Artificial intelligence is no longer a prediction for the future of the shipping industry. It is fully integrated into each stage of delivery, from the import of raw materials to last-mile delivery to customers’ doorsteps.

What exactly is artificial intelligence? In basic technical terms, it means that computers can process information on their own, with little more than datasets provided by humans. Put another way, it means that computers are able to actually learn; “machine learning” is a term that’s often applied to artificial intelligence  — and this technology is in high demand in multiple sectors of the global market.

Machine learning typically starts with “big data,” which refers to huge datasets of information that humans may not typically be able to synthesize. Computers look at all that data — in the case of shipping, it might be shipping profile data combined with weather, traffic, and global economic performance data — and synthesize it, drawing deeper and more powerful conclusions than humans can and encouraging supply chain growth and advancement.  

In the shipping industry, AI has been used for everything from optimizing the way boxes are packed on truck beds to making maps for UPS delivery drivers. And there’s a lot more on the horizon.

Related: Decoding Your Shipment Data: Surprising Information Your Analytics Reveal

Accurately Predicting Shipping Times

One of the most tangible benefits of big data is the ability to make accurate predictions based on historical patterns. Analysts can use huge datasets not only to understand the transport time of cargo ships, trucks and planes, but to understand how long they wait to unload cargo at warehouses, how many cranes are available at port, and how long inspections take.

Big data can also incorporate information about weather patterns, the relative busyness and slowness of certain shipping seasons, and congestion in shipping lanes.

AI can synthesize all that data almost instantly, offering better estimations of ship times than have ever been possible before. It can also spot potential problems well in advance, giving supply chain managers to make adjustments if necessary.

Improving Shipping Speed

AI has also made it easier to ship goods faster. This happens at a variety of levels, from savings of a couple seconds to those of days.

On the small end, AI helps shippers and third-party carriers optimize terminal operations, warehouse operations, and last-mile delivery route. At warehouses, for instance, many logistics providers give employees AI-powered electronic handsets or headsets that tell them which packages to pick up in which order.

AI has made trucking yards more efficient, helping trucks move through them, unload cargo, and get back out onto the road faster. And UPS recently re-designed the algorithm that guides its delivery drivers, eliminating as many left turns as possible to help them deliver a few additional packages per day.

None of these changes has a significant impact on its own. But at scale, across networks that deliver thousands of packages per hour, it can mean millions of additional parcels — and millions in additional profits.

Bigger savings come as a result of bigger changes. Logistics providers may use AI to predict the optimal location for a new warehouse, for instance, minimizing as many transport distances as possible. Trans-oceanic shippers can use predictive AI to avoid crowded shipping lanes or pack cargo more efficiently so that it reaches its destination sooner.

Related: How to Use Effective Analytics for Controlling Your Parcel Spend

Mitigating Inefficiencies and Optimizing Maintenance

AI has also helped shippers better maintain the equipment used across their logistics networks. AI can support predictive maintenance, flagging vehicles that are reaching certain milestones or identifying malfunctioning equipment.

Additional environmental regulations will make shipping companies even more dependent on AI. For example, the UN International Maritime Organization is capping sulfur content in fuel oil for container ships and other marine vessels at 0.5% beginning in 2020. AI will help marine shippers know when their container ships are at risk of violating those limits, and advanced modeling can help with the design of more environmentally efficient vessels.

With AI’s ability to regulate a vessel’s environmental efficiency, it can help reduce unnecessary fuel consumption, greenhouse gas emissions and carbon dioxide emissions. Shippers can save money on fuel prices while making an impact on the global shipping industry by mitigating freight carriers’ inefficiencies and reducing their carbon footprint.

Related: What is Big Data in the Parcel Shipping Industry?

Mimicking Human Perception to Make Decisions

AI is the type of technology that controls autonomous vehicles. In the shipping industry, that includes everything from self-driving trucks that will transport cargo across hundreds of miles to warehouse robots that negotiate tightly packed shelves and narrow aisles. Autonomous vehicles have already been deployed in warehouses, and experts agree that they’re on their way to our streets — it’s just a matter of how many years it will take to get there.

These vehicles need to be able to imitate human senses, including responding to visual cues and sound. Their ability to read and interpret data from powerful sensors is key to their success. If they can do that successfully, they can effectively replace many human logistics workers — they can do almost everything we can do without getting tired or needing salaries or benefits.

Improved Security

The shipping industry is also using AI to make itself more secure.

Shipping carriers now use drones to police the grounds around their warehouses. Big data can help logistics providers identify common sites of traffic accidents or package thefts and design their services around those. Locker services for apartment lobbies, like Hub by Amazon, are one such response.

Continued Transformation of the Shipping Industry

The shipping industry has always been made up of a complex network of transport needs. From the container ship that carries raw material to a factory to the cranes that unload components to the warehouses where finished goods wait for their final shipment — managing all those moving parts is so complicated that the people who specialize in it, supply chain executives, are now C-suite-level leaders.

Leaders who successfully incorporate AI into their supply chains can build networks that are more efficient than ever. This saves fuel, time, and most importantly, money — freeing up resources for shippers to invest in their products and teams.

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