considering the current optimism in shipping volumes and the ongoing challenges in global logistics, how do predictive logistics platforms help companies build more resilient and efficient supply chains?

These platforms give businesses the ability to look ahead, not just react. They combine real-time data with predictive analytics to identify potential disruptions — from port congestion to weather delays — before they impact operations. That foresight helps companies make faster, smarter decisions, whether it’s rerouting cargo or adjusting inventory planning. In a volatile environment, that kind of visibility can be the difference between staying ahead or falling behind.

At TransVoyant, you built a system that could successfully track cargo flows worldwide. What helped you make that level of integration work in practice?

At TransVoyant, we developed a platform that brought together data from a wide range of sources to track global cargo flows in real time. This included satellites monitoring vessel movements, IoT sensors placed on vehicles and containers to collect location and condition data, and freight APIs — digital interfaces that connected us with partners in air, rail, and maritime transport. By unifying these inputs, we gave our clients a clear, predictive view of their entire supply chain across all modes of transport.

One of your notable achievements was reducing AWS infrastructure costs by over 45% through architectural optimization. Can you elaborate on the strategies employed to achieve this?

We began with a detailed audit of all services and how they used resources within AWS — Amazon Web Services, the world’s largest cloud platform. It powers everything from computing to storage in our infrastructure. Many components were overprovisioned or poorly managed. We significantly reduced costs by introducing autoscaling groups, organizing data through storage tiering, and cleaning up redundant pipelines. But it wasn’t just about savings; these changes also made the system faster and more stable, which was critical for processing real-time logistics data at scale.

You also played a key role in stabilizing Docker Swarm clusters, which led to a 4x reduction in infrastructure size while increasing system reliability. What were the main technical hurdles, and how did solving them affect overall platform performance?

Docker Swarm is a system that lets you run containers — lightweight packages of software — across multiple servers as a single cluster. We were using it to manage a large number of services, but resource usage was inconsistent, and during traffic spikes, containers would crash. By setting proper CPU — the central processing unit that handles the system’s computing tasks — and memory limits and balancing workloads more effectively, we reduced instability and made the system much more efficient. In the end, we cut the infrastructure size by four times while improving overall performance.

At TransVoyant, you led the integration of complex transportation systems, including railways, airlines, and shipping lines. How did this integration impact your clients’ operational efficiency?

Integration across transport modes gave clients a unified control panel over their logistics operations. Whether a package was on a freight train in Nebraska or a ship crossing the Pacific, they could monitor delays, reroute cargo, or notify partners — all in real-time. We also ensured the system scaled globally. For example, we tracked the real-time location of all cargo ships and commercial planes — a significant technical challenge that had a big operational payoff.

Your work supported major companies like Merck and McKesson in making their supply chains more predictable and resilient. What societal demand did your platform address, and how did your engineering contributions help meet that demand?

The demand was very real: businesses — especially in healthcare and critical manufacturing — needed to see and predict what was happening across their supply chains in real-time. A delay in one shipment could cascade into severe shortages. Our platform helped clients prevent that. By giving them complete visibility and predictive alerts, we enabled faster reactions to disruptions — whether it was a late cargo ship, a grounded plane, or even a storm in a key port. For me, the most meaningful part was knowing the work we did had a ripple effect — helping essential goods reach people who needed them.

In the context of today’s supply chain volatility, what role do you see predictive logistics playing in the broader U.S. economic landscape?

These systems are becoming essential infrastructure — like railroads were a hundred years ago. A pharmaceutical company that gets real-time alerts about customs delays can reroute a shipment and avoid millions in losses. A manufacturer can predict port congestion and adjust production timelines. It’s that level of insight and control that’s turning predictive logistics from a “nice to have” into a competitive necessity.

Looking ahead, as supply chains become even more data-driven and interdependent, how do you see your role evolving — and what kind of projects are you personally aiming to contribute to next?

I see myself continuing to work at the intersection of infrastructure and intelligence — building systems that not only handle scale and complexity but actually help businesses make better decisions in real-time. I’m especially interested in projects that blend Big Data, automation, and real-world impact — like improving logistics for essential services or using predictive tools to reduce waste in global supply chains. I want to stay close to systems that move the needle — technically and societally.