Exploring trustworthy AI for smarter cargo operations

Supply chains today are more interconnected and vulnerable than ever. From global pandemics to geopolitical disruptions, the ability to predict and adapt quickly has become essential. But digital solutions like supply chain digital twins, virtual replicas that simulate the entire logistics chain, face a major obstacle: data sharing. Companies hesitate to share sensitive data and intellectual property, even when transparency could unlock significant gains.That is the core challenge we tackle in the Zero-Data Exchange for Engineering (ZEE) project. ZEE brings together industrial partners Ahlers, Globis, and Nallian, supported by our expertise at Sirris in secure, distributed AI and system architecture. Our goal: to research a reference architecture enabling secure, privacy-preserving data exchange in complex supply chain environments, making digital twins both practical and trustworthy.

Henrique Cabral, Sirris

What We Did

Together with Sirris we are advancing our framework by applying machine learning to predict cargo handling times dynamically. Using historical data, our models help optimize slot reservations for offloading goods, reducing delays and improving resource usage.

We applied federated learning and synthetic data techniques to predict cargo handling times at airports—helping carriers optimize slot reservations, reduce delays, and improve resource use—all without exposing raw data.

Why It Matters

In air cargo, unpredictability—from weather to logistics delays—is the norm. AI forecasts and slot optimization help keep operations smooth and efficient.

By combining privacy-preserving AI with robust security architecture, the ZEE project paves the way for resilient, transparent, and efficient supply chains, enabling companies to unlock the benefits of digital twins - without sacrificing their data.

Read the full article on the ZEE project, by Henrique Cabral, Data Scientist, PhD at Sirris on sirris.be

With the support of