The Role of AI and Machine Learning in Real-Time Decision-Making in Logistics

Logistics professionals must deal with an ever-changing assortment of variables when getting goods to their destinations on time. No universal solution can solve all these difficulties, but real-time decision-making tools could help. These offerings use artificial intelligence (AI) and machine learning to evaluate the impact of different variables, assisting people in making the best decisions. Here are some potential outcomes of combining AI and logistics.

Reducing Brexit-Related Workloads

The United Kingdom’s Brexit vote has complicated duties for many logistics professionals, resulting in increased paperwork and procedures to follow. However, one of the main reasons to pair AI and logistics is algorithms can handle vast amounts of real-time data and give professionals the information necessary to make confident decisions about complex situations.

Representatives from one company that offers global supply chain solutions indicated Brexit had caused a 500% workload increase within its United Kingdom operations. That is considerable — especially since there was only a 40% jump among European businesses. Moreover, estimates suggest organizations will process 200 million more customs declarations due to Brexit.

Fortunately, artificial intelligence can help because it excels at recognising patterns. Some AI tools can also automatically extract critical details from incoming text-based documents and take predefined actions based on what the words say.

Logistics professionals then have more time to focus on their job’s most challenging decision-making duties instead of getting bogged down by administrative tasks. Relatedly, some AI tools can track trends and show percentages based on real-time conditions. People can use that information to determine which responsibilities to tackle first or decide if they can delegate.

Helping Deliveries Arrive Faster

Many people without direct experience in the logistics industry do not realise the extraordinary number of factors that could contribute to late deliveries. Speed is key — especially with more customers wanting their items as soon as possible, sometimes on the same day. With the help of AI and logistics tools for better planning, people can understand what causes delays. Even better, they can receive suggestions on what to do to help drivers make up time.

That might mean an AI tool reroutes a driver based on an accident five minutes ago that has slowed traffic. Some platforms feature real-time shipment tracking, too, showing individual parcel locations. That information gives accurate details to customers enquiring about expected delivery times.

One of the convenient things about using AI to improve last-mile delivery is it can create a database full of more details than humans could remember. That is one of the defining concepts behind a company called Beans.ai.

The team has spent many years compiling information by paying delivery workers to record all the steps required to get a parcel to the right place. The content goes beyond addresses and includes details about whether the property has a receptionist and if there are specific rules about receiving large packages.

Once AI provides those details to a user, they can look at their options in real time and decide where to send drivers based on the information they have. That approach reduces surprises that could result in parcels getting lost or delayed.

Keeping Everyone Properly Informed

The supply chain often involves moving goods across borders and using multiple transport modes. Ensuring everything runs smoothly is not easy, but circumstances typically improve when people have accurate information to rely on during each step.

Combining AI and logistics may mean using algorithms to get the best prices with freight-forwarding tools — especially if those products use real-time information. Then, logistics leaders can know they got the best prices based on their inputted parameters.

Artificial intelligence also supports collaborative visibility by giving people immediate access to critical information. Suppose a perishable shipment is at risk of arriving in unsellable condition because the delivery driver left a refrigerated truck door partially open. A solution that combines AI with smart sensors could alert all relevant parties within seconds of the system recognising the open door.

Some AI tracking systems allow warehouse staff members to see the estimated arrival times of trucks in transit. Having that information in advance prevents potential backups by ensuring employees are ready to start unloading as soon as the vehicle arrives. Such systems significantly reduce driver waiting times.

Machine learning tools can also track specifics such as the percentage of time supply chain partners achieve minimum metrics for things such as on-time deliveries. That information justifies when it is time to talk with those parties and warn that their performance is not up to standards.

Saving Money Through Autonomous Decision Making

Most real-time decision-support solutions that blend AI and logistics provide all the information a person needs or wants to reach an informed conclusion. However, some tools take things even further, making choices on a user’s behalf.

Vorto’s Reload tool is one example of a platform that can do that. It automates most supply chain functions, from demand forecasting to driver dispatching. The organization estimates logistics professionals could save $400 million annually by using Reload. It works by creating an “automated marketplace” that handles all transactions between customers, suppliers and other parties.

People can then use a dedicated feature called AutoPilot to activate autonomous decision-making capabilities. It calculates the best courses of action based on current information, then proceeds without ongoing human intervention. Users then have more time to devote to other duties. This solution also frequently results in environmental benefits, such as reducing the number of trucks on the roads or cutting emissions in additional ways.

Understandably, some logistics professionals may not want to leave all decisions up to AI. However, they should feel excited and hopeful that autonomous options such as this one are available to help them balance their workloads.

AI and Logistics: A Smart Pairing

These are some of the many ways people can make better choices as logistics professionals by using AI tools with real-time decision-making support capabilities. Having accurate information is a good start, but it is even better when people receive guidance on what to do with the details they have. Artificial intelligence can do that and more, positioning users to thrive in the modern logistics landscape.

Eleanor is the founder and managing editor of Designerly Magazine. She’s also a web design consultant with a focus on customer experience. She lives in Philadelphia with her husband and dogs, Bear and Lucy