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Edge connectivity is more than a technology trend. It has become an important part of operational resilience and scalability. Manufacturers continue to face pressure to increase throughput, reduce downtime, and keep production moving. And that pressure increases with artificial intelligence (AI), Internet of Things (IoT), and robotics implementations that demand real-time data. In addition, manufacturers' IT teams need a solution that allows them to build secure, manageable, scalable systems as device ecosystems expand.
To overcome these challenges, many manufacturers are choosing to bring some of their workloads back from the cloud to the edge. MarketsandMarkets predicts the global edge computing market will grow from $111.34 billion in 2026 to reach $317.39 billion by 2031, a 23.3% compound annual growth rate (CAGR).
Edge computing processes data close to the source instead of sending every input to a centralized cloud or data center. In a manufacturing environment, where sensors, cameras, machines, scanners, and other devices collect or generate data, an edge server or node processes that information locally. Data and insights from the edge are immediately available to the people, processes, and systems that need real-time data for optimal performance.
This approach gives manufacturers more control over data management and data flows. For example, a smart camera system can process images, alert the security team in real time to potential issues, and send only essential data to the cloud.
Proofs of concept (POCs) and pilots are proving edge computing's benefits and return on investment (ROI).
Manufacturing equipment, safety systems, and robotics need to be able to deliver instant responses. When a sensor detects an anomaly or a vision system identifies a defect, waiting for data to travel to a distant cloud and return can create an unacceptable, and sometimes risky, delay. Edge connectivity brings the source of the data and the process that needs it closer together, so responses can be virtually instantaneous. It can also optimize data routing, reduce network traffic, and cache frequently accessed information locally to reduce response time and support fast decision-making. In a manufacturing facility, that can mean faster quality control (QC) processes, immediate shut-offs if safety is at risk, and detecting out-of-spec equipment operations to prevent failure and downtime.
Factory connectivity no longer only means supporting a few laptops and scanners. A connected factory ecosystem depends on handheld computers, tablets, wearables, printers, RFID readers, vision systems, autonomous equipment, sensors, cameras, and other operational technology. As a result, more manufacturing processes now slow down, or even grind to a halt, when connectivity fails.
Edge computing does not depend on an external network connection to process data locally. An edge node can continue to process and share data with mission-critical processes and later sync essential information with the cloud or a data center. That independence from an external network connection helps to increase manufacturing resilience and continuity.
Moving data processing to the edge can also help manufacturers control technology costs:
Manufacturers may use sensitive customer data in their processes and need to protect their intellectual property or proprietary processes. Edge computing processes data on-site, eliminating the need to send it for processing in the cloud or across a network to remote servers. This decreases vulnerability to data loss and data breaches, as long as systems and processes include access controls, device security, and data governance.
Manufacturers have been limited in automating with IoT because of barriers to real-time data, cost, security, connectivity, and data silos. Edge connectivity gives manufacturers a way to create reliable data flows for IoT systems. Manufacturers can run lightweight AI models, small language models (SLMs), vision language models (VLMs), and specialized AI agents at the edge to overcome those challenges.
Global X estimates that 62% of data will be processed on edge devices by 2027. Edge connectivity can also lead to industrial IoT applications, like predictive maintenance, quality control, and workplace safety, becoming more widespread.
Autonomous and collaborative robots need low-latency data processing and reliable connectivity to provide real value to manufacturers. Edge computing supports computer vision and AI inference within robotics systems, helping them identify objects accurately, inspect products, safely navigate facilities, and react to unexpected obstructions or changing conditions. Manufacturers can also coordinate multiple robots performing phases of a single production process or manage fleets of robots moving through a warehouse by leveraging edge connectivity.
Many devices that manufacturers deploy rely on wireless connectivity. For example, autonomous robots that move through a facility can't connect with a wired connection, and manufacturers may need to deploy IoT sensors in locations where cabling isn't possible. An edge-ready wireless network can provide reliable connections between edge nodes and the devices that depend on the data and inference they generate.
Building an edge-ready wireless network should start with a site survey that identifies interference from machinery, building materials, and other sources. The network should deliver high-density wireless coverage to support all edge appliances and mobile devices in the facility. For the best performance, a segmented network should keep mission-critical traffic, like safety solutions and robotics, separate from administrative tasks running on Wi-Fi. Some manufacturers may choose a private 5G network for that mission-critical traffic.
Manufacturers should also ensure an edge-ready wireless network can scale as they add more devices, applications, and automated systems without creating bottlenecks or slowing down innovation.
As manufacturers capture more data through scanners, RFID readers, inspection solutions, cameras, and IoT devices, they need infrastructure that can support real-time analytics without latency.
An experienced technology partner will help manufacturers evaluate workloads and design edge-ready wireless networks. The right partners will also build the security and management capabilities needed to operate a distributed environment and provide lifecycle services for the devices manufacturers rely on for data collection, application access, and communications.
Levata's wireless networking expertise can help manufacturers move computing and AI inference to the edge while supporting reliable, secure edge connectivity. Contact us to learn more.
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