The world of container shipping is going through a prolonged period of instability due to oversupply of new ships entering service and decreased demand for transporting 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 inevitable yield declines.
Everyone dreams of the rates going back up in the hurry. Not the wisest decision, as 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 only due to attracting customers with lower rates. Profits, if any, are eked out of the lower bunker costs. With freight rates under pressure from customers disregarding feelings of loyalty, the carriers have to learn how to become more effective retailers.
In the business of retail, where similar problems occurred ages ago, ability to act fast by recognizing splintering of customer segments into smaller and smaller chunks goes hand in hand with counterstrategy of personalizing each service with touches relevant to the customer. Retail has learned the simple truth that “one size fits all” approach only leads to a needless spiral of offering lower and lower prices and having year-round sales. A new approach, centered on the individual, offered the sellers promise of standing out from the crowd and generating profits instead of counting losses. They employed big data concepts with gusto.
Thus my case of shipping industry taking a page out of the retail industry handbook. There is no sense to expect that the world of carriers and ports will change for a while. The sooner the shipping industry undertakes changes 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, thus avoided as much as possible. The demand side is a mixed bag. Even a loyal shipper will not easily commit to more than 6 months of cooperation. 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.
In circumstances like those, what are the components of successful counteraction? First and foremost, it is data. You could think of it as “big” or just lots 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, 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, so not much upside to profit from. Then surcharges. Order taken. Done.
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 9 weeks (affecting 9 weekly services). The carrier creates a new factor for calculations performed by the price optimization engine, which in real time analyzes price options by customer, equipment, commodity, OD pair, etc. 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 an 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, 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 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 carrier’s ability to use port’s vessel movement data, that extra profit can be shared between the line and the port. In reality, beyond generating additional profit, it 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 port’s TOS system and intermodal transporter’s load planning system. This step creates another benefit: underutilized quay cranes and underutilized train sets at that location will be used more efficiently.
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 breaks on collaborative innovation. Currently, neither the carrier, nor the port, nor the 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 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 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 creation of a wide-reaching collaborative network. Whenever an example of such collaborative solution is discussed on public fora, we should embrace it, instead of rolling out counterarguments why carriers, ports, and inland transporters need to act separately.
In my opinion, with all the talk of big data and confusing cacophony of “cloud” and “Internet of Things” terms thrown into the big data discussions, we are still at sea when it comes to understanding the leverage that shipping industry has, but is not using, to aid their growth strategies. The first ones who manage to connect the dots, will get out of the morass of doing business as usual. The rest will wonder what hit them.