The air cargo industry is still taxiing onto the runway of AI opportunities. CargoTech already offers products to ensure the smoothest of technology take-offs, with AI serving as the fuel for sound business decisions. Cédric Millet, CargoTech’s President, and its members illustrate ways in which the group leverages AI to support decision-making and clear up some of the myths surrounding the technology.
“On a scale of 1-10, I’d say the air cargo industry is currently at 3 when it comes to adopting AI-assisted decision-making,” says Cédric Millet, President of CargoTech. AI is sporadically used in different segments of the air cargo industry – mostly in customer service and engagement functions, because these have the greatest similarity to processes in other, more digitally-developed industries. Much of the air cargo industry is still in the phase of digitizing its operations and starting to accumulate data volumes. “To embark on the journey of AI-assisted decision-making, it is crucial to extract a large volume of data to train the models and to identify anomalies for better decision-making, going forward,” Millet explains.
Enhanced data integration
Data is currently heavily fragmented across stakeholders, leading to huge inefficiencies. AI models have the potential to be capable of synthesizing data across the supply chain, thus promoting better end-to-end visibility and decision-making. CargoAi already offers advanced AI-driven tools that assist in streamlining decision-making for logistics professionals, such as its CargoCOPILOT product: CargoAi’s AI email plugin enables the frontline workforce to retrieve dynamic rates directly via their inbox, without having to search across platforms.
Another practical application of AI aimed at enhancing commercial decision-making processes and born of collaboration with a number of airlines, is Rotate’s ‘Fair Share Analysis’ which not only informs airlines about their market position regarding market share and yield level, but is also a critical component when it comes to optimizing an airline’s network and Origin-Destination (OD) sales mix. Here, AI leverages proprietary capacity data and machine learning algorithms, incorporating market data to generate fair share estimations.
Unlike the charter niche, the general air cargo industry faces the challenge of an abundance of data. “For a long period, the air cargo industry suffered from data scarcity, when it came to advanced data analysis. As data availability increased, Business Intelligence (BI) dashboards proliferated, sparking enthusiasm about the possibilities of applying Artificial Intelligence (AI) to revolutionize air cargo operations. However, there’s often a misconception that AI, in itself, will be able to solve some of the industry’s biggest challenges,” says Michael Teoh Head of Strategy at CargoTech. Experience has shown that AI does not replace the need for commercial teams to devise innovative use cases that drive value through better decision-making. AI should be viewed as an enabler rather than an end goal.
Wiremind Cargo has been implementing and delivering the benefits of AI to the air cargo industry since the company began. “it is important to remember that AI is quite a broad umbrella, not just ChatGPT/generative AI. Wiremind Cargo successfully deploys machine learning models that assist customers with commercial decisions regarding capacity and revenue management. Each of CargoStack Optimiser's modules is powered by different AI models trained on the customer's own data and tasked with trying to make specific predictions such as the amount of baggage expected on a flight, the show-up rate of bookings, or the optimal entry condition on a flight. By using Machine Learning models, CargoTech’s solutions are able to process vast data sets to spot trends and patterns, allowing the models to replicate what analysts would be doing at scale”.
