Complex, interdependent processes. Tons of historical data. A constant drive to create efficiencies and reduce waste. These characteristics of the freight and logistics industry all set the stage for significant impact from artificial intelligence technology.

 

As you might expect, AI technology is first and foremost assisting many freight and logistics companies with supply chain optimization.

15%
improvement in logistics cost

Data from management consulting firm McKinsey & Company revealed that companies using AI for supply chain management have improved their logistics costs by 15% and their service levels by 65% in comparison to peers who didn’t adopt these technologies.

Beyond supply chain optimization, the freight and logistics industry is also leveraging AI in increasingly innovative ways. Driver health, employee development, and customer service are all areas in which AI technology has made an impact. Given the technology’s ability to solve problems and make decisions in ways that feel surprisingly human, these “softer” AI applications feel almost natural.

 

Below, we’ll take a deeper dive into eight ways that AI technology within the freight and logistics industry is creating efficiencies, solving problems, and even promoting mental health.

Fully Autonomous Sales & Operations Planning Drives Revenue Increases

As a McKinsey & Company report revealed, integrating a company’s supply chain from end to end opens the door for fully autonomous planning, powered by AI. In other words, when an integrated system gathers data from events occurring up and down the supply chain, forecasts can be tweaked, supplies ordered, and production schedules adjusted (and more!)—all without the intervention of a human being.

↑ 4%
revenue increase

After studying large, Asia-based consumer packaged goods companies, McKinsey quantified the benefits from fully autonomous planning. Their data revealed revenue increases of up to 4% and decreases in supply chain costs of up to 10%.

However, 80% of the consumer packaged goods companies that McKinsey talked to were still using traditional sales and operations planning procedures—often because they lacked the necessary end-to-end integration. Ultimately, only 7% of the surveyed companies used real-time optimization across fully automated digital systems in their sales and operations planning.

 

As hard data continues to emerge supporting the benefits of fully autonomous sales and operations planning, more companies will likely make the investment—and turn over their planning processes to AI.

Route Planning Reduces Mileage & Emissions

To increase efficiencies, reduce fuel costs, and lower emissions, UPS developed ORION (On-Road Integrated Optimization and Navigation) route-optimizing software. The platform’s goal? To leverage predictive algorithms in concert with UPS’s tracking systems to create better routes than human drivers could develop on their own.

 

At first, the idea was a hard sell. Could an algorithm truly create outperform an experienced driver with years of experience on the road?

100m
miles saved per year

Ultimately, the answer was yes. In the first eight years of its deployment, ORION saved about 100 million miles and 10 million gallons of fuel per year. UPS anticipates even more savings as the technology continues to improve. More recent upgrades to the system have added real-time updates for drivers in transit, based on changing road conditions.

In addition to using ORION for route planning, UPS also uses the system to predict future package volume so they can adjust capacity accordingly, saving more than 85 million miles driven per year.

Chatbots Deliver 24/7 Customer Service

Love ’em or hate ’em, chatbots are becoming increasingly prevalent in logistics applications.

 

Take, for example, DHL’s chatbot, “Marie.” If you’ve got a relatively simple question, Marie can assist. The AI technology powering Marie allows her to field plain-language requests at all hours of the day. For example, if you need to know the status of a package en route, Marie can assist. More complex requests ultimately get passed off to a human customer service representative.

 

Other logistics companies have also hopped on the chatbot bandwagon. DB Schenker has “Betty,” their digital assistant who helps with tracking questions 24/7.

XPO is using their AI chatbot technology to facilitate last-mile service. Their chatbot makes automated phone calls to customers, leveraging real-time Google Maps data to confirm the exact timing of their delivery. It also diverts drivers when customers aren’t home to receive their packages, resulting in both fuel and emissions savings.

Mobile App Monitors Driver Health to Improve Safety

While startups launch pilots for autonomous robot deliveries in Europe, other startups are looking at ways that AI technology can assist human drivers still behind the wheel.

 

The AI Mood Mobile Application, an AI-based predictive mood tracking mobile application, was designed to help identify early signs of stress among courier drivers. The app was created through a partnership between SalesChoice, an AI SaaS startup; Purolator, a courier and logistics company; and Ontario’s Autonomous Vehicle Innovation Network.

 

The app monitors drivers for signs of stress, which can lead to higher road accident rates. The app got its first road test in 2022, during a pilot program that involved 125 drivers. That data will be analyzed to determine the scalability of the program, and its effectiveness in impacting health and safety targets.

AI Spearheads Employee Development & Promotes Retention

Kuehne+Nagel’s AI technology picks up where employee job boards end.

Rather than leaving it to their staff to peruse internal job listings for advancement opportunities, Kuehne+Nagel, a global logistics company, uses the power of an AI-driven internal talent mobility platform to nurture retain its 82,000 employees worldwide.

 

In addition to delivering tailored career content, the platform also serves up customized job recommendations from Kuehne+Nagel’s internal job board. It even offers tailored professional development suggestions to aid employees in their advancement through the company, promoting retention in a competitive job market.

 

AI Facilitates Smart Picking Solutions in the Warehouse

Picking can be one of the most labor-intensive parts of a warehouse operation, making it a ripe target for automation.

 

If there’s only one type of item in the warehouse, automating that process is one thing. However, most businesses have a warehouse full of items that vary in size, shape, and even color. (Talk about a challenge!)

 

Enter Fizyr’s deep learning algorithms. The technology proposes more than 100 grasp poses each second to handle items of varying size and shape. See this fascinating tech in action below:

 

 

Fizyr’s software has been deployed across several industries, including e-commerce, retail, food, manufacturing, and more. Ultimately, technology like Fizyr’s opens the door for automated picking of all types of items, which is poised to revolutionize warehousing going forward.

Digital Twins Model Solutions and Anticipate Challenges

Simply put, a digital twin is a virtual representation of a real-world system. For example, a company might make a digital twin of its warehouse or its entire supply chain.

 

A true digital twin, as defined by DHL research:

  • Is a virtual model of a real “thing.”
  • Simulates both the physical state and behavior of the thing.
  • Is unique and associated with a single, specific instance of the thing.
  • Connects to the thing, updating itself in response to known changes to the thing’s state, condition, or context.
  • Provides value through visualization, analysis, prediction, or optimization.

Given those conditions, a digital twin powered by machine learning technology can offer a number of benefits, including:

  • Proposing new workflows and testing them in a virtual setting to determine the most effective solutions before making changes in the real world.
  • Identifying more efficient warehouse layouts for improved storage and retrieval.
  • Running worst-case simulations to test the resilience of the system so supply chain managers can better prepare for challenges.
  • Predicting future scenarios based on incoming data from connected devices, sales dashboards, warehouse management systems, inventory management systems, supplier data feeds, and more.

Of course, building a true digital twin that’s integrated end-to-end with all available systems means a significant cash outlay. However, considering these benefits—and the insights that machine learning can deliver—some companies may deem it a worthy investment.

Dynamic Pricing Responds to Changing Conditions

Airlines are using it. Hotels are using it. And dynamic pricing, powered by AI, may come to LTL shipping sooner than many people think.

 

Airline and hotel revenue management systems leverage an algorithm to set fares in real-time based on factors like capacity and demand.

 

LTL pricing, in contrast, has been relatively static throughout its history. However, AI-powered dynamic pricing—similar to that leveraged by other industries—may soon arrive on the LTL scene, adjusting pricing based on client profiles and market conditions.

 

For now, it’s all speculative. However, if (when?) it arrives, dynamic LTL pricing will mean a big change, one that will mean an adjustment for many companies.

AI Technology in Freight & Logistics: Potential and Possibilities

The recent explosion of interest in AI technology means that even more applications will pop up in the freight and logistics industry in coming years. Expect continued innovation as this emerging technology moves beyond supply chain optimization into further applications in sales, marketing, human resources—and beyond.

 

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