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Cargo's Crystal Ball: The Power of Machine Learning

Many shippers were caught off-guard last August when the world's seventh-largest container line suddenly went into receivership. An estimated $14bn in cargo was left in limbo after two-thirds of South Korea-based Hanjin's fleet - some 93 vessels - were seized, stranded or denied entry into ports.

Shippers opened their contingency playbooks and began the costly process of rebooking shipments on other container lines; in some cases, shippers had to resort to alternative modes of transportation to mitigate shipment delays.

But what if shippers had known well in advance that Hanjin was on the brink of collapse? With the diffusion of so-called “machine learning” technologies, such foresight into looming supply-chain disruptions may no longer be the undertaking of a soothsayer with a crystal ball.

Although early warning systems have long been around to help cargo avoid weather hazards, such as an oncoming blizzard or hurricane, utilizing available data to detect the deteriorating financial state of a supply chain partner requires a different barometer. DHL Resilience 360’s Supply Watch, for instance, utilizes a machine-learning algorithm to recognize indicators for more than 140 different types of risk. Supply Watch was created out of a desire to pick up the “more nuanced risks, like supply failure, financial issues and compliance issues that may be very specific to companies and suppliers that our customers work with,” said Shehrina Kamal, senior product manager at DHL Resilience 360.

Machine learning represents the next iteration of data analytics, and finds applications in situations where rules-based programs cannot be developed to determine a solution, or when the volume of data is too large to handle manually. The algorithm “gives us an automated way of dealing with vast amounts of data in a proactive way,” said Kamal.

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