Sky’s the limit: How emerging tech can streamline air cargo operations
April 25, 2024 / Unisys Corporation
Short on time? Read the key takeaways:
- New technologies can help airlines achieve maximum efficiency and competitiveness, from optimizing load planning to streamlining compliance regulations.
- Next-gen technologies can help airlines solve three common cargo challenges: load planning optimization, exception management and efficient collaboration.
- Before implementing these solutions, airlines should address data debt, collaborate with partners and analyze their computing resources.
Flying with a half-full cargo hold may be a thing of the past, with the help of next-gen technologies. From optimizing cargo loads to streamlining compliance regulations, emerging technologies — such as AI, machine learning and advanced analytics — can help airlines find maximum efficiency in their cargo operations.
As the air cargo industry embraces digitalization, leveraging data becomes paramount to maintaining a competitive edge. Data can uncover efficiencies across your business, enhancing margins and boosting on-time performance. However, implementing cutting-edge solutions requires a nuanced understanding of how the technology could ease your pain points.
Begin by exploring how technology can help address three common cargo challenges. Then, follow practical steps to help you establish a solid foundation for implementing these technologies in your cargo operations.
How emerging tech can solve common cargo challenges
The supply chain is a puzzle of moving parts, and air cargo operators are key players in moving parcels from point A to point B. However, the route from origins to destinations does not always have clear skies. Inevitable disruptions can derail your best-laid plans. But new tools can help. Here’s how:
1. Optimize load planning
When it comes to building load plans — whether pallets, unit load devices (ULD) or another container — you’re dealing with a diverse range of shipments with varying dimensions, compliance regulations and structured product labeling codes. Finding the best combination of how to build your containers can be time-consuming — considering all the variables — and risky. If the build isn’t optimized – the container could have to be rebuilt at its next stop.
How can you solve this? An AI algorithm can generate cargo loading and unloading plans by considering cargo dimensions, stacking rules, customs regulations and storage capacity. This optimized plan can help fill space more efficiently and reduce the chance of having to rebuild the container along its journey.
2. Manage exceptions
Despite best efforts, exceptions are inevitable in the supply chain. However, excessive exceptions can significantly slow your operations, affecting asset utilization and your on-time departure metrics.
Airlines can leverage high-performance computing and quantum annealing to recover from disruptions quickly. These computing resources can help rapidly replan ULDs and container load plans, helping you rebuild and reload as soon as possible. Such technologies can keep your air cargo plans online and on track.
3. Streamline collaboration and communication
Your freight forwarders are invaluable partners, but the back-and-forth communication to send and receive shipment information can be time-consuming and error-prone, leading to delays and misunderstandings in shipment processing. However, conversational AI can help streamline your interactions. An AI chatbot on your website can engage with customers, answer FAQs and provide shipment updates, offering quick and accurate responses. This way, you have the shipment information you need to fill your cargo holds on more flights.
Establishing a solid foundation for your digital transformation
Emerging technologies have the potential to propel air cargo operators ahead of their competition. However, establishing a solid foundation is crucial before implementing next-generation tools. Apply these practical tips to kickstart this journey:
- Address your data debt: Reduce duplicate data, centralize master data and resolve data security issues.
- Talk with your partners: Know how comfortable your partners are with real-time data access and sharing.
- Analyze computing resources: Note the level of computing resources you might need to handle large datasets and run complex algorithms.
