Optimizing one aspect of your operations often results in new challenges arising in another area.
Case in point: Quality Custom Distribution, a cold chain logistics company, discovered that improved truck routing led to increased stress on its reefer trailers.
“You do a great job of routing,” says Tim Bates, corporate quality systems director at QCD. “But you’ve got all your stores on Main Street, USA, and they’re all a mile apart. That’s really contrary to how a reefer can work and recover after you have to open a side door or a rear door. Basically, the better job you do at routing, the harder it is on your trailer.”
That’s where predictive analytics supplied by artificial intelligence (via EROAD’s CoreTemp technology) comes into play.
“We can predict accurately that, if this trailer is operating with this kind of thermal abuse, here’s what the temperature of lettuce is going to be three hours from now if we don’t correct this,” Bates says. “So, we know before we get to a threshold to cause an alert or to cause product damage that it’s going to happen unless we change course or direction”
Related: Check out this case study to learn more about QCD’s experience with CoreTemp
Artificial intelligence will play an increasing role in mitigating such “conflicting priorities,” as the technology continues to develop, says Steven Perrin, product and solution manager at EROAD. “And there are a number of other things that we hope AI will be able to help us with because as this growth happens, the infrastructure and some of the supporting services that we may not think about are going to come under pressure as well.”
Bates and Perrin recently participated in a webinar during which they discussed current and future AI applications in cold chain logistics and transportation in general.
Being able to turn data into accurate and actionable predictions is a key benefit of AI, they said. It allows motor carriers to be proactive to ensure successful deliveries, customer satisfaction and regulatory compliance.
“The speed at which you get answers allows you to be a better manager because you’ve got more information to make a decision with,” Bates says.
AI also helps carriers reduce their administrative burden and lower operating costs. For example, real-time AI temperature monitoring removes the need for manual temperature checks, saving driver time and streamlining reporting to customers and regulators.
AI Is Poised to Transform Maintenance
AI can also benefit overall fleet health, replacing periodic maintenance checks with predictive maintenance alerts. By analyzing equipment components and trends, AI can predict when equipment is likely to fail and recommend preemptive repairs, reducing the chances of unexpected breakdowns and extending asset lifespan.
“We’re using some very high-level data science to actually predict if a piece of equipment is going to shut down in the next seven days,” Perrin says. “What we’re looking to do here is de-risk the use of equipment for our customers going forward so they can make really good decisions attaching equipment to routes.”
The Future of AI in Transportation
AI is already helping carriers make sense of vast amounts of data, empowering them to make better operational decisions. Someday soon, however, AI could take on some of that decision making, automating common tasks like trailer pre-cooling, reefer temperature adjustments during journeys and maintenance scheduling.
AI also has the potential to help ease other challenges facing the trucking industry, such as the driver shortage, infrastructure issues, rising fuel and equipment costs and supply chain issues.
“Where does AI fit into this picture?” Perrin says. “How can we solve a driver shortage with AI? How can we mitigate access to equipment with AI? We’re really talking about finding efficiencies – finding ways to do more with what we have to save some costs and create better incentives or better compensation for drivers so that it becomes a more attractive industry to bring more people into.”
