There have been massive supply chain disruptions since the pandemic began. These disruptions have driven up prices and led to shortages of goods. Congestion at ports has certainly played a role in these disruptions. The world’s fleet consists of approximately 6,000 ships. These ships carried nearly 150 million twenty-foot equivalent units (TEUs) of containers last year. Last October, over 100 ships, including 70 container ships, were waiting at anchor or in drift zones to unload at the twin ports of Los Angeles and Long Beach.
While conditions have eased in North America, port congestion has gotten much worse in China. China accounts for about 12% of global trade. China’s stringent COVID lockdown in critical port cities has impacted global supply chains. At the end of April, over 500 ships were awaiting berthing space at Chinese docks.
Shippers – the companies paying Ocean, Rail, and Truck carriers to transport their goods – can do little to speed shipments snarled by port congestion. But if shippers can get a better handle on how long it will take their shipment to cross the Ocean and then move on to one of their facilities, they can work to mitigate the impact of delays. To drive better estimated times of arrival (ETAs), better visibility is needed.
Hapag-Lloyd is one of the largest Ocean carriers. They have a fleet of over 250 container ships and a transport capacity of 1.8 million TEUs. They recently announced they will equip their entire container fleet with real-time tracking devices. The devices will improve visibility by transmitting data on a real-time basis from each container. Tracking devices from Nexxiot and ORBCOMM are being installed that will provide location data based on GPS, measure temperature, and monitor any sudden shocks to the container. In the future, additional sensors could be added through Bluetooth.
Is this the visibility shippers so desperately need? I talked to Steve Dowse, the senior vice president of international solutions at FourKites. FourKites provides a supply chain visibility platform that can ingest location and status data from trucks, trains, ships, and other transportation assets, and then provide visibility to where a company’s goods are, along with predictive times of arrivals. Mr. Dowse began his career in ocean shipping in the late 1980s.
Mr. Dowse explained that visibility to ships is already very good. Ships have an automatic identification system, or AIS, that transmits a ship’s position so that other ships are aware of its position. The International Maritime Organization mandates that large ships broadcast their position to avoid collisions.
But better visibility to containers is badly needed. The congestion at ports is largely a landside problem. For example, current congestion at China ports is based on COVID restrictions on cross-province trucking stoppages and port labor COVID lock downs in China that have prevented workers from off-loading and on-boarding shipping containers. Similarly, congestion at North American and European ports is due to landside congestion at Ocean terminals, rail ramps, inland ports, and shipper’s warehouses. Labor shortages have created havoc on end-to-end supply chain operations. An end-to-end journey from a plant in China to a shipper’s warehouse in the US that used to take 45 days, now takes over 100 days”, Mr. Dowse explained. So, visibility to a container on land moving by rail or truck matters more than visibility to when a ship will berth. “What does it matter if a container ship is slow steaming across the Pacific to save fuel, and will arrive 3 days later than expected, if it still takes ten days to unload at the port of Long Beach and then another 15 days to get to its destination?”
Similarly, the ETAs published by ocean shippers don’t really matter that much. Ocean ETAs are focused on when a ship gets into port and begins to unload its cargo. It is the end-to-end ETA that matters to shippers. “Machine learning and artificial intelligence, have a voracious appetite for data,” Mr. Dowse went on to say. “That is why Google Maps is so good. It has so many data points.”
When FourKites is making a predictive time of arrival for a final destination, they are using more data points than any Ocean carrier could hope to use. “We have many different data sources,” Mr. Dowse went on to say. “We correlate them and continue to update the ETA. An ETA 28 days out is not as accurate as one 7 days out. It is a rough prediction.”
In some cases, this rough prediction is useful. This early warning may lead a shipper to decide to expedite a shipment by air rather than risk it being late. “Air costs five times as much, but the goods get there on time.”
But more often, the closer a shipment gets to arriving at a shipper’s warehouse, the more important it is that the accuracy of the predicted time of arrival gets better. These warehouses need to make sure they have the capacity – the gate availability, the storage space, and the labor – to unload the shipment on the inbound side of the warehouse and then load these pallets or packages on outbound trucks.
At each node – truck to port, offloading the truck at a port terminal, goods loaded on ship, etc. – FourKites updates its predictive time of arrival based on the latest data. According to Mr. Dowse, “at each point along the way we adjust our prediction.” And these are not necessarily simple adjustments. “Assume the ship gets to Long Beach and sits for 5 days, but the plan estimated the ship would sit for 3 days. You can’t simply adjust the ETA by two days. You must understand the new train schedule. Our ETA is better than what any carrier could achieve.”
Similarly, FourKites can make better predictions for some shippers than others. Some shippers have more and better data sources that can be leveraged. For example, one shipper may have geofence data on when a truck is leaving a facility, while other shippers rely on less reliable EDI messages.
Mr. Dowse applauds Hapag-Lloyd for their container tracking program and hopes other large ocean carriers will follow suit. This becomes another data source that the FourKites platform can ingest.
But Mr. Dowse went on to assert that container level visibility is not sufficient. “Shippers want SKU (stock keeping unit) visibility, not container visibility.” For example, the closer a retailer gets to a product’s selling season, the better the retailer understands how the product should be allocated to different stores based on an updated demand forecast.
FourKites’ perspective is that improving the data on one leg of a shipment is laudable. But what shippers need is visibility across all nodes and modes, and good predictive times of arrival that account for an end-to-end journey down to the purchase order and SKU level.