Think Fast, Think Local: The Edge Advantage in Manufacturing

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Written By Jeff Winter

Edge computing has become one of the most talked-about tech strategies in recent years, especially in data-intensive industries like manufacturing. In simple terms, edge computing means moving some computing power closer to where the data is generated or used, whether that's on a factory floor sensor, an assembly line robot, or a telecom tower. This article explores how the concept came about, what problems it solves, the different forms it takes, examples of what you can do at the edge, and where this trend is heading. The goal is to give manufacturing and business leaders a clear, vendor-neutral understanding of edge computing in a casual, approachable way.

From Mainframes to the Edge: A Brief History

To appreciate edge computing, it helps to know a bit of its backstory. In the early days of computing, everything was centralized. Think huge mainframes in the mid-20th century that did all processing in one place. Even as personal computers emerged in the 1980s, they still processed data locally on the device. The shift toward distributed computing began with the growth of the internet. By the 1990s, as Tim Berners-Lee launched the World Wide Web, it became clear that centralized web servers would struggle with congestion as more devices came online. A pivotal moment came in 1998 when a small MIT team founded Akamai. Akamai pioneered using distributed servers located near users to cache and deliver web content, effectively creating one of the first content delivery networks (CDNs). This “edge” of the network offloaded traffic from central web servers and reduced internet bottlenecks – the first example of edge computing in action.

Throughout the 2000s, CDNs and similar services expanded beyond simple content caching. They began hosting parts of applications closer to users (for example, handling local shopping cart functions or real-time data feeds), foreshadowing modern edge services. However, the real surge in edge computing interest came later, alongside the rise of smartphones, cloud computing, and the Internet of Things (IoT) in the 2010s. Cisco introduced the term “fog computing” around 2014, describing a layered approach to push cloud capabilities closer to the ground (literally into the “fog”). Around the same time, standards bodies like ETSI began work on Multi-access Edge Computing (MEC) for telecom networks. In short, the idea of the edge matured as the number of connected devices exploded. What started as simple local web caches in the ‘90s evolved into a broad vision: computing anywhere outside centralized clouds, wherever it makes the most sense to do so.

Why Edge Computing Came Into Existence

Several technology and business pressures led to the emergence of edge computing. First and foremost is the massive growth of data being generated by distributed devices. According to Statista, we are anticipated to generate over 180 Zettabytes worth of data this year. Sending every bit of sensor readings, video streams, or machine logs to a distant data center is often impractical. Even with modern networks, bandwidth is limited and expensive, and central servers can become a bottleneck. This shift is driven by necessity: the rise of IoT devices at the network’s edge produces massive amounts of data, and trying to funnel it all to the cloud can strain networks to the breaking point.

Latency and real-time responsiveness

Certain applications simply cannot tolerate the delays caused by long-distance data travel. For instance, in manufacturing or automation, if a sensor detects a safety issue or a robot arm veers off course, the system needs to respond in milliseconds, not send data to a cloud and wait seconds for a reply. Edge computing addresses this by processing data near the source so decisions can be made immediately. As one industry report puts it, edge computing is best for situations where ultra-low latency or real-time processing is required, or where huge volumes of data would overwhelm a central site unnecessarily. Cutting down on latency isn’t just a nice-to-have; in many cases it’s critical. Imagine a self-driving vehicle that needs to recognize a pedestrian in its path, it can’t wait on the cloud, it needs local “on the edge” computing to avoid disaster. Manufacturing leaders know this well: if a machine on the factory floor is about to fail, detecting that on the spot (and shutting it down) prevents costly downtime. Edge computing came into existence largely to solve these problems of speed and bandwidth that traditional cloud architectures couldn’t always handle.

Reliability and connectivity

Not every site has consistent high-speed internet. Remote facilities like oil rigs, rural factories, or mobile assets (e.g. shipping trucks) may have intermittent connectivity. Edge devices can keep things running locally even when the connection to the central cloud is lost. This local autonomy ensures operations aren’t entirely dependent on the internet.

Data privacy and security

Companies often prefer to keep sensitive data (like proprietary production data or customer video footage) on-site or within country borders. By processing and storing data at the edge (for example, on-premises in a plant), firms can better control who sees the data and reduce the risk of large-scale breaches. In summary, edge computing emerged to bring computation closer for faster response, to reduce unnecessary data shipping, and to increase the robustness and privacy of distributed systems.

What Exactly Is Edge Computing?

Edge computing is a distributed computing model that brings data processing and storage close to the source of the data or the point of use rather than depending only on distant cloud data centers. In practical terms, this means placing computing power on-site or nearby so information can be acted on locally without unnecessary delays. The principle is simple: reduce the distance between data generation and data analysis to minimize latency, cut bandwidth costs, and improve reliability for critical operations. Standards bodies have echoed this definition. The Industry IoT Consortium (named Industrial Internet Consortium at the time), back in 2018 described edge computing as cloud systems that process data at the edge of the network near where it is generated. Likewise, the European Telecommunications Standards Institute (ETSI) defined Multi-access Edge Computing back in 2014 as providing cloud-computing capabilities in an IT service environment at the network’s edge (with ultra-low latency and local network awareness).

The definition of edge computing has evolved significantly over time. In the late 1990s, the first “edge” use cases appeared with content delivery networks, which cached websites and videos on servers closer to users to avoid congestion and speed up access. By the early 2000s, these networks expanded to run small pieces of application logic at those edge nodes, laying the groundwork for distributed computing beyond simple caching. In the 2010s, the rise of the Internet of Things added urgency and scale to the concept. Billions of connected devices began generating enormous amounts of data, and moving all of that back to centralized data centers proved impractical. The modern definition of edge computing solidified as the practice of analyzing and acting on data as close as possible to its source, particularly in industrial and manufacturing contexts where split-second decisions can make or break operations.

Importantly, edge computing does not replace cloud computing but complements it. Cloud systems still provide large-scale storage, global coordination, and heavy analytics, while edge deployments handle local, time-sensitive, or bandwidth-heavy tasks. In manufacturing, this might mean running predictive maintenance models directly on the shop floor, ensuring a machine is shut down before it fails, while the aggregated results of those predictions are sent to the cloud for long-term analysis and strategy. By combining both approaches, organizations get the immediacy and control of local processing along with the scalability and intelligence of the cloud. The consensus today is clear: edge computing has matured from a niche solution into a central piece of modern IT and operational strategy, defined by standards bodies, refined by industry practice, and evolving alongside technologies like IoT, AI, and 5G.

Types of Edge Computing

Not all edges are created equal. Edge computing can actually refer to a spectrum of locations and technologies. Here are some common types of edge computing categorized by where the computing is happening:

  • Device Edge: This is computing done on the devices or sensors themselves. Workloads run directly on physical hardware like IoT sensors, smart cameras, or even embedded controllers. Device-edge computing yields minimal latency because data is processed right at the source, and it avoids sending data over any network backhaul. Typically, device-edge tasks are simple (since a sensor has limited compute power). As an example, a security camera might do basic motion detection on-camera. This is useful when network connectivity is poor or you need instant response on-site.

  • On-Premises Edge: In this case, computing resources reside on-site at the customer’s location (for example, in a factory or a retail store). This could be an IoT gateway, an edge server, or even a small data center at the facility. On-prem edge is ideal when a company wants to keep all data within the premises for security or compliance, such as processing sensitive production data locally. It provides cloud-like capabilities (storage, compute) right at the site. Many manufacturing plants already have on-premises computing in the form of programmable logic controllers (PLCs) or local servers, these can be seen as early forms of edge computing.

  • Network Edge: Here the computing is done within the telecom or network provider’s infrastructure, closer to the end user than a central cloud. For example, a telecom company might host edge servers at a cellular base station or central office in your city. This network edge (sometimes called Multi-access Edge Computing) is useful when the end devices are mobile or widely distributed (like connected cars or city sensors) and you need a nearby point of presence. It provides low-latency services without each business having to deploy their own servers everywhere. As an example, a mobile carrier could run an edge cloud that gamers connect to within one or two network hops, enabling smooth cloud gaming experiences with minimal lag. Network edge sites are often used when there’s no single customer premise to put a server in (think smart city apps).

  • Regional Edge: This refers to small data centers or co-location facilities geographically near to a particular region or city (often tier-2 or tier-3 cities). These are run by third parties or service providers and can host workloads for many different customers, acting as a mini-cloud near the edge. Businesses can rent servers or space in these regional edge data centers (a co-lo model) to serve local users with lower latency. For instance, a video streaming company might use regional edge data centers to cache and stream content to nearby viewers, ensuring high performance. Regional edges, together with the network edge, form a “distributed edge” owned by service providers rather than the end customer.

In addition to these categories, you might hear related terms: fog computing (a concept by Cisco, essentially a framework to distribute cloud functions closer to devices), cloudlets (small-scale cloud data centers, often used to offload heavy computing from mobile devices nearby), and even micro data centers (self-contained racks that can be deployed in remote locations or facilities). The differences can be subtle, but they all fall under the big tent of edge computing. The key takeaway is that edge can live at many levels, from a tiny sensor, to a factory floor server, to a telco central office, to a small data center at a city’s outskirts. Each level serves the same principle: bringing data and processing closer together for speed, efficiency, and control.

How the Edge is Being Used

If you look at deployments through the 2024–2025 lens, a clear pattern emerges: organizations are concentrating edge compute where time‑sensitive machine data is born (plants, campuses, vehicles) and wiring it up with pragmatic stacks and protocols that developers already trust.

Where deployments are landing

Developer activity puts industrial automation at the front of the pack, with 34% of respondents building solutions for that segment in 2024 according to Eclipse Foundation. Automotive (29%), energy management (29%), environmental monitoring (23%), and home/building automation (25%/22%) follow close behind. In other words, most real‑world edge projects are anchored to factories, fleets, buildings, and grids—places where locality and uptime matter.

A lot of this sits on customer premises, but campus‑scale connectivity is expanding the footprint. GSA counted 1,427 unique organizations running private LTE/5G networks by 1Q 2024, and tracked 1,489 customer deployments across 80 countries by September 2024, a useful proxy for how many sites have the wireless fabric to support on‑prem edge.

What runs at the edge

According to Eclipse Foundation, control logic is the single most common workload on gateways/nodes (41%), ahead of AI inference (32%), with data exchange across nodes (33%), sensor fusion/aggregation (27%), and local analytics with ≥1 GB storage (21%) rounding out the top tier. That mix reflects a practical split: deterministic control and data plumbing alongside targeted AI at or near machines.

How teams connect and integrate it

Messaging and transport choices are consolidating. According to Eclipse Foundation, MQTT is the preferred IIoT protocol for 56% of developers (up seven points year over year), with HTTP/HTTPS (32%) and TCP/IP (30%) also common. MQTT + Sparkplug is reported by 8%, and newer options like Eclipse Zenoh are rising from a small base. On the wire (and over the air), cellular leads connectivity at 59%, a jump driven by 4G/5G, followed by Wi‑Fi (46%), Ethernet (45%), and Bluetooth (31%). Together, these figures describe a messaging‑light, pub/sub‑friendly edge that increasingly rides private/public cellular when mobility or coverage demands it.

Edge Computing Standards

There is no single standard that defines edge computing. Instead, several standards bodies cover different pieces of the puzzle. One set tells you where edge apps run and how networks expose capabilities. Another set gives you a general architecture so solutions from different vendors fit together. A third set standardizes how industrial data actually moves so machines and software can work together without custom glue.

  • Where the edge lives, and the APIs you can rely on. In cellular and private 5G environments, the anchor is ETSI Multi access Edge Computing. Its reference architecture defines the MEC host, platform, management, and service APIs, which makes it easier to place the same application near the line, near a campus gateway, or in an operator site with consistent behavior. See ETSI GS MEC 003 (V4.1.1, 2025). 5G adds native support through 3GPP so apps can discover capabilities like location, quality of service, and traffic steering, and even move across sites when needed for latency sensitive work like vision inspection or closed loop control. The core specification is 3GPP TS 23.558, and for unified exposure of APIs across networks you can also look at 3GPP TS 23.222. This pairing gives you portability across operators and campuses, and a clean way to program the network features your edge workloads depend on.

  • General edge and fog architecture for mixed or non telco environments. When your footprint spans plants, warehouses, and campuses with a mix of Wi Fi, wired Ethernet, and private cellular, IEEE 1934 OpenFog provides a vendor neutral blueprint for distributed systems, which helps your design survive product swaps and multi vendor deployments. To keep vocabulary, roles, and viewpoints consistent across vendors and integrators, pair that with ISO and IEC guidance. The high level IoT reference is ISO IEC 30141. For a focused overview of edge concepts and where standardization applies, see ISO IEC TR 30164, and for how cloud and edge fit together in the wider landscape, see ISO IEC TR 23188. Using these together gives you a common language for architecture decisions, makes RFPs clearer, and reduces integration risk over time.

  • Industrial data and messaging that make the edge useful. OPC UA standardized as IEC 62541 gives you secure, model rich information exchange from controllers to edge nodes to cloud applications, including structured information models so the meaning of data is preserved across vendors. For scalable streaming and near real time scenarios, the Publish Subscribe profile is defined in IEC 62541 Part 14. Alongside OPC UA, MQTT is the lightweight publish subscribe workhorse for telemetry, gateways, and brownfield device connectivity. It is standardized by OASIS as MQTT v5.0 and internationally as ISO IEC 20922. In practice, plants often pair OPC UA for rich semantics, discovery, and security with MQTT for simple and resilient message transport, which keeps integration effort low while still enabling analytics, digital twins, and cross vendor interoperability.

The Future of Edge Computing

Edge computing is evolving fast, and its role in enterprise tech stacks is poised to become even more significant. We are seeing a shift in a large majority of enterprise data (and industrial data) will be created and processed outside of centralized data centers or cloud. This is driven by continued growth in IoT (tens of billions of connected devices), but also by new demands like AI and machine learning at the edge. We’ll see more AI models (for image recognition, predictive analytics, natural language, etc.) being run on edge devices or edge servers. Rather than sending data back to a cloud AI service, the AI comes to the data at the edge. This convergence of AI and edge (often dubbed “Edge AI”) will enable smarter, semi-autonomous operations in factories, vehicles, and cities. For example, future factories might have AI-driven control systems at the edge that can adjust processes on the fly, or retail stores might use edge AI to dynamically adjust store layouts based on customer behavior in real time. The possibilities are exciting, essentially bringing intelligence to every corner of an operation.

Another future trend is the maturation of the edge computing ecosystem and services. Today, adopting edge often means piecing together hardware, software platforms, and network connectivity, which can be complex. But we’re seeing the rise of Edge-as-a-Service offerings, cloud providers, telecom companies, and startups are offering managed edge platforms so that businesses can deploy edge workloads without reinventing the wheel. These services abstract some of the complexity (similar to how cloud abstracted running on-prem servers). For instance, there are emerging platforms where you can write an application and the platform decides where to run it (on a device, at an edge node, or in the cloud) to meet latency requirements. Telecommunications companies are also partnering with cloud providers to host cloud extensions in 5G networks, so a company might deploy an application and specify it needs to run within 10 miles of end users, and the platform takes care of deploying to the right edge locations. This kind of seamless hybrid cloud-edge continuum is likely where we’re headed, enabling dynamic placement of workloads based on real-time needs.

In terms of industries, edge computing’s future is tied to continued digital transformation efforts like Industry 4.0 in manufacturing, smart infrastructure, and beyond. Manufacturing in particular will keep pushing the envelope, imagine more factories operating as fully connected, intelligent systems, where every machine’s data is analyzed on-site and processes are optimized continuously. Edge computing is a cornerstone to achieving that flexibility and efficiency. The advent of private 5G networks for industrial sites may further accelerate edge adoption, as manufacturers can have high-speed wireless connectivity on premises and connect many devices to on-site edge servers with minimal latency. We might also see standardization around edge, common standards for managing edge devices, for orchestrating updates, for security, making it easier to deploy at scale.

Speaking of security, as the edge footprint grows, so do security challenges. A future focus area is ensuring end-to-end security in a world where data is spread across thousands of distributed nodes. This means stronger encryption, zero-trust architectures, and AI-driven threat detection at the edge. On top of that, edge environments could be particularly vulnerable if not managed well (more devices = more targets). Therefore, expect significant innovation in edge security best practices, from secure device identity, to remote attestation (verifying edge hardware/software integrity), to robust patching mechanisms for far-flung devices.

Best Practices and When to Use Edge Computing

Edge computing isn’t a silver bullet for all computing needs, it shines in some scenarios and can be overkill in others. Deploying edge in an industrial environment isn’t as simple as rolling out new servers or gateways. It demands careful planning, alignment with business priorities, and attention to operational realities. Here are a few best practices and key considerations to keep in mind when deciding where and when to use edge computing:

1. Align Edge Deployment to Business Outcomes

The most common failure mode of industrial edge projects is starting with technology instead of purpose. Too many companies run pilots because edge is the hot new thing, without tying it to measurable business results. The first step should always be to define the problems that edge is uniquely positioned to solve. That could mean reducing unplanned downtime by enabling predictive maintenance close to machines, improving product quality through real-time inspection and anomaly detection, or enhancing worker safety by monitoring hazardous conditions without latency. It could also mean enabling adaptive process control in high-precision manufacturing.

This requires collaboration across IT, OT, and business leadership to prioritize outcomes and quantify success. Metrics such as reductions in downtime, improvements in yield, or measurable energy savings should anchor the investment case. If outcomes are not clear, even the most advanced edge infrastructure risks ending up as another pilot with no path to scale.

2. Engineer for Harsh and Variable Industrial Environments

Factories, refineries, and field operations are nothing like climate-controlled data centers. They are noisy, dusty, hot, humid, and often subject to vibration, chemicals, or fluctuating power quality. Hardware that is not built for these conditions will fail quickly, eroding confidence in the deployment.

When choosing edge devices, organizations need to look for ruggedized designs that are certified for extended temperature ranges, shock, and vibration. Redundancy and failover should be factored in, such as dual power supplies or clustering edge nodes for mission-critical systems. Maintenance accessibility is another practical concern since many devices will be placed in difficult-to-reach locations within production lines.

Networking is just as important. Industrial environments introduce electromagnetic interference and require connectivity that is both reliable and secure. Resilient architectures using industrial Ethernet, private 5G, or redundant wireless connections ensure that edge infrastructure continues to function under the realities of continuous production.

3. Treat Security as Foundational, Not Optional

Every edge node represents both a new source of value and a new potential point of vulnerability. The rise in ransomware targeting manufacturers makes it clear that cybersecurity cannot be an afterthought. Edge deployments are especially sensitive because they bridge IT systems, which are often well protected, with OT networks, which historically have been less secure.

Security best practices must be embedded from the start. Adopting zero-trust principles ensures that devices never implicitly trust one another, with strict identity management and least-privilege access as core features. Data must be encrypted both in transit and at rest, ideally supported by hardware-based encryption and secure boot mechanisms that guarantee device integrity. Lifecycle security is also critical. Automated patching, vulnerability scanning, and continuous monitoring must be planned up front, since manually updating dozens or hundreds of dispersed devices is unrealistic. Industrial standards such as IEC 62443 and NIST cybersecurity frameworks offer excellent reference points for organizations to benchmark their practices.

4. Standardize Interoperability and Data Flow

Industrial environments are an eclectic mix of legacy PLCs, proprietary machine protocols, and modern IoT sensors. Without standardization, edge projects risk becoming expensive integration exercises. Ensuring interoperability requires embracing open communication standards such as OPC UA and MQTT, which make it easier for heterogeneous devices to exchange information.

A robust middleware or edge platform can help abstract some of this complexity, enabling plug-and-play connectivity across different vendor systems. Establishing consistent data models and taxonomies early on is equally important. Companies need to align on what constitutes key terms like OEE, asset state, or quality defect across all plants to prevent silos and ensure meaningful comparisons.

Decisions about data flow should also be made strategically. Time-sensitive tasks such as safety triggers must stay local, while aggregated insights like energy efficiency trends can be transmitted to the cloud. This balance creates an architecture that is both reliable for critical operations and valuable for enterprise-wide analytics.

5. Establish Scalable Management and Lifecycle Processes

Edge projects that work at pilot scale often falter when expanded across multiple plants. The reason is that managing dozens or even hundreds of distributed devices introduces a completely different set of challenges. Success depends on establishing strong lifecycle management practices.

Centralized orchestration platforms are essential to allow IT and OT teams to monitor, configure, and update devices remotely. Automated update mechanisms reduce downtime and include rollback options in case new software disrupts operations. Observability tools for continuous monitoring of both hardware and applications prevent unexpected failures and provide insight into whether service-level expectations are being met.

Industrial assets are known to run for decades, but edge devices and software typically evolve on shorter refresh cycles. This makes it important to create an end-of-life plan from the start, with clear approaches for hardware replacement, software migration, and prevention of unmanaged device sprawl. The collaboration between IT and OT is crucial here. IT brings knowledge of distributed system management, while OT ensures that changes do not interfere with production safety or reliability.

6. Build with AI and Future Workloads in Mind

Most current edge deployments focus on filtering data, running local dashboards, or automating rule-based functions. But the true potential lies in AI at the edge, where machine learning models run directly on devices to identify defects, optimize processes, or make autonomous decisions.

Preparing for this shift means selecting hardware that can support AI acceleration with GPUs or TPUs, and designing hybrid pipelines where models are trained in the cloud but deployed for inference at the edge. It also requires robust data governance, since poor labeling or inconsistent data quality undermines AI capabilities no matter how powerful the edge devices are. Finally, modularity should be a guiding principle. Companies that design systems with the ability to swap in new or improved AI models without major re-engineering will stay ahead of the curve.

Forward-looking organizations treat edge not just as an enabler of immediate improvements, but as the long-term foundation for more intelligent, autonomous, and resilient industrial operations.

References:

Jeff Winter
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