PLC Archives | Max Autoland

Software-Defined Manufacturing Reshapes Industrial Automation and Semiconductor Strategy

11 Sep

Software-Defined Manufacturing Moves Toward Industrial Deployment

Software-defined manufacturing (SDM) is moving beyond technology discussions and into early industrial applications. The concept changes how manufacturers design, deploy, and maintain industrial automation systems.Traditional automation closely connects software with dedicated hardware. PLC programs typically run on specific controllers. DCS applications also depend on qualified hardware platforms.However, SDM introduces a different architecture. It separates automation applications from dedicated devices and runs them on more flexible computing infrastructure.Virtual PLCs, supervisory control systems, analytics, and other applications can operate in containers or virtualized environments. These workloads can run on industrial edge servers or selected on-premise infrastructure.As a result, factory automation increasingly treats control software as programmable infrastructure instead of fixed equipment.

Virtual PLC Technology Changes the Automation Architecture

Virtual PLC technology represents one of the most visible developments within software-defined industrial automation.A conventional PLC combines processing hardware, firmware, communication interfaces, and control applications within a dedicated device. Engineers often validate these elements as a single platform.A virtual PLC changes this model. The control runtime can operate independently from traditional dedicated PLC hardware.For example, an industrial edge server may host several virtualized control applications. These applications can support different machines, production cells, or auxiliary systems.Therefore, manufacturers can manage software resources separately from physical computing resources.This approach does not mean that dedicated PLC hardware will disappear. Instead, industrial automation will likely adopt a mixed architecture.Some control functions will remain inside dedicated PLCs. Others may move to industrial PCs, edge servers, or virtualized platforms.

Industrial Automation Will Continue to Depend on Physical Devices

Software-defined manufacturing does not remove the physical layer from industrial control systems.Sensors still collect process information. Actuators still execute commands. Drives still control motors and mechanical equipment.In addition, safety controllers must continue to meet strict functional safety requirements.Standards such as IEC 61131-3 remain important for PLC programming concepts. IEC 61508 and IEC 62061 also influence functional safety architectures.Therefore, SDM should not be viewed as software replacing industrial hardware.Instead, SDM changes where applications execute and how engineers manage automation resources.From practical industrial experience, the physical process often determines the final architecture. Motion control, high-speed synchronization, and safety applications may require dedicated hardware.However, supervisory control, data processing, visualization, and selected PLC workloads offer more flexibility.

Edge Computing Becomes a Key Layer for Software-Defined Control Systems

Real-time industrial control cannot simply move to public cloud infrastructure.Network latency, deterministic communication, cybersecurity, and system availability remain important technical concerns.Therefore, edge computing plays a central role in the SDM architecture.Industrial edge servers can operate close to production equipment. This arrangement reduces communication delays while supporting centralized software management.Moreover, manufacturers can use edge infrastructure for several workloads.These workloads may include:

Virtual PLC applications
SCADA and supervisory functions
Industrial analytics
AI inference
Machine monitoring
Data collection
Digital twin applications
Protocol conversion

Cloud platforms can then support fleet management, simulation, historical analytics, and large-scale data processing.This distributed model creates a clearer division between real-time factory automation and enterprise computing.

PLC and DCS Hardware Will Remain Important

Software-defined manufacturing will not immediately replace traditional PLC and DCS platforms.Major automation suppliers have spent decades developing dedicated control systems for demanding industrial environments.Siemens, ABB, Schneider Electric, Rockwell Automation, and other suppliers continue to support both traditional and software-centric automation architectures.Dedicated controllers still provide several advantages.They offer predictable hardware configurations. They also support long product lifecycles and established engineering workflows.In addition, many industrial sites already operate large installed bases of PLC and DCS equipment.Replacing these systems creates technical and financial risks.Therefore, most factories will probably adopt SDM gradually.The first projects will likely focus on applications that provide clear operational benefits.

Factory Automation Is Moving Toward Heterogeneous Computing

The most important change may involve the underlying compute architecture.Traditional industrial automation relies heavily on distributed dedicated devices.A factory may contain hundreds or thousands of PLCs, controllers, drives, gateways, and embedded processors.Software-defined manufacturing introduces greater compute consolidation.However, consolidation does not mean complete centralization.Future industrial automation will likely use heterogeneous computing environments.These environments may combine:

PLCs
Industrial microcontrollers
Industrial CPUs
GPUs
FPGAs
Edge servers
AI accelerators
Safety controllers
Motion controllers

Each technology will handle workloads that match its technical characteristics.For example, FPGAs may support deterministic high-speed processing. MCUs may continue handling embedded machine functions.Meanwhile, edge processors may host virtual PLCs and data applications.As a result, semiconductor value may shift toward integrated computing platforms.

Cybersecurity Becomes More Important in Software-Defined Factories

Traditional automation often isolates systems through dedicated hardware and network segmentation.Software-defined industrial automation increases software connectivity.Therefore, cybersecurity becomes an architectural requirement rather than an additional feature.Industrial systems increasingly connect PLC applications, edge servers, engineering tools, and enterprise platforms.This connectivity can improve operational visibility.However, it also increases the number of possible attack paths.Standards such as IEC 62443 provide important guidance for industrial cybersecurity.Manufacturers should apply security controls throughout the automation lifecycle.This includes secure development, identity management, network segmentation, software updates, and vulnerability management.In my view, cybersecurity may become one of the strongest factors influencing SDM adoption.Manufacturers will only virtualize critical control workloads when they trust the underlying architecture.

Why Manufacturers Are Exploring Software-Defined Manufacturing

Manufacturers face increasing pressure to adapt production systems faster.Supply chain disruptions can change production requirements. Workforce shortages can also increase automation demands.In addition, sustainability targets require manufacturers to monitor energy and resource consumption more closely.Traditional hardware-bound automation can make these changes difficult.Hardware replacement often requires engineering work, testing, downtime, and system validation.Software-defined architectures can reduce some of these limitations.For example, engineers may update selected applications without replacing the underlying compute hardware.Virtual commissioning can also support earlier software testing.Therefore, manufacturers can potentially shorten engineering cycles.However, the business case must remain application-specific.SDM does not automatically reduce costs in every factory.

Virtual Commissioning Offers an Early SDM Application

Virtual commissioning represents one of the most practical entry points for software-defined manufacturing.Engineers can test PLC logic and control systems before physical equipment becomes available.This approach can identify programming errors earlier.Machine builders can also simulate communication between machines and production systems.Moreover, digital models can support engineering changes before installation.In practical projects, commissioning delays often result from unexpected interactions between equipment, software, and field devices.Virtual commissioning cannot remove every problem.However, it can reduce risks before on-site startup.Therefore, manufacturers can use simulation and digital twins as early steps toward broader SDM adoption.

DCS and Supervisory Applications Can Benefit from Software Decoupling

DCS and supervisory control applications also provide potential opportunities.Many supervisory functions do not require the same execution characteristics as high-speed machine control.These functions may include visualization, alarm processing, historical data management, and production reporting.Therefore, manufacturers can move selected workloads toward centralized or virtualized infrastructure.This approach may simplify system maintenance across multiple production areas.For example, a large facility may manage several supervisory applications through standardized edge infrastructure.However, process control engineers must still protect system availability.Critical process applications require careful redundancy and validation.As a result, manufacturers should separate software flexibility from operational risk.

Manufacturers Should Avoid a Complete Replacement Strategy

Manufacturers should not treat software-defined manufacturing as a complete replacement program.Most industrial sites contain equipment with long operating lifecycles.A factory may operate PLC, DCS, and control systems for 15 years or longer.Therefore, gradual migration offers a more practical strategy.Manufacturers should first identify workloads that benefit from decoupling.Good starting points may include:

Virtual commissioning
Industrial data platforms
Analytics applications
Supervisory control
Edge computing
AI applications
Selected PLC workloads

This approach allows engineering teams to develop experience.It also reduces operational risks.In addition, companies can evaluate performance before expanding deployment.

Machine Builders Can Develop More Flexible Automation Platforms

Machine builders may also benefit from SDM.Traditional machine designs often depend on specific controller hardware.This dependency can create supply chain risks and redesign requirements.Software decoupling may provide more hardware flexibility.Machine builders could potentially validate applications across multiple qualified compute platforms.However, compatibility testing remains important.Industrial customers expect long-term support and predictable machine behavior.Therefore, machine builders must balance flexibility with engineering discipline.A portable control application still requires validated hardware, network behavior, and real-time performance.

Semiconductor Suppliers Should Focus on Complete Industrial Platforms

Semiconductor suppliers should avoid viewing SDM as a simple processor upgrade cycle.Industrial customers need complete platforms.These platforms should combine computing, connectivity, security, and lifecycle support.Important capabilities may include:

Real-time processing
Hardware virtualization
Industrial Ethernet support
Functional safety features
Secure boot
Hardware security
AI acceleration
Long-term availability
Industrial temperature support

In addition, software ecosystems will become increasingly important.A powerful processor provides limited value without compatible development tools and industrial software.Therefore, semiconductor companies should cooperate closely with automation suppliers and industrial software developers.

Industrial Automation Value May Shift from Devices to Software Platforms

Software-defined manufacturing may gradually change where value exists within factory automation.Dedicated hardware will remain important.However, software platforms may capture a larger share of system value.Engineering tools, application portability, cybersecurity, orchestration, and lifecycle management could become major differentiators.This shift resembles developments in other technology sectors.Standardized hardware can support multiple software services.However, industrial automation introduces additional complexity.Factories require deterministic behavior, long lifecycles, and strong operational discipline.Therefore, the industrial market will not simply copy the IT industry.Instead, industrial automation will develop its own software-defined architecture.

Application Scenario: Virtualized Production Cell Control

Consider a manufacturer operating several automated production cells.Each cell currently uses dedicated PLC hardware for control and monitoring.The manufacturer introduces an industrial edge server for selected applications.The server hosts virtual PLC instances for non-critical auxiliary processes.It also runs machine monitoring and local analytics.Dedicated PLCs continue controlling high-speed motion and safety functions.Meanwhile, engineers manage virtualized applications through centralized software tools.This hybrid architecture provides an important advantage.The manufacturer gains operational experience without replacing proven control systems.Over time, the company can evaluate additional workloads.This scenario reflects a realistic path toward software-defined factory automation.

Application Scenario: Multi-Site Factory Automation Management

A global manufacturer operates several factories with different automation platforms.The company wants more consistent application management.An SDM architecture can standardize selected software layers.For example, each site can deploy common edge infrastructure.Central teams can manage application versions and cybersecurity policies.Local facilities can continue operating site-specific PLC and DCS hardware.Therefore, the company can improve software consistency without forcing immediate hardware replacement.This model may prove especially useful for manufacturers with distributed production networks.

Author's View: SDM Will Change Industrial Automation Gradually

Software-defined manufacturing will not transform factories as quickly as enterprise IT changed data centers.Industrial environments operate under different conditions.Safety requirements remain strict. Equipment lifecycles remain long.In addition, manufacturers carefully evaluate operational risks.However, gradual change should not be confused with limited impact.The industrial automation architecture is already becoming more software-centric.Edge computing, AI, virtualization, industrial Ethernet, and digital engineering continue supporting this direction.In my view, the most successful SDM projects will follow a hybrid strategy.They will retain dedicated hardware where physics and safety require it.At the same time, they will virtualize workloads where software flexibility creates measurable value.

Software-Defined Manufacturing Requires Action Today

Manufacturers should begin preparing their technology architecture now.They should identify applications suitable for virtualization and edge deployment.In addition, engineering teams should develop skills in real-time computing, virtualization, networking, and industrial cybersecurity.Semiconductor companies should also review their industrial strategies.Future demand may increasingly favor heterogeneous computing platforms.These platforms will need to support control systems, connectivity, security, and AI.Therefore, industrial automation suppliers must look beyond standalone controllers.The next generation of factory automation will likely combine dedicated devices with programmable computing infrastructure.Software-defined manufacturing will not eliminate PLCs, DCS platforms, or industrial controllers.Instead, it will redefine how these technologies work together.The companies that build technical capabilities early may gain greater flexibility as industrial deployment expands.

Categories: News, Uncategorized

Schneider Electric Names Alex Richards to Lead Industrial Automation in UK and Ireland

09 Sep

Alex Richards Takes Leadership of Industrial Automation Business

Schneider Electric has appointed Alex Richards as Business Vice President for Industrial & Process Automation in the UK and Ireland.Richards will lead the company's regional industrial automation strategy and customer development activities. His responsibilities will cover automation, electrification, robotics, artificial intelligence, and digital technologies.The appointment comes as manufacturers increase investment in connected industrial automation and software-driven control systems.Schneider Electric aims to help industrial customers improve productivity, operational resilience, and energy efficiency.

Focus on Industrial Automation and Digital Transformation

Industrial automation continues to change rapidly across manufacturing, energy, infrastructure, and process industries.Today, manufacturers increasingly connect PLC, DCS, SCADA, robotics, and industrial software within integrated control systems.Therefore, leadership experience across automation and digital services has become increasingly important for technology suppliers.Richards will support customers as they modernise existing facilities and develop new factory automation strategies.The role also reflects growing demand for integrated automation architectures across the UK and Ireland.

Extensive Experience Across Schneider Electric Operations

Alex Richards joined Schneider Electric in 2008 and has held several leadership positions within the company.His previous responsibilities included Energy and Sustainability Services, Global Field Services, and Digital Services.In addition, he worked with Segments and Strategic Accounts across different industrial markets.Most recently, Richards served as Vice President for Segments and Strategic Accounts within Europe Operations.Before that position, he led Schneider Electric's Mining, Minerals and Metals activities across the EMEA region.This background provides experience across industrial services, strategic customers, digital technologies, and process industries.

Industrial Automation Requires Broader Technical Integration

Modern industrial automation projects rarely involve a single PLC or standalone control system.Instead, manufacturers increasingly combine PLC systems, DCS platforms, industrial networks, robotics, edge computing, and data analytics.Moreover, electrification and energy management now influence many automation investment decisions.Schneider Electric operates across several of these technology areas, including industrial automation and energy management.This broader technology portfolio may help customers coordinate production systems with energy and sustainability objectives.However, successful integration still requires careful engineering, cybersecurity planning, and lifecycle management.

Software-Driven Control Systems Gain Momentum

The industrial automation market increasingly focuses on software-defined and digitally connected operations.Traditional control systems remain important for deterministic and safety-critical industrial applications.However, manufacturers now expect greater interoperability between automation hardware and industrial software.For example, modern factory automation projects often connect PLC controllers with MES, SCADA, cloud platforms, and analytics systems.Process industries may also integrate DCS platforms with asset management and predictive maintenance applications.As a result, automation suppliers must provide both hardware expertise and software integration capabilities.

PLC and DCS Technologies Continue to Support Industrial Operations

PLC technology remains widely used in discrete manufacturing and factory automation applications.Typical applications include packaging, automotive production, logistics, and machine control.Meanwhile, DCS platforms remain important within continuous and batch process industries.Oil and gas, chemicals, power generation, mining, and pharmaceuticals often require integrated process control systems.In practice, many large facilities operate both PLC and DCS technologies.Therefore, industrial automation strategies increasingly focus on interoperability rather than replacing every existing control system.This approach can reduce engineering risks and support phased modernisation programmes.

Automation, Electrification and Decarbonisation Become Connected Strategies

Manufacturers face increasing pressure to reduce energy consumption and carbon emissions.At the same time, they must maintain production availability and product quality.Industrial automation can support these objectives through improved process visibility and control.For example, energy monitoring can identify inefficient motors, pumps, compressors, and production equipment.Advanced control strategies can also reduce process variability and unnecessary energy consumption.Moreover, connected control systems can provide operators with better production and energy data.However, automation alone does not guarantee lower emissions.Companies must combine automation investments with engineering improvements, operational discipline, and measurable energy management programmes.

Alex Richards Highlights Industrial Transformation Opportunities

Richards described the current industrial period as an important convergence of automation, digitalisation, and electrification.He believes these technologies can improve productivity, operational resilience, and long-term sustainability.His new role will focus on working with industrial customers and technology partners across the UK and Ireland.The objective involves turning technology investments into practical operational improvements.This approach remains important for industrial automation projects.In my experience, successful automation programmes depend on operational results rather than technology demonstrations alone.Customers usually measure success through availability, production capacity, energy consumption, maintenance costs, and product quality.Therefore, automation suppliers must understand both control technology and industrial operations.

Supporting Connected Factory Automation Projects

Factory automation increasingly depends on communication between machines, control systems, and enterprise software.Industrial Ethernet networks now connect PLCs, remote I/O systems, drives, sensors, and supervisory applications.In addition, industrial cybersecurity has become a major requirement for connected production environments.Companies must therefore consider network architecture during every automation modernisation project.A typical factory automation programme may include several technical layers.These layers can include sensors, field devices, PLC controllers, industrial networks, SCADA systems, and MES software.Higher-level analytics platforms may also process production and energy information.The challenge involves maintaining deterministic control performance while expanding digital connectivity.

Industry Perspective: Integration Will Define Future Automation Projects

From an industrial automation engineering perspective, the strongest technology trend involves system integration.Manufacturers no longer evaluate PLC, DCS, robotics, and energy systems independently.Instead, they increasingly evaluate how these technologies operate together.This trend creates opportunities for established automation suppliers with broad technology portfolios.However, customers should avoid unnecessary platform complexity.A successful automation architecture should remain maintainable throughout its operational lifecycle.Engineering teams should also consider spare parts availability, software compatibility, cybersecurity updates, and technical support.These factors often influence long-term project costs more than initial hardware prices.

David Hall Supports the Leadership Appointment

David Hall, Zone President for UK and Ireland at Schneider Electric, welcomed Richards' appointment.Hall said the leadership change would support automation, digitalisation, and decarbonisation initiatives across the region.The company expects Richards to help customers transform strategic objectives into operational projects.This includes industrial sectors with increasing demand for connected automation technologies.Moreover, regional leadership can help technology suppliers respond more effectively to local industrial requirements.Different industries often require different automation architectures and project strategies.For example, a mining operation has different requirements from a pharmaceutical production facility.Therefore, industry-specific engineering knowledge remains important for automation business development.

Application Scenario: Modernising a Manufacturing Plant

Consider a manufacturing plant operating ageing PLC systems and disconnected production equipment.The facility may experience limited production visibility and increasing maintenance requirements.A phased industrial automation programme could address these challenges.First, engineers can assess existing PLC, control systems, industrial networks, and field devices.Next, the company can upgrade critical controllers and communication infrastructure.The project may then connect production data with SCADA, MES, and maintenance platforms.In addition, energy monitoring can measure consumption across major production assets.Operators can use this information to identify operational losses and equipment inefficiencies.This phased approach can reduce operational disruption and control project risks.

Solution Scenario: Integrated Industrial Automation Architecture

A typical integrated industrial automation solution may include the following technologies:

PLC systems for machine and discrete process control.
DCS platforms for complex continuous process operations.
SCADA software for supervisory monitoring and operator interfaces.
Industrial networks for controller and device communication.
Robotics for repetitive production and material handling tasks.
Digital platforms for production analytics and asset performance monitoring.
Energy management systems for electricity and resource optimisation.
Cybersecurity solutions for protecting industrial control systems.

The exact architecture should depend on the application's safety, availability, and performance requirements.Therefore, companies should begin with operational requirements before selecting automation technologies.

What the Appointment Means for the UK and Ireland Automation Market

Alex Richards takes the leadership position during a period of continued industrial transformation.Manufacturers are investing in digital technologies, connected control systems, robotics, and energy efficiency programmes.Schneider Electric will likely continue promoting integrated approaches across automation and electrification.The long-term market opportunity remains significant.However, industrial customers will continue demanding measurable engineering and financial results.Technology suppliers must therefore demonstrate how automation improves specific operational indicators.These indicators may include production uptime, energy intensity, maintenance efficiency, and process stability.For the UK and Ireland, the next phase of industrial automation will likely focus on practical integration.Companies that successfully connect existing assets with modern digital technologies may achieve the strongest results.

Categories: News

From Industrial Automation to Industrial Autonomy: How AI Is Reshaping Control Systems

07 Sep

Yokogawa Presents the IA2IA Vision

Yokogawa recently presented its vision for the future of industrial operations at ARC Advisory Group's Industry Forum in Bengaluru. Dr. Rajeev Joshi, Deputy General Manager, introduced the IA2IA concept, meaning Industrial Automation to Industrial Autonomy.The presentation explored how industrial companies can introduce AI into existing control environments step by step. Instead of replacing current systems, AI can extend their capabilities.This approach matters because most industrial sites already operate mature PLC, DCS, and control systems. Therefore, manufacturers need an evolutionary path rather than a complete technology replacement.

Industrial AI Requires Higher Performance Standards

AI already supports many office activities, including content generation, data analysis, and project coordination. However, industrial automation environments demand much stricter performance and validation requirements.A poor office recommendation may waste time or require revision. In contrast, an incorrect industrial decision can affect production, equipment availability, product quality, and personnel safety.Therefore, industrial AI must operate within defined technical and operational boundaries. It must also work with established safety systems, control systems, alarms, and operator procedures.In practical industrial applications, engineers cannot treat AI as an independent decision-making tool. They must integrate it carefully with existing automation architectures.

Industrial Automation Remains the Foundation

Modern industrial automation already provides a strong technical foundation for autonomous operations. PLC systems execute machine sequences, while DCS platforms manage continuous and batch processes.PID controllers regulate process variables such as pressure, temperature, flow, and level. Meanwhile, Advanced Process Control can optimize more complex operating conditions.These technologies depend primarily on predefined engineering logic and mathematical models. Operators usually intervene when processes move beyond expected operating conditions.AI introduces another capability to this architecture. It can analyze larger datasets and identify patterns that conventional control logic may not recognize.However, AI should complement established automation instead of replacing proven control strategies.

From Fixed Logic to Adaptive Industrial Control

Traditional factory automation depends heavily on predefined instructions. Engineers configure control strategies according to known process requirements and expected operating conditions.This approach remains effective for stable and predictable applications. However, industrial processes often experience changing feedstock, equipment conditions, production targets, and environmental influences.Industrial autonomy introduces adaptive capabilities into this environment. AI models can analyze changing conditions and recommend or execute approved responses.The operating environment must remain bounded and secure. Therefore, engineers still need to define limits for AI-based decisions.This principle becomes especially important in industries such as oil and gas, chemicals, power generation, and pharmaceuticals.

Understanding the IA2IA Industrial Autonomy Framework

Yokogawa describes the transition through its IA2IA framework, which represents Industrial Automation to Industrial Autonomy.The framework does not suggest that companies should immediately build fully autonomous plants. Instead, organizations can gradually increase autonomous capabilities as their technology and operational readiness improve.This gradual approach reflects real industrial conditions. Most facilities operate equipment from different generations and manufacturers.For example, one site may combine modern DCS technology with older PLC systems, field instruments, historians, and third-party control packages.Therefore, successful industrial autonomy requires integration rather than replacement.

Stage One: Conventional Industrial Automation

The first stage consists of traditional industrial automation. PLC, DCS, SCADA, and other control systems execute programmed strategies.Operators monitor process conditions and respond to abnormal situations. Engineers modify control logic when production requirements change.This structure remains common across global manufacturing industries. It provides predictable control and clear engineering accountability.Moreover, established systems already support many international engineering and functional safety practices.

Stage Two: AI-Assisted Industrial Operations

The next stage introduces AI as a decision-support technology. AI analyzes operational data and provides recommendations to engineers and operators.For example, AI can identify abnormal equipment behavior before a conventional alarm reaches its configured threshold.Predictive maintenance represents a practical application at this stage. Machine learning models can analyze vibration, temperature, pressure, and electrical data.Operators and maintenance engineers still make the final decisions. Therefore, companies can validate AI recommendations without immediately changing control authority.From an engineering perspective, this stage offers one of the lowest-risk entry points for industrial AI.

Stage Three: Collaborative Human and AI Control

As confidence increases, AI can take a more active role in operational optimization. However, human operators continue supervising important decisions.AI may recommend production adjustments, energy optimization strategies, or equipment operating points. Operators can then approve, reject, or modify those recommendations.This collaborative model can improve consistency across shifts. It can also help less experienced operators access knowledge developed by senior engineers.However, organizations must carefully manage knowledge transfer and model validation. Historical plant data does not always represent future operating conditions.Therefore, engineers should continuously monitor AI performance.

Stage Four: Supervised Autonomous Operations

At higher maturity levels, AI can perform selected actions automatically within approved operating limits.For example, an AI application may optimize energy consumption while remaining inside predefined process constraints.The control system can continue enforcing interlocks, alarms, and safety limits. Therefore, AI does not replace the fundamental protection architecture.This distinction remains important for industrial control systems.AI may optimize operations, but safety instrumented systems must continue performing their independent protective functions where required.

Stage Five: Industrial Autonomy

The highest maturity stage involves increasingly autonomous operations across multiple industrial functions.Systems can perceive current operating conditions, predict future situations, evaluate available responses, and execute approved actions.However, complete autonomy does not mean removing people from industrial operations.Human expertise remains necessary for engineering design, safety management, strategic decisions, and abnormal situations outside validated operating boundaries.Therefore, industrial autonomy should focus on improving human capability rather than eliminating human responsibility.

AI Must Work with Existing PLC and DCS Systems

One major challenge involves integrating AI with existing industrial automation infrastructure.Most plants cannot simply replace their PLC or DCS platforms. These systems often support production assets for decades.Therefore, AI applications need secure connections to historians, industrial networks, asset management systems, and control systems.Common industrial technologies may include OPC UA, industrial Ethernet, edge computing, and secure data historians.The architecture must also address cybersecurity and network segmentation. AI applications should never introduce uncontrolled access paths into critical control networks.This requirement becomes increasingly important as industrial connectivity expands.

Data Quality Determines AI Performance

Industrial AI depends heavily on operational data quality.Many plants collect large volumes of data. However, incomplete tags, inconsistent timestamps, incorrect engineering units, and poor sensor maintenance can reduce model accuracy.Therefore, companies should evaluate their data infrastructure before deploying AI.Engineers should review instrument calibration, historian configuration, communication reliability, and tag consistency.In my experience, many industrial AI projects face greater challenges with data preparation than with AI algorithms.A technically sophisticated model cannot compensate for consistently inaccurate process data.

Experience Matters in Industrial AI Deployment

Industrial AI requires more than software expertise. Engineers must understand the physical process behind the data.For example, a vibration pattern may indicate several possible mechanical conditions. The correct interpretation often requires knowledge of machine design and operating history.Similarly, process temperature changes may result from feedstock variation, control valve behavior, heat transfer performance, or sensor problems.Therefore, domain experts must participate throughout AI development and deployment.The strongest industrial AI projects combine data science with automation engineering, process knowledge, and maintenance experience.

Functional Safety Cannot Become an AI Experiment

Industrial autonomy creates new opportunities, but safety requirements remain unchanged.Systems that perform safety-related functions must follow applicable engineering practices and standards. Depending on the application, these may include standards such as IEC 61508 and IEC 61511.AI-based optimization should remain separate from independent safety protection layers where appropriate.Moreover, engineers must define clear authority boundaries between AI applications and safety systems.An AI model may recommend an operating adjustment. However, the established protection system must still respond when hazardous conditions occur.This architecture supports innovation without weakening proven safety principles.

Cybersecurity Becomes Part of Industrial Autonomy

Greater autonomy requires greater connectivity. However, increased connectivity can also expand cybersecurity risks.Industrial organizations should apply established cybersecurity practices when connecting AI platforms to operational technology environments.Standards such as the IEC 62443 series provide useful guidance for industrial automation and control system security.Network segmentation, identity management, access control, logging, and asset monitoring should remain part of the architecture.Therefore, industrial autonomy projects should include cybersecurity engineers from the earliest design stages.

Practical Applications for Industrial AI

Industrial AI can already support several practical applications without requiring full autonomous control.Common examples include:

Predictive maintenance for rotating machinery and electrical equipment
Energy optimization for industrial processes
Production quality prediction
Process anomaly detection
Operator decision support
Alarm analysis and rationalization support
Production scheduling optimization
Equipment performance monitoring
Digital twin applications
Factory automation optimization

These applications allow organizations to gain operational experience before increasing autonomous control levels.

Application Scenario: AI in a Process Plant

Consider a chemical processing plant using a DCS for continuous process control.The facility collects temperature, pressure, flow, composition, and energy data through field instruments and historians.An AI application analyzes historical and real-time information. It identifies combinations of operating conditions associated with reduced energy efficiency.The system first provides recommendations to operators. Engineers compare those recommendations with established operating procedures.After sufficient validation, the organization may allow automatic optimization within predefined limits.The DCS continues controlling the process. Meanwhile, independent protection systems maintain their designated safety functions.This approach demonstrates how industrial AI can evolve alongside existing control systems.

Application Scenario: AI for Factory Automation

A manufacturing facility may use PLC systems to control conveyors, robots, motors, and production equipment.AI can analyze production cycle times, equipment status, and quality information.The system may identify recurring bottlenecks or predict machine failures. Maintenance teams can then schedule inspections before production interruptions occur.Moreover, AI can support production planning by comparing demand forecasts with equipment capacity.This approach allows factory automation systems to become more adaptive without replacing established PLC control logic.

Author's View: Industrial Autonomy Will Be a Gradual Transition

Industrial autonomy will likely develop gradually rather than through sudden technology replacement.Most manufacturers already depend on proven PLC, DCS, SCADA, and safety systems. These platforms will remain the operational foundation for many years.Therefore, the most practical strategy involves adding intelligence around existing automation architectures.Companies should begin with data quality, asset connectivity, and operator decision support. They can then move toward supervised autonomous optimization.In my view, the industry's greatest challenge will not involve choosing an AI model.Instead, organizations must establish engineering governance, operational boundaries, cybersecurity controls, and clear responsibility structures.The companies that combine AI expertise with industrial engineering knowledge will likely achieve the strongest results.

The Future of Industrial Automation and AI

The transition from industrial automation to industrial autonomy represents an important technology direction.However, autonomy should not become an objective without operational justification.Industrial organizations should evaluate where AI can improve safety, productivity, energy efficiency, maintenance, and decision-making.Yokogawa's IA2IA concept provides a practical perspective because it emphasizes gradual development.Instead of replacing existing control systems, industrial AI can build upon decades of engineering investment.PLC, DCS, control systems, factory automation, and industrial AI will increasingly operate as connected layers.The future industrial plant may become more autonomous. However, successful autonomy will still depend on sound automation engineering and experienced people.

Categories: News

MulticoreWare Expands Semiconductor Engineering for Industrial Automation and Edge Computing

06 Sep

MulticoreWare Strengthens Semiconductor Engineering Portfolio

MulticoreWare has acquired Simulus Automation to expand its semiconductor design and verification capabilities. The deal combines hardware engineering with MulticoreWare's software and AI expertise. Simulus Automation specializes in SoCs, chipsets, accelerators, and semiconductor verification services. Therefore, the acquisition supports a broader hardware-software development model for emerging computing platforms.

Hardware-Software Co-Design Gains Industrial Automation Momentum

Industrial automation increasingly depends on tightly integrated hardware and software architectures. Modern control systems now process larger workloads at the machine and edge levels. Traditional CPU, GPU, DSP, and NPU architectures can no longer address every workload efficiently. Consequently, engineers increasingly integrate programmable logic and specialized accelerators into SoC architectures.This trend also affects PLC, DCS, robotics, and factory automation platforms. Industrial controllers must process sensor, vision, motion, and AI data with limited latency. Meanwhile, edge devices must balance computing performance, power consumption, and system flexibility.

FPGA Technology Adds Flexibility to Edge Control Systems

The acquisition adds FPGA engineering capabilities to MulticoreWare's existing compute portfolio. The combined team supports FPGA implementations using RTL and High-Level Synthesis techniques. These technologies can support sensor fusion, robotics, and Physical AI workloads at the edge.For industrial automation engineers, FPGA-based processing can provide deterministic data handling. It can also reduce dependence on centralized computing resources in selected applications. However, system designers must evaluate development complexity, maintenance requirements, and lifecycle support.

CXL and PCIe Address High-Speed Industrial Data Movement

High-speed connectivity has become a major design consideration for heterogeneous computing systems. Simulus Automation brings design and verification experience with CXL and PCIe protocols. These interfaces support high-bandwidth communication between processors, accelerators, memory, and peripheral devices.This capability has potential value for industrial control architectures using edge AI. For example, machine vision systems can generate large data volumes near production equipment. Fast interconnects can therefore reduce data-transfer constraints between acquisition and processing hardware.

Cloud FPGA Supports Scalable AI Workloads

The combined engineering team also expands cloud FPGA capabilities. Cloud-based FPGA resources can handle repetitive workloads without dedicated local hardware. In addition, engineers can use these environments to emulate and deploy selected AI workloads.This approach may benefit industrial companies developing scalable edge and factory automation solutions. It allows teams to evaluate hardware acceleration before committing to dedicated silicon. However, engineers should assess latency, cybersecurity, data governance, and cloud dependency before deployment.

Acquisition Connects Semiconductor Design With Software Engineering

MulticoreWare aims to create a development path from system architecture through production software. The company combines semiconductor engineering with AI optimization and high-performance software development. As a result, customers can address hardware and software requirements within a more coordinated workflow.This model has particular relevance to industrial automation platforms. Modern PLC and DCS architectures increasingly exchange data with edge computers and AI systems. Therefore, semiconductor design decisions can directly influence controller performance and system integration.

Industrial Automation Could Benefit From Heterogeneous Computing

The acquisition arrives as demand grows for AI infrastructure, robotics, and edge intelligence. Industrial facilities increasingly use cameras, sensors, robots, and intelligent controllers together. These systems require faster data processing while maintaining predictable machine behavior.From an engineering perspective, heterogeneous computing offers both opportunities and trade-offs. Specialized hardware can improve performance for defined workloads. However, engineers must also consider diagnostics, firmware management, functional safety, and long-term maintainability.

Practical Scenario: AI-Based Factory Inspection

Consider an automated production line using machine vision for quality inspection. Cameras collect high-resolution images while PLCs manage machine sequencing and interlocks. An edge accelerator can process image data without sending every frame to a remote server.The PLC can then receive validated inspection results through the plant communication architecture. Meanwhile, the DCS or supervisory platform can collect production and diagnostic information. This architecture separates deterministic control from computationally intensive AI workloads.Such separation can improve system design when engineers define clear interfaces and failure responses. Nevertheless, safety functions should remain within appropriately certified control architectures.

Engineering Perspective on the Acquisition

The acquisition reflects a broader shift toward hardware-software co-design. Industrial automation suppliers increasingly combine control, computing, networking, and AI capabilities. This trend could influence future PLC, DCS, robotics, and edge-control architectures.In my view, the strongest opportunity lies in reducing unnecessary data movement. Processing relevant information closer to the machine can reduce bandwidth and response delays. However, industrial deployments require more than raw computing performance.Engineers must also verify deterministic behavior, cybersecurity, diagnostics, lifecycle support, and system interoperability. Therefore, semiconductor innovation should complement established industrial control engineering practices.

MulticoreWare Expands Its AI and Engineering Services

MulticoreWare provides AI software and engineering services for Physical AI, robotics, and accelerated computing. Its capabilities include multimodal AI, sensor perception, sensor fusion, embedded systems, and AI optimization. The company also works across automotive, robotics, industrial automation, smart cities, healthcare, defense, and edge applications.With Simulus Automation, MulticoreWare adds deeper semiconductor design and verification capabilities. The combined organization can therefore address a wider range of hardware and software engineering requirements.

What This Means for Industrial Automation Engineers

The acquisition does not directly change existing PLC or DCS platforms. Instead, it highlights a technology direction that automation engineers should monitor. Future control architectures may increasingly combine deterministic controllers with FPGA and AI accelerators.For system integrators, this development reinforces the value of modular architectures. Clear hardware interfaces can simplify integration between control systems and edge computing platforms. Moreover, standardized communication and defined failure modes can support easier system validation.

Conclusion

MulticoreWare's acquisition of Simulus Automation expands its semiconductor engineering capabilities. The combination brings FPGA, RTL, CXL, PCIe, and verification expertise into a broader AI portfolio. More importantly, it reflects the growing convergence between semiconductor engineering and industrial computing.For industrial automation, the long-term impact could appear in edge controllers, robotics, machine vision, and AI-enabled factory systems. However, successful deployment will depend on engineering discipline as much as computing performance. Industrial customers should evaluate deterministic operation, cybersecurity, maintainability, and lifecycle requirements alongside acceleration benefits.

Categories: Uncategorized

Qualcomm Dragonwing Pushes Edge AI Into Industrial Automation, Robotics and Factory Systems

31 Aug

Qualcomm Expands Beyond Smartphones Into Industrial Automation

Qualcomm is extending its computing expertise beyond smartphones and into industrial automation, robotics, and connected devices.The company is positioning its Dragonwing platform as a major foundation for its expanding B2B technology business.On August 31, Qualcomm Technologies hosted Qualcomm Dragonwing IoT Day in Seoul, South Korea.The event attracted about 500 representatives from customers, technology partners, manufacturers, and telecommunications companies.Participants included Samsung Electronics, LG Electronics, Hyundai Motor, SK Telecom, KT, and LG Uplus.The event demonstrated Qualcomm's growing focus on industrial edge computing and on-device artificial intelligence.

Dragonwing Targets the Industrial Edge Computing Market

Qualcomm introduced Dragonwing as a broad B2B platform rather than another processor product family.The platform combines computing, connectivity, artificial intelligence, software, and development tools.It targets industrial IoT, embedded systems, networking infrastructure, robotics, and smart devices.This approach reflects a major change in industrial computing architecture.Manufacturers increasingly want AI processing closer to machines, sensors, cameras, and control systems.Therefore, edge computing can reduce cloud dependency and shorten response times for time-sensitive applications.This capability matters particularly in machine vision, autonomous robotics, industrial inspection, and safety monitoring. Qualcomm Dragonwing Pushes Edge AI Into Industrial Automation, Robotics and Factory Systems

Qualcomm Dragonwing Pushes Edge AI Into Industrial Automation, Robotics and Factory Systems

Edge AI Creates New Opportunities for Factory Automation

Traditional factory automation relies heavily on PLC, DCS, SCADA, and dedicated control systems.These systems remain important for deterministic machine control and process management.However, AI introduces additional workloads that require high-performance computing and specialized acceleration.Vision analysis, predictive inspection, object detection, and natural-language interfaces can generate significant processing requirements.Edge AI allows these workloads to run directly on industrial devices.As a result, factories can analyze sensor and camera data without continuously transferring raw information to cloud servers.This architecture can also reduce network traffic and improve response times.For industrial engineers, the main question remains system integration rather than processor performance alone.The AI platform must communicate effectively with PLC networks, sensors, cameras, HMIs, and existing control infrastructure.

Qualcomm Linux 2.0 Supports Industrial AI Development

Qualcomm also highlighted software technologies designed to simplify industrial AI development.These technologies include Qualcomm Linux 2.0, the Qualcomm Intelligent Robotics Product SDK, and Qualcomm AI Workflow.The software environment helps developers move AI applications from development environments toward embedded deployment.This matters because industrial projects often face long development and validation cycles.Hardware acceleration alone cannot solve these integration challenges.Developers also need operating systems, middleware, AI frameworks, device drivers, development tools, and lifecycle support.Therefore, Qualcomm's platform strategy focuses on the complete development chain.

Robotics Becomes a Major Dragonwing Application

Robotics represents one of the strongest applications for edge AI technology.Industrial robots must process information from cameras, sensors, encoders, and other perception systems.They must then make decisions while maintaining precise motion and safety requirements.Qualcomm demonstrated its Dragonwing IQ10 Robotics Reference Design during the Seoul event.The design supports on-device AI, perception functions, and robotic control workloads.From an automation engineering perspective, this architecture can support applications such as autonomous mobile robots and intelligent inspection systems.However, AI computing should complement deterministic PLC and motion-control functions.Safety-related functions still require appropriate architectures, validation, and safety-rated components.

Sixteen Demonstrations Show Commercialization Potential

Qualcomm and ten partner companies presented 16 demonstrations covering industrial AI and connected-device applications.The demonstrations included robotics, machine vision, industrial cameras, smart home systems, and AI-assisted automation.Arduino presented the VENTUNO Q development platform using Qualcomm's IQ8 technology.Qualcomm also demonstrated an AI Media Station based on the Dragonwing Q8 Series.Other demonstrations showed real-time object recognition and Always-On Vision capabilities.These examples demonstrate how edge AI can move from laboratory development into practical embedded products.

Industrial Vision Connects AI With Quality Inspection

Industrial vision represents another important application area for Dragonwing technology.Advantech demonstrated industrial vision inspection and a vision-language model developed with Zetic AI.Such systems can analyze images while adding AI-based interpretation capabilities.Traditional machine vision often depends on predefined rules and carefully engineered inspection algorithms.AI-based vision can handle more complex visual patterns and variable production conditions.However, engineers must still evaluate detection accuracy, latency, lighting conditions, false positives, and model maintenance.These factors directly influence the suitability of AI vision for production environments.

Robotics Platforms Demonstrate Edge AI Performance

SST presented several Dragonwing-based systems during the event.Its demonstrations included a booster robot using the Dragonwing QCS8550 platform.The company also presented an AI home hub based on Dragonwing Q-8750 technology.A quadruped robot based on the Dragonwing QCS9075 further demonstrated robotic edge computing capabilities.These platforms show how one computing architecture can support different form factors.For industrial automation, this flexibility can simplify hardware development across multiple machine platforms.

Vehicle and Industrial Safety Applications Expand Edge AI

D-TEG demonstrated Dragonwing QCS6490-based AI applications for vehicle and industrial safety.The demonstrations included blind-spot detection and pothole detection.Such applications require continuous image processing and rapid local decision-making.Edge processing can therefore provide advantages when network latency or connectivity limits cloud-based analysis.Similar architectures can support industrial safety cameras, autonomous vehicles, mobile robots, and perimeter monitoring.Nevertheless, safety functions should remain separated from non-safety AI workloads when required by the application architecture.

Industrial AI Ecosystems Require More Than Semiconductor Performance

Other partners demonstrated increasingly sophisticated AI applications.Innodisk presented real-time vision-language model technology.Aetina demonstrated an agentic AI solution for industrial edge applications.Xslab presented a Phi-4 live demonstration.Integrit showed LLM and vision AI technologies, including a vision-language-action model.Namuga demonstrated real-time stereo depth vision.Wiseplus presented Orbit Eye, a multi-channel video analytics platform for industrial edge AI.These demonstrations indicate that industrial AI is moving beyond simple image classification.Systems can increasingly combine perception, reasoning, language, and physical actions.That trend could significantly influence the design of future factory automation architectures.

Korean Companies Strengthen the Dragonwing Ecosystem

Korean technology companies played an important role throughout the Seoul event.Presentations came from Hyundai Motor's Robotics LAB, SST, Integrit, D-TEG, Point Mobile, Xslab, Advantech, Innodisk, Aetina, and Wiseplus.Their participation demonstrates the importance of local ecosystems for industrial AI commercialization.Industrial customers rarely purchase processors in isolation.They require complete solutions involving computing platforms, cameras, sensors, software, AI models, communications, and system integration.Therefore, Qualcomm's partner strategy could become as important as its processor technology.

Dragonwing Could Complement PLC and DCS Architectures

From an industrial automation perspective, Dragonwing should not be viewed as a direct replacement for PLC or DCS platforms.PLCs continue to provide deterministic machine sequencing and real-time control.DCS platforms remain widely used for continuous process control, plant operations, and integrated alarm management.Dragonwing instead fits naturally into higher-level edge computing functions.For example, an industrial architecture could use PLC controllers for deterministic control.An edge AI computer could process camera streams and execute machine-learning models.The AI system could then send validated results to the PLC or SCADA layer.This division creates a practical architecture for combining conventional automation with AI.

Industry Outlook

Qualcomm's Dragonwing strategy reflects a broader shift toward distributed industrial intelligence.AI processing is moving from centralized cloud platforms toward machines, cameras, robots, and embedded controllers.For industrial automation, this trend creates new opportunities for intelligent inspection and autonomous machines.It also creates new engineering requirements around cybersecurity, functional safety, lifecycle management, and system integration.The long-term value of platforms such as Dragonwing will depend on their ability to operate within real industrial architectures.In our view, successful industrial AI will not replace PLC and DCS technology.Instead, it will add an intelligent computing layer around proven control systems.That combination could become an important architecture for the next generation of factory automation.

Categories: News

Schneider Electric Expands Automation Partner Network for Software-Defined Industrial Automation

28 Aug

Open Automation Is Reshaping Industrial Control Systems

Industrial automation is moving toward more open and software-centric architectures. Manufacturers increasingly need flexible control systems, better data access, and simpler system integration.Traditional PLC and DCS architectures often depend heavily on dedicated hardware and proprietary engineering environments. Open automation takes a different approach by separating software functions from specific hardware platforms.This model can improve interoperability across PLC, DCS, SCADA, and other factory automation systems. Moreover, it can help manufacturers adapt control applications as production requirements change.However, technology alone cannot guarantee successful deployment. Engineering teams need practical experience with system architecture, application development, testing, and commissioning.

EcoStruxure Automation Expert Supports Open Automation

Schneider Electric is expanding its partner ecosystem around EcoStruxure Automation Expert (EAE). The platform uses the IEC 61499 standard as a foundation for distributed automation applications.IEC 61499 supports a more modular approach to industrial control software. Engineers can organize control functions into reusable components and distribute applications across suitable computing resources.This approach differs from traditional PLC programming models based primarily on fixed hardware execution. Therefore, it can support more flexible application architectures for complex automation projects.For industrial users, the main value lies in architecture flexibility rather than software novelty. Open control environments can reduce dependence on a single hardware configuration when properly engineered.

Automation Partner Accelerators Build Engineering Skills

Schneider Electric organized Automation Partner Accelerator events between February and June 2026. The program reached more than 400 professionals from over 215 partner organizations.The initiative covered participants across nine countries. Schneider Electric designed the sessions around practical engineering and commercial requirements.Participants examined system architecture and application design during hands-on sessions. They also developed applications using reusable software libraries.Configuration and orchestration formed another major part of the training. Testing, validation, and commissioning activities helped connect software development with actual automation project execution.The program also addressed commercial considerations. Partners explored ways to explain open automation benefits and adjust their offerings to changing customer requirements.

System Integrators Become More Important in Open Automation

The move toward software-defined automation increases the responsibilities of system integrators. End users still need engineering teams that understand both operational requirements and automation technology.A system integrator must evaluate control architecture, communication networks, application structure, cybersecurity, and lifecycle requirements. These decisions directly influence system performance and long-term maintainability.In PLC and DCS projects, integration problems often appear between engineering disciplines. Open architectures can reduce some restrictions, but they do not eliminate engineering complexity.Therefore, integrators need strong knowledge of control systems and industrial communication technologies. They must also understand how software components interact with physical I/O, controllers, drives, and field devices.

Why Interoperability Matters for Factory Automation

Interoperability has become a major consideration as factories combine equipment from different suppliers. Modern production lines may include PLCs, robots, drives, vision systems, historians, and edge computing platforms.Proprietary architectures can make cross-platform integration more difficult. Open approaches can provide greater flexibility when engineers need to connect different systems.However, interoperability requires more than supporting common communication protocols. Engineers must also address data models, application behavior, timing, diagnostics, and lifecycle management.From a practical engineering perspective, standardized software structures can become valuable when plants operate multiple production lines. Reusable application components can shorten engineering work and simplify future modifications.

Software-Defined Automation Changes the Engineering Model

Software-defined automation shifts some engineering attention from hardware configuration toward application architecture. This change resembles broader developments in industrial computing and IT infrastructure.Control functions can become more modular and portable. As a result, organizations may gain additional options when selecting computing and control hardware.However, deterministic control requirements remain important. Motion control, high-speed processes, safety functions, and time-critical applications still require careful hardware and network design.For this reason, software-defined automation should not replace engineering fundamentals. Instead, it should complement established PLC, DCS, safety, and industrial networking practices.

Practical Experience Remains Critical During Commissioning

Training can provide a foundation, but project experience remains important. Engineers must deal with actual field conditions during installation and commissioning.Real plants rarely match engineering assumptions perfectly. Network behavior, instrumentation errors, legacy equipment, process constraints, and maintenance requirements can affect system performance.Experienced integrators therefore validate applications before full production deployment. They also establish clear testing procedures and commissioning documentation.This practical discipline becomes particularly important when organizations introduce new automation architectures. A technically flexible platform still requires structured engineering and controlled change management.

Schneider Electric Strengthens Its Partner Ecosystem

The Automation Partner Accelerator program reflects a broader industry trend. Automation suppliers increasingly depend on capable partners to deliver complex digital and control projects.For Schneider Electric, stronger partner capabilities can support wider adoption of EcoStruxure Automation Expert. Partners gain technical knowledge while end users gain access to trained engineering resources.Moreover, partner development can reduce the implementation barrier for open automation technologies. Customers are more likely to consider architectural changes when qualified engineering support exists.In my view, this partner-focused strategy is as important as the underlying software platform. Open automation requires an ecosystem that can design, implement, maintain, and upgrade the technology.

Open Automation Does Not Eliminate PLC and DCS Systems

The growth of software-defined automation does not mean traditional PLC and DCS systems will disappear. These technologies remain deeply established across process, discrete, hybrid, and infrastructure applications.Instead, the industry is moving toward greater coexistence between conventional controllers and software-centric architectures. Plants will continue using established control platforms while adopting new technologies where they provide measurable benefits.The most practical approach is therefore application-driven. Engineers should select an architecture according to process requirements, lifecycle costs, integration needs, and operational risk.

Application Scenario: Multi-Vendor Factory Automation

Consider a manufacturing plant operating PLC-based production lines from several equipment suppliers. The plant also uses drives, robots, SCADA software, historians, and energy monitoring systems.An open automation strategy can provide a framework for integrating these systems. Engineers can create reusable software functions while maintaining connections with existing equipment.The plant can then standardize selected application functions across multiple production areas. This approach may simplify future expansion and reduce duplicated engineering work.However, engineers should first identify integration boundaries and performance requirements. They should then validate communication, diagnostics, cybersecurity, and commissioning procedures before wider deployment.

Application Scenario: Modernizing Existing Control Systems

Another use case involves modernizing an established factory without replacing every controller. The organization can gradually introduce software-defined automation alongside existing PLC or DCS infrastructure.This phased approach can reduce disruption to production. It also allows engineering teams to evaluate new architectures against real operational requirements.In such projects, migration planning becomes particularly important. Engineers should document existing control functions, interfaces, alarms, sequences, and maintenance procedures before making architectural changes.

ARC Perspective: Skills Will Influence Adoption

The expansion of open automation depends on more than standards and software platforms. The industry also needs engineers who understand how these technologies behave in production environments.Schneider Electric's partner training initiative addresses this practical requirement. Its focus on architecture, reusable applications, configuration, testing, and commissioning aligns with the real workflow of automation projects.From an industrial automation perspective, this is a sensible direction. Technology adoption becomes easier when system integrators can demonstrate measurable engineering and operational benefits.The long-term question is whether open automation can deliver these benefits consistently across different industries. Its success will depend on interoperability, engineering quality, lifecycle support, and the availability of skilled professionals.

Conclusion: Engineering Capability Will Shape Software-Defined Automation

Software-defined automation is becoming an important direction for industrial control systems. It offers a path toward more modular applications and greater hardware flexibility.Schneider Electric is strengthening this transition through EcoStruxure Automation Expert and its partner development activities. The Automation Partner Accelerator program demonstrates the importance of building practical engineering capabilities alongside technology.For manufacturers, the decision should focus on measurable operational requirements rather than technology trends alone. PLC, DCS, and established control systems will continue to serve important roles.However, open automation can provide additional architectural options for organizations pursuing flexible and interconnected factory automation. The strongest results will come from combining open technologies with disciplined engineering and experienced system integration.

Categories: News

Mouser Expands Industrial Automation Portfolio to Accelerate Next-Generation Control Systems

17 Aug

Mouser Broadens Its Industrial Automation Supplier Network

Mouser Electronics has added nine manufacturers to its industrial automation portfolio during the first two quarters of 2026. The expansion strengthens its support for modern industrial systems and engineering teams.The new supplier lineup covers technologies across artificial intelligence, connectivity, power, control, and sensing. These technologies support increasingly integrated factory automation and industrial control architectures.For engineers, broader supplier access can simplify component sourcing during system design. It can also reduce delays when projects require specialized automation hardware.

Industrial Automation Needs Broader Technology Integration

Industrial automation systems now combine PLC, DCS, networking, sensing, computing, and power technologies. As a result, engineers must evaluate more interfaces and dependencies during system development.Modern control systems also generate larger volumes of operational data. Therefore, automation architectures increasingly require faster communication and stronger processing capabilities.Mouser's expanded portfolio reflects this shift toward connected industrial platforms. In addition, it supports applications that require closer coordination between field devices and control systems.

PLC and DCS Applications Continue to Drive Component Demand

PLC and DCS platforms remain important foundations for industrial process control. Engineers use these systems across manufacturing, energy, utilities, transportation, and process industries.New automation designs often connect controllers with distributed sensors and intelligent field devices. Consequently, component selection must consider communication protocols, power requirements, and environmental conditions.Industrial networks may include Ethernet-based architectures, industrial switches, remote I/O, and gateway devices. Engineers must also consider interoperability when integrating equipment from multiple suppliers.

Connectivity Becomes a Core Industrial Control Requirement

Industrial networking has become a central part of factory automation. Ethernet technologies now support communication between controllers, remote I/O, HMIs, drives, and higher-level systems.Moreover, connected equipment can provide operational data for diagnostics and performance analysis. This approach supports predictive maintenance and more structured asset management.However, connectivity also introduces additional engineering requirements. Network segmentation, cybersecurity, redundancy, and deterministic communication must match the application.

Robotics and AI Expand Factory Automation Requirements

Robotics represents another major application area for industrial automation technologies. Robotic systems require coordinated motion control, sensing, communications, safety functions, and power management.AI is also influencing industrial system architecture. Engineers increasingly use machine learning for inspection, predictive maintenance, process optimization, and anomaly detection.However, AI does not replace conventional control logic. PLC and DCS platforms still provide deterministic control for many industrial processes.The strongest architectures therefore combine AI analytics with established control and safety layers. This separation can help engineers maintain predictable machine behavior.

Safety Systems Require Structured Engineering Practices

Industrial safety systems require careful hardware and software design. Applications may include emergency shutdown systems, machine safety, burner management, and turbine protection.Engineers should evaluate applicable standards before selecting safety-related hardware. Depending on the application, IEC 61508 and related functional safety standards may influence system architecture.In practice, safety design also requires appropriate diagnostics and fault-handling strategies. Therefore, component selection should consider the complete safety lifecycle rather than individual specifications.

Industrial IoT Connects Field Data With Higher-Level Systems

The Industrial Internet of Things continues to connect field equipment with supervisory and enterprise platforms. Sensors can provide process, vibration, temperature, pressure, and equipment-status information.This data can support maintenance planning and production analysis. Moreover, centralized information can help engineers identify recurring equipment problems.Nevertheless, industrial IoT projects require disciplined architecture. Engineers should define data ownership, network boundaries, cybersecurity requirements, and system availability before deployment.

Mouser's Portfolio Expansion Supports Engineering Flexibility

From an engineering perspective, supplier diversity can provide practical advantages during procurement. Engineers can compare technologies while maintaining the required electrical and mechanical specifications.Mouser's broader manufacturer portfolio also supports projects that combine automation with embedded computing and connectivity. This approach reflects the convergence between traditional industrial control and digital technologies.The value of such expansion depends on product availability, technical documentation, lifecycle support, and application suitability. Therefore, engineers should evaluate suppliers against project-specific requirements.

Practical Application: Smart Factory Control Architecture

Consider a factory automation project that combines PLC control with robotic inspection. The PLC can manage machine sequences and deterministic I/O.Industrial Ethernet can connect the PLC with remote I/O, drives, safety devices, and HMIs. Meanwhile, sensors can provide process and equipment data for monitoring.An edge computer can process selected data locally. AI software can then identify abnormal patterns without interfering with the primary control loop.This architecture separates deterministic control from higher-level analytics. As a result, engineers can introduce digital functions without redesigning the entire control system.

Industry Perspective: Integration Matters More Than Individual Components

The current automation market increasingly favors integrated architectures. However, successful projects depend on compatibility across multiple technology layers.Engineers should therefore evaluate PLC, DCS, networking, sensing, power, and safety requirements together. Interface specifications often matter as much as individual component performance.In my experience with industrial control projects, integration issues frequently appear at system boundaries. Communication settings, signal conditioning, grounding, and power distribution require particular attention.For this reason, engineers should review complete system documentation before final procurement. A technically suitable component can still create problems when its interfaces do not match the installed architecture.

What This Means for Industrial Automation Engineers

Mouser's supplier expansion highlights a broader industry trend toward connected and data-driven automation. Industrial systems now combine established control technologies with newer computing and AI capabilities.For engineers, this trend creates more design options but also increases integration responsibilities. Careful specification review remains necessary across electrical, communication, environmental, and safety requirements.As industrial automation evolves, supplier availability will remain an important part of project execution. However, engineering validation should always determine the final component and architecture selection.

Categories: News

Data Center Environmental Monitoring: How Temperature and Humidity Control Protect Industrial Uptime

31 May

Case Study: Environmental Monitoring as the Silent Guardian of Data Center Uptime

Environmental Monitoring Has Become Critical for Data Center Reliability

Modern data centers power artificial intelligence, cloud services, financial platforms, and enterprise applications.These facilities host mission-critical servers, storage hardware, and networking systems that must operate continuously.Therefore, even a brief disruption can trigger financial losses, compliance exposure, and reputational damage.Many organizations prioritize power redundancy and cybersecurity. However, environmental monitoring plays an equally important role in protecting uptime.For this reason, leading operators increasingly integrate temperature and humidity monitoring into broader industrial automation and infrastructure strategies.

Temperature Control Protects Critical IT Equipment from Hidden Risks

Servers generate large amounts of heat during normal operation.Without sufficient cooling, temperatures can rise beyond safe operating limits.Consequently, hardware may throttle performance, malfunction, or shut down unexpectedly.Even short overheating events can corrupt data or shorten equipment lifespan.This challenge resembles thermal management issues found in factory automation, PLC cabinets, DCS rooms, and industrial control systems.In both environments, stable operating conditions directly influence system reliability.

Humidity Management Reduces Corrosion and Electrostatic Damage

Humidity control presents another critical challenge for high-availability facilities.When humidity levels rise excessively, condensation can form on electronic assemblies.As a result, operators may face corrosion, electrical shorts, or component failure.Conversely, extremely dry environments increase electrostatic discharge risk.Static discharge can damage sensitive circuit boards, processors, and communication equipment.Therefore, precise humidity control supports both equipment protection and operational continuity.

Energy Efficiency Depends on Intelligent Environmental Control Systems

Many operators attempt to avoid overheating through aggressive cooling strategies.However, excessive cooling increases energy consumption and operating costs.A more sustainable approach relies on precise measurement and adaptive environmental management.This strategy increasingly depends on control systems, SCADA platforms, and industrial automation architectures.Real-time monitoring enables HVAC systems to maintain stable environmental conditions without unnecessary energy use.Consequently, facilities can improve both efficiency and operational resilience.

Industry Standards Define Safe Environmental Operating Ranges

Industry organizations such as ASHRAE provide recommended environmental guidelines for data center operations.Most facilities target:

Temperature: 18°C–27°C (64°F–80°F)
Relative Humidity: 40%–60% RH

These ranges help operators balance thermal protection, energy efficiency, and equipment reliability.Moreover, documented environmental control supports service-level agreements and regulatory expectations.For organizations managing mission-critical infrastructure, environmental compliance increasingly carries business importance.

Sensor Placement Determines Environmental Monitoring Accuracy

Accurate environmental monitoring begins with effective sensor deployment.Poor placement often creates misleading measurements and delayed responses.Therefore, engineers position monitoring devices at critical airflow and thermal locations.Common installation points include:

Rack inlets and outlets
Cold aisles and hot aisles
Raised floor air plenums
Ceiling return ducts
UPS and battery rooms

This layered monitoring strategy provides stronger environmental visibility across the facility.In addition, dense sensor placement enables faster anomaly detection.

Data Centers Benefit from Multiple Monitoring Architectures

Modern facilities rarely depend on a single monitoring method.Instead, operators combine several environmental monitoring models.Typical approaches include:

Spot monitoring: fixed point measurements
Zone monitoring: multi-area environmental analysis
Mobile monitoring: temporary diagnostic assessment

Each method supports different operational requirements.Consequently, engineers often design hybrid monitoring strategies for large-scale facilities.This approach closely mirrors practices used across industrial control systems and advanced process automation environments.

DCIM Integration Strengthens Industrial Automation Performance

Environmental sensors generate greater value when operators connect them to Data Center Infrastructure Management (DCIM) platforms.DCIM software uses real-time data to optimize cooling, energy consumption, and facility performance.Therefore, sensor interoperability becomes essential.Many facilities prefer transmitters that support open industrial protocols and standard outputs.Common interfaces include:

4–20mA analog signals
HART communication
Modbus connectivity

These communication methods integrate smoothly with SCADA, PLC, DCS, and industrial automation control systems.As a result, operators can unify environmental management with broader facility automation.

Real-Time Environmental Monitoring Delivers Measurable Operational Benefits

Continuous monitoring creates clear operational advantages.Facilities that track temperature and humidity in real time can respond faster to environmental deviations.Key benefits include:

Reduced downtime risk
Longer equipment lifespan
Improved HVAC efficiency
Stronger SLA and compliance support
Better operational visibility

Moreover, predictive monitoring helps operators identify developing issues before failures occur.This proactive model increasingly defines modern high-availability infrastructure management.

Moore Industries HTZ Supports Continuous Temperature and Humidity Monitoring

Some facilities deploy dedicated industrial transmitters for long-term environmental control.One example involves the Moore Industries HTZ Humidity and Temperature Transmitter.The compact, loop-powered device measures both temperature and humidity with strong accuracy and repeatability.Originally developed for semiconductor cleanroom applications, the HTZ also suits data centers that demand uninterrupted performance.Its programming simplicity, industrial durability, and installation flexibility support mission-critical environments.

Case Study: Large-Scale Data Center Deploys Dense Environmental Monitoring Network

A large data center project required continuous environmental management for high-density server operations.The design team needed to maintain stable temperature and humidity conditions across multiple operational zones.Therefore, engineers installed monitoring points throughout:

Cold aisles
Hot aisles
Areas above server racks
Areas below server racks
HVAC supply and return ducts

This architecture enabled complete environmental coverage across critical airflow pathways.Moreover, it improved visibility into cooling system effectiveness.

Challenge: Precision Environmental Control Leaves No Margin for Error

The project team faced strict humidity management requirements.Operators needed to maintain relative humidity between 40% and 60%.Humidity below 40% increased electrostatic discharge risk.Humidity above 60% increased condensation exposure.Because the facility required uninterrupted operation, the monitoring system needed high accuracy, strong scalability, and dependable long-term performance.Furthermore, engineers required a deployment model that supported dense sensor coverage.

Solution: Industrial-Grade HTZ Transmitters Enable Reliable Monitoring

The project team selected the Moore Industries HTZ transmitter platform.The device offered several operational advantages:

Stable humidity and temperature measurement
4–20mA output with HART communication
Rugged industrial construction
Low maintenance requirements
Long-term operational reliability

Each facility deployed roughly 150 measurement points.This configuration created extensive environmental visibility across critical data center zones.Consequently, operators could identify abnormal thermal or humidity trends rapidly.

Results: Continuous Environmental Data Supports 24/7 Data Center Uptime

The monitoring deployment produced measurable operational improvements.The data center maintained humidity within recommended operating ranges.In addition, the facility reduced risks related to electrostatic discharge and condensation-driven equipment damage.Dense monitoring coverage also supported:

Lower downtime exposure
Fewer maintenance interventions
Continuous HVAC optimization
Stronger operational redundancy

High-resolution environmental data enabled teams to make faster, data-driven decisions.Therefore, the facility strengthened both reliability and operational efficiency.

Industry Perspective: Environmental Monitoring Acts as the Silent Guardian of Digital Infrastructure

Environmental monitoring rarely receives the same attention as power systems or cybersecurity.However, temperature and humidity control directly influence uptime, efficiency, and asset longevity.As data centers continue scaling for AI, cloud computing, and digital services, operators will need tighter environmental visibility.This trend creates new opportunities for industrial automation, PLC, DCS, SCADA, and intelligent control systems technologies.From an engineering perspective, environmental monitoring no longer represents a secondary utility function.Instead, it has become a foundational pillar of resilient digital infrastructure.

Application Scenario: Integrating Environmental Monitoring into Automated Data Center Operations

Consider a hyperscale data center expanding server density to support AI workloads.Traditional cooling practices may struggle with thermal variability and energy costs.By integrating industrial-grade transmitters with DCIM, PLC, SCADA, or control systems, operators can achieve:

Real-time environmental intelligence
Adaptive HVAC optimization
Faster anomaly detection
Improved energy performance
Higher infrastructure uptime
Scalable facility automation

This model demonstrates how industrial automation technologies increasingly support the future of mission-critical data center operations.

Categories: News

Siemens Xcelerator Helps ADMARES Transform Sustainable Housing Through Digital Twin and Industrial Automation

29 May

ADMARES Industrializes Sustainable Housing Design and Manufacturing With Siemens Xcelerator

Digital Manufacturing Brings Industrial Automation to Housing Construction

Siemens and ADMARES are redefining residential construction through digital twin technology, industrial automation, and advanced manufacturing.ADMARES, founded in 2016, applies manufacturing principles to housing production. Instead of relying on traditional construction methods, the company treats homes as standardized, digitalized products.This strategy addresses two major global challenges: affordable housing shortages and skilled labor constraints.As a result, the construction sector increasingly resembles modern factory automation environments.

Siemens Xcelerator Enables a Product-Based Housing Model

ADMARES uses the Siemens Xcelerator open business platform to design, manufacture, and operate sustainable homes.The platform combines software, automation technologies, and digital engineering tools into a unified development ecosystem.According to ADMARES CEO Mikael Hedberg, Siemens technology helps shift housing from labor-intensive construction toward industrialized production.This transformation matters because many regions face severe shortages of qualified construction workers.Moreover, productized housing models can improve scalability, cost control, and production consistency.

Digital Twin Technology Optimizes Housing Design and Manufacturing

At the center of the project lies Siemens’ comprehensive digital twin framework.ADMARES deploys Siemens Designcenter, Teamcenter, and Simcenter software to create, validate, and optimize modular housing designs.The digital twin does more than represent the final building.It also captures manufacturing data, process behavior, and production workflows.For engineers familiar with PLC, DCS, and industrial control systems, this approach mirrors digital engineering practices used across automotive, aerospace, and industrial automation sectors.Therefore, digital twins continue expanding far beyond traditional manufacturing applications.

Advanced Factory Automation Accelerates Home Production

ADMARES supports its housing model with a digital-first, automated greenfield smart factory.The production environment integrates Siemens Opcenter Manufacturing Execution System (MES) software with industrial automation hardware.This architecture supports a takt time of 22.5 minutes per building unit.The company reports that it can produce a fully completed 1,400-square-foot home in roughly 45 minutes.Such production rates highlight how factory automation principles can reshape conservative industries like construction.In practical terms, automated workflows reduce manual variability and improve operational repeatability.

Sustainable Construction Benefits from Automation and Process Optimization

Sustainability remains a core element of the ADMARES strategy.The company aims to cut residential construction carbon emissions by up to 75%.In addition, it targets approximately 80% less material waste compared with traditional building methods.These targets align with wider industrial trends toward resource efficiency and low-carbon manufacturing.Industrial automation often plays a critical role in achieving these outcomes.By improving process precision, manufacturers can reduce scrap rates, optimize material usage, and lower energy consumption.The same principles increasingly apply to modular housing production.

Smart Homes Combine Connectivity with Real-Time Monitoring

The homes produced through the ADMARES platform include integrated building software for continuous monitoring.Users can access real-time data related to:

Energy consumption
Water usage
Indoor air quality
Smart building performance

This functionality transforms each unit into a connected smart home environment.From a control systems perspective, these capabilities resemble Industrial IoT monitoring architectures used in intelligent factories and process plants.As digitalization expands, the boundary between industrial systems and smart infrastructure continues to narrow.

Siemens Demonstrates the Business Value of Open Industrial Platforms

Siemens views the ADMARES collaboration as an example of technology-driven business model innovation.According to Siemens Digital Industries sustainability leadership, the project shows how digitalization, automation, and advanced manufacturing can reinvent established industries.This point deserves attention.Construction historically adopted automation more slowly than automotive or electronics manufacturing.However, growing pressure around affordability, labor shortages, and sustainability may accelerate change.Open platforms such as Siemens Xcelerator could help lower integration barriers and speed industry adoption.

Industrialized Housing Reflects a Broader Automation Trend

The ADMARES project reflects a broader evolution across industrial sectors.Companies increasingly combine industrial automation, MES software, digital twins, PLC integration, and smart factory technologies to improve productivity and sustainability.Housing now joins a growing list of industries adopting manufacturing-style operating models.This shift may influence how engineers, system integrators, and automation vendors approach future building projects.For B2B readers, the case illustrates how automation expertise can create value beyond traditional factory environments.

Application Scenario: Digital Twin and Automation in Modular Housing Production

Consider a modular housing manufacturer planning rapid production expansion.Traditional workflows may involve fragmented design tools, manual planning, and limited production visibility.A Siemens-style digital ecosystem can connect engineering, simulation, manufacturing execution, and automation control into a single workflow.Potential operational advantages include:

Faster design validation
Improved production planning
Reduced material waste
Higher manufacturing consistency
Better lifecycle traceability
Scalable smart factory operations

These capabilities mirror proven best practices across industrial automation and advanced manufacturing industries.

Industry Perspective: Why Construction May Become the Next Automation Frontier

Construction stands at a turning point.Rising demand, environmental regulations, and labor shortages increasingly challenge traditional delivery models.Industrialized housing offers a practical alternative.By combining digital twins, control systems, factory automation, and software-defined manufacturing, companies can create faster, cleaner, and more scalable production models.The Siemens–ADMARES collaboration demonstrates how automation technology can move beyond factories and begin reshaping the future of housing.

Categories: News

Critical Manufacturing Showcases Advanced MES Solutions for Semiconductor Factory Automation at SEMICON West 2025

10 Apr

Critical Manufacturing to Demonstrate Advanced MES for Semiconductor Production at SEMICON West

Driving Efficiency in Semiconductor Production with MES

Critical Manufacturing, a leading provider of advanced Manufacturing Execution Systems (MES), will debut at SEMICON West 2025 in Phoenix, Arizona, from October 7–9. The company demonstrates how its MES and integrated data platform help semiconductor manufacturers optimize production lines, control processes, and enhance overall factory automation.

Real-Time Monitoring for High-Precision Operations

Visitors will see how Critical Manufacturing MES provides end-to-end traceability and real-time monitoring across wafer and die processing. By integrating with PLC, DCS, and control systems, the MES stabilizes operations with run-to-run process control, fine-tunes recipes per chamber, and coordinates wafer movement through automated sorters. This ensures consistent quality and reduced variability in advanced semiconductor production.

Leveraging IoT for Smart Manufacturing

Critical Manufacturing combines MES with an IoT-enabled data platform. This setup delivers contextualized insights for shop-floor teams, allowing rapid response to process deviations. As a result, manufacturers can maintain high throughput and minimize downtime, aligning with Industry 4.0 practices and modern factory automation requirements.

Enhancing Quality with AI-Powered Image Analytics

The company will also showcase c-Alice, an AI-driven image analytics tool acquired from Convanit. Designed for high-precision inspections, c-Alice analyzes images in real time, detecting and classifying defects while linking results to MES workflows. This integration provides engineers with actionable quality insights, enabling faster decision-making and higher yields in semiconductor operations.

Expert Insights from Critical Manufacturing

Jeff Peabody, VP of Sales & Business Development, stated: “SEMICON West allows us to demonstrate how a single platform can connect data, trace materials, and enhance quality control. With MES and c-Alice combined, manufacturers gain unparalleled visibility and control over complex processes.”

Visitor Opportunities at SEMICON West

Critical Manufacturing sponsors the Smart Manufacturing Pavilion at booth SM2. Attendees can schedule demos or consultations to explore how MES, IoT data platforms, and AI-driven analytics work together to support smarter, more efficient semiconductor production. Complimentary one-day passes are available for early registrants.

About Critical Manufacturing

A subsidiary of ASMPT and recognized in the Gartner Magic Quadrant, Critical Manufacturing delivers flexible, configurable MES solutions. Its systems help manufacturers meet traceability and compliance requirements, reduce risk through closed-loop quality, and integrate seamlessly with enterprise and industrial automation systems.

About ASMPT

ASMPT provides hardware and software solutions for semiconductor and electronics manufacturing. Headquartered in Singapore, the company supports wafer deposition, assembly, packaging, and surface-mount technology (SMT). Its solutions span electronics, automotive, industrial, and LED display production, enabling customers to enhance productivity, quality, and reliability in factory automation environments.

Application Scenarios for Industrial Automation

Semiconductor Fab Management: Real-time MES monitoring ensures stable wafer processing and traceability.
High-Tech Manufacturing: AI image analytics detect defects, enhancing quality control and throughput.
Factory Automation Optimization: PLC and DCS integration streamlines operations across complex production lines.

Categories: News

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