5G and edge computing transform manufacturing by enabling real-time decision-making, predictive maintenance, and intelligent automation. This shift is revolutionising operational efficiency and setting a new standard for connected industrial environments.
Manufacturers are on the cusp of a radical shift as 5G and edge computing move from promising technologies to critical enablers of real-time production, supply chain resilience, and intelligent automation. The fusion of low latency networks and localised processing power is reimagining what is possible on the factory floor, turning complexity into opportunity and setting a new pace for global industry.
Real-time data transforms production
Manufacturing has long been constrained by information flows’ slow and fragmented nature. As Fiona Treacy, Managing Director, Industrial Automation at Analog Devices, explains, greater factory efficiency is impossible without timely access to data. “With more than half of the world’s energy being consumed by the industrial sector, there is an urgent need to double the efficiency of factories around the globe,” she explains. “5G can enable this efficiency level within factories thanks to its high bandwidth and low latency. The real-time communication capabilities of 5G help machines sense and therefore navigate their surroundings in real-time, reducing errors and improving productivity.”
Kunal Shukla, Chief Technology Officer at Digital Barriers, agrees that the real breakthrough lies in faster speeds and in reshaping operational possibilities. “The combination of connectivity and compute is at the heart of manufacturing’s next transformation,” he says. “With 5G and edge computing, we are entering an era where low latency and high bandwidth make real-time decision-making a reality. This capability allows adaptive production lines, advanced predictive maintenance, and a more intelligent, connected supply chain.”
Pulsant’s Mark Lewis sees a new model emerging from this transformation. “5G and edge computing are set to revolutionise the manufacturing industry over the next five years by enabling higher levels of customisation at greater speed and lower cost,” he adds. “We are moving towards a model of ‘manufacturing as a service’ where consumers can expect more personalised products without compromising cost-effectiveness.”
At the heart of these shifts lies a fundamental change in how factories use and process information. Real-time data processing at the edge means production lines can respond to changes instantly, minimising waste, maximising uptime, and making it possible to mass-customise goods without sacrificing operational efficiency. This capability is already being demonstrated at major manufacturing facilities such as Airbus, where private 5G networks have allowed data from over 4,000 industrial assets to be integrated into unified operational and business applications. Such integration has enabled predictive maintenance, improved logistics, and reduced human error by automating quality control and order fulfilment processes.
Harnessing intelligence at the edge
The move to edge processing is not simply about speed. It is about rethinking the architecture of industrial intelligence. In traditional factories, data has been sent to centralised servers for analysis, introducing critical delays. Edge computing brings the intelligence to the source, allowing machines, sensors, and even video systems to analyse and act in real time.
“Edge computing is revolutionising how we process data in manufacturing by bringing data analysis capabilities close to the source,” Shukla explains. “When dealing with high volumes of video data from monitoring or quality control sensors, this ability to process locally is invaluable. It supports continuous monitoring, enabling manufacturers to identify issues early, avoid unexpected breakdowns, reduce waste, and ultimately boost efficiency.”
Lewis sees edge computing as the bridge connecting automation and artificial intelligence directly to the shop floor. “Edge computing creates numerous opportunities for increased automation on the factory floor, particularly through real-time analytics and machine learning,” he continues. It enables robots to react more quickly, learn from operational data, and optimise processes in real time, driving significant efficiency gains.”
This decentralisation of intelligence has profound implications for manufacturing design, moving industries away from rigid, linear production models toward dynamic, adaptable systems capable of learning and evolving over time. Manufacturers are also significantly enhancing security by pushing data processing to the edge. Local data processing reduces the volume of sensitive information transmitted across networks, lowering the risk of interception and cyberattacks.
Video as a sensor and the rise of digital eyeballs
One of the most striking manifestations of this change is the use of video as a real-time sensor. High-resolution cameras, connected by 5G and analysed at the edge, are becoming the digital eyeballs of smart factories.
“Video as a sensor represents a leap forward in manufacturing’s capability to monitor, control, and optimise in real-time,” Shukla says. “By deploying video streams and image data, we can continuously track and ensure product quality and monitor worker safety. Using AI and machine learning at the edge, paired with 5G, we can transform massive volumes of video data into actionable insights.”
The implications extend well beyond quality control. AI-powered video analysis can detect improper machinery use, identify non-compliance with safety protocols, and even spot signs of fatigue or hazard in real time. This level of insight was previously impossible without heavy human supervision or costly infrastructure.
Beyond the operational floor, video sensors combined with 5G open new remote collaboration and support possibilities. Augmented reality (AR) and virtual reality (VR) applications powered by low-latency networks enable remote experts to assist field workers, improving maintenance and reducing downtime.
Overcoming barriers to deployment
Despite the clear potential, deploying 5G and edge computing at scale has significant challenges. Legacy infrastructure, cost concerns, bandwidth constraints, and security risks are not trivial hurdles.
“Implementing video as a sensor on a large scale comes with a host of challenges,” Shukla warns. “High bandwidth costs, latency requirements, scalability concerns, and privacy and security considerations are all significant barriers. Our AI-based video codec is designed to operate efficiently in cellular environments, even at low bandwidths, reducing bandwidth requirements by up to 90 per cent, making real-time video possible across both private and public 5G networks.”
Lewis highlights the broader challenge of modernising factory networks for a new era. “One major challenge is updating legacy infrastructure, as traditional systems are often centralised, while 5G, AI, and edge computing favour more flexible, distributed architectures,” he explains. “Addressing these challenges will require an investment in new infrastructure and a shift in mindset towards a more decentralised, ecosystem-based approach.”
In this context, the emergence of private 5G networks explicitly tailored to the needs of individual sites is particularly significant. Airbus and Hamburger Containerboard, for example, have shown how private 5G deployments can transform production environments, offering reliable, low-latency connectivity even in challenging industrial settings.
Using private networks enables manufacturers to prioritise critical applications through network slicing, ensuring that mission-critical communications and automation systems always receive the necessary bandwidth and low latency. This ability to customise network behaviour in real time is becoming a competitive differentiator.
New frontiers for connected manufacturing
As early adopters like Airbus have demonstrated, private 5G and edge computing are not just enhancing traditional processes but are enabling entirely new capabilities. Autonomous Guided Vehicles (AGVs), predictive maintenance systems, dynamic quality control through digital twins, and remote expert support through augmented reality are no longer concepts for the future; they are operational realities.
Treacy emphasises the importance of the Intelligent Edge in unlocking this potential. “The highest quality data can be found at the Intelligent Edge, where data processing and computation are performed closer to the devices and sensors that generate the data,” he adds. “5G enables us to harness these insights at the edge and make better decisions, thereby enhancing overall efficiency.”
Shukla sees the convergence of 5G, edge computing, AI, and video analytics as the foundation for a more intelligent, agile, and resilient manufacturing sector. “The future of manufacturing will be increasingly data-driven and intelligent,” he concludes. “Video-enabled digital twins, AI-powered video analytics, and AR/VR tools for remote support will become crucial for predicting maintenance needs, managing workflows, and enhancing operational efficiencies.”
For manufacturers, the path forward will not be simply adopting new technologies but reimagining their entire operational fabric. The winners will be those who embrace connectivity and computation not as add-ons but as their enterprises’ new nervous system, enabling a leap from reactive production to proactive, intelligent manufacturing.
