AI in Logistics: Potential Benefits and Applications

A subset of supply chain management, which spans every stage of the process of creating and delivering a product to the end customer, logistics management involves the transportation of raw materials and the movement and storage of products. Logistics managers are constantly on the hunt for more efficient ways to manage this process. To date, they have long benefited from transportation and warehouse management software, as well as Internet of Things devices that facilitate tracking of trucks, delivery vehicles, freight trains, and other modes of transportation. Now that AI is being built into these and other applications and devices, logistics managers have ever more precise tools at their disposal.

What Is AI in Logistics?

AI is used in logistics for a variety of purposes, such as forecasting demand, planning shipments, optimizing warehousing, and gaining step-by-step visibility into routes, cargo conditions, and potential disruptions. AI algorithms can help logistics professionals predict transit times, determine the best carrier at the best price, and identify alternate routes and carriers in the event of transport disruptions. They can also be used to automate some elements of customer service, both via AI-powered chatbots that can help handle basic customer inquiries and through AI-based tools that analyze customer complaints and feed that data back to logistics teams.

Key Takeaways

• Companies are using AI to track the provenance of goods and components moving through different stages of the supply chain, helping them ensure that their suppliers are adhering to fair labor laws and sustainable practices.
• AI can optimize transport routes, accounting for traffic, weather, and delivery locations, as well as the impact of worker strikes. With optimized routes, companies can reduce carbon emissions and fuel consumption and move more products more quickly.
• AI can help resolve product return issues. For example, it can identify products with a high frequency of customer returns, then companies can use that feedback to uncover product defects or a mismatch between the product and intended market.

AI in Logistics Explained

The main goals of AI in logistics are to generate more accurate ETA predictions based on internal and third-party data (for example, weather forecasts and potential work stoppages) and identify at-risk shipments so that managers can take action (for example, by shifting shipments to a different route). AI models are trained on previously executed orders and user preferences, thereby helping improve operational performance and reducing the need for manual intervention. Early adopters of AI-powered supply chain management software have 15% lower logistics costs than their lagging competitors, while their inventory levels have improved by 35%, according to research from McKinsey & Company.

The Role of AI in Modern Logistics

The role of AI in modern logistics is expanding. A 2024 survey of manufacturing CEOs by Zogby Strategies and Xometry found that 97% of respondents said they’ll be using AI in their operations in the next two years.

Logistics managers are starting to use new AI capabilities to improve transportation efficiency, for example, by analyzing traffic and weather patterns to help identify the most fuel-efficient transport routes and avoid costly delays. Manufacturers count on the delivery of thousands of components from all over to world to assemble their products, and those deliveries need to be orchestrated to ensure that all the pieces are there when needed—but not too soon beforehand, since the cost of storing excess inventory can be significant.

Potential benefits of AI in Logistics

The volume of data generated during the transportation, storage, and delivery of products is immense. Data points include real-time location, temperature, shipping costs, and availability of carriers, to name just a few. The potential impact of AI-powered logistics—and associated on-time deliveries—on customer satisfaction is obvious, but there are many other ways AI helps improve logistics, described in more detail below.

• Inventory management
  AI-powered warehouse management tools can help logistics managers identify incoming orders with predicted fulfillment times that exceed the target. They can then share details of at-risk orders with fulfillment managers to prioritize picking for those orders, or adjust the stocking location of items to group products that are frequently ordered together. In addition, generative AI capabilities are designed to provide concise and structured summaries of relevant order revision histories and editable email acknowledgements of new and changed orders.
• Demand accuracy
  AI-embedded logistics applications can provide data that helps demand forecasters predict issues that might delay delivery of finished products. Also, data generated by demand forecasting applications can help logistics managers prioritize delivery of products likely to have the strongest impact on customer satisfaction and overall profitability.
• Overstock optimization
  Manufacturers can use AI-based predictive analytics to optimize stock levels, drawing from historical data and real-time demand data to help prevent stockouts and reduce the volume of surplus inventory.
• Fulfillment efficiency
  AI can assist in boosting fulfillment rates by helping warehouses to be more efficient—for example, by analyzing historical demand data to determine the best location for specific products and recommending floor layouts and worker routes to speed fulfillment. Warehouse managers can also use AI to help evaluate whether delivery containers are filled with the ideal volume of packages, with no wasted space.
• Order accuracy
  Manufacturers and logistics companies are training AI algorithms on data captured from cameras and sensors to help uncover and head off worker errors, such as pulling the wrong products from a warehouse or sending items to the wrong locations. These same systems can be used to analyze the captured data to better determine if there are frequent errors that can be avoided through process or design changes—for example, through better worker training, packaging alterations, or product location optimization based on demand levels.
• Picking optimization
  AI can optimize product picking—when warehouse staff gather products to fulfill an order—by uncovering order patterns and suggesting that products frequently ordered together be moved to the same part of the warehouse. Suggesting that products that have an earlier delivery date, such as perishable goods or time-sensitive orders, are stored in the most convenient sections of a warehouse is another way AI-powered demand predictions can improve product picking.
• Label automation
  GenAI tools can be used to automate the creation of shipping labels, previously a manual and error-prone task. The tools for this task can be integrated into logistics and warehouse management applications and support multilingual and international shipping requirements.
• Transportation management
  AI-powered transportation management applications can predict shipment ETAs at two different times—at the point where the logistics manager is planning the shipment and during the movement of products. At the planning stage, it’s helpful to know upfront whether shipments may be delayed so that logistics managers can choose alternate transport routes and carriers.
  
  Although it’s not always possible to change carriers during shipments, multi-leg moves can present more opportunities for optimization. Logistics managers can use AI-based data analysis to, for example, send a shipment to a different port or direct trucking services to a better route. AI tools can also be used to analyze predicted and actual shipping times side by side, so logistics managers can identify the riskiest lanes and avoid them whenever possible. Naturally, prediction accuracy improves as a shipment gets closer to the delivery point. Once the ETA reliability hits a certain threshold, AI management tools can be used to automatically send out a tender to the most appropriate transport carrier so it’s ready to go as soon as the shipment arrives.
• Fuel savings
  By 2050, the global aviation and shipping sectors will likely account for nearly 40% of global carbon dioxide emissions unless they take steps to reduce their current levels, according to the European Environment Agency. AI-optimized logistics can be help lessen the environmental impact of shipping products and materials by optimizing truckloads/shiploads and delivery routes. In a 2021 report, the World Economic Forum estimated that 15% of trucking miles were driven with no load.
• Delivery time optimization
  Logistics managers are using AI to optimize delivery routes so that companies have the raw materials they need, when they need them, and that they’re able to ship finished goods to warehouses or stores quickly and efficiently. Managers can set priorities based on almost any factor, such as order volume and product availability. They can even use AI to dictate that orders from high-priority customers receive special attention at every stage, should those orders appear to be in danger.
• Delivery safety
  AI-powered dashboard systems and other systems consisting of cameras and sensors can help detect in-vehicle risks, such as distracted or drowsy drivers, as well as external dangers, such as imminent collisions or sudden changes in road conditions. Logistics managers can also use the data from these systems to identify employees who don’t comply with safety protocols. If accidents occur, managers can use AI to help analyze the causes so they can take steps to help prevent such incidents in future.
• Warehouse and transport maintenance
  Forklifts, pallets, sorters, conveyors, loaders, and other types of warehouse equipment are prone to breaking down—as is key equipment on trucks, ships, railcars, railways, and other means of transport. Logistics managers can apply GenAI to data from sensors embedded in these machines and infrastructure to predict failures more accurately, enabling them to proactively schedule maintenance, help avoid unplanned downtime, potentially extend the life of expensive equipment, and ultimately help keep their supply chains running smoothly.
• Product returns
  AI can also help shine a light on reverse logistics (otherwise known as product return) issues. If a certain product is experiencing a high frequency of customer returns or is frequently returned from a particular region, AI algorithms can assist to quickly root out those trends, alerting the manufacturer to a potential design flaw or defect, or to a mismatch between product and market. In the event that a large number of products are recalled, AI can help streamline that process by establishing a more efficient return flow—for example, by setting up a special return code that directs recalled products to a designated location so they aren’t just lost among all other returns.

AI Applications in Logistics

Manufacturers are starting to use AI software to help automate tasks such as tracking equipment failures, improving product quality, and speeding the shipment of goods to customers. They’re also using AI to analyze vast amounts of data to help address their most complex logistics problems. Here are some specific ways logistics managers are using AI to achieve their goals.

• Route optimization
  Route planning used to be a manual, labor-intensive process. But AI systems can be used to optimize it by factoring in traffic and road conditions, weather, delivery locations, and other relevant data. With more efficient routes, companies can be better positioned to save money on fuel and reduce carbon emissions, while drivers can make more deliveries in the same period of time.
• Last-mile planning
  The cost associated with the final stage or “last mile” of fulfillment, from a distribution hub to the customer’s door, increased from 41% of the total delivery cost in 2018 to 53% in 2023, according to the CapGemini Research Institute. As customer expectations around speed of delivery increase, companies are responding by creating networks of small delivery depots, outsourcing to third-party suppliers, and using AI to optimize route scheduling. AI tools can help make vehicle routing more efficient by analyzing delivery locations and vehicle capacities and helping drivers adapt more quickly to unexpected slowdowns.
• Fleet management
  AI capabilities built into fleet management applications can help managers determine the best mix of for-hire carriers versus private fleet carriers. In addition, these tools can help autonomously assign loads to drivers and adjust the start times for shipments based on historical internal and external data.
• Demand forecasting
  Conventional demand forecasting relied almost exclusively on internal historical data. AI-powered demand forecasting tools also help analyze third-party data on weather, regional events, fluctuating customer demand patterns, and other factors to improve accuracy.
• Robotics and automation
  AI-powered robots can store and pick products faster and more efficiently than human operators. The benefits of automated robots include fewer errors and injuries and better use of space. Pilot programs for autonomous trucks hold promise for further cuts to transportation costs as well as improved delivery times due to near-24/7 vehicle utilization.
• Intelligent packing and sorting
  AI algorithms can suggest optimal warehouse floor layouts that help speed the movement of inventory into and out of these facilities. They can also assist in planning the most efficient warehouse routes for product pickers to fulfill orders. One of the biggest global package delivery companies is even using AI-powered robots to sort packages.
• Dynamic pricing
  Conventional, static pricing mechanisms are gradually being replaced in some industries by dynamic pricing, whereby AI algorithms facilitate the constant adjustment of the prices of goods and services based on ongoing analysis of market demand and other factors. The airline industry was a pioneer in this area, while hotels, retailers, ecommerce sites, ride-sharing companies, and professional sports teams were fast followers.
• Document automation
  GenAI-based document comprehension capabilities—sometimes called intelligent document recognition—are designed to automatically extract text from digital files, even those containing illegible or deteriorating documents. These capabilities can help streamline logistics tasks by, for example, creating digital receipts from bills of lading or digitizing paper invoices and importing them into the payables system. GenAI can also help extract text, tables, and other key data from documents to aid in expense payments, bill processing, and content management.
• Customer service and experience
  Companies are deploying GenAI-based chatbots to respond to customers’ most common logistics queries—such as whether a product can be shipped to a given address, or whether a carrier supports cross-country shipments or multipiece shipments in a specific country. Previously, customer service agents would need to consult a matrixed spreadsheet to answer such questions. AI systems are designed to comb through multiple variables and automatically update answers as these variables change. Natural language user interfaces allow people to access this information by conversing with the chatbot.

Examples of AI in Logistics

Western Digital uses a digital assistant named Logibot to provide logistics information to supply chain partners. After benchmarking its online store with those of competitors, Western Digital’s logistics executives set three goals: 24/7 response to queries, the ability to gather customer feedback and respond to comments, and the ability to handle most queries autonomously so customer service agents can respond to only the most critical issues. The end goal for the company is to track every interaction Logibot has with users, determine how many interactions are successful and how many aren’t, and use that data to make the tool more efficient and thus provide better customer service. Western Digital plans to expand Logibot from logistics to planning, procurement, and manufacturing.

For companies that grow or manufacture perishable goods—and those that rely on complex shipping networks to source goods and deliver the finished product to customers—being able to track and trace shipments is table stakes. AI offers the ability to autonomously track items that are already on the move and alert human agents if problems arise, such as an increase in temperature in a shipping container or an unexpected delay that may imperil a shipment. Logistics managers can use that information to reroute products and reset customer expectations. Even before shipment, logistics managers can use AI’s predictive capabilities to help uncover potential issues using historical internal data and third-party data on weather conditions, road and port closures, worker strikes, and other variables.

Challenges in AI Adoption

Although AI has the potential to improve how materials and products are stored and transported, implementation it isn’t always easy. Here are some of the challenges companies face when adopting AI.

• Cost to implement and train workforce
  Employees are sometimes intimidated by new applications, even intuitive cloud-based ones, and may resist adoption. Companies may want to consider building downtime into their schedules to familiarize employees with new capabilities. Companies should also consider working with their vendors to develop training programs suitable for a wide range of roles, including the logistics managers who will have to respond appropriately to AI-powered alerts and the drivers who will use automated driving features and follow AI-optimized routes.
• Integration with existing systems
  Integrating new AI capabilities into a legacy on-premises logistics application can be a daunting task, often requiring a systems integrator. Once the system is ready to go back into production, organization usually will experience some downtime. AI and other feature improvements in cloud-based applications are typically delivered much more seamlessly.
• Privacy and security concerns
  With legacy on-premises logistics applications, companies must constantly apply patches to address security vulnerabilities. With AI-powered logistics applications running in the cloud, however, the software receives regular, automated updates to help boost data security and privacy.

Maximize Fulfillment Faster with Oracle

Oracle Fusion Cloud Logistics, part of Oracle Fusion Cloud Supply Chain Management & Manufacturing, includes new AI capabilities to help streamline logistics tasks, optimize carrier routes, and reduce inventory holding costs. Such capabilities could be applied to help manufacturers lower costs, shorten delivery times, improve employee safety, and reduce their carbon footprint.

AI in Logistics FAQs

How can AI be used in logistics?
AI is used in logistics mainly to forecast demand, plan shipments, monitor cargo conditions, and optimize warehouse space and transport routes.

How is AI changing the shipping industry?
Shipping companies are using AI to analyze factors such as traffic, sea currents, and weather conditions to fine-tune their routes or map out alternatives, reducing their fuel consumption and the risk of costly delays. They’re also using it for predictive equipment maintenance.

How can AI make supply chains more sustainable?
The main way AI can make supply chains more sustainable is by optimizing transportation routes, which can help reduce transport vehicle fossil fuel consumption and lower carbon emissions.