The industrial landscape is undergoing a significant transformation with the rise of digital technologies. One of the key drivers of this shift is industrial mobility, a concept that is changing how factories and manufacturing processes operate. At its core, industrial mobility involves the use of advanced technology to streamline operations, improve efficiency, and enable greater autonomy on the factory floor.
The impact of industrial mobility is already being felt in industries like manufacturing, logistics, and transportation. Autonomous vehicles and robots are being used to manage inventory, map factory layouts, plan efficient routes, and even control entire production systems autonomously. As these technologies continue to develop, investment in private mobility companies has surged, with $6.8 billion invested during the 2012-2017 period. This trend is expected to accelerate as the technology matures.
Industrial mobility refers to the use of autonomous vehicles and robotic systems to automate the movement of materials and goods within industrial settings. Unlike traditional mobility, which focuses on transporting people, industrial mobility is tailored to meet the unique needs of the manufacturing, logistics, and process industries. For example, AGVs, AMRs, and autonomous industrial trucks are increasingly used to streamline material handling and reduce labor costs.
With more than 3.6 million tons of freight moving in the US daily, the demand for industrial autonomous vehicles is growing. Large industrial players like Amazon, Walmart, and BMW are heavily investing in autonomous mobility to:
As a result, they are better able to meet changing demands and improve overall operational performance. Industrial mobility testing ensures that these systems can operate reliably under real-world conditions like dynamic environments, making autonomous technology a practical solution for smart factories and logistics hubs.
Industrial mobility is a key driver of Industry 4.0. The Fourth Industrial Revolution, also known as Industry 4.0, signifies a major leap forward in manufacturing through the integration of advanced technologies. At the core of industry 4.0 is the Industrial Internet of Things (IIoT), which connects machines, sensors, and systems to enable real-time data exchange and enhanced decision-making. In fact, according to the PwC/MI Industrial Mobility Survey, 65% of US manufacturers believe it will become mainstream within the next decade.
This integration facilitates a more intelligent and efficient manufacturing environment by leveraging technologies such as artificial intelligence (AI), machine learning, robotics, and Industrial Internet of Things (IoT). For example, in factories and digital supply chains, IIoT supports a range of applications, including inventory management, asset mobility, and real-time location systems (RTLS).
As Industry 4.0 continues to evolve, it further drives the rise of industrial autonomy, where systems operate with minimal human intervention. As a result, smart factories are becoming a reality, and are expected to grow from $223.6 billion to an impressive $985.5 billion by 2032. This shift towards greater autonomy significantly reduces costs and improves operational efficiency, leading to more reliable and scalable manufacturing solutions.
For example, autonomous long-haul trucking could save manufacturers nearly 30% in total transportation costs by 2040, assuming aggressive adoption rates. Similarly, fully autonomous trucks are expected to save up to 25% of total trucking costs once mainstreamed.
Industrial mobility solutions comprise several key components that work synergistically to automate and optimize operations:
Equipped with sophisticated sensors, machine learning algorithms, and precision actuators, industrial robots and autonomous robots are being deployed to perform complex tasks that are hazardous, repetitive, or require high precision.
For example, Delta robots, which use advanced machine learning and real-time sensor data, are designed for tasks like packaging and handling in industries such as pharmaceuticals and food, where speed and precision are critical. These robots can handle light, repetitive applications with high accuracy, making them ideal for environments that require meticulous quality control. By operating continuously without breaks, they significantly enhance productivity and reduce the risk of human error. Adopting such technologies is also crucial for achieving greater operational efficiency and safety.
Autonomous industrial vehicles, such as Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs), are increasingly being adopted. A 2023 Modern Materials Handling study found that more than 2/3rds of all recipients were looking to adopt AGVs or AMRs in the next 24 months.
AGVs are used primarily for material handling within manufacturing facilities and warehouses. They follow fixed routes like those in Ford’s manufacturing plants, where AGVs transport heavy automotive parts along predefined paths to assembly lines, significantly improving productivity. These systems are ideal for repetitive tasks such as transporting materials from one location to another, like moving raw materials from a storage area to the production line. In fact, according to the American Society of Mechanical Engineers (ASME), AGVs have the capacity to increase efficiency by 50-70%.
Unlike AGVs, AMRs don’t follow predefined routes and are instead equipped with advanced sensors and AI capabilities that enable them to navigate dynamically through complex environments. AMRs, like Cyngn’s fleet of self-driving vehicles, can adapt to changing conditions on the factory floor, making them more versatile and flexible than AGVs.
AMRS are ideal for tasks like:
Connectivity is the backbone of industrial mobility solutions, enabling seamless communication and coordination between devices. Advanced sensors gather critical, real-time data on machine performance and environmental conditions, providing insights for optimizing processes. Reliable network infrastructure then ensures this data is transmitted in real-time between sensors, robots, and automation components, facilitating immediate decision-making.
Horizontal and vertical integration are key strategies for optimizing industrial mobility. Horizontal integration connects similar processes or departments across multiple locations. Imagine several factories: one assembles products, another handles packaging. With horizontal integration, these factories share real-time data, so when one finishes assembly, the next is ready to package immediately—like synchronized hands working together.
Vertical integration, on the other hand, connects different stages within a single factory. From raw materials to final shipping, each step is linked by sensors and AI. If a machine slows down, the system automatically reroutes tasks or alerts maintenance—like the factory has its own brain, constantly optimizing the workflow.
Together, these integrations make factories smarter and more autonomous.
According to the U.S. Chamber of Commerce’s AI Commission Report, AI will enable about $13 trillion in economic growth by the end of the decade. Machine learning and AI are critical components of industrial mobility, driving advancements in automation and operational efficiency.
General Motors (GM) provides a notable example of how a company "won" by leveraging AI. GM uses AI-driven predictive analytics and machine learning tools to optimize its manufacturing processes, particularly in its vehicle production lines. By analyzing vast amounts of data from sensors in real-time, GM's AI systems help predict equipment failures before they occur, reducing downtime and increasing productivity. This proactive approach enabled GM to achieve more efficient production cycles, significantly lowering operational costs and enhancing vehicle quality.
According to a recent survey by McKinsey, it has the potential to reduce operational costs by up to 30% in some industries by optimizing processes and improving predictive maintenance capabilities. These technologies enable systems to learn from historical data, optimize their performance, and predict future trends, thereby reducing costs and enhancing efficiency.
While cloud computing offers centralized data storage and processing, edge computing brings computational power closer to the data source. This reduces latency and enables real-time decision-making, which is critical for autonomous operations.
Picture an automated warehouse where autonomous mobile robots (AMRs) move materials between production lines. With edge computing, sensors on the robots process data locally, allowing them to instantly detect obstacles, reroute, or adjust speed without waiting for instructions from a distant cloud server.
For example, if a forklift unexpectedly crosses the path of a robot, the edge system enables it to react immediately, stopping or navigating around the obstacle in milliseconds. Meanwhile, cloud computing manages long-term data like performance metrics and fleet optimization, ensuring smooth operations across the entire facility. Together, these technologies make real-time autonomy possible in fast-moving environments like material handling.
As industrial systems become more interconnected, they are also more vulnerable to cyber threats. A recent report highlighted that over 40,000 industrial control systems (ICS) in the US are exposed to potential cyberattacks, with threats targeting critical infrastructure such as power grids and water systems.
Ensuring robust cybersecurity measures is essential to protect sensitive data and maintain the integrity of autonomous operations. To combat these issues, effective strategies include regular patch management, advanced network monitoring, and employee training on cybersecurity protocols. Additionally, deploying predictive security tools to identify and close vulnerabilities before they can be exploited has been crucial in reducing downtime and preventing attacks.
The applications of industrial mobility are vast and varied, spanning multiple industries and driving significant improvements in efficiency, safety, and sustainability.
Real-time tracking of inventory levels and locations using RFID and RTLS systems helps reduce inventory-related costs and optimize stock levels. These technologies, used by companies like Walmart as early as 2006, can reduce errors by up to 50%.
Automated movement of materials and finished goods throughout the supply chain improves logistics efficiency and reduces manual handling costs. Technologies such as Automated Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs), and conveyor systems enable continuous, real-time movement of materials with minimal human intervention. These systems reduce manual handling costs by minimizing labor-intensive tasks like loading, unloading, and transporting goods across facilities.
AI and machine learning algorithms optimize routes and workflows for autonomous vehicles, improving efficiency and reducing travel times. Tools like digital twin models and systems such as Cyngn’s navigation platform enhance this process by first creating a detailed map of the environment. Cyngn’s system then overlays the map with semantic information and "rules of the road," allowing autonomous industrial vehicles to navigate safely and efficiently. These technologies help autonomous systems adapt to real-time changes, minimizing delays and maximizing productivity.
These systems can independently control operations, reducing the need for human intervention. By making real-time decisions based on data from sensors and environmental inputs, they allow for more efficient management of complex processes. In continuous-process industries, where production runs continuously, the integration of autonomous control systems ensures uninterrupted operations and enhances overall process efficiency.
The integration of information technology (IT), which focuses on the use of computers, networks, and data management systems, and operational technology (OT) systems, which involves hardware and software that detects or causes changes in industrial equipment, helps streamline industrial processes. This enables more informed decision-making and improves overall operational efficiency.
Advanced analytics and AI-driven decision-making tools are used to optimize manufacturing processes, reducing waste and improving quality. Siemens, for instance, uses its MindSphere platform, an AI-driven IoT system, to collect and analyze data from manufacturing equipment in real time. This helps manufacturers predict equipment failures and optimize production schedules. Similarly, General Electric (GE) leverages its Predix platform to analyze industrial data, which improves efficiency by reducing downtime and enhancing predictive maintenance, ultimately optimizing production and reducing operational costs.
Autonomous systems help reduce energy consumption and minimize waste, contributing to more sustainable industrial practices. For instance, the use of autonomous electric vehicles in logistics can reduce carbon emissions and lower operational costs, leading to a smaller environmental footprint.
Industries such as manufacturing, logistics, and transportation are at the forefront of adopting industrial mobility solutions, with some sectors expected to see up to 30% cost reductions through the use of autonomous systems by 2040.
In the manufacturing sector, only 9% of companies have currently adopted autonomous technologies, but this is expected to grow as the benefits of improved efficiency and reduced costs become more apparent. Manufacturing 4.0 represents the next phase of digital transformation in manufacturing, where smart factories leverage advanced technologies like AI, IIoT, and robotics to achieve higher levels of automation and efficiency. For example, semi-autonomous vehicles such as free-range AGVs and autonomous forklifts are being used to automate material handling within factories.
The logistics industry is set to be one of the biggest beneficiaries of industrial mobility, with autonomous trucks alone expected to reduce total transportation costs by up to 30% when fully mainstream. Add to that, the power of additional technologies such as drones for last-mile delivery and warehouse automation, and you’re looking at significant improvements to speed and accuracy.
70% of the people in the transportation industry believe autonomous trucks will play a significant role within 20 years. Autonomous trucking and autonomous transportation solutions are paving the way for safer and more efficient freight transport. As fully autonomous trucks become more mainstream, they have the potential to significantly lower transportation costs and enhance logistics efficiency.
At its core, industrial mobility is about making manufacturing smarter and more autonomous. This is precisely what our fleet of AMRs delivers. At Cyngn, our self-driving AMRs—including our autonomous tugger, autonomous forklift, and autonomous stockchaser—can automate a variety of repetitive hauling tasks without requiring special infrastructure. This allows your team to work faster and at a lower cost. Our high-capacity industrial AMRs never take vacations, don’t make human errors, and always handle your most demanding industrial jobs.
As a result, our DriveMod vehicles have been shown to be: