This commentary appeared in the print edition of the Jan. 6, 2020, Journal of Commerce Annual Review and Outlook.
In the business of shipping cargo internationally by water, we always seem to be living in interesting times. Today is no exception.
Today, we face continuing demands from customers for shorter and shorter delivery times that necessitate higher-velocity supply chains, while the assets — ships, terminals, trucks, and trains — that carry cargo continue to move slowly and unreliably. For example, container ships are on time only 75 to 80 percent of the time, and when they are late the average delay is 3.5 days. US intermodal train speeds are almost 12 percent lower in 2019 than they were in 2016. In many cases it now takes four or five days from vessel arrival to container out-gate, when it used to be 2 days. Truck wait times at marine facilities continue to be an industry issue. At the same time, consumers now expect the longest they have to wait for the delivery of a purchase is two days, and Amazon advertises deliveries within two hours.
How can this issue be addressed by supply chain managers? There are two choices: A) Carry more inventory or B) Utilize enhanced visibility along your supply chain as a substitute for speed. In this case, plan (B) is a much better option, and new technology is making it a viable option. Using big data and machine learning, transportation providers can now predict with much greater accuracy where their cargo will be at any point in time and produce reliable vessel and door ETAs. This will allow supply chain managers to use predictive analytics to move from static “descriptive” visibility to “prescriptive” visibility and manage their cargo proactively to better meet their customers’ needs, while still controlling inventory and other supply chain costs.