How AI Will Continue to Transform Logistics

When it comes to AI, logistics businesses should take their lead from digital marketplaces such as Amazon and DoorDash, says VP of Engineering, Invisible Technologies, Adam Haney.  

“Both Amazon and DoorDash are using AI to transform the way they do everything from recommending products through to last-mile delivery,” he explains. “By harnessing the power of AI they have streamlined their operations, improved their services, and expanded their operations.”

“Many logistics businesses can do the same - and for far less than they probably think.”

How E-Commerce Showed the Way Forward

Haney notes that, while DoorDash is the United States’ most popular food app with around 65 percent market share, the core of what it does comes down to logistics.

“DoorDash used AI to help solve the challenge of effectively connecting individual merchants and customers with its deliverers, in an environment where efficiency, productivity, and cost are everything.”  

This included optimizing routes, forecasting delivery times, and allocating orders to the most suitable driver (known as Dashers). It also implemented dynamic delivery pricing based on factors such as demand, weather conditions, driver availability, and other variables. 

Meanwhile, Amazon began implementing AI almost 25 years ago in its personalized recommendations. Since then it has integrated AI into every step of the buying and delivery process, including inventory management, stock picking and packing, and shipping and delivery. It also uses AI to optimize drivers’ delivery routes.

Complex, Data-Heavy, and Disparate

While both DoorDash and Amazon may be digital giants, Haney argues that smaller logistics companies also have many of the ingredients at hand to emulate these practices, including the most important one - lots of data. 

“Data is the fuel that powers AI, and even the smallest logistics companies generate significant volumes of it,” Haney explains.

“A typical logistics operation is likely to have mountains of customer and order data, as well as tracking information and delivery schedules. Then there will be bills of lading, bills of materials, and other documents to understand the content of different containers or vehicles or vessels.”  

The main challenge, according to Haney, is that this data is unstructured, or from disparate - often outdated - sources.

“Some logistics companies are still using technology like the fax machine to process orders, " he says. “Others try to get around this by using a protocol known as EDI (electronic data interchange) but it is often criticized for being complex, inflexible, and expensive.” 

How Data Can Propel Logistics Businesses Further

To make their data effective enough to use for AI, logistics companies need to collect, ‘clean’ and ‘annotate’ it. That means labeling or tagging it in a way that makes it understandable to AI algorithms. However, this isn’t out of reach for most logistics companies, according to Jean-Paul Biondi, Invisible’s VP of Operations.

“This is the same issue a major retailer client faced,” Biondi explains. “With the rapid rise of online shopping in the COVID pandemic, digital ordering took off massively. To keep up with demand, it suddenly found it needed to catalog hundreds of thousands of items to make them available to shoppers.” 

“The challenge was that each of these items needed to be described in a way that was accurate and searchable, but also resonated with their audience. That might sound straightforward but telling for instance, whether one bottle being displayed is mascara and one is eyeliner, can be a lot more difficult than it sounds.” 

Biondi says that Invisible used a team of humans to complement AI by going through each image and making sure that its contents were described properly. 

“This allowed us to cut the average onboarding time significantly,” he says.

Making AI Cost-Effective

After data is annotated, it can be fed into an AI/ML model, which then also needs to be trained, evaluated, and fine-tuned before it can be evaluated and deployed - something that can take months and requires the input of SMEs, as well as ongoing maintenance.

But, even then, Biondi says logistics companies shouldn’t be deterred. 

“AI isn’t necessarily that expensive, especially in light of the efficiencies it can bring to an organization and the savings that can be made in a very short space of time,” he explains.

“For logistics companies, AI projects can also be small, manageable and discrete - revolving around the ‘low hanging fruit’ of inventory management, demand forecasting or route optimization.”

“Once it has been embedded in that process, it can then be rolled out more comprehensively,” he says. “So that it really optimizes all operations and provides a genuine competitive edge.” 

Finally, Biondi points out that logistics firms don’t need to go to the expense of building in-house capacity to start capitalizing on AI. Instead, they can outsource integrating AI to an experienced third-party provider.

“Partnering with a firm that specializes in AI can provide smaller logistics companies with easy access to advanced technologies,” Biondi concludes. 

“This way, they can enjoy the benefits of AI-driven logistics solutions without the challenges of building and maintaining these systems in-house.”