How 5G and
edge computing
can help improve
manufacturing
productivity

Manufacturing productivity has seen steady growth following a big drop during the COVID-19 pandemic. At the same time, continued labor shortages in manufacturing have caused companies to explore how to build a connected factory of the future to help with growth and productivity. Over 80% of manufacturers said reducing costs and improving operational efficiency are the most important reasons for implementing smart factory technologies.

While many of these new technologies can help to improve manufacturing productivity, having an outstanding wireless network is critical. Newer applications within manufacturing require lower latency, faster data speeds and more bandwidth.

Fortunately, 5G and mobile edge computing (MEC) technologies are helping to contribute to these innovations, offering faster networks and more efficient compute processing capabilities—which is perhaps why 91% of manufacturers say 5G connectivity will be important to the overall future of their business. For companies asking how to improve productivity in manufacturing, exploring 5G and edge computing technologies is a great start.

Across the manufacturing landscape, companies are exploring new connected factory technologies to help them improve their operations.¹ Here are a few examples:

• Industrial Internet of Things, or IIoT, involves placing sensors on specific factory equipment to perform tasks such as measuring, monitoring and managing performance,  running proactive maintenance analytics and scanning for problems like temperature extremes, leaks or other environmental hazards. Data generated by IIoT devices can be used to help manufacturers monitor, maintain and prevent a machine from breaking down, which avoids costly downtime and improves productivity by keeping production running. IoT Analytics found that predictive maintenance boosted equipment efficiency by 17%, according to TechHQ.
• Advanced and intelligent robotics, such as industrial arms that include computer vision capabilities, or mobile robots, such as automated guided vehicles and autonomous mobile robots, can complete material handling tasks that help to replace manual carts on a factory line. By having the right materials provided to factory workers at the right time, downtime can be avoided instead of having to wait for workers to manually deliver critical materials to a production location. An American Economic Association paper found that firms using robotics had "increases in value added and productivity."
• Artificial intelligence (AI) and machine learning (ML) can help manufacturers gain a better understanding of all aspects of their factory. According to Deloitte, one connected factory saw production double on existing capital equipment after using predictive capabilities driven by advanced data analytics.
• Augmented and virtual reality systems, also known as AR/VR, can be used to provide product demonstrations for workers as well as for training front-line workers to better handle equipment and avoid hazardous materials to help prevent or reduce workplace injuries.
• Digital twin technologies create virtual environments for running simulations. Many of these simulations can also provide predictive maintenance capabilities, identifying potential parts that could break down soon and prompting teams to replace them before they break. Leveraging 5G and edge computing-powered digital twin solutions could boost manufacturing productivity through continuous performance adjustments based on real-world data streams.

The combination of 5G and edge computing helps to enhance these modern applications by hosting them at the edge of the network, closer to devices and endpoints. This process can help to lower response times and boost performance, allowing companies to collect, process and analyze data faster.

In order for an edge network to succeed, several elements are required:

• High bandwidth: For applications to transmit an increasing amount of data, edge networks will need bandwidth that can scale on demand.
• Reliability: Once processing moves to the edge, applications will grow even more mission-critical, which requires a network that can minimize loss of availability.
• Security: All data must be encrypted end to end. This will typically require additional tools that can stop malware and alert IT security to any possible network attacks to ensure customer data and proprietary information is safe.
• Low latency: Many modern manufacturing applications require a reduction in the time from data capture to processing in order to make faster and safer decisions.
• Flexibility and Scalability: Edge networks must be agile enough to support rapid changes, whether this is from a new service or application being rolled out or a surge in demand. Modern networks need the ability to slice both network bandwidth and resources in order to accommodate critical applications to ensure high-quality service.

In all these areas, 5G can perform better than the alternatives, which is why investing in 5G and edge computing gives manufacturers the opportunity to maximize the productivity benefits of these connected factory technologies. Indeed, according to a survey by the Manufacturing Institute, manufacturers agreed that 5G should make it easier to perform key tasks such as automation, predictive maintenance, AR and robotics compared with wired, Wi-Fi or 4G connections.²

Deploying AI and ML algorithms on industrial robot arms and mobile robots requires very high speed data transfers in order for the robots to perform certain tasks, such as obstacle avoidance for safe navigation around the production floor. Robots that pick and place objects out of a bin require computer vision processing that needs to be speedy in order to increase their productivity. Even standalone video cameras that monitor quality control on finished products (e.g., scanning for defects) require a speedy, high-bandwidth network to find those defects fast.

A further benefit of 5G and edge computing is the opportunity to combine both in a private on-site 5G network. Edge computing infrastructure can be deployed in either a public MEC (at the edge of a public 5G cellular network) or a private MEC scenario where computing and network infrastructure is installed on-premises. This involves a dedicated platform that delivers low latency to warehouses, factories and even large campuses, providing greater security for those organizations. Companies that are looking for the lowest available latency or enhanced safety along with data sovereignty for applications should explore a private MEC deployment.

Applications that could benefit from a private MEC deployment include:

• Computer vision and ML tasks for item picking
• Predictive maintenance gathered from IoT sensors and video cameras to improve machine utilization
• Quality assurance automation that uses low-latency computer vision combined with ML to catch production defects
• Automated guided vehicles for materials handling
• Infrastructure monitoring that can reduce inspection costs, monitor safety and manage physical assets in near-real time