Bench Talk

Industrial Ethernet vs Fieldbus: Architecture, TSN, and Migration

Thu, 23 Jul 2026 14:51:23 GMT

(Source: Michelle/stock.adobe.com)

Fieldbuses once replaced bundles of wiring with shared, deterministic networks. For years, they carried control signals reliably and predictably. However, as equipment, sensors, and data systems proliferated, fieldbus limitations emerged. The central issue today is real-time operational visibility. Engineers need networks that move control data, diagnostics, and process information across the plant as reliably as they move control signals.

Industrial Ethernet connects every layer of the production process, improving uptime and insight. Transitioning to Ethernet represents a disciplined engineering approach. This article examines the technical drivers behind the shift from fieldbuses to Ethernet, including architecture design, determinism, coexistence strategies, cybersecurity considerations, and emerging technologies such as Time-Sensitive Networking (TSN) and Single Pair Ethernet (SPE).

Rewiring Factories for Ethernet

Though fieldbuses remain reliable, their isolated architectures limit system-wide visibility and efficiency. Protocols such as PROFIBUS, DeviceNet, and Modbus often operate in isolated architectures. Integrating them with plantwide or enterprise systems may require gateways or translation layers, which can add latency, limit diagnostics, and increase maintenance overhead.

Ethernet-based protocols such as PROFINET, EtherNet/IP, and EtherCAT bridge the gap between operational technology (OT) and information technology (IT). They transmit process variables, diagnostics, and analytics traffic over shared infrastructure, creating a cohesive network connecting controllers, sensors, and enterprise systems.

Most facilities adopt Ethernet incrementally. Modernization typically occurs one cell or one production line at a time, often aligned with equipment replacement cycles. This phased approach maintains operational continuity while steadily enhancing existing systems.

With Ethernet, operators can view live dashboards displaying real-time temperature, throughput, and energy data. Continuous visibility enables maintenance teams to identify deviations before they lead to material waste or downtime. The primary advantage lies in situational awareness and informed decision-making.

Ethernet integration also forces a new kind of discipline. Because the network reaches beyond the plant floor, cybersecurity and effective segmentation become integral to the control design. Every open port adds risk, making containment strategies essential.

Successful Ethernet adoption strengthens uptime, improves performance, and reinforces confidence in the network as a critical foundation for modern industrial operations.

Designing Scalable Ethernet Architectures

Ethernet reshapes how engineers think about control systems. Fieldbus networks are static; Ethernet is adaptable. Topologies can grow and reroute without major rewiring, a significant advantage for plants that seek to evolve while operating continuously.

Engineers must design with their operating environment in mind. In a plant full of vibration and noise, the goal is predictable performance and resilient infrastructure. Managed switches segment traffic and prioritize critical control data. Redundant paths sustain communication when individual links fail. Resilience technologies such as Media Redundancy Protocol (MRP), Device Level Ring (DLR), and High-availability Seamless Redundancy/Parallel Redundancy Protocol (HSR/PRP) ensure sub-second recovery times, preventing a single cable failure from becoming a process failure.^[1]

Deterministic performance in Ethernet environments arises from deliberate engineering. Scheduling, traffic prioritization, and precise time synchronization—enabled through technologies such as TSN, EtherCAT, and PROFINET IRT—establish predictable communication behavior. Ultimately, network resilience requires validation, documentation, and periodic reassessment as new devices, segments, and protocols are added.

Bridging Old and New Networks

Many facilities operate as brownfield sites with legacy equipment. Integrating Ethernet into legacy systems requires careful alignment with systems originally designed for distinct communication methods, ensuring compatibility while preserving existing performance.

Ethernet and fieldbus technologies often operate side by side within the same plant. While one production line may continue running the stable PROFIBUS network, the next production line might run PROFINET. Gateways enable communication between these systems by translating legacy signals into new structures, allowing data to flow without interrupting production. Over time, the plant evolves into a layered ecosystem of protocols, network topologies, and vendor platforms that function together as an integrated whole.

Successful coexistence relies on planning and operational trust. Operators and electricians must see the new system working before they trust it. Clear documentation, thorough testing, and well-defined procedures provide operators and electricians with the assurance needed to support and maintain the evolving network.

In hazardous areas, intrinsic safety (IS) requirements guide technology selection. Protocols such as PROFIBUS PA and HART remain common, while Ethernet gradually extends into these zones via SPE and future IS-certified Ethernet hardware.

Legacy fieldbus and distributed control system (DCS) devices were never designed for Internet Protocol (IP) networking; therefore, connecting them to enterprise systems raises new security and timing concerns. Firewalls, virtual local area network (VLAN) segmentation, and least-privilege access maintain controlled communication boundaries. Expanded visibility enhances operational insight while reinforcing the importance of defined network segmentation. Additionally, TSN uses mechanisms such as time synchronization and scheduled traffic to support bounded latency and predictable delivery. This approach ensures that each signal is delivered within a precise and dependable timeframe.

Securing the Connected Plant

Ethernet provides greater access to operational data, but every new switch, sensor, and connection also creates an additional access point that must be secured. Engineers will not stop every intrusion, but they can control its spread. As networks grow more complex, it becomes increasingly important to understand what is connected, how systems communicate, and who has access to them.

Industrial network security requires clear boundaries, fault isolation, and preparation for failure. This approach operates as a continuous discipline that includes regular patching, active monitoring, and periodic reassessment as both the facility and external threats evolve.

The safest architecture is one where a compromised device cannot influence the control strategy. Ethernet-based systems require deliberate containment strategies that limit the scope of disruptions. Thoughtful segmentation and protective design measures act as built-in safeguards, ensuring disturbances remain localized and the broader system continues to operate reliably.

Turning Connectivity into Insight

Ethernet’s biggest advantage is visibility. By merging control and information networks, engineers can monitor process variables, energy use, and equipment behavior in real time.

Predictive maintenance depends on that transparency. Vibration, current, and temperature readings feed dashboards that instantly flag deviations. Maintenance teams gain precise insight into developing failures, including the specific component involved and the conditions driving the issue. Reliable timing ensures that this information reflects actual system behavior, supporting confident decision-making.

Ethernet transforms raw process data into actionable operational intelligence. When engineers can trace the sequence of events leading to faults, engineering efforts extend beyond reactive maintenance toward sustained continuous improvement. The network becomes an instrument for operational control, supporting efficiency, reliability, and informed optimization.

Engineering the Next Generation of Ethernet

Industrial networking is advancing toward convergence, bringing all devices, from controllers to sensors, onto a single deterministic infrastructure. TSN and SPE are driving that shift.

TSN enhances timing precision and traffic scheduling, enabling motion control, safety communication, and monitoring data to share the same cable without interference. SPE streamlines wiring by carrying both power and data over a single twisted pair. These advances will push Ethernet deeper into the field level, connecting devices that once lived on proprietary buses. Though the technology is ready for implementation, multi-vendor interoperability is still developing.

Ethernet’s evolution reflects continuous refinement, with each advancement strengthening reliability and further integrating control, monitoring, and analytics into a cohesive network architecture.

The Engineer’s Takeaway

Industrial networking has progressed from point-to-point wiring to shared fieldbuses and now to Ethernet-based architectures that function as distributed control systems. Each stage has expanded visibility, strengthened integration, and increased the need for disciplined design and validation. Successful Ethernet implementation depends on careful planning, testing, documentation, and verification to ensure reliable operation and long-term system integrity.

Network reliability is built on a well-defined structure. Every signal benefits from a clear purpose, a known source, and a predictable transmission path. This level of organization allows faults to surface clearly through data, enabling faster diagnosis and resolution. Ethernet provides comprehensive visibility into system performance, enabling teams to monitor uptime, quality, and safety in real time and respond early to emerging issues.

[1] https://industrialethernet.net/technology/industrial-ethernet/operational-solutions-for-industrial-network-resiliency/

Designing Wireless Coexistence for Factory Floors

Mon, 20 Jul 2026 14:52:43 GMT

Coordinating radio technology can prevent simple overlaps from having major consequences

(Source: karyamanswasta/stock.adobe.com; generated with AI)

Wireless networks have become essential on the factory floor, carrying everything from pressure and temperature readings to video, diagnostics, and mobile data. Plants often run multiple systems side by side—Wi-Fi^®, Bluetooth^®, Zigbee^®, WirelessHART, ISA100 Wireless, and others—frequently added at different times by different teams. When those systems share spectrum without coordination, they can quietly drift into conflict.

Consider the following example. At a chemical plant, a team installs Zigbee sensors along a fence line to track emissions. The sensors share the same frequency band as the plant's WirelessHART network, and no one coordinates the rollout. Within three weeks, plant sensors begin draining batteries faster than usual and miss scheduled update windows. Channel checks trace the problem to overlapping frequencies. Once each network is assigned its own slice of spectrum, performance returns to normal, but not before retry counts climb and one-second updates stretch to four. A simple coordination step could have prevented these errors, highlighting the importance of ownership in today’s multi-protocol industrial networks.

The consequences of this example capture the core challenge of wireless co-existence: managing and coordinating multiple radio systems that share the same spectrum without causing interference or starving one another of timely, reliable updates. Plants now demand wireless to effectively manage immense amounts of data, as well as to reduce facility expenses. Wiring often costs more than the sensors themselves, and moreover, radio links expand monitoring of pressure, temperature, level, and flow without tearing up existing runs. The result is visibility into previously hidden conditions and decisions that land on time. Coexistence is the plan that makes this scale.

In this blog, we explain why intentionally designing for coexistence is essential to achieving reliable wireless communication on the factory floor. We also highlight how selecting the right radio, ensuring timely sensor updates, and maintaining predictability in mixed networks—through band and channel separation, proper spacing, thorough site surveys, and clear ownership—contribute to effective coexistence strategies in industrial environments.

Match the Radio to the Job

Understanding why coexistence matters begins with recognizing that different wireless technologies serve different purposes, while also knowing which radio fits which job.

In practice, most facilities run more than one wireless system because no single radio fits every application. WirelessHART and the ISA100 series of standards commonly carry continuous monitoring, delivering on-time readings with multi-year batteries, even when radio frequency (RF) is busy. Typical update rates sit between 100 milliseconds and one second, configured per device based on how quickly the process variable can change. At scale in major facilities, few alternatives match that mix today.

Wi-Fi, on the other hand, prioritizes overall throughput. It is ideal for information technology (IT) traffic like reports, tablets, and video, where total throughput matters more than precise timing. During heavy activity, such as software updates or large file transfers, Wi-Fi timing can vary. That variation is acceptable for user devices, but not for control inputs that expect a steady cadence.

Bluetooth remains useful for walk-around diagnostics and hand-held access. Lower-frequency links around 900MHz reach assets across long distances—hundreds of meters or farther—with clear line of sight. These links also deliver data from behind heavy steel, where 2.4GHz struggles, making this approach fit for tank farms and pipe racks but unsuitable for time-critical sensor inputs.

Since the 2.4GHz band does not pass through steel effectively, many process plants lean on mesh topologies for ISA100 and WirelessHART, allowing sensors to relay signals around obstructions. The outcome is a portfolio approach that improves coverage without major rewiring.

Tailor the Wireless Combination to the Facility

It is important to remember that coexistence strategies are not universal. What works depends on the type of facility and the nature of its operations.

The right portfolio depends on what the plant makes and how it operates. A refinery and a robotics cell share the need for reliable wireless, but their priorities differ, meaning their technology choices will also diverge from each other.

Continuous process plants optimize for predictable, long-lasting monitoring. WirelessHART and ISA100 form the backbone for pressure, temperature, level, and flow, while Wi-Fi, Bluetooth, and lower-frequency links add convenience, logistics visibility, and non-critical telemetry around that core.

Discrete manufacturing and robotics follow a different pattern. Private 5G—dedicated cellular networks operated by the facility itself—and IO-Link Wireless—a short-range protocol designed for fast sensor and actuator communication—support short, fast exchanges where tight timing outweighs multi-year battery life. The application sets the requirement and the technology follows. Standards-based systems tend to integrate more smoothly as deployments grow.

On-Time Delivery Versus Best Effort

Once you know which radio does what, the next question becomes why they cannot simply share the same spectrum without consequence. The answer lies in how each technology treats time.

Coexistence is ultimately about timing. A reading is only useful if it arrives exactly when needed. Wi-Fi is designed to move high volumes of data overall, not to deliver every packet at a fixed moment. While that works for reports and screens, it does not work for signals feeding optimization or safety logic. Industrial sensor networks use scheduled exchanges—time slots assigned in advance so each device knows exactly when to transmit—resulting in readings that arrive on a steady rhythm that the control system expects, even when air traffic is heavy.

Separation and Structure Keep Networks Predictable

Recognizing that different systems have incompatible timing needs means determining how to organize them so each gets what it requires?

When coexistence is engineered intentionally, each system finds its lane. Industrial mesh networks, such as ISA100 and WirelessHART, operate at 2.4GHz because that is where their ecosystems live. Employee Wi-Fi often moves to 5GHz or 6GHz, where more channels and wider bandwidths accommodate user traffic. Deterministic or safety-critical signals still travel on cable. Lower-frequency links near 900MHz serve assets set back by distance or equipment-dense areas. Effective coexistence requires channel planning, where teams record channel choices so adjacent networks do not compete for the same slices of spectrum.

Physical placement is equally important as channel planning. High-power transmitters can desensitize nearby receivers whether they sit on the same or different frequencies. Placing a WirelessHART gateway within a meter of a Wi-Fi access point can degrade receiver sensitivity, even when the two operate on non-overlapping channels. In tighter areas, plants often schedule high-bandwidth user traffic outside expected update windows in order to keep their cadence.

How Coexistence Fails in Practice

Even with clear roles and separation strategies in place, coexistence can still unravel when those principles are not maintained with new additions.

Conflicts rarely announce themselves with a single alarm. More often, they drift in. A new network raises the background noise on the same part of the band that another system uses. Sensors begin re-sending messages. Update intervals stretch into multi-second territory, while batteries empty sooner than expected. The system still works—but out of rhythm, and at mounting cost.

Once teams identify the overlap and move off the noisiest slices, cadence returns to normal. The pattern is straightforward. Uncoordinated additions create invisible collisions that only surface later as performance issues. It rarely fails loudly. It drifts, and the cost shows up later.

When radios collide, the early signs come from operations rather than specialized tools. In practice, teams look at message retries and signal quality in the affected area, compare local channel choices, and check whether any access points were moved or added. Small course corrections at that stage usually restore the expected cadence before visibility is lost.

What Site Surveys Reveal About the Factory Floor

Theory helps, but the physical environment has the final say. That is where site surveys come in.

However good separation strategies and channel plans look on paper, the true test is executing those principles in the physical environment. Steel structures, rotating equipment, and existing transmitters create a radio landscape that drawings alone cannot predict. This is why site surveys are necessary for success, and they should be done before deployment to establish a baseline, as well as after problems emerge to pinpoint what changed.

A survey often confirms what drawings cannot. In steel-heavy areas, small shifts in sensor location change reflection paths and can turn a weak link into a strong one. Gateways that look convenient when located together often work better when spaced away from persistent noise sources like welders and large motors. Survey data also reveals whether channel assignments made in the planning phase actually deliver clean spectrum at each sensor location, or whether local interference demands adjustments.

Even in well-designed systems, the final step into control is usually cleaner over fiber. Wireless domains aggregate at the gateway, and timing stability is preserved on the wired side. A thorough survey documents not just signal strength but also the noise floor, retry rates during typical operations, and the locations of any RF-noisy equipment. This information proves invaluable when troubleshooting later.

Shared Ownership Keeps Systems Stable

Technical strategies only hold if someone is accountable for maintaining them.

Networking failures like the chemical plant example discussed in the introduction read less like a technical defect and more like a gap in ownership. Plants that treat wireless as shared infrastructure avoid such pitfalls by keeping a simple register of who operates what, on which channels, and where gateways and access points sit. This register also supports cybersecurity reviews, since every radio is a potential network entry point. New deployments should pass a quick review against that map so additions align with what is already working. Deterministic traffic stays on wires, wireless domains end at gateways, and control systems see the additions over fiber. Over time, this becomes a habit rather than a checklist—one that prevents surprises and allows for growth that does not disturb what already works.

Conclusion

Coexistence is achievable, but only when it is treated as part of the design, not an afterthought.

Plants adopt wireless to observe more points and respond faster at lower cost. Success comes from treating radio links as infrastructure for environments dense with steel and electrical noise. Clear separation between wireless domains, sensor networks built to tolerate interference, and placements validated by field surveys all contribute to predictable behavior.

With intentional design and simple monitoring, Wi-Fi, Bluetooth, lower-frequency links, and industrial mesh networks can function as a single reliable system. Coexistence is not automatic—it is engineered. Treat the airwaves like infrastructure and the result is simple: signals arrive when they should and teams trust what they see.

"Good Enough" Digital Twins Drive Competitive Edge

Thu, 12 Mar 2026 05:01:00 GMT

(Source: EmmaStock/stock.adobe.com; generated with AI)

In smart manufacturing, the digital twin is a prediction and insight engine for the factory floor. But how accurate does a digital twin need to be to deliver value? The answer isn’t straightforward. Accuracy depends not only on how closely the twin mirrors the real-world system, but also on who is using it, for what purpose, and under what constraints.

This is where most discussions about digital twins fall short. Engineers know that a twin mirroring measured torque and pressure signals while estimating internal states or degradation trends using data-driven models will require a very different fidelity profile than one forecasting long-term asset utilization. Yet conversations about “increasing accuracy” can tend to overlook this context. In practice, accuracy is a relative metric, and not all use cases demand perfection.

This blog discusses what makes a digital twin suited for practical use in industrial settings, arguing that accuracy must be determined by context, user needs, and return on investment (ROI)—not perfection. By reframing accuracy in this way, manufacturers can balance fidelity, cost, and purpose to deploy digital twins that deliver measurable value and improve over time.

Accuracy for Whom?

The first step in evaluating digital twin fidelity is defining the consumer of its outputs. Are the predictions intended for a human operator, or are they feeding supervisory control layers that inform automated decisions?

For human-in-the-loop systems, data is often visualized in dashboards, reports, or alerts. Latency tolerance is higher, and approximations can be smoothed or contextualized. But when a machine consumes the data directly (e.g., a control system adjusting a robot arm’s trajectory based on feedback from a twin), the expectations for real-time accuracy rise sharply. Minor delays, rounding errors, or missing variables can cause cascading system-level faults.

Even within a smart factory, digital twins may serve radically different roles. A discrete-event simulation modeling material flow through a packaging line doesn’t need millisecond accuracy. A high-fidelity twin of a pick-and-place robot, on the other hand, might require precise motor temperature, torque, and position feedback at high sampling rates to allow the control system to maintain precision and uptime.

Without clearly defining who needs the data and how they will use it, discussions around digital twin accuracy remain vague and difficult to operationalize.

Precision Comes at a Cost

Building a more accurate digital twin is always possible, but it’s rarely free. Increasing fidelity usually demands more sensors, higher-speed data acquisition, more frequent calibration, tighter synchronization, and more computationally intensive models. In some cases, the final few percentage points of accuracy might require disproportionately higher investment.

Seeking higher fidelity creates a cost-benefit tradeoff for manufacturers to consider. Many manufacturers only deploy digital twins once they’re accurate enough to provide actionable insights that justify their usage. Instead of striving for perfection upfront, they focus on refining the twin over time by layering in new data and using machine learning (ML) to improve predictive quality.

This iterative approach also reduces time-to-value. Deploying a 95 percent twin today means collecting real-world feedback sooner. Waiting months to push accuracy from 95 percent to 98 percent may delay production improvements without significantly increasing ROI. In most practical cases, the benefits of continuous learning outweigh the theoretical appeal of near-perfect replication.

Understanding the Sources of Error

To manage accuracy effectively, manufacturers need to understand where errors enter the system. Three primary sources dominate.

Initial Model

Model design forms the foundation of twin accuracy. If the initial model omits key variables, reuses a configuration from a different site, or fails to capture unique plant behaviors, it will never match reality. Digital twins must be optimized for the physical and operational context of each deployment.

Missing Data

Sensor coverage and quality create the data backbone. Missing or inaccurate data are a major contributor to poor fidelity. Insufficient sampling, uncalibrated sensors, or misaligned sensor placement can inject noise and blind spots into the twin.

Latency

Latency and synchronization determine real-time relevance. Even accurate data might become useless if they arrive late. For time-sensitive applications, the speed of the data pipeline can determine whether a prediction adds value or misses the moment entirely.

Mitigating these factors requires system-level planning. A robust twin depends as much on the physical architecture of the factory—including sensors, Programmable Logic Controllers (PLCs), computational infrastructure, and communication infrastructure—as it does on the sophistication of the model itself.

Discrete-Event vs. High-Fidelity Twin

Two modeling approaches dominate digital twin construction: discrete-event simulation (DES) and high-fidelity physics-based modeling.

DES models operate at a macro scale. They simulate how parts move through a process, when resources become available, and how system-level delays emerge. This approach is widely used for scheduling, throughput analysis, and layout optimization. Such models are less concerned with physics and more focused on flow and timing.

High-fidelity models, by contrast, zoom in on specific equipment or processes. These may simulate the internal temperature of a welding head, the angular velocity of a spindle, or the pressure inside a hydraulic cylinder. These twins rely on granular sensor data and are usually tied directly to control loops.

Many modern digital twin platforms support both types of modeling. The trick is to match the technique to the use case. Simulating a supply chain bottleneck with a high-fidelity robot model wastes compute. At the same time, using a DES model to diagnose bearing degradation on a lathe is equally ill-suited.

Learning from the Unknown

Even the best-designed twins will eventually encounter events they weren’t built to predict. In a manufacturing context, these could include unexpected equipment failures, sudden environmental changes, or unpredictable interruptions like supply chain disruptions.

Manufacturers can prepare for these events over time by designing twins to learn. When failures do occur, the control system should replay historical data, comparing what the twin predicted against what actually happened. This forensic loop allows teams to identify gaps, retrain models, and improve future accuracy.

Moreover, shared learning across assets is becoming more common. If one wind turbine in a fleet fails due to a rare vibration signature, operators can inject that failure signature into every other twin to prevent repeat failures. Some OEMs now offer shared digital twin frameworks where field data is exchanged bidirectionally between customers and vendors to enrich fault prediction models.

Validation and ROI

Validating accuracy is an ongoing process that requires multiple complementary approaches. Historical data replay allows teams to test prediction accuracy with known outcomes, creating a baseline understanding of model performance. Statistical error analysis helps quantify false positives and negatives, revealing patterns in where the twin succeeds or fails. Root cause mapping proves equally valuable, tracking model deviations back to their source to identify whether issues stem from sensor placement, model assumptions, or data processing gaps.

Similarly, ROI evaluation becomes meaningful when assessed across interconnected dimensions. Financial returns typically emerge through reduced waste, energy savings, and improved yield—direct bottom-line impacts that justify continued investment. Operational improvements manifest with better overall equipment effectiveness, higher throughput, and shorter changeovers, creating compound benefits across production lines. Strategic advantages encompass enhanced safety, sustainability metrics, and operator satisfaction, delivering longer-term competitive positioning that extends beyond immediate manufacturing metrics.

The most accurate digital twin is not the one with the most inputs or the lowest error margin. It's the one that delivers measurable improvements aligned with the factory's business goals.

Conclusion

As manufacturers move toward smarter, more connected operations, they will look less at building the perfect digital twin and more at building one that is fit for purpose. A “good enough” twin delivers meaningful insights, adapts with new data, and evolves alongside the plant itself. By aligning reliability with user needs, understanding the true sources of error, and focusing on iterative improvement rather than theoretical precision, manufacturers can unlock ROI without unnecessary complexity. In the era of digital transformation, the most valuable digital twins will be those that scale, learn, and drive measurable outcomes.

Future Trends in Control Panels

Mon, 02 Feb 2026 16:27:31 GMT

(Source: andrew_shots/stock.adobe.com)

As Industry 4.0 gives way to 5.0—and even early ideas of 6.0—control panels are becoming more intelligent, more connected, and far more adaptable.

Industry 4.0 introduced digitalization and connectivity to manufacturing. It brought us smart factories where machines talk to each other, collect real-time data, and make automated decisions.

Industry 5.0 builds on that by putting people back in the center—combining human creativity with advanced robotics and artificial intelligence (AI) to create more personalized, sustainable production.

Industry 6.0, though still conceptual, looks even further ahead. It imagines a future of hyper-personalized manufacturing where machines not only collaborate with humans but evolve in real time—driven by AI, seamless human-machine integration, and the potential of quantum computing.

Each phase builds on the last: 4.0 connected the machines, 5.0 reconnected the humans, and 6.0 aims to fully merge the two.

In this blog, we walk you through the technologies and design strategies already reshaping control panels—and we look at what is just around the corner.

How Industry 4.0 and 5.0 Are Transforming Control Panels

The core function of the control panel has not changed; it is still a container for the plant’s brain. But what is built into these panels is evolving rapidly. Technologies like the industrial Internet of Things (IIoT), edge computing, 5G, and cloud connectivity are enabling panels that are much smarter and more connected than ever.

Sensors have become smarter while delivering more power and versatility. For example, we now have multivariable smart sensors that can simultaneously read pressure, vibration, and temperature in a single unit. This creates an explosion of data flowing in and out of the panels.

More data also means more gateways, more antennas, and more network infrastructure packed into the same limited panel space. While wireless technology is gaining popularity—especially for non-critical monitoring—most users still prefer wired systems for mission-critical control due to data reliability and cybersecurity risks.

In short, control panels are becoming the nerve centers of intelligent, data-rich ecosystems.

Why Modularity and Scalability Are So Critical

As the nerve centers of these rapidly evolving industrial ecosystems, modularity and scalability are critical design features in control panels. Modularity means more than just putting things into blocks. It means flexibility. We need panels that allow builders to move shelves around, swap out modules, or expand the footprint without having to start from scratch. Power supplies, I/O cards, or controllers need to be accessible and, ideally, hot-swappable.

Scalability is about anticipating growth. Vendors must consider how to design a panel that meets today’s needs while accommodating tomorrow’s technologies. Whether that’s more processing power, expanded connectivity, or new cooling systems, the layout must support it.

Meeting these design needs requires innovative partnerships between the vendors and the panel builders. The builders design the enclosure, but the vendors must integrate everything—cabling, cooling, and power—without overloading the space.

Cybersecurity’s Role in Control Panel Design

Cybersecurity is one of the fastest-changing and most vital areas in industrial control. Attacks on infrastructure can originate from thousands of miles away. While physical access security is important, most of the cybersecurity burden falls on the electronics and software inside the panel.

That means embedded firewalls, secure communication protocols, and rigorous software design. But the panel builder also has a role to play. More panels now include electronic locks, biometric readers, or key fobs that control access to them. Access logs are becoming increasingly common, allowing companies to track who interacts with the panel and when.

We can expect to see increased pressure on panel builders to meet stricter cybersecurity standards. Today, you might get by with basic locking mechanisms. Tomorrow, compliance with standards like UL 2900 and IEC 62443 will be essential.

AI and Machine Learning Are Shaping Future Control Systems

AI will turn control panels from passive equipment into proactive, intelligent systems. In the future, control panels might have the capability to monitor for temperature, detect early signs of failure, and send a maintenance request before a technician even realizes something is wrong.

We’re also moving toward an environment where panels provide recommendations. For example, they might say, “This I/O module has not been used in six months. Is it still required?” Or, “This cooling pattern is inefficient—here’s how to reduce energy use.”

In the future, as machine learning analyzes and develops an understanding of machine operations and control systems, it will help us evolve from predictive maintenance to self-optimizing systems. Eventually, we will see panels that interact across the plant, understand broader environmental conditions, and act accordingly. It is a bit like giving each panel a nervous system and a brain of its own.

Sustainability Is No Longer an Afterthought

Sustainability is already reshaping panel design. That means considering the materials we use, how panels are painted or coated, and how easily they can be disassembled and recycled when they are retired.

AI could help design recyclable panels the same way it helps develop new drugs or materials. With Industry 4.0, 5.0, and the emerging 6.0 advancing rapidly, panels will likely be replaced or modified more frequently during upgrades. So, panel builders must make sure they can be responsibly dismantled and reused.

Energy efficiency is another key aspect of sustainable panel design. Active cooling systems add cost and draw power. That is why hybrid designs are gaining ground—using passive ventilation first, and switching to active cooling only when needed.

Digital Twins’ Growing Impact on Control Panel Design

Digital twins let us simulate and test a panel design before it is built. We can visualize airflow, heat zones, wiring constraints, and even maintenance accessibility. This is a huge opportunity to reduce design cycles and prevent real-world deployment issues.

We can also use digital twins to evaluate trade-offs: Should I use this layout or that one? Does this module placement make the panel run hotter? Does it make maintenance harder? The answers to questions like these help us refine the design virtually, saving time and cost.

In the future, digital twins may be paired with AI to create AI-native workflows, with software tools like Siemens NX or Autodesk’s generative design platforms creating new opportunities. Instead of applying AI after a design is finished, we will build processes from scratch that assume AI will guide every step.

Conclusion

We’re entering an era where control panels are no longer passive pieces of equipment—they are active players in the broader industrial ecosystem of the plant. They gather and act on data, support AI-driven decision-making, and serve as gateways between the physical and digital worlds.

These control panel advancements match the rapid pace of change underway in industrial automation, and it means everything needs to move faster: design, compliance, deployment, training. Panel builders must keep pace not only with technological advancements but also with changes in the workforce. Training skilled operators is becoming increasingly challenging as more people transition into higher-paying tech jobs.

Success will require a new mindset. It’s not enough to optimize old workflows with new tech. We need to reimagine panel design from the ground up, building for speed, intelligence, and sustainability. The panel of the future is already taking shape. Now is the time to design like it.

Wireless Technologies Reshape Industrial Automation

Tue, 29 Apr 2025 22:23:34 GMT

(Source: Natchooda/stock.adobe.com; generated with AI)

Industrial automation systems are notoriously complex. With an amalgam of sensors, actuators, control systems, enterprise resource planning, supply chain software, and more, industrial automation relies heavily on the cohesive operation of these otherwise disparate subsystems. As such, communication and networking are often the enablers for applications requiring large amounts of data.

Wired systems have long been the backbone of communication in industrial settings, as their reliability, determinism, and integration with legacy equipment made them the default choice across a variety of sectors. However, the increasing demands of industrial digitization—combined with new imperatives around mobility, flexibility, and cost—are pushing wireless technologies to the forefront of today’s automation strategy. Technologies like 5G, WirelessHART, LoRa^®, Bluetooth^®, Wi-Fi^®, and Zigbee^® are unlocking new architectures, applications, and performance benchmarks in Industry 5.0, which emphasizes human-centricity, sustainability, and resilient manufacturing in industrial applications.

In this blog, we look at the ways emerging wireless technologies are reshaping industrial automation by removing many of the constraints that come with wired systems.

Shortcomings of a Wired Approach

In traditional industrial deployments, wiring frequently represents the largest share of total system cost. A single sensor may be priced in the hundreds or even thousands of US dollars, depending on its function and specifications. However, the cost to connect that sensor, including trenching, conduit installation, labor, and provisions for fault tolerance, may exceed the sensor’s price by an order of magnitude. These installation expenses quickly scale across large facilities, turning wiring into a major barrier to broader sensor deployment.

Beyond cost, wired systems are also susceptible to a range of physical and environmental hazards. In industrial settings, high temperatures and corrosive substances can degrade cables over time. Meanwhile, fires or equipment collisions can sever wired connections and lead to unplanned downtime and repairs. As a result, systems in these environments often require frequent maintenance and inspections to ensure that wiring remains intact and functional, further increasing the cost of wired systems.

Why Wireless Is the Answer

Wireless communications eliminate many of the physical and financial constraints associated with wired systems.

Without the need for trenching, conduit installation, or extensive labor, wireless sensors can be deployed rapidly across a facility, even in areas where wiring would be impractical or hazardous. This feature reduces capital expenditure and accelerates time-to-insight by allowing faster rollout of monitoring and sensing infrastructure. Eliminating physical wiring also minimizes maintenance overhead and reduces the risk of communication failure due to cable damage.

More importantly, the transition to wireless supports a level of mobility and flexibility that wired systems cannot match. Modern industrial environments increasingly rely on reconfigurable production lines, autonomous guided vehicles, collaborative robots, and mobile asset tracking—all of which require communication networks that support movement and dynamic layouts. Wireless technologies provide the necessary infrastructure for these applications by delivering reliable connectivity without being tethered to fixed points. As a result, wireless systems are more flexible and future-ready than their wired counterparts.

Matching Wireless Technologies and Protocols to Use Cases

Naturally, no single wireless protocol or technology can address all industrial use cases. Instead, engineers must select based on application requirements, such as latency, power consumption, reliability, data throughput, and certification needs.

5G

5G has emerged as the leading candidate for low-latency, high-bandwidth, and mission-critical applications in manufacturing. Its deterministic performance and capacity to support thousands of simultaneous connections make it uniquely well suited for applications like real-time robotic control, autonomous vehicles on factory floors, and latency-sensitive safety interlocks. Private 5G networks offer even further enhanced control by allowing manufacturers to optimize quality of service without depending on public carriers.

WirelessHART

WirelessHART remains the standard in refineries and chemical processing due to its intrinsic safety certifications and proven determinism. Built on the legacy of the (wired) Highway Addressable Remote Transducer (HART) protocol, which has more than forty million installed devices, WirelessHART offers reliable, secure, and deterministic communication. Its value lies not only in its performance but also in its compatibility with explosion-proof enclosures and adherence to industry-specific safety certifications. Its predictability makes it trusted for tasks that were historically wired, such as process variable measurement and supervisory control.

LoRa

Long rage (LoRa) communication and similar low-power wide area network (LPWAN) technologies support long-distance, low-data-rate applications where battery life and cost are paramount. Their ultra-low power draw allows sensors to operate for years without battery replacement. As such, LoRa is a great choice for remote or dispersed assets, such as environmental monitoring, remote utility management, and agriculture.

Bluetooth

Bluetooth, while traditionally consumer-focused, is making inroads into healthcare and personal monitoring applications due to its ubiquity in smartphones and wearables. Its short-range operation and limited throughput are well suited to body area networks and localized data collection. For this reason, lower power variants of Bluetooth, such as Bluetooth Low Energy, are commonly used for applications like asset tracking. Other industrial Bluetooth uses include handheld testing equipment—typically tablets—connected to vibration sensors that attach magnetically to machines for short-term measurements.

Wi-Fi

Wi-Fi is widely used in industrial environments where high data throughput and broad device compatibility are required. It supports bandwidth-intensive applications such as video monitoring, diagnostic data transfer, and firmware updates. While not as deterministic as 5G or WirelessHART, Wi-Fi is suitable for non-critical tasks in controlled environments. Its vast presence and ease of integration make it ideal for brownfield retrofits and facility-wide networking.

Zigbee

Zigbee is a low-power, short-range mesh networking protocol designed for environments with dense sensor deployments. It is a good fit for lighting control, HVAC monitoring, and other low-bandwidth applications. Its mesh topology increases coverage and redundancy in indoor industrial settings.

Risks and Challenges in Wireless Deployment

Although wireless deployment brings numerous cost and efficiency advantages over wired infrastructure, the transition to wireless introduces its own set of risks.

Reliability remains a chief concern. Even the most robust wireless protocols fall short of the “six nines” (99.9999 percent) reliability offered by wired systems. While protocols like 5G and WirelessHART can approach four nines, the final decimals matter in critical applications. High-reliability systems must often include redundancy and fallback mechanisms to compensate for wireless variability.

Cybersecurity is another escalating issue. As devices become increasingly connected, attack surfaces expand. Wireless sensors and controllers must incorporate encryption, authentication, and update mechanisms without compromising uptime. Firmware updates and patch management are especially sensitive in production environments, as even minor interruptions can cause process deviations or safety risks.

Signal integrity adds further complexity. Physical barriers like metal enclosures, reinforced concrete, or equipment-generated electromagnetic interference (EMI) can disrupt wireless transmission. Pre-deployment surveys and radio frequency simulations are therefore necessary to determine optimal antenna placement, identify shadow zones, and plan for repeater usage.

Strategies for Deployment Without Disruption

Adopting wireless should not mean removing what already works. Most successful deployments begin with hybrid architectures that augment existing systems. For this reason, one should retain wired infrastructure for necessary controls and introduce wireless to enable flexibility, expand sensor coverage, or add mobility. This reduces risk while capturing early value.

Other considerations for successful wireless deployments include the following:

Conduct a site survey before deployment to map signal coverage, identify physical obstructions, and evaluate sources of EMI.
Use gateways that support multi-protocol translation to unify data pipelines across diverse devices and networks.
Select vendors that follow mature industry standards and avoid solutions that create proprietary lock-in.
Invest in training for installation, commissioning, and ongoing maintenance to equip internal teams with the skills needed to manage and troubleshoot the system effectively.

Conclusion

Industrial applications are changing, and communication infrastructure is evolving with them. While wired systems remain necessary for the highest levels of determinism and reliability, wireless technologies now offer a viable path to greater scalability and flexibility. With careful planning, integrating wireless technologies can unlock a new era of connectivity and monitoring for industrial automation.

Sensors and Analytics in Industrial Automation

Mon, 25 Nov 2024 23:23:31 GMT

(Source: Bounpaseuth / stock.adobe.com; generated with AI)

One of the cornerstones of Industry 5.0 is its use of advanced technologies to create more holistic and effective manufacturing solutions. Naturally, sensors and data collection build the foundation for such an effort. With data offering manufacturers unprecedented insights and transparency into their operations, Industry 5.0 unlocks new opportunities for efficiency, sustainability, and quality on the factory floor. In this blog, we will look at the role of sensors and analytics in industrial automation and discuss how these technologies are central to Industry 5.0.

Developing Sensor Technology in Industrial Automation

Sensors have always been key to industrial automation. Early implementations focused on basic physical measurements, specifically pressure, temperature, level, and flow. But, as automation advanced, so did sensor technology. In recent years, new categories of sensors—image, proximity, torque, vibration, and speed—have emerged to address the needs of more complex and diverse manufacturing environments.

In the context of Industry 5.0, manufacturing processes often involve human-robot collaboration, which necessitates additional safety measures. For example, proximity, image, and even infrared sensors have become essential to detect collisions in environments where humans work alongside collaborative robots (cobots). These sensors protect operators by detecting motion in real time and triggering safety responses if a human enters a high-risk area.

Beyond safety, torque and displacement sensors assist robots in accurately positioning objects on conveyors to ensure operational accuracy and quality control. Such sensor diversity and adaptability make advanced industrial automation possible and support various manufacturing needs.

Optimizing Processes and Resources

In Industry 5.0, real-time data collection enables responsive and adaptable production environments. Here, sensors gather data on everything from conveyor speeds and object dimensions and shape to product completion rates and assembly quality. For example, manufacturing operations on assembly lines use real-time sensor data to ensure that product components are correctly aligned and within tolerance, which prevents costly rework.

Real-time data are particularly important in applications where safety is a priority, such as automated assembly lines or hazardous manufacturing setups. In these scenarios, sensors provide instantaneous feedback to control systems, enabling preventive actions that protect personnel and maintain operational integrity. By leveraging real-time sensor data, manufacturing systems can optimize the balance between productivity and safety, with minimal downtime from manual checks or disruptions. The role of generative AI (GenAI) has become increasingly pivotal to the significant increase in the computational power of modern smart factory control systems.

In a similar vein, sustainable productivity, one of Industry 5.0’s pillars, aims to reduce resource consumption and minimize waste while maximizing output. Sensors play a role in this effort by measuring energy consumption, production speed, material use, and enabling overall equipment effectiveness (OEE) calculations—a metric that indicates a machine’s effectiveness across factors like quality, performance, and availability. With real-time data, manufacturing systems can quickly identify and address inefficiencies, such as machines running at suboptimal speeds and loads or producing excessive waste.

Advanced analytics platforms aggregate sensor data to calculate OEE. When a machine operates below target OEE levels, analytics can help identify specific inefficiencies, such as excessive idle time, poor energy use, and downtime, allowing managers to adjust for optimal performance. For instance, analytics might suggest reducing the operation of a machine that consumes high energy or optimizing material use to minimize scrap. As a result, manufacturers achieve more sustainable operations while simultaneously improving profitability.

Enabling Predictive Maintenance

Predictive maintenance, which depends on long-term and real-time data collection from sensors, is one of the most valuable applications of analytics in industrial automation. With years of historical data on equipment performance, analytics can predict maintenance needs before failures occur. While real-time data are crucial for immediate operational decisions, predictive maintenance relies on data trends gathered over months or even years.

For effective predictive maintenance, companies often integrate data from original equipment manufacturers (OEMs) with their own collected data. Given that OEMs have years of operational data from similar machinery, this collaborative approach allows access to a wider dataset. For example, sensors installed in turbines or conveyor belts to monitor vibration, oil debris, temperature, or pressure, flag deviations that indicate wear or potential failure. Advanced analytics can then process these data and identify patterns that suggest when equipment likely will require servicing, thereby minimizing downtime and repair costs and extending the equipment’s operational life.

However, implementing predictive maintenance requires significant data volume and historical trend analysis. Thus, cloud computing becomes an integral part of the equation. By processing data at scale, cloud-based analytics platforms support high-precision maintenance scheduling and operational planning, directly optimizing productivity and cost efficiency.

Integrating Sensors in Legacy Systems

While Industry 5.0 emphasizes interconnected systems and data-driven processes, many industrial facilities still operate with legacy equipment designed for Industry 3.0 or 4.0 environments. Integrating modern sensors into these outdated systems presents unique challenges. For example, legacy systems typically use analog sensors, which may not support the advanced data-processing capabilities required for Industry 5.0.

Retrofitting or adding sensors involves ensuring accurate placement, which may be difficult when access to certain machine components, such as rotating shafts or internal bearings, is restricted. Furthermore, retrofitting requires infrastructure upgrades to accommodate digital communication, as analog wiring and interfaces often lack the bandwidth needed to handle real-time data transmission from multiple sensors. To bridge this gap, facilities may need to install data converters or even wireless communication systems to facilitate data transfer without overhauling the entire infrastructure.

Moreover, when integrating digital sensors, compatible software to analyze and visualize sensor data is needed, posing another challenge to legacy control systems. Therefore, companies moving toward Industry 5.0 must invest in sensor technology and supporting ecosystems, such as fiber optics, edge computing devices, and analytics platforms, that can manage and process data from these sensors.

Conclusion

As Industry 5.0 continues to evolve, sensors and analytics will only become more important. With their ability to monitor real-time conditions, predict maintenance needs, and support sustainability goals, sensors enable manufacturers to optimize processes while safeguarding workers and reducing environmental impact. Meanwhile, advancements in analytics driven by GenAI will provide the intelligence necessary to make sense of the vast data these sensors generate. Together, these technologies are the foundation of Industry 5.0, turning data into actionable insights and paving the way for a more adaptive and resilient industrial future.

Prioritizing Sustainability with Industry 5.0

Fri, 22 Nov 2024 22:16:54 GMT

(Source: TakoyakiAI / stock.adobe.com; generated with AI)

Responding to the intensifying pressures of climate change and resource scarcity, today’s companies focus on reducing environmental impact through smarter processes, energy-efficient systems, and artificial intelligence (AI)-enhanced operations. With emerging technologies designed to face modern challenges, Industry 5.0 is positioned to carry on this focus by placing a unique emphasis on sustainability. This blog explores how Industry 5.0 capabilities such as advanced smart automation, Generative AI, and circular economy practices are shaping the future of sustainable industrial processes.

Applying Automation Technologies for Energy Efficiency

An immediate benefit of Industry 5.0 is the application of advanced automation technologies to reduce energy consumption in industrial environments.

Smart sensors are able to continuously monitor energy usage across equipment such as compressors, heaters, and pumps. The data collected by these sensors are then fed into AI-powered energy management systems, which automatically adjust equipment operations to prevent unnecessary energy use.

For example, when production is low, predictive algorithms can determine the optimal time to turn off or reduce the output of energy-intensive equipment, such as heaters or compressors. This precise control reduces energy consumption and contributes to lower carbon emissions. Moreover, integrating AI into energy management systems means factories can achieve significant savings by ensuring that machines only operate when necessary.

AI’s capabilities also extend to designing more energy-efficient products. AI-driven design tools can suggest product configurations that reduce the need for energy-intensive manufacturing processes, such as welding or soldering. By simply rethinking how parts are assembled, manufacturers can lower their energy consumption and carbon emissions. In these ways, automation technologies reduce energy waste and improve the overall operational efficiency of industrial plants. Furthermore, new products will also be easier to recycle.

Optimizing Resource Use With AI

AI algorithms can revolutionize how industries manage resources, from raw materials to human capital.

In the context of Industry 5.0, AI is applied to production processes and supply chain optimization. Smart factories now leverage AI to forecast demand, plan inventory, and schedule production more accurately. With an optimized supply chain, companies can reduce material and energy waste.

For example, AI can predict the exact number of materials required for production to minimize overstock and excess waste. It can also consolidate shipments to reduce transportation emissions, particularly when shipping parts or raw materials from overseas. By optimizing shipping routes, AI can ensure that the greenest possible transportation methods are chosen, such as combining multiple shipments into a single container or selecting transportation options with lower carbon footprints.

In addition, AI enables predictive maintenance for better resource optimization. Traditional maintenance schedules often lead to either premature replacements or costly breakdowns. By continuously analyzing data from machines, AI systems can predict when maintenance is truly necessary, which extends the life of equipment and reduces the need for new parts and lubricants, not to mention the excessive use of resources. A well-maintained machine requires fewer repairs, generates less scrap, and uses less oil—all contributing to a more sustainable operation.

Enabling the Circular Economy Through Industrial Automation

The circular economy is a concept that aims to minimize the waste associated with manufacturing. This is achieved generally through two approaches: recycling materials within the manufacturing process and encouraging product take-back programs.

In the first scenario, manufacturers can use industrial automation to recycle heat, water, and materials within the plant, achieving a closed-loop system. For instance, Subaru factories recycle wastewater into pure water for reuse.^[1] In other cases, heat generated from industrial ovens can be captured and reused for other processes, such as heating water for facility use.

Product take-back programs are another way automation supports the circular economy. An excellent example is the pump manufacturer Grundfos, which has implemented a take-back program. Through this initiative, customers can return old pumps to the company, where they are either refurbished or recycled.^[2] In this context, AI systems can aid in identifying which components can be salvaged and reused in new products.

Integrating Renewable Energy into Automated Systems

Many large manufacturers are already integrating solar, wind, and other renewable energy sources into their grid. However, the complexity of managing renewable energy within automated systems presents unique challenges, such as dealing with the intermittency of renewable energy sources.

AI can monitor energy production and storage levels to allow factories to shift between renewable energy sources and the grid as needed. As part of this process, AI can optimize the use of stored energy when renewable sources are unavailable so that operations remain smooth, even when renewable energy production fluctuates.

Overcoming Organizational Challenges in Sustainable Automation

While the technologies enabling sustainable industrial automation are advancing rapidly, their success depends on effective organizational strategies. All departments within an organization—including engineering, information technology, operations, and supply chain—must be on the same page when implementing new automation systems. However, achieving this alignment often requires significant cultural shifts within companies, as sustainability goals should be prioritized across all levels of the organization.

Once alignment is achieved, the next challenge is integration. New automation technologies must work cohesively with existing infrastructure to maximize their effectiveness. For example, adding robotic systems or smart sensors in one part of the factory should not create isolated pockets of efficiency. Instead, these systems should be integrated into the broader ecosystem to apply energy savings, predictive maintenance, and circular economy practices uniformly across all operations.

Meanwhile, scalability is a concern. As more parts of the operation adopt sustainable automation technologies, the system must grow without losing efficiency. A strong focus on continuous improvement is necessary as new technologies and best practices continue to evolve.

Conclusion

As Industry 5.0 advances, the convergence of automation, AI, and human expertise creates a powerful platform for promoting sustainability. The possibilities for reducing the industrial sector’s environmental impact are vast, from energy-efficient automation technologies to AI-driven resource optimization and the circular economy. However, achieving these goals requires technical innovation and a concerted organizational effort to align, integrate, and scale sustainable practices. Industries adopting these technologies will be better equipped to meet their sustainability goals and build a more resilient and resource-efficient future.

Sources

[1] https://www.subaru.co.jp/en/csr/environment/waterresources.html
[2] https://www.grundfos.com/solutions/support/takeback

Using Technology for Resilience

Wed, 30 Oct 2024 17:55:12 GMT

(Source: Suriyo / stock.adobe.com; generated with AI)

From a business continuity perspective, improved resilience is one of the most significant impacts of the Industry 5.0 movement. In this context, resilience refers to an organization's capacity to endure and adjust to shocks, disturbances, and variability that impact its normal operations.

Generally, resilience has two key aspects. The first is the ability to withstand disruptive situations like pandemics, wars, cyberattacks, and natural disasters for as long as they last without experiencing significant negative impacts. The second is the ability to adapt to the disruption and emerge better after the situation is resolved.

In this blog, we examine some fundamental Industry 5.0 technologies that support new paradigms, such as predictive maintenance, adaptive manufacturing, and digital twins, and how they contribute to a more resilient manufacturing sector.

Predictive Maintenance

Predictive maintenance is an approach to maintaining industrial machinery that monitors and analyzes its condition and performance data to predict when maintenance will be required.

This process relies on Internet of Things (IoT) sensors installed on industrial equipment to collect real-time data on parameters like vibration, temperature, and pressure. These data are fed into artificial intelligence (AI) and machine learning (ML) models trained to recognize normal operating patterns. By continuously analyzing these data, the models can identify anomalies that may indicate an impending failure and accurately determine the best time for maintenance.

For example, increased vibration levels in a motor or bearing could signal the need for lubrication or replacement weeks or months before it breaks down. Here, the AI system raises an alert, allowing maintenance to be scheduled at the optimal time—after the current production cycle but before the component fails. This paradigm leads to significantly lower operating costs than the traditional corrective or preventive maintenance strategies from the past.

In fact, this predictive approach has several advantages over traditional preventive maintenance schedules, which are based on usage hours or calendar intervals. Predictive maintenance minimizes unplanned downtime by avoiding unexpected breakdowns. Moreover, equipment lifetime is extended by repairing or replacing components only when truly required. Finally, overall maintenance costs are reduced through optimized planning and inventory management.

Naturally, predictive maintenance is a major contributor to resilience in manufacturing operations. By avoiding disruptive breakdowns, it allows production to continue uninterrupted and optimizes overall equipment effectiveness (OEE).

Furthermore, this approach’s AI and analytics capabilities also have potential to improve processes, maximize product quality, and reduce waste and energy consumption. It can also provide resilience during supply chain issues or demand fluctuations. Such a holistic approach enhances the organization’s ability to withstand shocks, recover quickly from any disruption, and emerge in an improved state.

Adaptive Manufacturing

Adaptive manufacturing refers to an organization’s ability to adapt its manufacturing processes and operations rapidly in response to disruptions or changing demands. This technique is enabled through a confluence of AI, automation, and flexible production systems.

At its core, adaptive manufacturing is about having flexible and reconfigurable production lines that can be reprogrammed to change the product mix or production volumes with minimal changeover time and costs. AI is key in optimizing these reconfigurations based on live data regarding forecasts, inventory, and supply chain constraints.

For example, if a particular product model experiences a surge in demand, the AI system can adjust the manufacturing schedule, reallocate resources, and even modify the product design for easier production—all to prioritize the high-demand item. Conversely, adaptive manufacturing can switch quickly to an alternative design using available components if certain parts face a supply crunch.

This responsiveness makes manufacturers more resilient to fluctuations in customer demand and supply chain disruptions resulting from natural disasters, geopolitical events, or pandemics. They can continue fulfilling orders and generating revenue instead of being forced to stop production. Adaptive manufacturing, enabled by AI and automation, enhances resilience by improving quality, reducing inventory costs, and extending product life cycles.

Overall, the ability to adapt manufacturing processes gives organizations the agility to withstand and recover rapidly from internal and external disruptions. They can respond nimbly to changing market conditions, thereby mitigating risks and maintaining business continuity.

Digital Twins

Digital twins are computerized representations of physical assets or systems that use live data to enable simulation, monitoring, and optimization. In the context of manufacturing, digital twins enhance resilience in several ways.

First, digital twins allow organizations to validate changes in their system’s behavior in a risk-free digital environment before introducing changes in the physical world. This “trial without error” approach minimizes disruptions and downtime when introducing new products, processes, or technologies. Companies can experiment, identify potential issues, and find optimal solutions before real-world deployment.

Second, digital twins leverage machine learning and data analytics to monitor the performance and health of physical assets and processes continuously. Any deviations from the ideal operating parameters are quickly detected, enabling predictive maintenance and timely interventions before failures occur. The result is minimized downtime and extended operational life of equipment while operating at optimal performance.

Moreover, the insights from digital twin simulations allow organizations to optimize their overall manufacturing operations for improved efficiency, quality, and sustainability. AI-driven “what if” analyses can identify bottlenecks, wastages, and opportunities for process improvements. Such insights allow operators to reduce costs and environmental impact while boosting productivity.

Finally, digital twins play a role in supply chain resilience. By integrating data from suppliers, logistics providers, and other partners, companies can model and stress test their entire value chain. This end-to-end visibility enables risk mitigation strategies like diversifying suppliers, rerouting shipments, or building redundancy.

Conclusion

The goal of any business is to be resilient, adapt to disruptions, and emerge better as a result. With Industry 5.0, technologies like IoT sensors and AI enable new paradigms, including predictive maintenance, adaptive manufacturing, and digital twins.

With these advances, companies can become more resilient to inevitable market volatility and unexpected disruptions, leading to a more secure and reliable global industrial sector.

Safety in Industry 5.0

Wed, 23 Oct 2024 22:34:21 GMT

(Source: HadK / stock.adobe.com; generated with AI)

Without question, workplace injuries and illnesses significantly affect a company’s bottom line. In fact, according to a 2021 OSHA report,^[1] estimates show companies pay almost US$1 billion per week for direct workers’ compensation costs. Beyond these costs, company's face indirect costs for workplace injuries and illnesses, including lost productivity that might result from training new employees or repairing damaged equipment.

When we discuss the benefits of Industry 5.0 in the modern factory, we must remember the most important element—the people. This new manufacturing era, with its advanced technologies, is not just about improved operational efficiency and productivity. It’s also about creating a safer and more efficient work environment, with the human worker at the center of it all.

In this blog, we’ll examine operational and functional safety in Industry 5.0 and how new technologies like collaborative robots (cobots) and drones, as well as an overall human-centric approach, are creating a safer environment for workers.

Operational vs. Functional Safety

As a whole, Industry 5.0 is unique in emphasizing a human-centric approach to safety. In the context of the modern factory, safety is defined as either operational or functional.

Functional safety involves designing and integrating safety systems that prevent accidents and system failures. These safety systems include establishing processes that mitigate potential faults and failures, incorporating equipment safety technologies, and creating safety designs that comply with industry regulations. Wearable sensors are one example of Industry 5.0 safety systems. The wearable sensors monitor workers’ compliance with safety protocols, ensuring they wear the necessary protective gear and avoid restricted areas. Sensors embedded in gloves and helmets, for instance, can detect whether workers are wearing the required safety equipment and restrict access to hazardous areas if they are not.

Operational safety focuses on the day-to-day implementation and maintenance of these safety measures. It involves training personnel, maintaining equipment, and ensuring all safety protocols are followed during routine operations. In a manufacturing plant, operational safety includes ensuring workers follow safety guidelines and maintaining equipment properly to prevent accidents.

An example of operational safety in Industry 5.0 could be an artificial intelligence (AI)–based coaching system that can provide continuous feedback to workers on improving their safety practices. Such a system can analyze workers’ movements and suggest adjustments to their postures or actions to reduce the risk of injury.

New Technologies and Safety

Industry 5.0 is marked by the integration of new factory technologies, many of which are designed to enhance workplace safety.

Cobots, for instance, are meant to operate cohesively with humans, offloading intense and repetitive tasks from the worker. Such a technology reduces the risk of musculoskeletal injuries and allows workers to focus on more complex and creative tasks. To achieve this, cobots are equipped with specialized force and proximity sensors that allow them to perceive and interact with their environment—including the humans—to better prevent collisions and reduce risks to human workers.

Similarly, a drone is another technology that improves safety in Industry 5.0. Drones are valuable in this context because they can reduce the need for human exposure to dangerous environments, including confined spaces, high elevations, or areas with toxic gases. Equipping drones with sensors, such as cameras, allows them to gather real-time data and quickly identify and resolve potential issues.

Finally, the exoskeleton is an Industry 5.0 innovation that assists workers in lifting heavy loads, thereby reducing the risk of injury. These wearable devices provide mechanical support, allowing workers to perform physically demanding tasks easily. Exoskeletons contribute to a healthier and safer workplace by minimizing the physical strain on workers.

The Role of AI and IoT

AI and the Internet of Things (IoT) fundamentally transform the concept of safety in the modern factory. Industry 5.0 leverages AI and advanced sensors to predict and prevent equipment failures. These systems monitor parameters such as temperature, pressure, and vibration to detect potential issues before they lead to catastrophic events. Predictive maintenance uses sensor data to identify patterns and predict when maintenance is needed. Such a proactive approach minimizes downtime and enhances overall safety by addressing problems and potential accidents before they escalate.

Similar to a high-speed conduit, IoT connects all devices and systems within a plant, enabling real-time monitoring of all operational parameters and quick response to anomalies. For instance, IoT-enabled sensors can monitor the environmental conditions within a plant continuously, such as humidity, temperature, and gas levels, and provide immediate alerts if any parameter deviates from safe limits. Instant feedback allows for swift corrective actions, minimizing the potential for developing hazardous situations.

With an interconnected approach, IoT allows for comprehensive safety management, which reduces the likelihood of accidents and improves overall operational efficiency. For example, if a sensor detects a temperature spike in equipment, the IoT system can shut down the equipment automatically and alert maintenance personnel.

Collectively, AI and IoT enable a holistic approach to safety management. With all safety systems and devices interconnected, environment, health, and safety (EHS) managers can access a centralized platform to monitor and manage safety protocols. A comprehensive view combined with the analytical capabilities of AI allow for better decision-making and more effective coordination of safety efforts across the entire plant.

Conclusion

Industry 5.0 transforms how we view operational and functional safety in the factory. By integrating advanced technologies, AI, and IoT, as well as focusing on human factors, Industry 5.0 creates safer, more efficient, and more productive manufacturing environments.

Sources

[1] https://www.osha.gov/businesscase/costs

AR, Robotics, Exoskeletons, and Drones

Mon, 26 Aug 2024 14:53:33 GMT

New Technologies in Industrial Automation

(Source: Gorodenkoff/stock.adobe.com)

One of the defining features of Industry 5.0 is the widespread adoption of advanced technologies that create a more efficient, sustainable, and effective manufacturing cycle. Some of the technologies with the greatest potential to impact the industrial sector include augmented reality (AR), robotics, exoskeletons, and drones.

While each of these technologies brings different distinct benefits, their individual integration and synergistic overlap—and interaction with the operator—are expected to be the backbone of Industry 5.0. This article discusses how each of these technologies will be employed in the next wave of industrial automation and how the manufacturing sector stands to benefit from such advances.

Augmented Reality

AR is considered one of the key technological advancements driving Industry 5.0. AR technology uses a confluence of advanced hardware, optics, processing, and software to merge digital elements with the physical environment. This technology enhances the capabilities of factory operators and improves industrial processes through real-time data collection and interactive feedback systems.

One of AR's primary impacts in Industry 5.0 is in the domain of quality control, as it stands to transform inspection processes. Traditionally reliant on human observation, these processes are being revolutionized by AR’s ability to provide enhanced visual inspections. For instance, AR can overlay digital markers on physical objects to indicate tolerance levels, defects, or assembly instructions. This accelerates the inspection of incoming materials and ensures higher accuracy by highlighting deviations from expected standards.

In assembly operations or maintenance procedures, AR can guide operators through complex manufacturing tasks by displaying step-by-step instructions directly in their field of vision. This results in fewer errors, streamlined training, and significantly increased operational efficiency.

However, integrating AR into industrial operations is challenging. The main hurdle lies in developing robust software that can integrate seamlessly with AR hardware to process and display complex industrial data effectively. Software must be intuitive and tailored to specific industrial applications to ensure that the information presented is relevant and actionable.

Robotics

Since its inception in the 1960s, robotics has been a fundamental automation tool in manufacturing. Now, with the rise of Industry 5.0, robotics is positioned to adopt new roles.

Today, robots are equipped with specialized end-of-arm tools such as grippers, suction cups, cutters, and tools for soldering, painting, or applying adhesives. These modern robots can perform complex assembly tasks with high precision, creating avenues for automation that were not previously possible.

Robots have also diversified in size and capability, which is matched by advancements in actuation technology. While large robots often rely on hydraulic systems, mid-sized models may use pneumatic actuators, and most modern robots employ electric actuators. In the context of Industry 5.0, electric actuators are particularly beneficial because they easily integrate with the Internet of Things (IoT) to enhance connectivity, human transparency, and control.

The most transformative aspect of robotics in Industry 5.0 is the rise of collaborative robots, commonly known as cobots. Cobots are designed to work alongside human operators by sharing tasks and responsibilities (Figure 1). For example, cobots can handle heavy or repetitive tasks on manufacturing lines, allowing humans to focus on fine-tuning and quality assurance.

Figure 1: Cobots align with Industry 5.0’s human-centric approach to automation by enhancing productivity through collaboration. (Source: gumpapa/stock.adobe.com)

As we advance further into Industry 5.0, the interaction between humans and robots will likely evolve into more integrated and intelligent systems. Robots will share tasks as well as sensory inputs and decision-making processes, blurring the lines between human and machine capabilities. Ultimately, this synergy will enhance efficiency, reduce human error, and foster environments where human-robot interactions optimize creative and analytical tasks.

Exoskeletons

With respect to human safety and performance, exoskeleton technology is poised to be transformative for Industry 5.0. Exoskeletons are wearable devices that support and augment the user’s strength and endurance. Active exoskeletons, equipped with motors and battery packs, assist operators by providing additional strength to perform tasks that exceed typical human capacities. This capability allows workers to handle heavier loads or perform repetitive tasks without fatigue, thereby increasing operational efficiency and reducing the risk of injury.

One significant benefit of exoskeletons in Industry 5.0 is their potential to democratize physically demanding jobs. By augmenting human strength, exoskeletons can make physically challenging tasks accessible to a broader range of people. With greater job inclusivity, industries can tackle labor shortages and increase workforce diversity.

However, several challenges must be addressed to realize the full potential of exoskeletons. The comfort and ergonomics of these devices are crucial. Current exoskeleton models often require considerable adjustment to fit comfortably and allow for natural movement. Improvements in design and the use of lightweight materials like carbon fiber stand to make exoskeletons more user-friendly and less cumbersome.

Another hurdle is the technology’s responsiveness and intuitive control. Operators need exoskeletons that can interpret their movements and intentions accurately without delay. Advanced sensors and artificial intelligence algorithms are required to integrate human intent with mechanical action, ensuring that the exoskeleton acts as a true extension of the human body.

Drones

In Industry 5.0, drones are expected to significantly impact inventory management, safety, and quality control. Traditional methods of inventory management are struggling to keep pace with the high demands of modern warehouses, which may contain hundreds of boxes and crates in difficult-to-reach locations. Instead, drones equipped with advanced sensors and imaging capabilities that can identify smart tags and QR codes can offer a new means of navigating these vast warehouse spaces (Figure 2). These systems can efficiently track and manage inventory location and status in real time. Compared to traditional inventory management, drones reduce the need for human intervention in unsafe zones and allow for faster, more detailed, and more accurate data collection.

Figure 2: Drone technology enables manufacturers to perform quality control inspections and inventory management with efficiency never seen before. (Source: Creative_Bringer/stock.adobe.com, generated with AI)

For the automotive industry, drones can also play a role in enhancing quality control by conducting aerial inspections of vehicles during various production stages. For instance, drones equipped with high-resolution cameras and sensors can inspect car bodies for defects such as dents, scratches, or inconsistencies in paint applications.

The real-time data collected by drones can be coupled with an advanced quality control system, in which advanced algorithms analyze the images to detect irregularities or imperfections. This process increases the accuracy and safety of inspections by eliminating oversights while significantly accelerating the quality control process.

Conclusion

As Industry 5.0 gains momentum, AR, robotics, exoskeletons, and drones are poised to be mainstays. By collectively enhancing production efficiency, decreasing human error, and creating a safer environment for workers, each of these technologies is well aligned with Industry 5.0's goals and constitutes the key pillars of the future of smart factories.

The Evolution from Industry 4.0 to Industry 5.0

Thu, 06 Jun 2024 05:01:00 GMT

(Source: KPs Photography / stock.adobe.com)

The manufacturing industry has always been at the forefront of technological innovations. From the advent of the steam engine in the 1700s to the invention of the assembly line by Henry Ford, manufacturing has been one of society’s greatest drivers of change.

Today, the manufacturing industry is once again experiencing a series of unprecedented changes.

The proliferation of robotics, advanced sensors, device connectivity, and advanced analytics has led to modern Industry 4.0 manufacturing. Now, the field is poised for yet another change, with a transition into the world of Industry 5.0.

This blog discusses the differences between Industry 4.0 and Industry 5.0, the challenges facing the evolution from Industry 4.0 to Industry 5.0, and some ways to make the transition as seamless as possible.

Industry 4.0 Versus Industry 5.0

At present, most modern factories are classified as “Industry 4.0,” and are commonly referred to as “Smart Factories.”

Industry 4.0 is marked by a significant shift toward a more interconnected and intelligent manufacturing environment, leveraging advancements such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and edge computing. This era introduces the capability to gather, analyze, and use vast amounts of data in real time, which enhances decision-making processes, predictive maintenance, and overall operational efficiency. Ultimately, the result is a comprehensive advancement over Industry 3.0 to create more agile, efficient, and responsive manufacturing systems. However, Industry 4.0 remains focused mainly on the manufacturing side of things, answering questions about how to make more products faster, more consistently, of better quality, and at lower cost.

Industry 5.0 expands the technological advancements of Industry 4.0 and augments them by considering human factors. It seeks to redefine roles within manufacturing and beyond—encompassing supply chains and entire operational landscapes—to create a more cohesive, adaptive, human-centric, and sustainable industrial environment.

This paradigm shift moves beyond viewing machines as mere tools for productivity and advocates for a collaborative synergy in which technology enhances human capabilities, creativity, and decision-making processes. The fundamental aim is to achieve a balanced symbiosis between advanced technological systems and the unique insights and values humans bring to the table, ensuring that industrial progress supports both efficiency and the well-being of society at-large. Within this industrial revolution, a significant focus lies on fostering seamless communication and interactions between humans and machines.

Drivers of the Evolution

While the differences between Industry 4.0 and Industry 5.0 are clear and defined, understanding the larger societal and geopolitical factors driving this evolution is essential.

One major driver of Industry 5.0 is resilience in the face of global challenges.

Starting in 2020, the COVID-19 pandemic and subsequent disruptions exposed vulnerabilities within global manufacturing networks. In the face of these challenges, Industry 5.0 emphasizes resilience and flexibility, aiming to build systems that can adapt and recover from unforeseen events more efficiently.

In a similar vein, the COVID-19 pandemic heightened consciousness surrounding worker health and safety. Industry 5.0 seeks to leverage the technological advancements of Industry 4.0—namely, sensors and vision systems—to mitigate risks, create safer work environments, and minimize accidents.

Naturally, another significant driver of this evolution is the demand for more innovative, sustainable, and reliable products that can reach the market faster. But how will Industry 5.0 enable these changes?

Industry 5.0 will leverage the integration of digital twins with generative AI, which simplifies the prototyping of new concepts. This integration accelerates the development process and enhances the quality of the final product by allowing for the evaluation of multiple design options in terms of cost, performance, quality, and durability before a product reaches the market. Furthermore, Industry 5.0 has a major focus on sustainability, with products designed to be more recyclable and less polluting. Thus, driven by technological advancements and changing consumer demands, Industry 5.0 holds the promise of delivering innovative, high-quality, and sustainable products more efficiently.

Hurdles for the Evolution

Despite the clear benefits and reasons for moving toward Industry 5.0, the evolution is not without its challenges. Paradoxically, the still ongoing adoption of Industry 4.0 is a major hurdle to adopting Industry 5.0.

Between the advent of Industry 3.0 and the emergence of Industry 4.0, nearly four decades elapsed. Remarkably, in just one decade, we transitioned from Industry 4.0 to the dawn of Industry 5.0, a testament to the rapid pace of technological advancement and its transformative impact on manufacturing and production.

Consequently, now we are witnessing an overlap in which many companies are still in proof-of-concept evaluations of Industry 4.0 while others are leveraging Industry 5.0 already. Currently, while large corporations have made substantial investments in Industry 4.0, the adoption rate among smaller manufacturing sites and processes through full-scale digitalization projects remains relatively low.

Major barriers to adopting of Industry 4.0 technologies include the high capital expenditures needed to purchase new equipment, as well as new skill sets and expertise, along with a lack of trust in modern paradigms such as cloud computing and reliance on data security. Until the industry at large fully embraces Industry 4.0, Industry 5.0 will take some time to reach widespread adoption.

Another challenge of adopting Industry 5.0 is that it represents an inherently different mindset and approach to manufacturing.

Unlike previous evolutions (e.g., Industry 3.0 to Industry 4.0), the Industry 4.0 to Industry 5.0 evolution is not so much a challenge of technological advancement but more so a challenge of mindset advancement. Integrating new digitalization projects requires strong alignment all the way from the chief executive officer to the operator to the manufacturing floor. The organization needs to have a clear vision of what its version of Industry 5.0 will look like and then deploy this idea at all levels. This calls for new processes and ways of operating, including more delegation from the operators to those on the floor.

Conclusion

Like the many industrial revolutions in human history, modern manufacturing is currently on the precipice of another remarkable evolution. The transition from Industry 4.0 to Industry 5.0 marks a significant change in the manufacturing world.

Achieving this transition requires an industry-wide mindset shift in how our factories operate, how workers interact with machinery, and how the whole ecosystem can converge. Once this happens, we can enter the era of Industry 5.0, enabling greater sustainability and resilience, increased worker safety, and a more robust supply chain.