Both fleets and shippers are already optimizing business processes with the help of machine learning (ML) and artificial intelligence (AI), but as advanced as these processes appear, it is often a matter of using existing data. It doesn’t need to be limited to the data points in a spreadsheet, either. Pierre Pelletier, director of strategic growth at Nuvoola AI, said during a webinar hosted by LP Tardif and Associates that his company was able to leverage existing CCTV video feeds to aid security gate work. Even a stream at only 15 frames per second can be sufficient with some tweaking . (“Often we have to add a couple of cameras and readjust a few of the angles.”)
That alone can leave an algorithm to identify things like licence plates, fleet logos and addresses, he explained. “We call this ‘in the wild’. So this is not standardized information.”
When the various types of data are drawn together, tasks can then be automated to give staff “super human powers” and help them escape manual and repetitive tasks such as looking at trailer seals, he said.
One of the large couriers is already using cameras to upload bills of Lading to customer locations, saving around half an hour per truck and around 15 hours a day at the terminal. The courier also has 12 different terminals, he added. Human voices can be captured, just like the Alexa systems found in many households. And Pelletier said it was important to address outliers that occur in the real world. “We are talking about humans, and things happen.” Louis-Paul Tardif of LP Tardif and Associates added that the benefits of AI and ML can be as varied as solutions to optimize routes and facilities, manage driver detention, improve maintenance practices and streamline back office work.
“With AI and machine learning, it’s one of the main pillars of having a competitive advantage in the market,” said Bissan Ghaddar, a partner in OptiAI and associate professor at Western University’s Ivey Business School.
It’s why one customs brokerage approached her company admitting that it had plenty of data but didn’t know how it could be used. “They want us to come up with insights,” she said, referring to the data that eventually helped to profile customers and products, analyzing supply chains and forecasting revenue.
One benefit is that deep learning and machine learning can take into account external factors when data sets are more complicated, Ghaddar said. For example, working with Transport Canada, OptiAI helped identify the trucking operations that had the greatest economic impact during Covid19.
Optimizing different networks can also help to retain drivers, she added. “Use machine learning to find driver preferences, driver behavior, and provide accommodations to make them happier.” In the meantime, dispatching processes can be refined to minimize empty miles and maximize the revenue per mile.
Novoola even helps government officials recognize TDG signs using cameras and match them to specific license plates. And a large courier in Atlantic Canada turned to AI to measure pallets because the process was typically done by hand. Algorithms can monitor heat maps that track traffic in a yard, too.
The goal is to transform such information into meta data, and centralize the information to automate tasks, Pelletier said. “We like to have it as real time as possible, to make specific business decisions.”
Ivado Labs, meanwhile, has over the past two years worked with the Port of Montreal to optimize terminal operations. The end result has helped better predict arrival times, volumes and destinations between ships and rail. “There are key components in that project that can be applied to trucking,” said director of product machi David Lederhendler.
The challenge is that the internal data was fragmented. “When we pose questions around data, it’s always a challenge,” he said.
It was a challenge worth taking. The algorithms now help the port to determine which containers are to be assigned to certain modes of transport and can even take restrictions such as capacities into account.
“The algorithm has helped us highlight a lot of inefficiency in the system,” Lederhendler said, noting that the port expects to boost capacity by 10-15% as a result.
In another case, a fleet used the centralized data as “one source of truth” to renegotiate with partners and address least-minute changes through a series of “what-if” scenarios, he said.
But there also needs to be a willingness to use the data as it emerges, especially since the outcomes are not always understood until the work is completed.
“How will users interact and use that information that is sometimes counterintuitive to their gut feeling?” Lederhendler asked. “It’s a long process. It takes time, and data will be key, but also the human behind the data, to make sure they understand what’s going on.”
