A case study arising from an innovative partnership, featuring Erez Agmoni, global head of innovation with Maersk, and Krenar Komoni, founder and chief executive officer of Tive.

The challenge for Maersk was figuring out how to acquire insights into shipment status for general cargo, Agmoni says. Tracking devices were relatively expensive, and not practical to be applied to that category of goods (as opposed to high-value or perishable cargoes). And the available software wasn’t up to the job. What Maersk needed, he says, were insights into aggregated data, not on a shipment-by-shipment basis.

Komoni says that approach made it possible to derive value from information about general cargo movements. Aggregated data gave the carrier the necessary level of visibility, justifying an investment in tracking hardware.

Maersk was motivated to seek a solution because the lack of transit time visibility for general cargo shipments made it impossible to plan routings and predict how they would be executed. So the Maersk Innovation Center designed a proof of concept for a means of keeping tabs on thousands of shipments without having to view each one individually. 

The higher-level view yielded some surprising insights. Shipments moving by truck from Los Angeles to Memphis have a choice of two main routes, one close to the U.S./Mexico border and the other farther north. The latter seemed to be the faster alternative — until a review of aggregated data revealed the opposite. It turned out that drivers taking the northern route were stopping more frequently because rest areas along the way were smaller and more crowded, so they would start looking for a place to stop sooner because they didn’t trust that one would be available when they maxed out on their hours of service. Maersk would never have thought to ask drivers to explain their behavior without an aggregated view of transit times. “You can’t find this information if you follow one shipment at a time,” Agmoni says.

In the end, he says, the solution produced more precise transit times and even generated “push” notifications well in advance of a truck’s arrival at destination.