How is artificial intelligence transforming the logistics industry?

How does AI impact the logistics sector?

Logistics has always involved complex workflows and multifaceted processes, requiring comprehensive planning and the coordination of numerous stakeholders to ensure the uninterrupted flow of goods and services across the supply chain. Moreover, the industry is constantly affected by economic, political, social, and environmental factors, including gross domestic product (GDP), national policies and regulations, evolving customer demands, climate change, pandemics, and more. AI allows logistics companies to predict the impact of these factors on the industry and adjust their processes accordingly. What, though, is AI in logistics all about?

AI in logistics focuses on using machine learning (ML), deep learning (DL), computer vision (CV), natural language processing (NLP), and predictive analytics to automate and optimize tasks of varying complexity across logistics and supply chain processes. By analyzing vast amounts of relevant data, AI provides valuable insights into logistics operations and existing bottlenecks, predicts possible outcomes, supports data-driven decision-making, and enables more efficient, accurate, and strategic logistics processes. Companies that incorporate AI into their supply chains can reduce operational expenses, maximize performance and productivity, increase customer satisfaction, and achieve other key benefits. For instance, McKinsey reports that early adopters of AI in supply chain management have enjoyed 15% lower logistics costs and significant improvements in inventory management (35%) and service levels (65%) compared to companies slower to embrace AI. Given the current transformative impact of AI, we can expect it to continue expanding, significantly impacting key logistics aspects like inventory management, warehousing, order processing, distribution, and transportation.

What are the most popular use cases of AI in logistics?

Autonomous cars rushing to make last-mile deliveries, drones navigating the shortest possible routes for delivering medical supplies, and human-like robots shuttling goods in warehouses—these are just a few examples of how AI is transforming the logistics sector. Are you curious to learn more? Read on as we explore AI logistics use cases.

Intelligent inventory management

An efficient inventory management process ensures that the right products are available at the right time, and the use of AI in logistics makes this possible. Through predictive analytics and ML, businesses ensure real-time visibility across the entire supply chain, gaining an accurate view of available inventory and optimizing stock levels based on current demand. With intelligent inventory management, logistics companies can expedite the fulfillment of customer orders and the delivery process, minimize the risks of overstocking and understocking, reduce material waste, and achieve other significant improvements.

Demand forecasting

To forecast demand precisely, ML algorithms analyze extensive amounts of real-time information, historical sales data, market trends, consumer behavior, competitor activity, and external factors like seasons, weather conditions, and geopolitical events. This enables businesses to quickly respond to fluctuations in demand, ensuring effective resource allocation, optimizing warehouse and inventory management, streamlining transportation planning, and more.

Smart route optimization

It’s time for logistics companies to move beyond manual planning for transportation route optimization and fully embrace the power of AI. By evaluating variables like traffic patterns, fuel usage, and road and weather conditions, ML models for supply chain and logistics management help identify the optimal transportation routes and adjust them to changing conditions in real time. AI-powered route optimization significantly reduces not only shipping costs and delivery times but also the levels of fuel consumption and, thus, carbon emissions.

Autonomous vehicles

Self-driving vehicles are no longer sci-fi concepts from books and movies but powerful real-world tools that can revolutionize logistics. Powered by ML algorithms, self-driving trucks and cars can navigate the best possible transportation routes, respond to current traffic conditions, and avoid road hazards, delivering goods to delivery hubs and consumers much faster. AI-enabled autonomous drones can easily make last-mile deliveries to difficult-to-reach areas, such as densely populated regions, zones with congested traffic, and locations where ground transportation is impossible. Mordor Intelligence predicts that the drone delivery market will grow from $690 billion in 2024 to $1.75 trillion in 2029, with a CAGR of 20.33% over the forecast period. Although still in the nascent stage, autonomous vehicle technology is already revolutionizing the logistics sector, making the delivery process more efficient and accessible to all.

Smart warehouses

The synergy of ML, CV, robotics, and Internet of Things (IoT) devices and sensors elevates warehouse automation to the next level. AI-powered robots can autonomously navigate warehouse premises to fulfill a wide range of tasks, including tracking inventory levels, transporting goods, sorting and picking packages, monitoring shipments in real time, and more. Logistics companies that integrate AI into various aspects of warehouse automation maximize space usage in their storage facilities, enhance the accuracy of warehouse processes, and increase operational efficiency and delivery speed.

Predictive maintenance and damage detection

The potential of AI in logistics extends to predictive maintenance and damage detection. By blending ML capabilities with predictive analytics, businesses can identify potential equipment breakdowns and enable prognostic maintenance before failures cause operational disruptions and financial losses. Additionally, logistics firms can use ML and CV to identify defects in goods during transportation and shipment, allowing for quick resolution and reducing the impact on customer satisfaction.

Dynamic pricing

Logistics businesses can confidently respond to changing market conditions and fluctuations by personalizing their shipping costs with AI. ML algorithms and predictive analytics allow supply chain firms to easily adjust their prices in real time based on supply, demand, competitors’ rates, and even customer purchasing patterns. AI-driven dynamic pricing offers organizations virtually unlimited flexibility in optimizing their costs, giving them an edge over the competition and increasing sales and revenue.

Customer service

Chatbots, powered by NLP and generative AI (GenAI), can redefine customer service across numerous industries, including logistics and supply chain management. By using these smart tools, logistics companies can provide consistent and round-the-clock support for a variety of tasks, including changing order details, tracking shipments, requesting deliveries, and answering frequently asked questions (FAQs). AI-powered chatbots significantly elevate the quality of customer service, delivering hyper-personalized experiences and increasing customer engagement.

Businesses that use AI in logistics

Logistics businesses willing to stay ahead of the curve have already made AI part of their digital transformation strategy. Next, we outline several prominent AI in logistics examples among industry leaders.

Amazon

Amazon is an e-commerce giant that delivers goods to customers worldwide through its Amazon Logistics service. The company has implemented AI technology to streamline its logistics operations across the supply chain, accelerate the delivery process, and drive customer satisfaction. Amazon uses AI to accurately predict customer demand; uncover damaged products using Project P.I. (Private Investigator), its proprietary AI model; and optimize warehouse and inventory management through AI-powered robotic systems, such as Sequoia and Digit. Currently, over 750,000 robots operate in Amazon’s fulfillment centers, tackling time-consuming and repetitive tasks. Its other proprietary AI model, the Package Decision Engine (PDE), enables the organization to use the most efficient and sustainable packaging for customer orders, significantly reducing package waste in the eco-friendly era.

FedEx

FedEx’s numerous AI initiatives over the past few years have solidified its reputation as an AI logistics company. By using AI to analyze weather and traffic conditions, FedEx provides precise delivery time estimates, improving delivery experiences and increasing customer satisfaction. Additionally, FedEx capitalizes on ML capabilities to forecast customer demand and the volume of packages to be delivered daily, provide personalized customer support, and drive smart logistics. In collaboration with Dorabot, the company has introduced an AI-powered sorting robot, DoraSorter, that arranges inbound and outbound parcels based on their destination, increasing operational efficiency and decreasing labor costs. The FedEx Sustainability Insights platform is another example of how the firm has employed ML algorithms to help companies evaluate and manage their carbon footprints, eventually reducing their environmental impacts.

DHL

DHL, a global leader in the logistics industry, is neck-and-neck with the competition when it comes to implementing AI technology. The organization has revolutionized its warehouse and distribution operations by deploying AI-powered Locus Autonomous Mobile Robots (AMRs) that help employees automate and optimize picking and order fulfillment tasks. By implementing over 3,000 Locus AMRs in its global facilities, DHL has dramatically improved productivity and accuracy, reduced physical strain for employees, and cut labor expenses. To decrease the usage of outer cardboard packaging by 50%, thereby minimizing carbon emissions, the company has developed AI-powered software, OptiCarton, that can determine the ideal shipment overpack based on product characteristics, volume, and dimensions. In addition to AI-enabled robots and the OptiCarton solution, DHL leverages ML models and predictive analytics to optimize transportation routes, gain end-to-end visibility into its supply chains, and provide seamless customer service 24/7.

Maersk

Maersk is an undisputed leader in maritime logistics that manages a wide variety of operations on a daily basis, including warehousing, shipping, transportation, distribution, and more. Driven by the idea of “zero touch logistics,” which minimizes human intervention in logistics processes, the conglomerate has embraced a broad spectrum of next-gen advancements, including AI. To optimize warehouse and inventory management processes, Maersk has implemented AI-powered Robotic Shuttle Put Wall (RSPW) systems. These solutions excel in sorting customer orders, picking batch inventory, and managing order profiles and packages, accelerating operational speed and efficiency threefold. Additionally, Maersk uses AI tools to handle contract negotiations, provide real-time visibility and traceability for supply chain operations, increase customer service quality, improve transportation routes, and more.

How to integrate AI into logistics operations

If your business aims to achieve significant logistics outcomes, incorporating AI into relevant operations isn’t a luxury anymore—it’s a necessity. Below, we highlight the recommended steps to seamlessly integrate AI into your logistics and supply chain processes.

Step 1. Define your business needs and objectives

• Comprehensively assess your current logistics operations and needs.
• Pinpoint specific areas and processes that AI can improve.
• Establish clear objectives and goals you expect to achieve through AI implementation.

Step 2. Identify data sources and collect data

• Identify the sources of your logistics data, including transportation and real-time traffic information, historical sales records, sensor data from warehouses and vehicles, customer information, and more.
• Gather raw data from the identified sources.
• Remove inconsistencies and errors from the collected data to ensure accuracy and quality.
• Establish data governance protocols to guarantee your data is secure and complies with industry regulations.

Step 3. Integrate and transform data

• Use the ETL (extract, transform, load) and ELT (extract, load, transform) processes to integrate and transform the collected data into a usable format, ensuring it’s suitable for further analysis and usage by AI systems.

Step 4. Select the appropriate AI implementation strategy

• Given your specific needs and constraints, decide on the most suitable AI implementation strategy. This can involve either integrating existing AI tools into your logistics and supply chain operations or building tailor-made AI software with custom ML models.

Step 5. Implement the chosen AI strategy

• Embed off-the-shelf or bespoke AI software, ensuring seamless integration with existing logistics and supply chain systems.
• Make the required changes to current workflows, processes, and job roles to enable a smooth transition to AI.
• Provide the necessary training to empower employees to use AI tools effectively.

Step 6. Monitor and maintain your AI system

• Establish and track key performance indicators (KPIs) to assess the impact of AI solutions on your logistics processes.
• Implement the necessary security measures and contingency plans to ensure your AI solution is robust, safe, and resilient; promptly resolve issues; and protect your sensitive data.

Step 7. Embrace continuous improvement

• Stay current with the latest AI advancements.
• Optimize the performance of your existing AI tools and refine relevant strategies accordingly to continue obtaining maximum value from your logistics operations.