Machine learning (ML) algorithms are helping to solve problems throughout the supply chain, from the back office to the warehouse and even on the road—primarily as a driving force behind the many tech tools on the market that are designed to improve visibility into logistics operations. Just ask leaders at Express Logistics, an Iowa-based third-party logistics service provider (3PL) that recently implemented ML-based decision intelligence technology in response to disruptions that hampered its transportation operations throughout 2022. The nonasset-based 3PL specializes in less-than-truckload (LTL), truckload, and intermodal freight solutions, and like many companies, couldn’t escape the effects of natural disasters and other events that snarled supply lines last year.
“When you’re in this industry, you know disruptions are inevitable,” Melissa Marcsisak, Express Logistics’ vice president of operations, said in a statement describing the project, pointing to the impacts of natural disasters and other events, such as the Canadian truck driver protests of early 2022. “A total of 41% of the state of Michigan’s $56 billion in exports goes into Canada, and the protests impacted our shipping costs for about three weeks. In the fall, Hurricane Ian and Hurricane Nicole severely disrupted shipping lanes just 43 days apart from each other. That was the tipping point—we knew we needed better visibility to react faster.”
Express Logistics turned to tech provider Third Axiom Solutions to address the problem, implementing the company’s Axiom-One decision intelligence platform. The software platform uses artificial intelligence (AI) and machine learning capabilities to provide insight into Express Logistics’ shipping operations, sending alerts and identifying alternatives when volatility occurs in the market. The system combines learning algorithms, natural language processing, forecasting, statistics, and dashboarding to create a view of what’s happening across the company’s transportation network—so that managers can make quick decisions in turbulent times.
CREATING SMARTER SUPPLY CHAINS
Machine learning is a branch of AI and computer science that uses data and algorithms to imitate the way humans learn, improving its accuracy over time. Using statistical methods, the algorithms are trained to make classifications or predictions and to mine data for key insights. This is especially helpful in business applications because it allows workers and managers to make better, more informed decisions. Tech providers and logistics professionals alike say the rise of AI and ML is helping to create smoother-running, smarter supply chains.
And that’s exactly what’s happening at Express Logistics. As Third Axiom’s leaders explain, Axiom-One connects with the 3PL’s transportation management system (TMS) and other existing systems to extract transportation data and provide actionable insights for better decision making. The platform models each action for Express Logistics as a set of processes, using intelligence and analytics to inform, learn from, and refine recommendations for a given situation.
“We were able to provide Express Logistics with the tools and resources to become proactive instead of reactive during transportation disruptions,” Tim Story, the tech firm’s co-founder and managing partner said in the statement. “They now have insight into what carriers are charging for specific lanes so that they can respond quickly—saving time, money, and additional resources across their network.”
Express Logistics’ leaders also have more up-to-date information at their fingertips. Among other benefits, the platform provides updated reports as often as four times a day instead of the nightly uploads the company received in the past from its business intelligence system. What’s more, Express Logistics can now compare its historical data back to 2006, showing how the company has performed year over year alongside the broader industry during some of the most volatile times.
Express Logistics’ leaders say they plan to use the platform as a daily data source, eventually integrating other business processes into the system.
“This year, we’re expecting to have all of our data regarding our lanes on the Axiom-One platform so that we can make informed decisions quicker,” Marcsisak said. “The platform will be our daily data source, and we’ll eventually integrate our forecasting, budgeting, and goals into it as well. It’ll ultimately be our single source of insight into our entire network.”
