Automation to tackle reshoring manufacturing, tariffs and labour shortages

Opinion | June 30, 2025

By Jean-Pierre Joosting

Opinion

Supply chains are typically complex logistics systems that rely on entire ecosystems of suppliers, resources, skilled labour, and manufacturing capabilities. China excels in this area, particularly evident in 5G communications and electric vehicles. Furthermore, Chinese companies produce the majority of the world’s electric vehicle batteries and its factories are implementing state-of-the-art automation technologies. This competitive landscape is being perceived as a threat other country’s markets. The resulting trade tensions are driving a push of reshoring manufacturing and supply chains.

Initially, most countries began to seriously consider reshoring manufacturing and the role of automation during the COVID-19 pandemic, which severely disrupted international supply lines between countries. However, due to COVID-19, many companies managed to implement resilience through multiple manufacturing points and shorter supply lines rather than reshoring to high-wage countries. Today, the situation is far more serious, as reshoring is once again at the forefront due to protectionism in the form of tariffs and geopolitics forcing organisations to consider manufacturing in expensive countries, many of which have meagre manufacturing resources that have been hollowed out over decades.

The uncertainty caused by sudden tariffs has led to a reevaluation of where to prioritise reshoring manufacturing and whether this is even possible. For many industries, it will simply be too expensive unless prices rise significantly, which, unfortunately, will drive inflation once again or slow down the economy. The initial reason for offshore production was to leverage cheap labour and regulation-light manufacturing, such as in China, India, Mexico and Eastern Europe. Since then, whole ecosystems have developed in such countries and cannot be reversed quickly without economic dislocation.

Several factors determine whether production can be reshored. The cost of automation is paramount in deciding whether high-wage countries can bring production back and mitigate labour costs. The availability of sufficient resources and skills is also key. Other important aspects include the cost of input materials for local manufacturing, the regulatory environment and the availability of parts and technology needed to produce products cost-effectively. In the United States, tariffs will ironically increase the cost of many input materials for local manufacturing.

The idea of a factory employing thousands of workers to produce goods is a remnant of a bygone era. Today, standards are being created for the factories of the future, often referred to as Factory 4.0, to ensure modern, fully automated, interactive, and flexible production lines.

Challenges of automation

Most factories have some form of automation, especially in the automotive industry. Pick-and-place machines for electronics assembly and welding robots in the automotive industry are two notable examples of success. However, one key challenge in electronics manufacturing is assembling non-standard or irregular components, which is where manual labour often comes in. These components make their handling and precise placement challenging for insertion machines, especially as electronic designs become more intricate and the diversity of irregular components increases. In other industries, manual labour is still required for many assembly tasks, which cannot be addressed by current automation technology. Humanoid robots could play a key role here in future factories. Some other key challenges include various disruptions, such as labour shortages, limitations of current technologies, and costly downtime on production lines.

Advancing automation technology

To increase the efficiency of an assembly line with automated processes and provide the ability to reconfigure production lines quickly, Factory 4.0 digitises manufacturing and industrial processes, creating a manufacturing environment where systems can exchange information, trigger actions, and control each other autonomously. In such smart factories, machines, devices, and systems are interconnected, enabling the collection of real-time data, advanced analytics, predictive maintenance, digital twins, and autonomous decision-making.

Tackling labour shortages or enabling reshoring in high-labour-cost economies is a key driver of investment in automation. Training and upskilling programmes also need to be considered, not only to supply skilled labour for the required roles but also to ensure workers possess the necessary expertise. Here, the use of Generative AI is viewed as a potential facilitator that not only reduces costs but also prevents accidents and damage to machinery.

To illustrate this, researchers from the University of Georgia developed a new virtual reality (VR) space named VR Co-Lab to help humans working with robots train more efficiently and effectively. Detailed in a recent study, VR Co-Lab trains employees digitally to practice disassembling recyclables without damaging materials and to learn how to avoid injuries and collisions with robots [1].

To assist companies in reducing the initial costs of automation, Robotics as a Service (RaaS) is a business model similar to Software-as-a-Service (SaaS). RaaS enables organisations to access robotic technologies via a subscription or leasing plan, allowing them to deploy, manage, and expand their robotic operations more flexibly and cost-effectively. RaaS is used to automate repetitive tasks, including warehouse management, customer service, and complex surgeries, among others.

Manufacturing Operations Management (MOM) and the Industrial Internet of Things (IIoT) are transforming manufacturing, with the IIoT enabling real-time data collection and analysis to optimise MOM processes. MOM encompasses a wide range of activities, including planning, scheduling, and quality management. At the same time, the IIoT focuses on connecting devices and systems to gather and act on real-time data from the shop floor. The integration of IIoT with MOM systems delivers enhanced efficiency, predictive maintenance, improved quality control, and more effective supply chain management.

AI and machine learning can identify subtle anomalies that might lead to a breakdown, enabling manufacturers to carry out repairs before a breakdown causes costly downtime or damage. Known as predictive maintenance, this operating process requires a continuous stream of operational data from sensors installed on the machine, as well as data persistence and other sources of information, such as maintenance records, failure events, inspection records, and engineering diagrams, to enable holistic monitoring. AI in predictive maintenance can identify patterns and anomalies that traditional data analysis and condition monitoring cannot easily detect. Furthermore, it can employ a feedback loop to improve over time.

For example, Senseye Predictive Maintenance is a cloud-based software platform that combines AI with human insights to automatically generate machine behaviour models that analyse machine data and predict potential failures. It is machine-agnostic and integrates with any asset, system, or data source, using either existing data or newly installed sensors. Senseye is used across various industries, including pulp and paper, metals and mining, and manufacturing, to provide proactive maintenance, minimising unplanned downtime and its associated costs. Siemens also offers generative AI functionality to make predictive maintenance more conversational and intuitive [2].

IIoT and 5G tie it all together

Automation in factories requires communication between machines, sensors, and computing platforms, often referred to as IIoT. Traditionally, communication is facilitated by Ethernet-based protocols such as PROFINET, POWERLINK, EtherCAT, EtherNet/IP, SERCOS III, CC-Link IE, and Modbus TCP. However, wireless protocols such as Wi-Fi, Bluetooth, Zigbee, WirelessHART, and LoRaWAN are increasingly being implemented in modern factories to reduce installation and maintenance costs, enable the quick movement of equipment on the factory floor, and facilitate remote access and control of machinery and processes. This results in improved asset management and is key to enabling predictive maintenance. Furthermore, wireless technology is also well-suited for real-time data collection and control. However, in wireless systems, security and interference are key concerns, but they can be addressed with careful planning.

To further advance factory automation, 5G is being increasingly considered due to the benefits it brings to robotics and automation, including real-time communication and enhanced operational efficiency. Features such as high bandwidth, low latency, and reliability make it ideal for demanding industrial applications. With the use of AI and machine learning, the IIoT uses vast amounts of data, making the speed and capacity of 5G key requirements.

The high bandwidth and low latency of 5G networks enable more advanced and accurate automation tasks, including the use of robots and drones, as well as remote control of machines, processes, and automated systems. Remote operation also reduces the need for human workers in hazardous environments. 5G also supports rapid inter-machine communication, increasing efficiency and allowing quick responses to changing production demands by enabling switching between product batches or types with minimal delay. 5G can also cover extensive areas with ease and link smart factories with their supply chains and logistics.

Since Private 5G offers greater reliability, control, and security, it is being increasingly deployed in factories and plants. 5G can also easily cover large areas and connect smart factories with their supply chains and logistics. Private 5G networks offer dedicated connectivity, ensuring high performance and dependability while giving manufacturers more control over network parameters to optimise performance. However, the key feature driving Private 5G for many organisations is that these networks are isolated from public networks, enhancing security and minimising the risk of cyberattacks, an increasingly important consideration as threat levels continue to rise and state-sponsored cyberattacks become the norm.

Additive manufacturing

Additive manufacturing is also emerging as a useful technology in manufacturing. It excels at prototyping and making obsolete parts, but it is alos entering more mainstream processes in industrial applications as the technology matures. In electronics, it can create 3D parts, such as antennas, that would not be possible using traditional methods.

To illustrate this, the Italian automation company Comau is collaborating with several international companies involved in digital additive manufacturing to bring its technology to new market segments that require affordable and easy-to-use advanced automation. For example, CEAD leverages Comau robotic arm technology, integrated into its Flexbot system, to help companies worldwide revolutionise their manufacturing processes by delivering large-format thermoplastic composite 3D printing. Comau is also collaborating with Titomic, a world leader in cold spray technology, enabling large-scale additive manufacturing, coatings, and repairs utilising novel and high-performance materials [3]. In another example, Comau and Prima Additive have joined forces to deliver dual-layer laser cladding innovation, having developed a high-speed, fully automated brake disc coating system [4]. Coma customers will be able to print parts on demand with consistent, repeatable results, allowing them to optimise material usage, reduce waste, and improve overall production efficiency.

Additive manufacturing extends far beyond electronics manufacturing, covering almost every industry and is a field undergoing rapid development as materials and processes improve. The technology can print tiny parts as well as large formats, such as boat hulls, car parts, or surface coatings.

Fitting into an integrated supply chain

An integrated supply chain encompasses not only factories but also inventory management and warehousing for raw materials and goods, as well as the dispatching of finished products. Automated warehousing is essential for overseeing inventory movement to and from smart factories. These warehouses rely heavily on robotics, particularly Autonomous Mobile Robots (AMRs), which transport parts and goods throughout the facility. AMRs can also interface with automated systems to select necessary parts with minimal or no human input. Wireless communications such as 5G, Wi-Fi Halow, Bluetooth, and LoRaWAN are vital for enabling AMRs. These robots require low latency, accurate positioning, obstacle detection, collision avoidance, and rapid rerouting. Small, modular factories and regional warehouses could offer solutions to challenges posed by tariff trends, geopolitical upheavals, pandemics, or natural disasters.

Looking to the near future

Smart factories are poised to leverage robotics and automation software and systems to deliver reconfigurable, fully automated factories and supply chains with minimal labour requirements. Although automation addresses current labour shortages in manufacturing and logistics, it will likely result in virtually no growth in jobs.

Reshoring driven by tariffs and sovereign concerns requires the cost of automation and robotics to decrease to a level that makes economic sense. The International Federation of Robotics (IFR) predicts that the use of physical, analytical, and generative AI will expand, enabling robots to perform a wide range of tasks more efficiently. Analytical AI, for example, allows robots to process and analyse considerable amounts of sensor data for managing variability and unpredictability in high-mix and low-volume production, as well as in public settings. Physical AI enables robots to train themselves in virtual environments and operate based on experience rather than programming. Generative AI will permit robots to integrate seamlessly into human environments and query information for maintenance, servicing, and operational procedures. Although the IFR finds that humanoid robots are currently limited to performing single-purpose tasks in industrial manufacturing, they see potential for humanoid robots in logistics and warehousing, especially if issues related to cost and complexity can be resolved [5]. Other reports predict that humanoid robots will number in the hundreds of millions within a decade or two.

Automation is the way forward

Tariffs and digital sovereignty are driving the need for automation and robotics in manufacturing and supply chain logistics, which in turn is prompting countries to push for reshoring. However, due to the high costs of materials and manufacturing in advanced economies, this is generally not feasible without significantly higher prices. Automation and robotics in smart reconfigurable factories are seen as a key enabler to drive reshoring, address sovereign and tariff issues, as well as remove labour costs and shortages.

To illustrate the potential disruption that automation technology can cause, Space Forge has just confirmed the successful launch of its first in-space manufacturing satellite, developed entirely in Wales. This first-of-its-kind in-orbit manufacturing demonstration is designed to prove the viability of producing advanced materials in the unique environment of space [6].