This report is the third and final installment in our 2025 logistics series, Automating Logistics. The series has systematically examined how automation and emerging technologies are reshaping logistics, first by analyzing warehouse robotics, then exploring the complexities of automating last-mile delivery. In this concluding paper, we turn our attention to the beginning and middle stages of the supply chain—often referred to as the “first mile” and “middle mile.” This part of logistics, frequently overlooked despite its outsized impact on overall efficiency, is rapidly becoming a focal point for innovation.

The First Mile

The “first mile” refers to the initial movement of goods from factories, farms, or suppliers into the broader logistics network. Unlike warehousing and last-mile delivery—where automation is already well underway—the first mile remains largely manual, with persistent gaps in visibility, standardization, and system integration.

Smart labels, IoT sensors, and real-time tracking are beginning to bring much-needed visibility to the first mile—transforming what was once a logistical blind spot into a source of continuous, actionable data. As goods move from origin to port or airport, emerging technologies in air cargo and maritime handling are streamlining handoffs and reducing friction through automation and robotics. What starts with better data capture is quickly translating into faster, more reliable, and more adaptive freight movement.

Supply Chain Data Tracking & Visibility

Automation begins with knowing where goods are and what condition they’re in, in real time. But most first-mile freight still starts in a fragmented state—relying on manual scans and siloed systems.

Smart labels and IoT sensors now allow shipments to report their own location and condition as they move, feeding into connected platforms that consolidate this data across modes. Layered with analytics, these systems shift logistics from reactive problem-solving to predictive, data-driven management—unlocking the early building blocks of automation.

Smart Labels & IoT Packaging

At the heart of digital visibility is the ability to continuously track individual shipments at the package or container level. Traditional logistics processes rely on periodic manual scans, leaving lengthy gaps without status updates. Smart labels and IoT packaging are transforming this by embedding real-time tracking directly into shipments.

Companies such as Reelables have introduced innovative, disposable shipping labels containing tiny, thin-form 5G-connected GPS trackers, enabling continuous live tracking of a package’s exact location rather than relying on infrequent carrier scans.

Similarly, Wiliot’s battery-free Bluetooth sensors, powered entirely by harvesting ambient energy, continuously broadcast detailed sensor data—such as temperature, humidity, and motion—directly from the packaging itself.

As these technologies scale, every parcel or container within first-mile logistics will have the potential to actively communicate its precise journey, significantly improving accuracy and timeliness of logistics data.

End-to-End Visibility Platforms

Smart labels and IoT devices generate massive amounts of shipment data—but without structure, that data is hard to use. Visibility platforms like project44 and FourKites aggregate this information across modes (road, rail, sea, and air), creating a unified view of freight in motion.

By layering predictive analytics and AI on top, these systems help operators forecast delays, monitor exceptions, and optimize routing in real time. As logistics networks grow more complex, this type of integrated visibility is quickly becoming essential infrastructure.

Maritime: Smart Shipping and Port Automation

Maritime logistics, particularly the global container shipping sector, is undergoing a digital transformation. Historically defined by labor-intensive manual processes and limited visibility at sea, maritime logistics is increasingly adopting automation, robotics, and AI solutions. The goals driving this transformation include reducing costs, improving reliability, and addressing critical sustainability challenges in a traditionally energy-intensive sector. Maritime automation begins with digitizing physical assets—containers, ships, and ports—and continues by using this digital layer to optimize operations and eventually automate entire processes.

Smart Containers & Tracking

At the core of maritime digitization are smart containers—standard shipping containers upgraded with embedded IoT sensors. Traditionally, containers provided little insight into their journey once loaded onto vessels, becoming opaque “black boxes” traversing global supply chains. Today, solutions from companies like Nexxiot and ORBCOMM allow containers to continuously report real-time GPS location, temperature, humidity, shock, and even potential intrusion attempts.

Major shipping lines such as Maersk and Hapag-Lloyd are rapidly adopting these devices, enabling shippers to remotely monitor sensitive or high-value cargo throughout its ocean transit. As these sensor systems become more affordable and widespread, the industry is moving toward proactive intervention—such as addressing temperature fluctuations or responding immediately to unauthorized container access—instead of reacting passively after an issue has already occurred.

AI-Powered Route & Voyage Optimization

Maritime analytics systems enable vessel operation optimization. With fuel being a major cost, companies like Nautilus Labs use machine learning to optimize maritime voyages. Their systems analyze vessel position, weather, and performance data to recommend efficient routes and speeds. "Slow steaming" practices reduce fuel use and port congestion waiting times, achieving 5-10% fuel savings. AI also optimizes vessel allocation and scheduling, improving maritime logistics efficiency.

Autonomous & Remote-Controlled Vessels

Taking digitization and optimization one step further, maritime operators are beginning to experiment with fully autonomous and remote-controlled vessels. The Yara Birkeland, launched in Norway in 2021, became the world’s first fully electric, autonomous container ship designed for short-haul coastal routes. Using advanced sensors, cameras, radar, and machine-learning navigation software, the ship operates without crew, significantly reducing labor costs and environmental footprint.

Boston-based Sea Machines Robotics has also demonstrated autonomous vessel control technology on harbor tugs and cargo boats. These smaller, autonomous vessels promise to alleviate road congestion—each autonomous ship potentially replacing dozens of truck journeys—and provide significant cost advantages in niche logistics markets.

Port & Terminal Automation

Maritime automation does not stop at vessels—it extends critically into ports and terminals, where inefficiencies and bottlenecks often arise. Historically reliant on intensive manual labor, ports are now adopting advanced automation technologies to streamline cargo handling, boost efficiency, and reduce operational costs.

Automated Guided Vehicles (AGVs) & Autonomous Yard Trucks

Unmanned AGVs autonomously transport containers between dockside cranes and storage yards, enabling continuous, 24-hour operations without human intervention. These systems integrate sensor arrays, lidar, cameras, and AI navigation software to operate safely in dynamic terminal environments.

Autonomous Stacking Cranes (ASC)

Robotic stacking cranes systematically manage storage yards by autonomously stacking and retrieving containers, optimizing storage density, and reducing operational bottlenecks. These cranes operate autonomously within predetermined yard configurations, often coordinated centrally by terminal operating software.

Automated Gate Systems

Automated gate systems utilize Optical Character Recognition (OCR), RFID tags, and sensor networks to rapidly identify trucks and containers upon arrival and departure. This technology significantly reduces congestion at entry points, streamlines truck flows, and eliminates time-consuming manual checks.

For instance, the Port of Los Angeles has deployed OCR-based gate entry systems integrated with real-time appointment scheduling, reducing truck turnaround times by over 30%.

Ultimately, maritime logistics is progressing steadily along the automation curve—from smart containers that provide real-time shipment data, to AI-optimized voyages, autonomous coastal vessels, and increasingly automated port infrastructure. These technologies are not only improving efficiency and reliability but also laying the groundwork for more integrated, data-driven global supply chains. While challenges around labor, regulation, and capital expenditure remain, the trajectory toward smarter, more automated maritime logistics is clear—and increasingly foundational to first-mile performance.

Air Cargo: Automation and AI in Air Freight

Air cargo is critical for rapid, high-value, or perishable shipments, yet remains one of the most manual and costly segments of first-mile logistics. Historically, air freight operations have depended on manual cargo handling, paper documentation, and limited shipment visibility—leading to bottlenecks, delays, and high operating costs. Driven by surging e-commerce demand, growing labor shortages, and stringent reliability requirements, air cargo operations are rapidly adopting automation, robotics, and AI-powered solutions to streamline processes, optimize capacity, and enhance visibility.

Robotic Cargo Handling

Cargo warehouses and airside operations have traditionally relied heavily on manual labor—forklift operators, cargo handlers, and inspection staff. Labor constraints and operational pressures have led companies to deploy robotic systems capable of autonomously performing repetitive tasks. For example, Lufthansa Cargo, in partnership with Boston Dynamics, has piloted robotics projects at Munich Airport using Boston Dynamics’ Spot robot to autonomously inspect warehouses and identify available cargo storage space. Such robotics applications promise substantial cost savings, productivity gains, and operational reliability, presenting clear investment opportunities as these technologies scale beyond pilot programs.

AI for Capacity & Routing Optimization

Air cargo operations have traditionally managed capacity and route planning manually, often leading to suboptimal aircraft loading and underutilized space. Now, AI-driven solutions are improving cargo capacity management and route optimization. Startups like cargo.one have developed digital marketplaces that leverage AI to dynamically allocate cargo space, forecast demand patterns, and optimize cargo loading. By analyzing cargo dimensions, urgency, historical booking trends, and pricing, cargo.one helps airlines like Emirates SkyCargo and Lufthansa Cargo boost cargo revenue per flight, reduce unused capacity, and improve overall efficiency.

Similarly, companies like Accelya provide AI-powered analytics platforms that enhance cargo pricing strategies and flight-load optimization. These solutions typically deliver capacity utilization improvements of up to 10–15%, translating into substantial operational and economic benefits for cargo carriers.

Tracking & Smart Unit Load Devices (ULDs)

Enhanced shipment tracking and visibility have become essential, particularly for sensitive, high-value, or time-critical shipments. Traditional barcode-based tracking systems offered limited insights, leaving substantial gaps in real-time visibility. Now, smart Unit Load Devices (ULDs)—standardized air cargo containers equipped with integrated IoT sensors—are transforming cargo visibility. Companies such as Unilode Aviation Solutions have developed smart ULDs that continuously transmit real-time data on cargo location, temperature, humidity, shock, and tampering attempts.

These smart ULDs integrate seamlessly into end-to-end visibility platforms (e.g., those provided by project44), enabling operators to track cargo precisely, enhance security, and continuously optimize route planning based on historical and live sensor data.

Autonomous Aircraft Operations (Emerging Opportunities)

Looking further ahead, air cargo logistics is beginning to explore autonomous flight as a means to address ongoing pilot shortages and reduce operating costs. Startups like Reliable Robotics are developing retrofit solutions to enable existing cargo aircraft to operate autonomously, particularly targeting short- to medium-haul freight routes. Reliable Robotics recently demonstrated remote-controlled cargo flights and is working toward fully autonomous commercial cargo operations, which could significantly enhance the economics of air cargo by lowering crew-related expenses and operational complexity.

The Middle Mile

“Middle-mile” logistics refers to the critical but often-overlooked transportation segment connecting major logistics hubs such as ports, rail terminals, and warehouses. Although frequently short-distance, these repetitive movements significantly impact overall supply chain efficiency and reliability. Middle-mile automation addresses persistent pain points—labor shortages, inefficiencies, and congestion—through targeted robotics, electrification, and AI-driven management.

Trucking: Autonomous, Connected, and Green Trucks

Trucking is the workhorse of middle-mile logistics, responsible for moving freight between ports, rail yards, warehouses, and distribution centers. But the sector faces mounting challenges—driver shortages, fuel costs, congestion, and tightening emissions regulations. These pressures are accelerating a shift toward automation, connectivity, and low-emission vehicles.

In this section, we examine how autonomous trucks, electrification, and AI-powered freight matching are reshaping middle-mile transport. While some technologies are nearing commercial deployment, others remain in early stages—highlighting both the complexity and the opportunity in modernizing this critical link in the logistics chain.

Autonomous Trucks

Autonomous trucking technology is poised to transform middle-mile logistics—particularly long-haul routes between ports, warehouses, and regional distribution centers. Startups like Waymo Via, Aurora, Kodiak Robotics, and Plus are developing fully autonomous Class-8 trucks designed for highway operations, where traffic patterns are more predictable and infrastructure is standardized.

These vehicles combine LiDAR, radar, cameras, and AI to operate with minimal human intervention, enabling nearly continuous use and reducing reliance on a shrinking driver workforce. Trials have shown promising results in terms of safety, uptime, and cost savings. Major OEMs including Daimler, Volvo, and PACCAR are partnering with these startups to bring autonomous trucks to market by 2026.

Middle-mile routes—with long distances, high freight volume, and limited urban complexity—offer the clearest early use cases for autonomy. While regulatory and technical challenges remain, this segment of the supply chain is likely where we’ll see widespread autonomous deployment first.

Truck Platooning

Truck platooning—digitally linking multiple trucks to travel in close formation, reducing aerodynamic drag—is a semi-autonomous technology initially viewed as a near-term solution. Despite demonstrating fuel savings (4% for lead trucks, up to 10% for followers), platooning has struggled with real-world adoption. Startups like Peloton Technology and Locomation, once promising players, have ceased operations or scaled back significantly due to the complexity of deployment, safety concerns, and limited market uptake.

Electric & Hydrogen Freight Trucks

Zero-emission trucking is a regulatory inevitability, especially in port and urban environments. However, the space is dominated by large OEMs—Tesla, Volvo, Daimler, Hyundai, Toyota/Kenworth—with deep capital reserves and in-house manufacturing capabilities. While startups like Nikola made early moves, most hardware-focused entrants have struggled to scale or differentiate.

Digital Freight Matching

Digital freight matching platforms aim to replace manual broker calls and static load boards with real-time, algorithmic matching between shippers and carriers. While early players like Convoy and Transfix helped modernize the space, the category has since matured and consolidated. Flexport, through its acquisition of Convoy, has signaled interest in expanding its surface freight capabilities, but most platform innovation now centers on enterprise-scale logistics orchestration rather than true automation.

Rail Freight: AI-Enhanced and Autonomous Trains

Rail plays a central role in middle-mile logistics, moving high volumes of freight efficiently across long distances and between major logistics hubs. It’s particularly well-suited for intermodal containers, bulk goods, and coast-to-coast shipments. However, rail networks have traditionally been limited by rigid infrastructure, slow scheduling cycles, and manual operations.

That’s starting to shift. Advances in AI, autonomy, and robotics are beginning to modernize rail, making it more adaptive and competitive with long-haul trucking. From autonomous train systems and battery-electric railcars to predictive dispatching and automated inspections, rail is becoming an increasingly important platform for automation across the middle mile.

AI-Based Scheduling & Control

Railroads are adopting AI tools to optimize dispatching and fuel use, but most solutions—like Wabtec’s Trip Optimizer or Hitachi’s train planning software—are developed and deployed by incumbent OEMs. These systems focus on incremental gains in efficiency within complex, highly regulated networks.

Autonomous Trains

Building on the digitization and analytics foundation, autonomous operation is becoming a realistic vision for freight rail. While autonomous passenger metro systems have existed for decades, fully autonomous freight trains—particularly over long-distance routes—are now emerging.

In Australia, Rio Tinto’s AutoHaul system represents the industry benchmark for rail autonomy. Launched in 2019, AutoHaul uses autonomous trains—each spanning over 2 kilometers—to transport iron ore across hundreds of kilometers of remote track without onboard crew.

These trains rely on onboard sensors, GPS, and AI-enabled control systems to operate without human crews, with centralized monitoring systems managing exceptions in real time. While adoption in North America and Europe is more gradual—due to regulatory hurdles and mixed-use tracks—startups like Parallel Systems and Intramotev are developing autonomous, battery-electric railcars designed for flexible, short- to mid-range freight. These systems can operate individually or in platoons, offering a more responsive and modular alternative to traditional unit trains. Recent regulatory approvals for pilot programs suggest that autonomous rail, though early, is gaining momentum as a middle-mile innovation.

Automation & Robotics in Rail Operations

Rail’s efficiency is not solely about locomotion but also about managing and maintaining rolling stock and track infrastructure. Traditional inspection and maintenance practices have been manual, time-consuming, and often reactive. AI-driven robotics and automated inspection systems are now transforming these supporting operations.

Companies like KONUX use IoT-enabled sensors and AI algorithms to proactively detect maintenance issues like worn rail switches or developing track defects. Automated wayside inspection systems equipped with high-speed cameras, infrared sensors, and machine learning analyze passing trains for defects—such as overheated bearings, broken wheel components, or damaged freight cars—instantly and continuously.

Digital Automatic Coupling (DAC) technologies are also gaining traction, particularly in Europe, as operators look to eliminate one of the last remaining manual bottlenecks in rail yards. Unlike traditional couplers—which require workers to manually connect air hoses, power lines, and mechanical links—DAC systems automate the entire coupling and decoupling process. This not only reduces safety risks and labor costs but also speeds up train formation, improving yard throughput. While adoption is still early and largely regional, DAC represents a foundational step toward fully automated freight rail networks.

Middle-Mile Logistics Infrastructure

Autonomous & Electric Drayage Trucks

Drayage—the short-distance movement of containers between ports, rail terminals, and nearby distribution centers—is especially well-suited for automation. Routes are repetitive, operations are time-sensitive, and congestion is common. These conditions make drayage an ideal use case for autonomous and electric vehicle technologies.

Companies like ISEE are deploying autonomous trucks in controlled environments such as yards and port-adjacent roads. These vehicles can operate continuously and handle tasks like trailer connection and repositioning without human intervention.

Battery-electric trucks, including the Volvo VNR Electric, are already operating in pilot programs at ports like Los Angeles, supported by zero-emission mandates. Hydrogen fuel-cell trucks from Toyota and Kenworth are also in trial for longer-haul or heavier-load applications, offering faster refueling and extended range.

Taken together, these solutions reduce labor demands, lower emissions, and improve terminal efficiency—addressing several of drayage’s most persistent challenges.

AI-Based Drayage Scheduling & Coordination

AI-based scheduling platforms are streamlining drayage operations by dynamically assigning containers to available trucks, reducing idle time and port congestion. Companies like NEXT Trucking and Dray Alliance integrate directly with terminal systems, using predictive analytics and real-time data to automate appointment scheduling and optimize dispatch.

These platforms aim to minimize dwell times and empty miles—two persistent pain points in port logistics. While adoption is challenged by fragmented stakeholders and legacy IT, early deployments point to real efficiency gains, especially in high-volume terminals where small improvements have outsized impact.

Autonomous Yard Operations & Robotics

Yard operations—managing the flow of trailers between parking zones, docks, and gates—have historically relied on manual driving and dispatch. These tasks are repetitive, time-sensitive, and well-suited for automation, especially within the controlled, geo-fenced environments of ports, warehouses, and distribution centers.

Companies like Outrider are deploying autonomous yard trucks capable of repositioning trailers without human intervention. Outrider’s platform also includes a robotic arm that autonomously connects trailer brake and electrical lines, eliminating the need for manual coupling. These systems increase throughput, reduce labor dependency, and support round-the-clock operations.

In parallel, robotic forklifts and pallet movers are being tested for trailer loading and unloading, further extending automation across yard workflows. When deployed together, these technologies can significantly reduce turnaround times and increase site productivity—making yard automation one of the most near-term, investible opportunities in middle-mile logistics.

Yard Management Systems (YMS)

To scale yard automation effectively, operations must be coordinated with precision. Yard Management Systems (YMS) provide the control layer that tracks trailer movements, assigns dock doors, and manages gate activity in real time.

These platforms use data from sensors, RFID tags, and AI-driven analytics to maintain accurate visibility and automate decision-making across the yard. When fully integrated, a YMS helps reduce idle time, eliminate manual coordination, and increase throughput—unlocking the full value of autonomous yard technologies.

Conclusion

Automation in logistics has long focused on the warehouse and the last mile—but the first and middle miles are now catching up. As goods move from origin to hub, and from hub to hub, new technologies are starting to replace fragmented, manual processes with systems that are more visible, responsive, and resilient.

Smart labels, IoT sensors, and AI-powered visibility platforms are helping digitize the earliest stages of the supply chain, setting the foundation for more adaptive operations downstream. In parallel, air and maritime freight are beginning to integrate robotics, automation, and remote operation into previously manual workflows. Across the middle mile, autonomy is taking root in linehaul trucking, rail networks, yard operations, and drayage—each shaped by predictable routes and infrastructure-rich environments that favor automation.

While the pace of adoption will vary across regions and modalities, the direction is clear. As these technologies continue to scale and integrate, the first and middle miles will become key leverage points for efficiency, cost reduction, and competitive advantage across the logistics chain.