As the shipping and logistics industry moves toward a fully digital, data-driven future, a powerful new tool has appeared on the horizon: artificial intelligence.

According to Gartner’s Hype Cycle for Supply Chain Strategy, 2017, the shipping and logistics industry may be on the verge of a completely digitized future. “We expect that artificial intelligence (AI), machine learning, corporate social responsibility, and cost-to-serve analytics will all drive significant shifts in supply chain strategies within the next decade,” reports Gartner Research VP Noha Tohamy, the chief analyst on the report.

In shipping and logistics specifically, continuing advances in machine learning and other AI technologies stand to benefit companies beyond existing or emerging automated solutions like self-driving trucks and human-free ports. When paired with the deluge of data gathered by Internet of Things (IoT) devices in the broader supply chain, the massive analytical power of machine learning algorithms has the potential to improve the efficiency — and, ultimately, the cost-effectiveness — of the entire shipping process.

Next-Level Preventative Maintenance

Mechanical failures are one of the most expensive problems shipping companies currently face — both in terms of actual repair costs and time lost. If a truck breaks down on an isolated stretch of highway, for example, it’s almost inevitable that the shipping company will end up taking a loss on the load being transported.

Some are striving to address this problem by creating systems of IoT sensors that help fleet managers perform preventative maintenance with remarkable precision. For example, advanced machine learning algorithms are capable of rapidly processing the voluminous data captured by the sensors and “learning” which statistical indicators most accurately predict when a vehicle will be in need of repair.

In effect, these technologies are becoming something of a “crystal ball” for the industry. Companies can predict when issues and/or breakdowns are likely to occur, thereby enabling them to address them proactively rather than reactively. This minimizes downtown and keeps the cargo moving, ensuring partners remain satisfied and ROI remains high.

Better Disruption Management

Machine learning algorithms are also well-suited for broader disruption management operations. A shipping company may have a fleet of well-serviced trucks and a comprehensive catalog of optimized routes at its disposal, but variables ranging from highway closures to adverse weather conditions can easily throw a wrench into even the best-laid plans.

A number of machine learning innovators have started entrusting algorithms with selecting the best alternative port when the original port is blocked, estimating times-of-arrival, and even gauging the likelihood that a carrier will cancel a booking or roll a container and leave it on the dock.

In a similar vein, IBM and its subsidiary The Weather Company have jointly created a project known as “Deep Thunder,” a machine learning model designed to help companies — in shipping and other industries — gain a deeper understanding of the impact that severe weather will have on their operations. By applying the computing power of IBM’s renowned Watson to more than 100 terabytes of weather data a day, Deep Thunder produces “far more reliable weather forecasts, including the kind of location-specific information about impacts of storms, hurricanes, and typhoons that is vital for supply chains to know.”

Optimizing Chassis Pools

Regarding infrastructure, Dr. ManWo Ng, Professor of Maritime and Supply Chain Management at Old Dominion University, has used machine learning algorithms to improve the management of port terminals’ chassis pools. Dr. Ng’s algorithms integrate both current and historical data about how many vessels are at berth in a given port, how many import containers are discharged on specific days, how many empty containers are received, and how many gate transactions are recorded to forecast the number a chassis a port will require with a great deal of accuracy.

“Not only can chassis demand be better predicted,” Ng points out, “the availability of predictive models also means lower repositioning costs and a reduced environmental impact due to the elimination of unnecessary chassis repositioning trips.”

Preparing for the Future

As the Gartner report makes clear, widespread AI adoption in the shipping and logistics industry is still years — and possibly decades — away, but this doesn’t mean that companies should sit back while their forward-thinking competitors pass them by.

At Primary Freight, much of our success over the past 20 years can be attributed to our commitment to deploying cutting-edge, custom technology for our partners. Regardless of your company’s tech-savviness, we can work with you to develop a specialized shipping and logistics solution that will position you for success both right now and in the AI-driven landscapes of tomorrow.

If you’d like to learn more about what makes Primary Freight an ideal shipping and logistics partner, give us a call at (800) 635-0013 today!