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Trucking Executives Cite Opportunities for AI Adoption
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LAS VEGAS — Artificial intelligence and machine learning promise to unlock greater efficiency and improve decision-making in the freight transportation industry, but implementing AI functions is not without its challenges, trucking executives said.
Use cases for AI range from simple chatbots to answer drivers’ questions to generating insights on preventive maintenance and pricing decisions, said Daragh Mahon, executive vice president and chief information officer at truckload carrier Werner Enterprises.
By examining the many data points flowing from modern trucks, for example, AI capabilities can help predict when a component should be replaced before a costly breakdown occurs.
“The possibilities are endless,” Mahon said during a Feb. 7 panel discussion at the Manifest 2024 supply chain and logistics conference.
Werner, based in Omaha, Neb., ranks No. 17 on the Transport Topics Top 100 list of the largest for-hire carriers in North America.
Despite the opportunities, putting AI into practice can be complex.
David Broering, president of integrated logistics at NFI Industries, said the trucking and logistics provider has taken a deliberative approach to using AI focusing on areas where the company trusts the data and the technology creates clear value.
“I think it’s overrated,” he said. “It’s not as cheap as people think it is. Transportation is a pennies business.”
Employees sometimes struggle to interact effectively with AI tools, which may not return the right result if they use different terminology, such as “load” rather than “shipment,” Broering said.
“You have to work really hard in those generative models to get the AI to really work with your users across a broad spectrum of queries to get true value of it,” he said.
Apart from enterprise-level AI implementations, transportation professionals can use commercially available AI tools to streamline a variety of business tasks.
For example, Werner’s development teams use Microsoft Copilot to save time and effort when writing code, Mahon said.
“The amount of mundane code writing that these guys don’t have to do anymore is just fantastic,” he said.
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Another challenge for AI implementation is properly structuring and formatting supply chain data, which resides in different locations and comes from a variety of sources.
“That’s the big challenge — how do you aggregate all this information and put it in one place,” said Erik Kiser, CEO and founder of Orderful, a software firm working to streamline electronic data interchange, or EDI, in the logistics industry.
Transportation companies are collecting and storing massive amounts of data in the hope that it will lead to business improvements, but much of that information is not being put to use today, Werner’s Mahon said.
“I think we have to get to a place where we’re collecting just the data we need,” he said.
Mahon also made the case that the transportation industry ultimately should move away from EDI and embrace open application programming interfaces, or APIs, to exchange data.
EDI technology has been in use for several decades and hasn’t changed much during that period, he said. “I think APIs are the way to go. I think that’s the way we are all headed.”
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Broering said NFI has been working with Orderful to utilize EDI more efficiently in its operations.
“It makes the really brutal pain of EDI sort of fade away,” he said. “I don’t really care if EDI goes away anymore. For me it’s fine, let it continue.”
Other industries such as health care also are harnessing AI to make better use of data.