Even with the use of advanced technology and innovative business models, air cargo services are facing operational challenges in the 21st century.
In fact, due to container costs, missed revenue, aircraft inefficiencies, and re-positioning, about $2 billion is left on the table each year.
Challenges of the 21st century, such as operational complexity, market volatility, and unpredictable customers, are making it difficult to optimize operations. Navigating a complex global trade network is tricky for air cargo carriers, and the sheer amount of data is simply overwhelming for many systems. Clearly, there needs to be a solution that address such hurdles.
Thankfully, data science and predictive intelligence can be utilized to leverage the mountain of data that results from all the industry’s moving parts. Analyzing and extracting insights from the data is the best way to understand thousands of possibilities for every cargo shipment, and ultimately predict the best course of action.
For air cargo carriers, data science and predictive analysis stand as the only true solution to maximizing efficiency. Here’s how this technology can be utilized to delight customers and boost profit.
1. Predict Shipper Behavior and Future Demand
Shipper behavior is always changing, constantly presenting new challenges for air cargo services. For instance, in the 1990s and early 2000s, e-mail and other types of electronic transmission methods reduced the amount of small parcels and documents that required shipment. What was once a huge source of revenue for air cargo carriers vanished seemingly overnight.
For air cargo carriers, figuring out where shipper needs will increase and decrease relies on looking at historical and present data. Trends of the past and present can tell a lot about the future.
With so much data to mine through, any uncertainty can be resolved by predictive intelligence solutions that deliver proactive decisions. The good news is that such technologies, which include advanced probability simulations, artificial intelligence, and machine learning, are already available for air cargo carriers.
2. Address Market Changes to Reduce Costs
Market fluctuations have long been making it tough for air cargo carriers to provide attractive prices to customers, as everything from currency rates and environmental regulations to airport curfews change often. It’s no surprise that the World Bank estimates air cargo is 12 to 16 times pricier than sea cargo.
This high cost has made shipment via the skies inaccessible for many businesses, particularly those that buy or sell low-value goods. Data analytics can help drive prices down, and make air cargo a more viable option.
Consider fuel, which is the largest operating cost for air cargo carriers. One way to save on this resource is through fuel hedging. If executed correctly, air cargo carriers can protect themselves against rising or volatile prices.
The opportunity to cut expenses here is great, but predictive intelligence is required to know when it is best to engage in such a practice, because fuel hedging can lead to losses if data science is poorly applied. Delta Airlines lost $450 million on a fuel hedge when oil prices stayed low; with better data analysis, this could have been avoided.
3. Take Advantage of Visibility Across the Supply Chain
In 2015, 74% of companies that use air shipping reported some sort of disruption in their supply chain. There are countless reasons for a disruption, including natural and manmade disasters and aircraft maintenance. Inefficiencies in air cargo operations often play a role too.
For instance, ULDs (unit load devices), a highly valuable asset for carriers, require about $300 million in repairs and replacement annually across the industry. Not only is fixing or losing these aircraft pallets costing companies serious cash, it’s also leading to flight delays and late shipments.
Technology already being employed by carriers does allow for visibility across the supply chain. For instance, United Airlines’ use of radio frequency identification tracking (RFID) technology helps it get real-time updates on the status of cargo pieces. However, if logistics companies are to take advantage of such real-time visibility, they need to effectively employ data science. It’s the only way to optimize management of assets like ULDs and overcome unexpected obstacles.
This is because predictive analysis gives carriers the insights needed to quickly and effectively respond to problems in the supply chain.
4. Optimize Asset Allocation
By using predictive intelligence to understand shipper behavior, forecast future demand, navigate market changes, and capitalize on increased supply chain visibility, air cargo carriers are in a position to fully optimize allocation of resources.
With data science, those in the logistics industry could anticipate demand levels and ready staff, services, and supplies accordingly. Air cargo carriers could also optimize inventory levels, and then schedule vessel shipments and disperse containers more effectively. This could help reduce unused container spots, which has long been a pain point for the air cargo industry.
Simply put, data analytics offer air cargo carriers the only chance to manage assets effectively and improve margins. It is the way to find the problem areas, predict solutions, and prescript suitable paths to take.
A Promising Future
Using data science and predictive analytics intelligently can go a long way in helping improve not only the air freight industry itself, but also the global economy.
Consider this: air cargo transport handles 35% of the goods involved in world trade by value. Obviously, there’s a lot at stake, and it’s paramount that air cargo carriers take advantage of all the tools at their disposal.
Data analytics are being put to work in the skies with exceptional outcomes. The future looks bright. But further adoption of predictive intelligence by air cargo carriers will make that future look even brighter.