Open Automation Architectures Enable Industrial DevOps

As industrial automation becomes increasingly software-centric, manufacturers are reassessing how control systems are developed, tested, and maintained. DevOps, long established in IT environments, is emerging as a relevant methodology for automation engineering—provided the underlying control architecture is open enough to support it.

From static machine software to continuous engineering
Manufacturing environments are under pressure from rising product variation, shorter production cycles, and higher availability requirements. These demands mirror the conditions that originally drove the adoption of DevOps in IT: frequent changes, complex systems, and the need to deploy updates without disrupting operations.

In traditional automation projects, control software is often treated as fixed once a machine is commissioned. Engineering workflows are separated between development and operations, with changes implemented manually and tested directly on physical equipment. This approach limits iteration speed and increases downtime when modifications are required.

DevOps challenges this model by promoting continuous integration, automated testing, and repeatable deployment processes. Applied to automation, it reframes machine control software as a system that evolves throughout its operational lifetime.

Why closed PLC environments limit DevOps adoption
While DevOps concepts are well defined, industrial automation cannot simply replicate IT practices. Many PLC-based systems rely on proprietary development environments and closed interfaces, which restrict access to standard tools such as version control systems, automated build pipelines, and virtual test frameworks.

These constraints affect collaboration and traceability. Even small changes can require manual code merging, on-site testing, and extended shutdowns. Under such conditions, the core DevOps principles—frequent iteration, early validation, and reliable deployment—are difficult to implement consistently.

Openness as a technical prerequisite
For DevOps to function in automation engineering, control platforms must integrate with mainstream IT tooling. This requires support for open communication protocols, access to standard operating systems, and compatibility with commonly used programming languages and development environments.

Open architectures allow engineering teams to work from shared codebases, automate testing in simulated environments, and establish standardized deployment workflows. These mechanisms reduce commissioning time and make software changes more predictable, even in complex automation systems.

Beckhoff’s PC-based control approach aligns with this requirement by running control software on standard PC hardware and operating systems. TwinCAT supports multiple programming languages and interfaces, enabling tighter integration with established DevOps toolchains used in software engineering.

Software-defined automation in practice
The impact of open, DevOps-compatible automation platforms is visible in sectors where flexibility and performance are tightly coupled. In packaging, software-defined motion control has replaced mechanically fixed designs, allowing format changes to be handled through software rather than hardware modification. Systems developed around modular motion and software-based coordination can be updated and optimized with minimal disruption.

In the energy sector, particularly in hydrogen production and refuelling infrastructure, open control architectures support real-time data processing, safety functions, and connectivity within a unified system. Here, the ability to test and validate changes virtually before deployment is critical for maintaining safety and uptime.

Across these applications, faster development cycles and earlier fault detection have been achieved by shifting testing and validation earlier in the engineering process.

Operational and lifecycle implications
When DevOps practices are combined with open automation platforms, several operational effects emerge. Software changes can be validated in simulation rather than on live equipment, reducing risk during commissioning. Updates can be deployed more frequently without extended shutdowns because processes are standardized and repeatable. Defects are identified earlier, improving overall system quality.

There are also implications for cybersecurity and maintenance. Consistent update mechanisms make it easier to apply patches across an entire facility, improving transparency and control over software versions in operation.

Automation software as a living system
Beyond tooling, DevOps introduces a cultural shift in automation engineering. Control software is no longer viewed as static but as a continuously evolving component of the machine. This aligns industrial automation more closely with software-intensive industries, where iterative improvement is standard practice.

For manufacturers, this reduces reliance on mechanical redesigns to meet new requirements. Instead, performance enhancements and functional changes can be delivered through controlled software updates, extending machine lifecycles and improving return on investment.

As automation systems continue to converge with IT practices, industrial DevOps is likely to become a standard approach rather than an exception. Its effectiveness, however, depends on the openness of the control architecture that supports it.

www.beckhoff.com