You don’t have to look very far or very hard to find ample evidence of AI in the supply chain and logistics. It was such a natural application area that teams in areas like logistics, fulfillment, and shipping/delivery quickly latched onto AI’s huge potential to drive efficiency, improve decision-making, automate processes, and reduce costs.
While we’re still in relatively early days, companies that leverage AI’s analytical and continuous learning capabilities across supply chain functions have already made tremendous gains. This article shares some use case examples and looks ahead a bit to what’s next.
Creating the Climate for AI Advances
The expansion of computing power, the proliferation of big data and interconnected devices (IoT), the globalization of supply chains, and cloud computing are all factors contributing to the growth in generative AI usage.
As highlighted by Maersk, key supply chain applications already leveraging AI are route optimization, demand forecasting, warehouse and inventory management, real-time tracking and visibility, risk management scenarios, and dynamic pricing.
“Really in the last few years, we’ve had the computing power and the enormous storage requirements for these technologies to work,” said Tom McLeod, CEO of McLeod Software, referring to applications in freight optimization. “They’ve reached a price point making them accessible and potentially available for widespread use.”
Companies Leveraging AI in the Supply Chain
Here are some examples of companies leveraging AI in logistics and supply chain:
Amazon
The eCommerce giant has been finding a variety of ways to integrate AI into the supply chain, including predictive analytics for demand forecasting, warehouse automation with AI-powered robots, and route optimization. As a result, it has been able to optimize inventory management, speed up deliveries, and improve customer satisfaction.
Amazon’s Supply Chain Optimization Technology (SCOT) uses deep learning algorithms and massive datasets to forecast demand for over 400 million products daily. It provides intelligence on which products to stock, in what quantities, and at which facility, while coordinating shipments from millions of sellers worldwide.
On route optimization, Amazon uses large language models (LLMs), neural networks, and 20 different machine learning (ML) models to direct drivers toward the safest, most efficient routes. The company considers variables such as weather predictions, driver feedback, and historical data, according to the Sourcing Journal. Generative AI also helps drivers quickly navigate to hard-to-comprehend delivery destinations.
DHL
The global logistics company uses AI to drive predictive maintenance for its fleet of vehicles, in warehouse robotics, for smart delivery routing and demand forecasting. With these tools, DHL is able to reduce operational costs, improve delivery times, and enhance customer interactions.
DHL uses autonomous mobile robots (AMRs) from Locus Robotics for order picking and Boston Dynamics for unloading trailers at the dock, both of which leverage AI intelligence. DHL Supply Chain has also partnered with Robust AI on the development of its Carter AMR, which will initially be used for picking optimization.
DHL Express has also added a trade lane comparison feature to its free, AI-powered My Global Trade Services (MyGTS) platform, which lets users navigate international shipping regulations. Companies can use the feature to find references to existing trade lane regulations and requirements between exporting and importing countries as they prepare their market expansion strategy.
Maersk
Maersk is leveraging AI for predictive analytics to optimize shipping routes, minimize fuel consumption, proactively plan for capacity spikes and dips, and track the movement of goods in real time. The technology has helped Maersk reduce shipping costs, improve delivery accuracy, and decrease its environmental impact.
The company uses AI to optimize container loading, scheduling, and route planning, leading to significant fuel savings and reducing its environmental footprint. It also uses AI to collect and analyze data from ships, ports, and warehouses, gaining real-time visibility into supply chain operations. It can then identify potential issues, such as predicting when a ship will be delayed by port congestion and proactively rerouting it.
FedEx
The purple package delivery company is using AI for demand forecasting, dynamic pricing models, route optimization, and automated package sortation. FedEx has been able to improve operational efficiency and maintain a high degree of delivery accuracy during peak seasons.
FedEx Surround is a platform that uses real-time tracking and predictive analytics to enhance shipment visibility and insights. Using sensor-based data, AI, and machine learning, Surround helps businesses monitor shipments, predict potential delays, and optimize delivery routes. The solution, currently available in Hong Kong and Singapore, improves the reliability and efficiency of FedEx’s logistics operations.
The company has also invested in Nimble, a company offering a fully autonomous, robotic 3PL model.
Walmart
The world’s largest retailer utilizes AI-driven inventory management and demand forecasting, along with warehouse robotics and autonomous delivery vehicles. AI helps Walmart ensure product availability, optimize stocking processes, and streamline its supply chain operations. For instance, the company trained its AI to recognize exceptions so something like an unexpected weather event doesn’t affect the company’s forecasts.
Walmart uses AI and ML-powered inventory management to optimize product placement in its warehouses and stores at the right time and in the right quantities. It does this by tapping historical sales data, eCommerce searches, and pageviews paired with third-party data like climate and weather patterns, local trends, and demographics. The model then predicts future demand associated with specific products during peak season and throughout the year.
Solution Providers Using AI in the Supply Chain
Here are some providers that illustrate how AI can be utilized to optimize different aspects of the supply chain, from visibility and route planning to freight consolidation and demand forecasting.
FourKites
FourKites provides AI-driven real-time tracking and visibility solutions, including predictive ETAs and disruption alerts. Its platform improves supply chain resilience by informing shippers of potential delays and disruptions.
FourKites’ Fin AI uses a natural language interface built on top of its LLM to surface insights from across the company’s data network, which tracks 3 million shipments daily. Users can assess the impact of disruptive events on their supply chain, for instance, by diagnosing why a shipment isn’t tracking. Future iterations will help them optimize labor, transportation, and inventory management and automate workflows.
FarEye
FarEye’s AI platform optimizes last-mile delivery, route planning, and shipment tracking.
It helps reduce delivery times and fuel costs, and enhances the customer experience by providing real-time delivery updates.
The company’s delivery orchestration tools and real-time visibility solutions, integrated with AI technology, help retailers simplify complex last-mile delivery logistics. This includes leveraging aggregated data and real-time feedback to drive autonomous deliveries using driverless vehicles and drones.
FarEye’s AI-based dynamic routing optimization engine analyzes real-time data such as road restrictions and tolls. It generates optimized routes that reduce miles driven and fuel consumption, taking into account regulatory compliance requirements.
Flock Freight
Flock Freight leverages AI to consolidate LTL shipments, optimize routes, and maximize capacity utilization. Its AI platform helps reduce carbon emissions and improve cost efficiency by minimizing empty space in truck trips.
Flock Freight’s shared truckload (STL) model uses AI to combine shipments from multiple shippers with similar routes or destinations. It optimizes capacity and drives efficiency by sharing costs among multiple customers. Also, empty return miles, aka “deadhead” runs, are reduced, optimizing fuel efficiency and reducing the number of trucks on the road.
Coupa
Coupa provides AI-powered supply chain modeling and optimization, including demand forecasting and scenario planning, building on technology acquired with Llamasoft in 2020. This allows shippers to predict disruptions, simulate scenarios, and optimize inventory and logistics strategies.
Coupa’s Supply Chain Prescriptions provides insights into cost drivers, proactively identifying cost savings by presenting options such as node skipping, mode switching, and freight consolidation. Shippers can prioritize scenarios, and supply chain modelers can ensure the recommendations align with business priorities.
Shipwell
Shipwell utilizes AI to provide real-time freight visibility, automate carrier selection, and optimize routes. Its platform helps shippers reduce shipping costs and improve efficiency through smarter decision-making.
Shipwell’s TMS is integrated with ChatGPT, so shippers and carriers can communicate with its platform in natural language and get accurate, timely query responses. It can also automate pickup and delivery scheduling and use historical data to suggest optimal times based on traffic and other factors, reducing the risk of delays.
Charting the Future of AI in Supply Chain and Logistics
AI in supply chain and logistics is a transformative technology that increases efficiency, improves decision-making, and automates key processes, as exemplified by these companies. By increasing real-time visibility, optimizing route planning, and tapping predictive analytics, AI is helping reduce costs and boost productivity. As adoption grows, its potential in supply chain management will continue to expand.
EAIGLE, an innovator in vision technology for yard operations, is another company advancing the state of the art in AI-powered supply chain and logistics applications. Combining real-time data from security cameras with powerful analytics, EAIGLE is driving greater efficiency at the gate, the yard, and the docks.
EAIGLE’s AVAC™ (Automated Vehicle Access Control) optimizes gate traffic routing, increasing security and accuracy, lowering costs, and reducing truck dwell time. Its YardSight™solution provides real-time visibility into loading docks and parking spots, allowing drivers to quickly navigate to their destination and improving traffic flow.
Request a demo today to learn more about how EAIGLE is harnessing technology to measurably improve the performance of yard operations.