2021-09-29

Driving AI forward in transportation and logistics

by Vawn Himmelsbach, a technology and business writer
Montreal, Quebec - September 30, 2021
Transportation
When it comes to artificial intelligence, self-driving vehicles and intelligent drones tend to make the headlines—but anyone working in IT or in lines of business know that applications in the backend have huge potential to drive business forward, particularly through connected supply chains.
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Making the case for AI requires getting the C-suite on board to invest in the technology and train the human capital required to go the distance — since it may take two to three years to see a return on investment. One way to do this is by demonstrating how AI can help the business be more resilient.

These days, resiliency is top of mind in the transportation and logistics industry. At the start of the pandemic, companies were hard hit with global supply chain disruptions, extreme surges in demand for certain goods and plunging demand for other goods — combined with rising prices, container shortages and inventory challenges. And it could take several years to get global supply chains back on track.

But it’s also forced companies to digitize processes and look for new ways to be efficient and resilient, in light of this uncertainty and disruption. It’s not about building bigger, but building smarter — especially in transportation.

“At some point you reach full capacity. We’ve seen it with the Suez Canal — those ships are becoming so large that you’re not going to be able to increase capacity by building bigger, so you have to work on the efficiency of your system to increase how fast it goes through.”

Clement Bourgogne, VP Strategic Programs at Scale AI, Canada’s artificial intelligence supercluster.

For IT directors, it’s an opportunity to build automation, machine learning and artificial intelligence into digitized processes and eventually embrace new partnerships and collaborations that will improve operations and visibility for all stakeholders in the supply chain. And two areas to start — for easy wins — include route optimization and asset management.

While most transportation and logistics companies are already using route optimization systems based on operational research, IT directors can take it up a notch by incorporating data analytics and AI to optimize routes based on big data.

For example, Scale AI is working with Montreal-based Ray-Mont Logistics on the Ares.AI project, which involves developing an AI-based decision support system to optimize its fleet of trucks, drivers, chassis and rails — helping to eliminate bottlenecks, reduce empty runs and improve asset utilization. In terms of rail, it will help minimize switch changes, car movements and operational delays, ultimately reducing expenses.

AI can also be used in combination with the Internet of Things to move from a model of preventative maintenance to a model of predictive maintenance — to repair assets when failure is impending, rather than through arbitrary check-ups.

The next step will be expanding route optimization and asset management across networks. “This is where bridging the gaps between different players is going to unlock significant value,” says Bourgogne. “Big rail companies, big shipping lines and even some big trucking companies all have to interact together at some point or another, but they’re not necessarily collaborating in terms of sharing data, so increasing predictability helps all of these companies maximize the use of their capacity.”

This would result in a more fluid, more predictable supply chain that’s easier to track, “so having that collaboration is going to be key to unlocking value, but also in increasing the efficiency of supply chains because that means less idle time,” he says.

While today this isn’t often the case, we’re starting to see examples of how this could work. Scale AI is working with the Port of Montreal, for instance, to build a ‘smart’ port that will navigate the movement of containers through a network driven by a complex web of information exchanges between numerous stakeholders.

By using AI and predictive analytical tools, they’ll be able to synchronize all elements of the rail and marine supply chain and improve upstream visibility. Ultimately, this will help all stakeholders reduce cargo delays and overall dwell time while maximizing throughout to the port’s logistics hub.

“It’s really about being more efficient with what you already have,” says Bourgogne. But getting there means coming up with the right business case to get stakeholders on board — and building from there.

“If the problem is not dire enough for the company, then the technology might not be perceived as valuable. But if you’re solving the company’s biggest problem and toughest issue, then people are going to flock to it because it’s serving a very concrete need. This is where IT directors can directly play a critical role in the design and implementation of a project — by identifying what issue needs to be solved.”

Clement Bourgogne, VP Strategic Programs at Scale AI, Canada’s artificial intelligence supercluster.

The success of a company’s first AI deployment will be defined by the level of adoption across the organization, but also by what else it triggers. “If it triggers more investments into this technology,” he says, “this is a positive sign that the organization is embarked on the AI journey and that it’s starting to evolve.”

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Gouvernement du Québec
Gouvernement du Canada