Expert Talk: The Future of Smart Containers and Logistics

Meet the Experts

Visibility in Logistics: What Has Changed? 

Just a few years ago, technologies like AI and automation were far from being widely adopted in logistics. Processes were largely dependent on transactional systems and often fragmented across multiple platforms. According to Käber, a key shift occurred around 10 to 15 years ago with the introduction of milestone-based supply chain models, which revolutionized how goods were tracked. 

Today, we're entering a new phase. With the help of AI and end-to-end automation, visibility systems are capable of identifying trends and patterns across entire container fleets which go far beyond traditional milestone tracking. These insights are driving better decision-making and enabling a more predictive, data-driven approach to logistics. 

“We’re not just asking where the goods are, we want to know when they will arrive, and whether they’ll reach the warehouse or store on time,” says Lars Schwabe. “That means integrating data in real-time and focusing not only on location, but on all the contextual data we can derive - on both the container and fleet level.” 

APIs and Machine Learning: Enablers of Real-Time Logistics 

One major breakthrough in simplifying logistics data has been the use of APIs. They allow different data sources to be integrated directly into customer systems while streamlining processes and preserving data quality. 

Olaf Habert explains how this approach is central to Hapag-Lloyd’s Smart Visibility Tool Live Position. “It’s a kind of entry ticket to a world where you can actively manage your supply chain. And we’re not just covering the ocean leg; we’re extending visibility to the road and rail parts of the journey as well.” Live Position is also available via API, providing even more flexibility for Hapag-Lloyd's customers. 

Still, Habert emphasizes that visibility is not Hapag-Lloyd's main field of operations: “Our goal is to be the quality leader in shipping—not to become the next big visibility platform.” 

When it comes to predicting ETAs, machine learning plays an increasingly vital role. It enables new ways of planning and cargo handling, albeit it’s not a silver bullet. Real-world factors like road traffic or terminal closing times can still affect outcomes, even when the system predicts a perfect schedule. 

Data-Driven Decision-Making: Shaping the Future 

According to Lars Schwabe, the proper use of data will be a game-changer for the industry: “By embedding data into supply chain systems the right way, we can not only boost efficiency but also set new standards for sustainability.” He also sees potential in using algorithms to share location data without compromising business confidentiality. 

André Käber agrees, pointing to managing bottlenecks as a core challenge in logistics. “To solve them, you need to identify where they occur and then be willing to rethink and adapt processes for greater efficiency,” he says. Technologies like AI and digital twins are key enablers here. When applied correctly, they can reduce costs, enhance sustainability, and transform how decisions are made.