Carrying Smart Cargo
An enterprise platform powered by AI can take the air cargo industry further than ever before
Cargo has always been an important part of air transport. On the very first commercial flight in 1910 there were 200 pounds of silk but no passengers. Fast-forward to the present and once again cargo is at the heart of aviation, carrying COVID vaccines and wrangling gnarly supply chains both when the container ship Ever Given blocked the Suez Canal in 2021 and more recently when logistics in the port of Shenzhen were disrupted by a lockdown.
Air cargo can be divided into two categories – freight and airmail. Within air cargo there are differentiated service products with value-added premiums like cold chain, high security and live animals. But since mangoes and postcards do not need flat beds and are not concerned where on the plane they sit, cargo is not as snazzy as the passenger side of aviation.
Nonetheless the technology of cargo and passengers have both advanced together. One Record is the cargo compliment to One Order and One ID, products introduced by industry trade association IATA to make it easier for airlines to serve their customers by integrating everything about people and their air travel needs into one place.
Core operating systems and mobile apps for cargo have developed like iCargo from IBS, an Indian software company. Digital distribution platforms like CargoAI, cargo.one and webcargo are finding novel ways to bring cargo into the modern commerce age.
But despite progress the processes behind air cargo are still rather clunky. Personal relationships between airlines, freight-forwarders, and their end-users they serve matter. There is no well-established loyalty programme (see article) and the cargo professionals I know tell me their tools are not smart enough (see article) and make their lives difficult. IAG Cargo, the freight arm of a British-Spanish airline group with many brands (see article) has moaned that youngsters are not attracted to careers in cargo.
Read on to find out why I think this is, and what solutions I think the industry will come up with…
Freighter overcapacity is on the horizon
Try to lease an air freighter today and you will be hard pushed to find one available. Miami-based Eastern Airlines has 33 large Boeing 777 freighters going. Even ocean freight carriers are getting in on the action – French line CMA CGM has had four mid-size Airbus 330s in service since 2021 and has another four large Airbus 350s on order.
Come 2023 it is likely that there will be excess capacity and declining unit revenues (revenue per available tonne kilometre, see article). It would not be the first time something like this happened – after Air Canada invested in converting six of it’s McDonnell Douglas DC-8s into freighters in 1984, normally a way of extending an aircraft’s life by decades, the aircraft were all retired in less than ten years.
Today’s new single-aisle freighters like Lufthansa’s Airbus 321F are much more fuel efficient and longer-range than the old DC-8s, but their profitability will ultimately be driven more by revenue determined by supply and demand conditions in the wider economy than their impressively low cost base.
Disruptive new entrants will outmanoeuvre incumbents
Cincinnati-based Amazon Air exists entirely to shift Amazon parcels. With most of its operations in the United States, it also flies around a few airports in Europe. But it’s also offering capacity in the public market.
Amazon has vast amounts of data and impressive analytical capabilities regarding forecasting consumer trends in the wider economy, I would bet on them being much more commercially nimble than both dedicated freighter-couriers like FedEx or DHL and the cargo arms of passenger airlines like KLM Cargo, Air Canada Cargo and Lufthansa Cargo.
Tension in the business model can be overcome by advanced analytics
These airlines have to trade-off block space agreements (BSA) and spot capacity when dealing with their freight forwarders, the businesses who accept an end-user’s cargo and arrange for it to be transported. The idea is that freight forwarders value having negotiated space available for their end-users’ cargo, even if some of it is released back when it’ll go unused. Meanwhile, the airline wants as much space as possible to be sold to ensure an efficient operation (spot capacity), so last-minute requests from the freight forwarders may not be accommodated.
These two competing business models create tensions between freight forwarders and airlines and agreements are often manually overridden during the very short booking cycles of a few days.
Fortunately smart automation and technology is on hand to help introduce new structure and improved relationships. Artificial intelligence (AI) and machine learning have roles to play in cargo sales and loyalty.
Combining classical schedule data published by OAG (formerly the Official Airline Guide) and real-time flight movement information from flightradar24 yields an estimate of how much cargo there is out there. Blending capacity with an airline’s own commercial data yields a freight rate elasticity of demand. High frequency updates can then be used with AI-based deep learning methods to create new and automated pricing models that are more powerful than those used before.
KLM Cargo has used analytics based on past and predicted demand for specific routes. They use these to building business cases for air bridges to China with their large corporate customer Philips, a medical equipment and consumer electronics company. In terms of internal structure the Dutch carrier’s cargo arm is also ahead of the crowd – their organisation has unified goals and teams working across business units (see article).
New cargo platforms powered by AI can take the industry further
One of the airline industry’s buzzwords is dynamic pricing. Everybody has a slightly different understanding of exactly what this might be, but thinking about prices that respond automatically to market conditions would be a common place to start.
Cargo is notorious for short one-to-five-day booking cycles, a focus on shifting crates rather than more strategic thinking and fraught relationships between airlines and their freight forwarders. These attributes make it a tough use case for dynamic pricing as the necessary inputs are subtle and easy to overlook. Machine learning however can spot the patterns that busy human managers might miss.
Eventually more consumer-friendly cargo platforms should emerge. Cargo loyalty is already emerging from South African LoyaltyPlus. These new programs will have two components:
First they will need a digital assistant for cargo sales professionals that does more than simply issuing price quotes and booking space. It should be a real assistant allowing them to build and develop business based on a deeper understanding of the market. Showing how demand conditions are evolving will help sales specialists target their capacity and automate aircraft deployment decisions based on real-time demand forecasts.
The second component of cargo platforms will be whizzy AI algorithms that look at the supply chain and air cargo holistically, ingesting real-time data and “listening” to the market. The associated deep learning models will integrate with the digital assistant to “push” recommendations to sales teams. The underlining analytics will incorporate at least:
Manufacturing & sourcing shifts
Current market rates, capacity and trends
Economic growth & commodity markets
Ocean, rail & road feeder services capacity
Interest rates & foreign exchange prices
BSA vs spot capacity commercial logic
Trade & border restrictions
New entrant freighter capacity
Freight forwarders launching in-sourced & leased freighters
Changes in alliance partner strategies (incl. JVs)
Value-added service potential (e.g. temperature control, security & live animal carriage)
Space & revenue optimisation across both freighter & passenger fleets
Circular flight vs. origin-destination network controls.
A tool like this would help cargo airlines sift through all the noise, solve problems and produce compelling recommendations based on robust analytics. It will show the low-hanging fruit as well as harder to spot opportunities.
All strategic and commercial logic starts with a unified goal. Sales people like to know how to achieve it and how they can get support in finding opportunities through trends they cannot pick up manually.
Creating this workflow, identifying all possible data sources and writing the set of rules to be executed using enterprise-level AI will power “digital carriers” and enable strategic sales efforts that will ultimately make airlines more commercially successful.
Imagine having this available on a tablet. It’s coming.
Do you work in cargo commercial strategy, revenue management, sales or a similar role? Would you like to know more? Get in touch to find out more about the “digital carriers”.
ricardo DOT pilon AT millavia DOT com, author
oliver AT ransonpricing DOT com, editor
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