For decades, the PLC (programmable logic controller) has been the backbone of industrial automation. It is predictable, deterministic and engineered for harsh environments and long lifecycles. Those requirements are not changing.

Here is what is changing — everything around the PLC.

Plants now expect real-time visibility, unified data models, cybersecurity controls and AI-assisted decision-making — without sacrificing uptime. The result is a new automation stack: PLCs still do what they do best, but they increasingly share the stage with industrial PCs, edge gateways, modern human-machine interfaces (HMIs), and cloud services. This is why many manufacturers are talking less about “upgrading PLCs” and more about evolving toward “controller + compute” architectures and edge AI-enabled systems.

Industrial automation architectures are undergoing a structural shift. Here are the major trends reshaping the automation market — and the practical ways original design manufacturers (ODMs) help engineering leaders at original equipment manufacturers (OEMs) keep pace without derailing product roadmaps.

Trend 1: PLC to “Controller + Compute” Architectures

The classic model — PLC controls the line, SCADA visualizes, historians store — still exists. However, it is no longer sufficient when OEMs need:

• High-frequency data preprocessing near machines
• Multiple protocols and vendor ecosystems to coexist
• AI inference close to sensors and cameras
• Modern software deployment patterns (containers, remote updates, fleet management)

That is why hybrid architectures have evolved: PLCs handle hard real-time control and safety logic, while industrial PCs or edge devices manage higher-level computation, including analytics, vision, orchestration and data normalization. Many organizations treat edge nodes as the “application layer” sitting next to deterministic control.

What changes are for the engineering teams:
OEMs must manage software lifecycles as products, employing versioning, patching and remote diagnostics. Validation extends beyond proving control logic to encompass the interaction among control, data, and computing operations.

Where this goes wrong:
Teams bolt on edge compute late, then discover networking, timing and security assumptions were not designed into the system. That leads to rework and delayed launches.

Trend 2: Edge AI Shifts from Pilot to Production

Edge AI is first appearing where ROI is easy to justify: machine vision quality inspection, anomaly detection, predictive maintenance and operator-assist systems. Edge inference reduces latency, bandwidth costs and cloud dependence while keeping sensitive production data closer to the process.

But the “AI model” is rarely the hardest part. The hard part is operationalizing it: reliably collecting and labeling data, handling drift, monitoring model performance and updating models without disrupting validated control behavior.

Essentially, edge AI is a systems engineering problem as much as a data science problem.

Trend 3: Interoperability and Semantic Data Models Get Serious

Manufacturers no longer want custom integrations. Engineering teams want to avoid building fragile middleware layers that become technical debt.

Open Platform Communications Unified Architecture (OPC UA), an interoperability standard for secure, reliable data exchange in industrial automation, has long been a cornerstone of interoperability. What is new is the desire to bring interoperability closer to the field level — where timing, determinism and multi-vendor control-to-control communication matter.

That’s where OPC UA FX (Field eXchange) enters the story. The OPC Foundation has been advancing OPC UA FX specifications and releases, including components covering connecting devices, networking, offline engineering, and profiles. Major automation players describe OPC UA FX as a way to deliver secure, deterministic, interoperable communication—often leveraging time-sensitive networking (TSN) concepts to enable low-latency behavior.

Another advancement in this field is the Open Resource Interface for Network (ORIN) software development kit, developed by DENSO WAVE. This software program enables computers to connect to factory automation devices (robots, PLCs, and NC machine tools from various manufacturers) via common protocols and supports the simple development of higher-level application software using general-purpose languages such as C#, C++, VBA, Java, and Python.

Why it matters:
Interoperability is not just “getting the data out.” It is about consistent data models, predictable communication, and reducing the integration tax that slows every deployment and every product iteration.

Trend 4: Cybersecurity Becomes a Product Requirement

In industrial automation, cybersecurity is no longer a “customer IT issue.” Cybersecurity has become a requirement in RFQs, audits, and vendor qualification. The ISA/IEC 62443 standards are widely used to define requirements and processes for securing industrial automation and control systems.

At the same time, broader security approaches, like Zero Trust verification and monitoring concepts, are influencing how organizations think about segmentation, identity, and access control across operational technology (OT) environments. NIST has published detailed guidance on implementing Zero Trust architectures, and the Cybersecurity and Infrastructure Security Agency (CISA) has published guidance on micro-segmentation as a practical control. Micro-segmentation divides a network into small, isolated zones for greater system security.

Translation for automation product teams:
Threat modeling and secure-by-design practices must start during architecture design — not after commissioning. Development teams must consider: documentation, testing artifacts, secure development practices, and supply chain controls.

Trend 5: IIoT and “Edge + Cloud” Becomes Workload Placement

Most manufacturers want the right workload in the right place: control stays local, time-sensitive analytics stay at the edge, and fleet-wide learning and optimization can live in the cloud. Edge and cloud are complementary tools, chosen based on latency, reliability, and operational constraints.

Manufacturers that rely entirely on the cloud limit the flexibility and performance of their systems.

Trend 6: Workforce Constraints Accelerate Strategic Partnerships

Even highly capable engineering organizations face a talent squeeze – especially for cross-disciplinary roles that blend controls, embedded software, networking, and security. Deloitte and The Manufacturing Institute have highlighted large projected workforce needs and persistent gaps in manufacturing skills.

This doesn’t mean “outsource everything.” It means organizations need to be increasingly selective about what must stay in-house versus what can be accelerated through partners—especially when timelines are tight and the ODM has access to differentiated technology.

How ODMs Help You Keep Up on Custom Automation Solutions

A strong ODM partner doesn’t just “build to print.” In modern automation, ODMs help most when they bring a platform approach, integration discipline, and manufacturing expertise to a development project. ODMs can offer the following:

1. Platformizing the architecture
   ODMs can help define a reusable reference design for the controller + compute split (PLC + industrial PC/edge), incorporating modular I/O and gateway approaches, and standardized telemetry and data model strategy. The payoff is simple: faster next-generation products and fewer surprise integration issues.
2. Building interoperability and multi-vendor test coverage
   If an OEM’s roadmap touches OPC UA or OPC UA FX-style interoperability, it needs the following for regression testing: consistent information models, rigorous integration testing across vendor stacks, and repeatable test harnesses. This is unglamorous work, but it’s where timelines live or die.
3. Operationalizing Edge AI without destabilizing validated control
   ODMs can contribute the “glue” work: data ingestion pipelines at the edge, model packaging/deployment patterns, monitoring hooks, field update strategy, compute selection, and thermal/mechanical design for industrial environments. This is how edge AI moves from “demo” to “product.”
4. Engineering cybersecurity as part of the product lifecycle
   IEC 62443-aligned approaches emphasize lifecycle processes, not just point solutions. ODMs that have security engineering discipline can help with secure boot, signing, credential handling, update mechanisms, segmentation strategy, and documentation that customers increasingly demand.
5. Making products manufacturable, sourceable and supportable at scale
   Many automation innovations fail at scale because components go end-of-life (EOL), supply chains break, manufacturing test coverage is weak, or field service procedures are inadequately planned. ODMs live in this world. They can harden prototypes into real products with design for manufacturing (DFM) and design for test (DFT), supplier qualification, and lifecycle management.

Where ODMs Should Not Replace OEMs

A quick reality check: ODMs accelerate execution and provide custom automation solutions — but they should not own the OEM’s customer problem definition and product positioning, the business tradeoffs that shape requirements, or the core differentiated technology. Also, OEMs must be responsible for final safety/regulatory accountability. An ODM can provide support to the OEM, but ownership must remain internal.

The DENSO WAVE Advantage in Custom Automation Solutions

PLCs remain essential — but the competitive advantage is shifting to OEMs that combine deterministic control with edge compute, standardized interoperability, and production-ready AI — securely and at scale.

ODMs help when they are more than manufacturers: when they bring systems engineering, platform reuse, compliance discipline, differentiated technology, and supply-chain execution that keeps an OEM’s roadmap moving even as the automation stack evolves.

DENSO WAVE has been working with an OEM that makes PLCs for over 40 years and has been helping the OEM evolve its automation stack for PLCs over numerous product generations. DENSO WAVE leverages automotive technology expertise, Toyota Quality Management processes, and the power of a $50 billion supply chain to provide OEMs with reliable, rugged, and resilient-sourced products.

Contact DENSO WAVE today to assist you in keeping up with the trends in PLCs and other industrial automation products.