How Jettainer’s CEO Balances AI Innovation With Human Expertise in ULD Management

How Jettainer’s CEO Sees AI and the Human Factor in ULD Management

In cargo automation, many leaders focus on replacing labor with machines. At Jettainer, the approach is different. CEO Dr Jan-Wilhelm Breithaupt emphasizes enhancing human capabilities through digital tools rather than removing people from the process.

For him, smarter container management means leveraging data and technology to support operational teams on the warehouse floor. Since June 2024, Breithaupt has accelerated Jettainer’s digital transformation. “We are massively pushing forward the digitalisation of Jettainer,” he said, highlighting the overhaul of their data warehouse as the foundation for improved reporting and management.

The new Jettainer NG platform, launched in March, has already been upgraded to offer tracking down to sub-location levels. This shift turns ULD tracking from a passive feature into a tactical asset. Breithaupt expects further technological enhancements by autumn, aiming to reduce lost units fleet-wide by measurable percentages.

Efficiency gains are even more significant. Jettainer achieves 15-20% fleet size reduction and boosts utilization rates by 25-30% in flight legs per month. This success comes from combining experienced ULD controllers, advanced digital platforms, and transparent airline handling data enhanced by tracking technology.

AI: Still a Human Puzzle

Artificial intelligence is beginning to influence container management but comes with clear limits. AI can ease repetitive tasks and help sift through increasing data volumes. However, the real challenge lies in improving data quality and interpretation.

Breithaupt points out that AI helps understand repair shop locations and container dwell times, but predictive maintenance remains complex. Unlike aircraft engines, where damage accumulates predictably, ULD damage can be sudden and random—often caused by inconsistent ground handling.

AI can model trends across large data sets but cannot predict specific damage events for individual containers. Interpreting AI outputs requires expertise. Breithaupt expects significant AI benefits within 12 to 18 months, though full system integration is still underway.

Automation Where It Fits

Breithaupt is cautious about using traditional robots in air cargo handling due to the varied shapes and sizes of cargo. While robots suit uniform packages typical for integrators, air cargo demands versatility.

Instead, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) are seen as future forces behind the scenes. Digitization will focus on administrative functions, data sharing, and stock management, while physical freight handling will remain largely manual for at least a decade.

Jettainer plans to shift some back-office staff to customer sites, enabling real-time ULD management and ground handling optimization. This is not a reduction in workforce but a repositioning to create more skilled, customer-focused teams.

Accountability at Ground Level

Ground handling quality remains a major challenge, especially in the Americas. Breithaupt notes that improper handling—like cutting rubber doors to speed freight unloading—leads to costly repairs and container downtime.

Damage rates are lower in Europe and Japan, where handling practices are stricter. Handling containers directly on the floor, common in some regions, significantly increases damage risk. This impacts costs and fleet size for customers.

Jettainer emphasizes transparency to help clients negotiate better terms or apply penalties with ground handlers. Training and accountability are critical. Containers should never be stored or handled on the floor but always on rollerbeds or pallets to avoid damage from steel forks.

High turnover among ground handling staff undermines consistent training efforts. Ultimately, leadership at ground handling operations must enforce proper ULD handling standards to prevent damaging practices from becoming routine.

AI and the Repair Dilemma

To improve damage assessments, Jettainer experiments with AI in real-world settings. They sponsored an AI challenge at the IATA One Record Hackathon, encouraging teams to develop AI-based tools for ground handlers.

The winning app combines AI language models with image recognition to guide damage inspections. It retrieves container-specific assessment instructions, prompts users to photograph damage, and evaluates severity to decide if a container is non-serviceable.

This approach aims to standardize and speed up damage evaluation while reducing human error. It’s an example of AI supporting frontline staff rather than replacing them.

For managers interested in AI’s practical applications in operations, exploring courses on AI and automation can provide valuable insights. Resources like Complete AI Training offer tailored programs focused on integrating AI into business functions.