For this report on the Industrial Edge, the Industrial Ethernet Book reached out to industry experts to get their perspective on the technologies that are impacting industrial edge computing and its use in the smart factory.

Industrial edge computing solutions involve processing data closer to its source in industrial settings, enabling real-time analytics, automation, and improved decision-making. These solutions typically involve ruggedized edge devices, specialized software, and connectivity options to handle the unique demands of industrial environments while also integrating solutions from the IT world.

Secure and reliable communication between edge devices, the cloud, and other systems is crucial and increasingly involves utilizing industrial communication protocols including as OPC UA, MQTT, and REST APIs.

AI and machine learning

Data processing on dedicated edge devices is becoming increasingly complex and extensive.

According to Daniel Mantler, Product Manager HMI/IPC at Phoenix Contact GmbH & Co. KG, the current megatrends in edge computing applications are ‘AI and Machine Learning at the Edge’ and ‘Containerization and Virtualization’. As AI continues to penetrate edge applications, the data processing on dedicated edge devices is becoming increasingly complex and extensive.

“AI-based analytics on an edge device can deliver highly accurate reports about the connected machine. Use cases such as predictive maintenance and anomaly detection can be installed directly on a machine—even retroactively,” Mantler said. “The great advantage is that, by implementing these analytics on a dedicated device, there is often no need to intervene in the machine or its control program—especially when the edge device, like those from Phoenix Contact, supports all industrial protocols such as Profinet or OPC UA.”

All these functions can be easily and rapidly scaled thanks to the second megatrend: the virtualization of the entire edge functionality. For example, the PLCnext control program can be deployed virtually as an OCI container across entire server infrastructures, enabling the execution of highly complex applications and the direct reading and analysis of machine data.

“AI-based analytics on an edge device can deliver highly accurate reports about the connected machine. Use cases such as predictive maintenance and anomaly detection can be installed directly on a machine—even retroactively,” — Daniel Mantler, Product Manager HMI/IPC at Phoenix Contact GmbH.

Benefits for smart manufacturing

Virtual PLCnext Control offer greater scalability, flexibility, and cost efficiency in automation. As software in an OCI container, it enables seamless integration, combines OT and IT security, and increases the hardware independence of control solutions.

“Edge computing significantly simplifies the entire workflow. By integrating directly into the machine, data is collected right at the source. There’s no need for complex and secure data transmission paths ‘upward’ to a cloud or central server. Edge devices typically offer far greater performance than traditional PLCs. Performing analytics—potentially AI-driven—directly at the point of data generation makes perfect sense, especially for retrofitting existing systems,” Mantler said.

He added that the true added value of ‘AI on Edge’ only partially stems from the edge device itself—software is the key! It requires software that enables any plant operator to extract meaningful insights from their equipment. This calls for a simple app-store-like experience for the edge device. In our case, this is made possible through the PLCnext Store.

Using ‘MLnext’, a data-driven anomaly model can be created without any prior experience in AI or programming. Through a graphical user interface, MLnext only needs to be fed with data—typically from a time-series database that we’ve previously installed on the edge device. MLnext learns from collected ‘good data’ and generates reference curves. The model can then be deployed directly on the edge device to compare live data against the learned patterns, identifying and visualizing anomalies—such as a worn motor shaft—simply and precisely.

“Edge devices are not new—the term has long existed in the industrial sector. What’s new and decisive for any edge device today is the software it runs, and how user-friendly it is in enabling data analysis for the operator,” Mantler added.

In the case of Phoenix Contact and its new edge device, the ‘VL3 UPC 2440 EDGE’, the hardware is based on a high-performance industrial PC featuring a QuadCore Atom CPU and 16 GB of RAM. The device runs a user-friendly Ubuntu Pro operating system, allowing software to be extended either natively in Linux or via OCI containers.

Additionally, the device comes pre-installed with Phoenix Contact’s virtual controller, ‘vPLCnext Control’—a fully functional, real-time-capable PLC. This enables automation solutions to be developed using IEC-61131-3 standards. It also allows direct communication with field-level sensors, actuators, and other controllers via industrial protocols such as Profinet, Modbus, or OPC UA, making data collection seamless.

Software for data analysis and entire pre-defined applications are available through the PLCnext Store, enabling easy expansion of the device’s functionality.

Applications and industries

Mantler said that applications range from retrofitted to new installations. Anywhere a large data stream is generated, an edge device can add value. Even systems that cannot be connected to the internet can benefit. In mechanical engineering, for example, analyzing data can be highly useful for detecting potential failures in advance.

In safety-critical use cases, the edge device can also monitor anomalies within the IT network itself, making it the ideal central interface for data flowing into and out of the system.

Edge devices can also be deployed in small-scale systems, such as an e-mobility charging station, where the edge device handles both control logic and data analysis. In such cases, there’s no need for a separate controller in addition to the edge device.

“Edge devices and AI-based analytics on them address a key pain point for plant operators: cybersecurity largely remains under their own control. While analytics via cloud systems are certainly possible, the existing IT infrastructure often does not permit this,” Mantler said.

“High-performance edge devices provide the foundational platform for increasingly complex analytics. To make these advanced mechanisms accessible to a broad range of users, there is a strong focus on offering AI and analytical models in a way that is simple and usable for everyone,” he added.

“As more sensors, controllers, and smart devices come online, the amount of data at the edge keeps growing. Sending all of it to the cloud isn’t practical—or affordable. Edge computing lets you clean up, contextualize, and analyze data locally, so you send only what matters for long-term storage or deeper analysis in the cloud,” — Dan White, Director of Technical Marketing, Opto 22.

Edge and cloud computing

As data at the edge keeps growing, sending all of it to the cloud isn’t practical or affordable.

According to Dan White, Director of Technical Marketing at Opto 22, it might sound ironic, but cloud computing is what’s driving edge computing.
“As more sensors, controllers, and smart devices come online, the amount of data at the edge keeps growing. Sending all of it to the cloud isn’t practical—or affordable. Edge computing lets you clean up, contextualize, and analyze data locally, so you send only what matters for long-term storage or deeper analysis in the cloud,” White told IEB recently.

White said that, as cloud computing expands, cybersecurity is another megatrend shaping edge devices. The more connected your operations become, the more critical it is to protect data in motion. Edge computing gives you a checkpoint at the edge of the network, where you can enforce encryption, authentication, and filtering before anything leaves the plant. At the same time, software-driven automation is changing how systems are built and maintained. Instead of relying only on fixed-function hardware and rigid programming environments, modern automation uses open protocols, scripting, APIs, and containerized applications. That means more flexibility, faster changes, and easier integration across systems—from the plant floor to business software.

Industrial edge benefits

Industrial edge computing offers a series of specific technical benefits for smart manufacturing, and now advances in AI are factoring into new developments as well.

“You get better data resilience. When network connections fail or cloud services go down, edge systems can keep collecting and storing data locally. That’s critical in industries like pharmaceutical and food and beverage, where gaps in data could mean lost product or compliance issues,” White said.

“You also get more control over how and where data flows. Today’s edge devices are open enough to support multiple protocols securely. Maybe one system needs MQTT, another polls OPC UA, and a third pulls from a REST API. The edge can serve them all—without middleware or duplicated effort,” he added.

He also said that security has also taken a leap forward. Modern edge platforms support TLS encryption, certificate-based authentication, and firewall rules that let them publish data securely over the public internet, without opening inbound ports. That kind of architecture simplifies remote access for integrators or OEMs while keeping the factory network locked down.

When it comes to AI, the edge currently provides critical support, preparing the data those models need. Clean, contextualized, and consistent data makes upstream AI smarter. No missing units, no mismatched formats—just usable information the model can trust.

What makes the newest technologies unique, and how it is different from solutions commonly implemented in the past.

“Modern edge technology is designed for both control engineers and software developers. You’re not locked into one programming language or one way of thinking. Whether you’re building flow logic, writing Python scripts, or connecting to a cloud API, the tools are flexible enough for OT and IT teams to collaborate,” White said.

Security is no longer an afterthought. Today’s edge systems support TLS encryption, client certificates, and strict firewall rules—features expected in IT, but rarely seen on PLCs in the past. Built-in security makes it safer to move data within the factory, across facilities, and into cloud applications.

“Integration has also gotten a lot cleaner. You don’t need layers of middleware to move data anymore. Edge devices can publish directly to brokers, databases, or dashboards, using standard protocols and APIs. And the architecture itself has changed. Instead of pushing everything to a central system, you’re processing and storing data right where it’s created. It’s faster, more resilient, and reduces network load. Maybe most importantly, it’s all open. No vendor lock-in, no black boxes—just tools that talk to each other,” he added.

Industry impact

White said that industries that need reliable, traceable data are seeing the biggest gains from edge technology. In pharmaceutical and life sciences, for example, edge systems can log data locally during network outages, protecting audit trails and maintaining compliance without gaps. That kind of redundancy is critical when product integrity is on the line.

Remote operations are another perfect fit. In sectors like water treatment or energy, where assets are spread out and sometimes hard to reach, edge computing provides real-time monitoring and control even without a persistent connection. Users can process data onsite, push summaries to the cloud, and keep critical systems running no matter what’s happening upstream.

Machine builders are using edge devices to stay connected to the systems they ship. Once a machine leaves the floor, it’s still carrying your brand. Remote diagnostics, usage tracking, and performance feedback help OEMs support customers better and improve designs over time. With enough data, that feedback loop can even shape future AI models.

And across industries, the need for predictive maintenance is growing fast. Whether it’s compressors, pumps, or entire production lines, edge systems enable condition-based monitoring without adding complexity. Even data centers—where uptime is king—are starting to use edge architectures to keep critical infrastructure in check.

Industrial Edge solutions, the IIoT and Industry 4.0

White told IEB that “edge technology is what makes IIoT and Industry 4.0 real—not just possible, but practical. In brownfield environments, edge devices can pull data from existing systems without touching control logic or disrupting operations. That’s key for plants that can’t afford downtime or don’t want to replace or revalidate equipment just to get data out.”

“But in greenfield designs, the edge can be the control system. Modern platforms combine control, data processing, visualization, and networking into a single device. That means you start with secure, structured, accessible data by default, and you don’t need to add integration layers later,” he said
“In addition, edge solutions scale cleanly. You can roll it out gradually—one line, one machine, one site at a time—without having to commit to a rigid, top-down architecture. That flexibility makes it easier to move from pilot to production, and from one use case to many.”

“Software driven automation is reshaping traditional manufacturing by enabling more flexible, responsive, and data centric operations. Industrial edge computing supports this transformation by providing the infrastructure to connect legacy systems with modern control platforms – bringing data processing closer to the source and reducing dependence on centralised systems,” — Ruth Williams, Product Marketing Manager, Moxa Europe GmbH.

Hyperconnectivity becoming a core requirement

Enabling communication between a wide variety of industrial assets, often spanning different protocols, vendors, and technology generations.

Ruth Williams, Product Marketing Manager at Moxa Europe GmbH said that “several megatrends are accelerating the adoption of Industrial Edge Computing including the convergence of IT and OT, the rapid growth of the Industrial Internet of Things (IIoT), and increasing demand for secure, real-time access to operational data. These developments are prompting industries to rethink how they collect, process, and act on information across distributed environments.”

“Hyperconnectivity is emerging as a core requirement, enabling communication between a wide variety of industrial assets, often spanning different protocols, vendors, and technology generations,” Williams said. “Solutions such as smart I/O modules and protocol gateways, including those offered by Moxa, support this evolution by making it easier to bring legacy systems into integrated, data-driven environments without major infrastructure overhauls.”

She said that, at the same time, cybersecurity has become a fundamental consideration in edge deployments. The shift toward adopting international standards such as IEC 62443 reflects a growing need to secure operational data, particularly as edge computing extends beyond the factory floor to remote or mission-critical sites. Ruggedised, security-aware hardware is increasingly important for ensuring the resilience and integrity of these systems.

Combined with emerging technologies such as 5G and artificial intelligence at the edge, these trends are helping to establish a more intelligent and responsive industrial landscape. Local data processing, real-time decision-making, and machine learning at the edge are enabling new use cases, from predictive maintenance to adaptive process control, while supporting the broader move toward software-defined automation.

Industrial edge technologies

“Software driven automation is reshaping traditional manufacturing by enabling more flexible, responsive, and data centric operations. Industrial edge computing supports this transformation by providing the infrastructure to connect legacy systems with modern control platforms – bringing data processing closer to the source and reducing dependence on centralised systems,” Williams said.

This decentralised model allows for real time data acquisition and analysis, which is essential for applications such as predictive maintenance, quality control and adaptive process optimisation. Devices such as remote I/O modules, protocol gateways and serial to network converters (common in Moxa’s industrial connectivity portfolio) help manufacturers integrate existing equipment with new software environments, ensuring interoperability across diverse systems.

Advances in artificial intelligence further amplify the potential of edge computing. Running AI algorithms locally enables factories to detect anomalies, optimise energy use and adjust processes dynamically, without relying on constant cloud connectivity. Combined with developments in 5G, which deliver high speed, low latency communication across industrial sites, these technologies are establishing a more intelligent and responsive production environment.

Secure, centralised network management also underpins effective edge deployments. Tools such as Moxa’s MXview software allow operators to monitor distributed assets, manage configurations and ensure secure remote access, ultimately reducing downtime, engineering overhead and lifecycle costs. As more factories adopt cloud integrated, software defined architectures, robust, interoperable edge infrastructure will remain critical for scalability and long-term resilience.

Breaking away from the past

Williams said that traditional industrial systems have often operated in silos, relying on proprietary protocols and centralised data processing, which limited real-time visibility and flexibility. In contrast, Industrial Edge computing enables smart factories to access fast, secure, and reliable real-time operational insights directly at the edge. This shift supports immediate decision-making and improves responsiveness to changing conditions.

Moxa’s rugged hardware is designed to operate in harsh industrial environments and supports both legacy serial and field protocols alongside secure, scalable integration with modern IT platforms. This hybrid capability allows manufacturers to modernise incrementally without discarding existing infrastructure.

By preprocessing and filtering data locally, edge computing reduces reliance on cloud connectivity, enhancing efficiency and data control. As AI-driven applications such as predictive maintenance and anomaly detection become more prevalent, Moxa’s solutions ensure consistent, protocol-agnostic data flow between machines, gateways, and cloud systems. This unified and cybersecurity-conscious approach enables manufacturers to adopt next-generation automation, unlocking the full potential of AI and data-driven operations while preserving prior investments.

Applications focus

Williams said that industries that require real-time reliability and distributed operations are well-positioned to benefit from the latest Industrial Edge solutions. In discrete manufacturing, for example, integrating legacy equipment with modern Industry 4.0 platforms is essential for supporting agile production processes. Moxa’s smart I/O modules and protocol gateways facilitate this integration by bridging older serial and fieldbus protocols with Ethernet-based systems.

In the power and energy sector, continuous and secure data transmission from remote substations, solar farms, and wind parks to central control systems is critical. Moxa’s rugged edge devices are designed to operate reliably in harsh environments, providing secure and stable connectivity that supports critical operations.

Critical infrastructure and smart cities depend on consistent edge-level data acquisition for managing intelligent systems and public utilities. Moxa’s modular I/O and protocol converters enable interoperability across diverse device ecosystems, helping to maintain operational efficiency and regulatory compliance.

In semiconductor and electronics manufacturing, where yield, traceability, and uptime are paramount, Moxa’s secure gateways and remote I/O solutions support precise monitoring and control, contributing to process optimisation and enhanced data integrity.

Across these industries, Moxa delivers edge connectivity solutions that combine flexibility, resilience, and protocol interoperability, allowing organisations to modernise their operations incrementally while ensuring continuity and security.

IIoT and Industry 4.0 solutions

Moxa contributes to the advancement of IIoT and Industry 4.0 by providing connectivity solutions that help transform standalone machines into networked, intelligent components within industrial systems. Its edge devices facilitate the integration between operational technology (OT) and information technology (IT), enabling secure and reliable data exchange to local human-machine interfaces (HMIs), analytics platforms, and cloud-based applications.

“By emphasising protocol interoperability, network resilience, and cybersecurity, Moxa supports manufacturers in adopting digital transformation in a controlled and compliant manner. Its edge solutions enable key Industry 4.0 principles such as decentralisation, transparency, and predictive analytics through support for edge networking, real-time control, and remote diagnostics,” Williams said.

“This approach helps manufacturers shift from reactive maintenance to more proactive, data-driven operations that can scale and adapt over time,” she added.

“Megatrends such as Industrial IoT, IT/OT integration, AI on the shop floor, the adoption of software-defined principles across multiple domains, and, more broadly, the adoption of cloud and IT technologies—such as virtualization and containerization—are the driving forces behind the rise of industrial edge computing,” — Francisco Javier Franco Espinoza, Strategy and Innovation Manager Factory Automation, Siemens.

Adoption of cloud and IT technologies

Cloud and IT technologies such as virtualization and containerization are the driving forces behind the rise of industrial edge computing.

“Megatrends such as Industrial IoT, IT/OT integration, AI on the shop floor, the adoption of software-defined principles across multiple domains, and, more broadly, the adoption of cloud and IT technologies—such as virtualization and containerization—are the driving forces behind the rise of industrial edge computing,” Francisco Javier Franco Espinoza, Strategy and Innovation Manager Factory Automation at Siemens told IEB.

“These same trends are also driving the shift toward software-driven automation—or, as we call it at Siemens, Software-Defined Automation (SDA). SDA is prompting manufacturers, automation suppliers, and system integrators to rethink every aspect of automation. The goal is to evolve from merely automated production to adaptive production, and ultimately to autonomous production—making every step of the process, from engineering to operations, more efficient and resilient,” he added.

Espinoza said that, at Siemens, we have been working hard over the past few years to equip our customers and partners with the right tools for this journey. Case in point: tools like Siemens Industrial Edge, our edge computing platform and ecosystem; SIMATIC AX, our IT-like engineering environment for PLCs; and the SIMATIC S7-1500V virtual PLC—the first virtualized control system ever used in a real production environment.

Key benefits of Industrial Edge

Espinoza said that key benefits of edge computing include localized data preprocessing, low-latency response, and reduced dependency on cloud and connectivity. A scalable edge computing platform should also allow secure remote management of edge devices and applications, enabling the efficient deployment of production-critical applications across the shop floor, while reducing maintenance and operational costs. This is the case, for example, with Siemens Industrial Edge, which manufacturers are using to break down data silos in their operations and integrate their IT and OT systems at scale.

Many companies that leverage edge computing in their manufacturing operations do so to implement AI- and ML-supported smart manufacturing use cases—or are planning to do so in the near future. This has been our experience with Siemens Industrial Edge and our Industrial AI portfolio.

“When it comes to generative AI, this technology can help streamline tasks like PLC and SCADA engineering or enhance the way operators interact with and troubleshoot machines. This is exactly what we do with our Siemens Industrial Copilot for Engineering and Industrial Copilot for Operations, respectively. These are just two examples of many possible use cases,” he said.

Impact of new technology

“Automation solutions in the past tended to be monolithic applications, often tied to specific hardware and heavily reliant on proprietary technologies and protocols. This frequently led to vendor lock-in and rigid architectures,” Espinoza said. “Modern technologies stand in contrast to that legacy, emphasizing modularity, openness, and hardware independence. This enables manufacturers to select best-of-breed solutions for each task, rather than being locked into a single vendor’s portfolio. In addition, there is an increased focus on cybersecurity, scalability, and portability across different platforms.”

Applications edge

Espinoza said that industrial edge solutions are gaining rapid traction across sectors such as automotive, food and beverage, pharmaceuticals, and logistics—where real-time data analytics and zero downtime are critical. We have customers in various discrete manufacturing industries using Siemens Industrial Edge to successfully implement and scale AI-supported visual quality inspection, process monitoring, and production analytics. Asset health monitoring and plant optimization are examples of applications successfully implemented by our customers in the process industries.

Edge architectures are particularly valuable where regulatory compliance, fast cycle times, and adaptive production are essential—for example, in pharmaceuticals for batch traceability or in automotive for predictive maintenance. Across these verticals, edge computing empowers manufacturers to realize their digital transformation ambitions while maintaining operational continuity.

A prime example of industrial edge computing in a real-life scenario is the Audi factory in Neckarsulm, Germany, where S7-1500V virtual PLCs control the car body assembly, and advanced AI models run on Siemens Industrial Edge for real-time quality control.

Edge solutions

Espinoza concluded by noting that edge solutions are foundational to the Industrial Internet of Things (IIoT) and Industry 4.0.

“Edge computing forms the connective tissue that merges data from machines, sensors, and business systems in real time. This integration creates the “digital thread” that underpins smart manufacturing,” he said.

Siemens’ portfolio illustrates how edge computing—through its modular approach, openness and focus on OT environments—enables manufacturers to create value from their data no matter their digital maturity level. This is achieved by implementing new and flexible architectures, breaking down data silos, and deploying analytics and AI at scale in both brownfield and greenfield environments.

“Edge architectures close the loop between the plant floor and the enterprise, unlocking continuous innovation in line with Industry 4.0’s vision for fully digitalized factories,” Espinoza said.

“The necessity to secure networks from increasingly sophisticated and persistent cyber-attacks combined with the need to have predictable and manageable costs have led to the industrial edge taking a very prominent role in operational network architecture design,” — Dr. Al Beydoun, ODVA President and Executive Director.

Edge computing and cybersecurity

Industrial edge is taking a very prominent role in operational network architecture design.

“The necessity to secure networks from increasingly sophisticated and persistent cyber-attacks combined with the need to have predictable and manageable costs have led to the industrial edge taking a very prominent role in operational network architecture design,” Dr. Al Beydoun, ODVA President and Executive Director told IEB recently. “Additionally, the demand for consistent uptime and low latency in operations environments lends to reliance on edge appliances.”

His point is that while the cloud offers the benefit of a host environment that is constantly updated with the latest hardware and backbone software, the possibility of misconfigured cloud security combined with the uncertain cost of computing and storage has made the edge a strong consideration for operations environments. Additionally, edge appliances offer the advantage of not having to worry about compromised cloud neighbors leading to unintentional security breaches.

While redundancy is still important, it can be accomplished through back up edge devices located either onsite or split among the local area. Further, virtual controllers can be hosted in edge devices for less critical applications to help manage software updates and reduce hardware reliance in operations facilities. Note that industrial Ethernet networks that support TCP/IP and TLS/DTLS such as EtherNet/IP are well positioned for virtual controller usage.

Industrial edge computing benefits

“Industrial edge computing enables smart manufacturing via usage of data models and algorithms to optimize applications in real time. Data models such as OPC-UA and PA-DIM are supported by leading industrial networks including EtherNet/IP,” Beydoun said.

For example, the parameters of a solenoid coils response rate or the output measurements of a bottle capping machine can be monitored, and warnings can be issued to ensure quality isn’t compromised if the process gets out of specification. This transitions maintenance from a scheduled or reactive activity to a predictive one. Additionally, the inputs for a tensioning machine can be monitored and adjusted as components wear and raw material variances are encountered to ensure that output tolerances are met. The usage of AI combined with industrial edge computing allows not only algorithm optimization for predictive maintenance and loop tuning but can also aid in cybersecurity. AI can help to provide automated initial security alerts to experienced IT security staff based on unusual logins, unexpected elevations of privileges, and other unexpected packet patterns.

Newest solutions

The newest edge, virtual controller, and AI solutions offer the potential to solve existing problems in new ways that can help drive down costs and improve output and quality,” Beydoun added. “Edge appliances include significant computing resources, security patch and OS updatability, and containerization for scalability and flexibility. This enables edge devices to support virtual controllers and AI solutions for low-risk operations applications.”

Virtual controllers are capable of decoupling software from hardware that can help with device maintenance and updating. Additionally, AI can take over tasks that used to require time consuming, custom algorithms and can more flexibly take on new jobs as applications evolve. In the past, devices were limited to being on machine, the software was tied to the hardware provided, and automation required highly specialized engineering. These new edge, virtual controller, and AI solutions provide a greater amount of flexibility to tackle operations challenges both at scale and with fewer resources.

Beydoun said that industrial edge appliances can add value to applications such as quality optimization of pharmaceutical, food and beverage, and consumer goods packaging machines as well as automotive component and final assembly line operations. These types of applications allow for the opportunity to improve quality, throughput, and energy usage via traditional algorithms and AI with an acceptable level of risk. The usage of virtual controllers and edge appliances together can also help to control non-critical systems such as lighting, heating, ventilation, and air conditioning.

Critical national infrastructure such as oil and gas refineries, power plants, and chemical facilities still require traditional onsite physical controllers with redundancy to help maintain safety and security. It’s also important to realize that AI relies on mathematical probability based on real time inputs and models created based on historical data. AI is best leveraged as a powerful tool to enhance and optimize operations, rather than serving as the sole foundation of critical processes.

IIoT and Industry 4.0

“Edge appliances are enabling the promise of IIoT and Industry 4.0 to come to life by providing the computing power and low latency necessary for resource intensive operational technology solutions. An example of this is the usage of digital twins on the factory floor to help workers visualize and replace failed components through augmented reality inclusive of on demand installation and maintenance documentation,” Beydoun said. “The usage of edge appliances also helps to maintain a strong cybersecurity posture, in conjunction with cybersecurity network solutions such as CIP Security for EtherNet/IP, while allowing for the implementation of AI for predictive maintenance.”

He added that edge appliances also allow for greater stability since they aren’t at risk of cloud or network cable outages and can keep costs predictable and manageable to enable the ROI needed to scale IIoT and Industry 4.0 solutions. An example of this is an edge device collecting data from external vibration, temperature, and moisture sensors that can serve as an additional warning sign of future failures. Edge appliances can also help manage the control of Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs) as well as enable custom engineering tools for onsite workers via onsite Wi-Fi or private 5G networks.

“he industrial landscape is shifting as more operational technology (OT) data is produced and processed at the edge. This trend is fueled by IoT sensors, real-time processing needs, and data security concerns which increase with the rising proficiency of threat actors and sophistication of the tools they employ,” Georg Stöger, Senior Principal Technology Specialist at TTTech.

Software-driven automation

Containerized architectures for flexible software deployment on edge computing equipment, making updates and patch management efficient and scalable.

“The industrial landscape is shifting as more operational technology (OT) data is produced and processed at the edge. This trend is fueled by IoT sensors, real-time processing needs, and data security concerns which increase with the rising proficiency of threat actors and sophistication of the tools they employ,” Georg Stöger, Senior Principal Technology Specialist at TTTECH told IEB.

“Software-driven automation is transforming factories with edge applications for real-time decisions, boosted by AI integration. Of course, edge platforms use containerized architectures for flexible software deployment on edge computing equipment, making updates and patch management efficient and scalable,” Stöger said.

He added that cybersecurity is also crucial, with 80% of CIOs increasing budgets for zero-trust architectures and advanced threat detection at the edge to protect distributed systems. A major challenge is integrating a multitude of legacy OT equipment with little or no cybersecurity capabilities.
As manufacturers face “cloud regret” due to latency and costs, edge computing offers a strategic alternative, with global spending projected to reach $378 billion by 2028 (Source: IDC’s Worldwide Edge Spending Guide), highlighting the importance of processing data where it is created.

Performance benefits

Stöger said that the most immediately obvious benefit of industrial edge computing is performance: Industrial edge computing processes data directly on the factory floor, possibly reducing processing latency and response times to single-digit milliseconds and thus enabling real-time quality control and machine optimization.

AI integration at the edge allows real-time analysis of production data, enabling predictive maintenance that can reduce downtime by up to 50%. These systems use machine learning models that improve continuously.

Another key advantage is data sovereignty: By processing data locally, it can be much easier to ensure compliance with regional regulations and to keep sensitive data within known physical boundaries. Edge computing, properly done, addresses security concerns with role-based access controls and encryption for data that must leave the factory floor. Besides, edge processing also provides resilience against network disruptions, thus increasing robustness and reducing data loss or even downtimes.

Through local processing, edge computing also enhances resource efficiency by massively reducing internet bandwidth consumption, which is a real concern in some situations such as remote locations or sites with limited connectivity.

An example is TTTECH Industrial’s IIoT edge platform for machine builders, Nerve, that allows companies to collect, process, and analyze machine data in real-time, as well as manage devices and deploy applications remotely via the online Management System. It forms a secure base with industrial cybersecurity certification according to IEC 62443, to run and maintain (legacy) applications on the edge.

Unique technology

“One major change – at least for OT systems – concerns the handling of software,” Stöger said. “The current software management technology, which is widely used in IT already, is containerization, which packages applications with their dependencies for consistent deployment across different hardware. Unlike past monolithic systems, today’s modular edge platforms allow dynamic workload management, letting manufacturers update components without disrupting the entire system. This flexibility of a distributed model brings intelligence directly to data sources.”

He added that security has also advanced significantly. Traditional perimeter-based security has been replaced by zero-trust architectures that verify every access attempt, not only by human actors, but also between software services, electronic components, and computing devices. Modern platforms use defense-in-depth strategies, continuous vulnerability monitoring, secure boot processes, and cryptographic verification of workloads.

Data handling has improved, too. Current edge platforms use time-series databases optimized with configurable retention policies to balance analytical needs and storage constraints. This allows to keep relevant parts of data over long time horizons for different types of analyses and applications without needing vast amounts of nonvolatile storage at the edge.

Most importantly, today’s edge solutions integrate operational technology with cloud environments, creating hybrid architectures that leverage the strengths of both. This creates a continuous digital thread from the shop floor to enterprise business systems.

Newest Industrial Edge solutions

From TTTECH’s perspective, manufacturing is currently leading in industrial edge adoption, using it for predictive maintenance, quality assurance, and adaptive production. Smart factories process machine vision data for real-time defect detection, achieving high inspection rates with sub-millimeter precision.

Off-highway also shows compelling use cases, for example in smart farming, where autonomous weed control and harvesting systems are harnessing the power of edge-local AI and controls.

The energy sector uses edge computing in generation, transmission, and distribution. For example, wind farms optimize blade pitch based on real-time weather and wind data, optimizing energy output while reducing mechanical stress on the turbine structure. Monitoring of energy consumption and production in industrial sites over time allows to predict future energy flows, enabling cost optimization, peak shaving, and even the trading of flexibilities on energy markets based on AI models.

Process industries like chemical, pharmaceutical, and food production use edge computing for continuous monitoring of critical parameters, detecting subtle deviations that might indicate contamination or quality issues.

Across all these industries, edge computing meets the need for real-time processing, operational resilience, and secure data handling.

Edge solutions: IIoT & Industry 4.0

“By combining computational resources and versatile networking capabilities with state-of-the-art cybersecurity at the industrial site, edge solutions bridge the gap between operational technology and information technology, driving industrial digitalization,” Stöger said.

“Edge computing platforms significantly improve data utilization. Traditional industrial environments generated vast amounts of data that were often untapped due to bandwidth and processing limitations. Edge computing filters, aggregates, and analyzes data locally, sending only select relevant data and actionable insights to higher-level systems or the cloud. This has enabled digital twins that accurately mirror physical assets, supporting simulation and optimization across production lines,” he added.

Security in edge platforms addresses the expanded attack surface of connected industrial systems. Modern solutions use defense-in-depth strategies, continuous vulnerability monitoring, and secure communication channels to protect critical infrastructure from sophisticated threats.
Edge computing also supports decentralized decision-making, a key feature of Industry 4.0. By distributing intelligence throughout the production environment, these systems enable autonomous operation of equipment and processes, with local analytics driving real-time adjustments.

“The flexibility of containerized edge applications accelerates innovation cycles, allowing manufacturers to deploy new capabilities without changing hardware,” Stöger said. “This agility, combined with the performance benefits of local processing, makes edge computing a cornerstone of industrial transformation.”

Al Presher, Editor, Industrial Ethernet Book