There are always supply chain lessons to be learned from catastrophic events like Hurricane Harvey. Stepping into our data science lab over the course of the past week has been an interesting experience for me. Lining the exterior of this war room of sorts are a slew of monitors showing the real-time movement of ocean vessels, aircraft, trucks, railcars, parcel shipments, weather cells, earthquakes, pockets of civil unrest, port congestion, airport traffic and a host of other supply chain related events around the world. It’s daunting to see this mass of moving icons on a screen, each depicting a different event or conveyance. Fortunately, users can filter on any number of events, effectively pulling hay off the haystack to see only the information they are interested in. And the truth is, our system automatically removes hay from the haystack for each of our customers, only showing them risks that pose a current or predicted risk to their conveyances, assets or supply chain nodes. After all, why would a company want to see an earthquake in Mongolia that has no bearing on their supply chain. But I digress.
Relating back to Harvey, on August 25, as the brunt of the hurricane was making landfall in Corpus Christi, our real-time control tower showed an inordinate amount of ocean vessels holding off the coast near Houston, roughly 225 miles north of the hurricane’s eye.
These vessels, of course, were waiting to see what path the hurricane would take, before mooring in the port of Houston. Late on the night of August 27th, the hurricane took a sharp right turn and headed up the coast of Texas toward Houston. As news of that shift made its way to the ocean carriers, the cargo vessels holding outside of Houston began clearing the area, and the real-time dots on our control tower showed the mass exodus.
In the days following, those vessels originally headed to the port of Houston began lining up at alternative ports. Understandably, the dwell times at those ports began to stretch out considerably, and there was a run on over the road carriers to transport the goods coming off those vessels to their ultimate destinations, causing spot rates to spike. Those shippers that anticipated this run, and secured transportation capacity earlier, have made out much better than those who waited. The early bird gets the worm, as they say, and the customers expecting goods from the more forward-thinking shippers will get their goods before the laggards.
This game of anticipation, educated speculation and action has played itself out for years in supply chains all over the world, sometimes as a result of catastrophic events like Hurricane Harvey, and much more often as a result of more pedestrian disruptions resulting from supplier capacity issues, run-of-the-mill port congestion, or unscheduled port stops by ocean carriers.
Heretofore, supply chain professionals have had to rely on latent notifications of external disruptions and carrier visibility, and their own experience to anticipate and react to these issues. How nice is it to get further left of these issues through real-time visibility and predictive analytics?
Our customers were the beneficiaries of real-time insights related to the hurricane and it predicted impact on their shipments and assets. It’s a good time to be in supply chain!