The world of container shipping is going through a prolonged period of instability due to oversupply of new ships entering service. There is also a fall in demand for the transportation of goods between major economies. Falling container shipping rates created a windfall for shippers and brought tears to shipping lines and ports. Uncertain recovery provides an opportunity to look at how better pricing, capacity management, and business optimization could combat the inevitable decline in yield.

Everyone dreams that the rates will quickly go back up. But don’t count your eggs before they hatch. Between the new builds and laid up vessels, there is enough spare capacity waiting to enter the market to last us a decade or two. In the current highly commoditized and oversupplied market, revenues increase because customers are attracted to lower rates. Profits, if any, are eked out of lower bunker costs. Disregarding feelings of loyalty, customers pressure for lower rates. Carriers have to learn how to become more effective retailers.

Learnings from retail

The retail business was facing similar problems a long time ago. The ability to act fast by recognizing the splintering of customer segments into smaller and smaller chunks goes hand in hand with counter-strategy of personalizing each service with relevant touches. Retail has learned the simple truth that the ‘one size fits all’ approach only leads to a needless spiral of lower and lower prices, and year-round sales.

A new approach, centered on the individual, promises the ability of sellers to stand out from the crowd and generate profits instead of counting losses. They employed big data concepts with gusto.

Thus my case of the shipping industry taking a page out of the retail industry handbook. There’s no point expecting carriers and ports to change. The sooner the shipping industry opens up to changes and borrowing ideas from other commoditized industries, the better.

As in retail, shipping faces imbalance between supply and demand. The supply side is constrained by fleet makeup, fixed rotations, timetables, and contracted port and terminal capacity. Breaking from, or renegotiating, contracts on the supply side is financially punitive, therefore should be avoided as much as possible. The demand side is a mixed bag. Even a loyal shipper will be hesitant to commit to more than 6 months of co-operation. Let’s assume that normally freight purchasing is split 50/50 between contracts and spot, but in the era of low rates, the ratio tilts in favor of using spot rates.

The three conditions for successful balance of supply against demand

In circumstances like these, what are the components of successful counteraction? First and foremost, it is data. You could think of it as ‘big’ or just a lot more of it than you could access before. The second condition is the technology of using this data to calculate customer offers and to optimize network behavior. The third condition is collaboration between all participants in the physical chain of container custody – carrier, feeder, port, inland transporter.

Let’s quickly step through a containerized supply chain to highlight how big data could be leveraged better. Typically, a container is offered at the basic rate, stripped of any additional services or add-on products. It means that any extra products and services are completely de-bundled from the shipping rate. There are extras that could be added (e.g. generator of x-capacity to a reefer), but they are also commoditized, therefore there is not much upside to profit from. Then surcharges. Order taken. Done.

Profit-generating pricing within big data

Could big data and data-driven optimization change that kind of selling interaction? Imagine a carrier that concludes, on the basis of their own operations data, as well as, data from port operations system for vessel movements, 3rd party feed for vessel locations, and social media feeds, that the destination port requested by the shipper is congested and it will remain so for about nine weeks (affecting nine weekly services). The carrier creates a new factor for calculations performed by the price optimization engine, analyzes price options by customer, equipment, commodity, OD pair, etc. – in real time. It then prescribes the price (or a price range) to be offered to the shipper.

Using big data analysis and that new factor, the price engine creates a new optional offer. In addition to the requested OD, it offers the shipper the option of dropping the load off at one port before or after the preferred destination port. The calculation is done using data on lift capacity and costs, capability and contract terms with those ports, as well as intermodal contracts applying to container delivery from this optional destination.

Calculating on top of available data, the carrier’s network optimization engine matches the original delivery SLA and discovers that there is an option of faster delivery. The carrier now makes an optional offer of faster delivery service for an additional charge. The shipper can stick with the original plan or agree to the new offer. Let’s assume the shipper chooses the offer.

This creates additional profit for the carrier. If done collaboratively with the port, e.g. in exchange for the carrier’s ability to use the port’s vessel movement data, that extra profit can be shared between the line and the port. In reality, beyond generating additional profit, this creates an opportunity to utilize their capacity that otherwise might be wasted.

Now imagine the offer extends also to inland transporter, which increases the value of the offer. The carrier shares data from their new plan with the port’s TOS (Terminal Operation System) and intermodal transporter’s load planning system. This step creates another benefit: Underutilized quay cranes and train sets will be used more efficiently.

The problems holding back big data potential

While we are able to access and use (big) data today, and even ensure data collaboration between parties, we still need information technology to stop putting the brakes on collaborative innovation. Currently, neither the carrier, nor the port or inland transporter have processes and technology nearing the capability implemented by retailers to solve similar problems. Processes in shipping are not aimed at sharing the data and quickly producing complex data-driven decisions to sell and execute the offer. There is still too much focus on data-based business intelligence, and too little work done on data-driven automated decision and execution.

There is also the problem of technology investments. Transporters and ports focus on running reasonably efficient operations and not on running highly flexible and responsive trading business of ‘container-as-a-commodity’. The cross-enterprise processes are lacking. The focus is still on automating processes within functional silos instead of taking a holistic view of the enterprise. Yet, making technology-aided rapid and optimal decisions across pricing, capacity, and yield is fundamental to growth.

While individual businesses often struggle to implement necessary process and technology changes, even less thought is given to rolling out optimization solutions outside of one enterprise to facilitate the creation of a wide-reaching collaborative network. Whenever an example of such collaborative solution is discussed on public forums, we should embrace it, instead of rolling out counterarguments on why carriers, ports, and inland transporters need to act separately.

In my opinion, with all the talk of big data and confusing terms like ‘cloud’ and ‘Internet of Things’ thrown about in big data discussions, we’re still at sea when it comes to understanding the leverage that shipping industry has. We’re not using this to aid their growth strategies. The first ones to connect the dots will get out of the maze of doing business as usual. The rest will wonder what hit them.