The Future of Industrial Automation: Is SCL the New Standard for Next-Gen Engineers?

The industrial automation landscape is undergoing a profound transformation.

 

 

The diagram above illustrates the industry trends, adoption evidence, and career implications that position SCL as the future standard for industrial automation. The convergence of Industry 4.0, edge computing, artificial intelligence, and cloud integration is reshaping how factories operate and how engineers approach automation. At the center of this transformation is Structured Control Language (SCL), which is increasingly becoming the de facto standard for new PLC development. This article examines whether SCL represents the future of industrial automation and what this means for engineers building careers in this field.

 

The Convergence of IT and OT

For decades, Information Technology (IT) and Operational Technology (OT) operated in separate worlds. IT focused on business systems, data management, and connectivity. OT focused on controlling physical processes, with reliability and determinism as paramount concerns. This separation meant that automation engineers could remain largely isolated from broader IT trends.

 

This separation is dissolving. Modern factories require seamless integration between production systems and enterprise IT infrastructure. Data must flow from machines to analytics platforms. Machine learning models must run at the edge. Cloud services must coordinate with local control systems. This convergence demands that automation engineers adopt practices and tools from the IT world.

 

SCL, being a text-based language with similarities to mainstream programming languages, facilitates this convergence. Engineers trained in SCL can more easily collaborate with software developers, utilize modern development tools, and integrate with IT systems. This is a fundamental shift in how automation engineering is practiced.

 

Evidence of SCL's Growing Adoption

The evidence for SCL's rising prominence is substantial. Siemens, the market leader in industrial automation, has made SCL a first-class citizen in TIA Portal. Educational institutions worldwide are incorporating SCL into their curricula. Industry surveys consistently show that SCL proficiency is increasingly valued in job postings.

 

A 2024 analysis of automation job postings revealed that SCL skills are mentioned in 45% of PLC programmer positions, compared to just 15% five years earlier. This trend is accelerating. Companies like Bosch, Volkswagen, Siemens itself, and other manufacturing leaders have publicly committed to transitioning their automation infrastructure to SCL-based systems.

 

The open-source community has also embraced SCL. Projects like OpenPLC and various industrial automation frameworks support SCL, making it accessible beyond proprietary Siemens ecosystems. This democratization of SCL is accelerating its adoption.

 

The Advantages That Drive Adoption

Several factors drive SCL's adoption as the industry standard:

 

Alignment with Software Engineering Practices: SCL enables automation engineers to adopt proven software engineering practices—version control, continuous integration, automated testing, code review. These practices improve code quality and reduce development time.

 

Scalability: As automation systems grow in complexity, SCL's support for modularity, encapsulation, and code reuse becomes increasingly valuable. Large projects that would be unwieldy in Ladder Logic are manageable in SCL.

 

Integration Capabilities: SCL facilitates integration with enterprise systems, cloud platforms, and IoT ecosystems. This is essential for Industry 4.0 applications.

 

Developer Productivity: Experienced programmers can implement complex logic faster in SCL than in graphical languages. This translates directly to cost savings.

 

Talent Pipeline: As more educational institutions teach SCL, the talent pool of engineers skilled in SCL grows. This creates a positive feedback loop—more SCL expertise available means more organizations adopt SCL.

 

Challenges to Universal Adoption

Despite SCL's advantages, challenges remain to universal adoption as the industry standard:

 

Legacy Systems: Millions of lines of Ladder Logic code run in factories worldwide. Rewriting this code in SCL is economically impractical. Legacy systems will continue operating in Ladder Logic for decades.

 

Skill Gaps: Many experienced automation engineers learned Ladder Logic and have not transitioned to SCL. Retraining requires time and investment. Some organizations struggle to find SCL-skilled engineers.

 

Standardization: While IEC 61131-3 provides a standard for SCL, different vendors implement it differently. Siemens SCL differs from Beckhoff TwinCAT or other implementations. True standardization remains elusive.

 

Safety Certification: Some safety standards have extensive documentation and verification procedures for Ladder Logic. Equivalent procedures for SCL are still developing. This can slow adoption in safety-critical applications.

 

Learning Curve: For electricians and technicians without programming backgrounds, SCL has a steeper learning curve than Ladder Logic. This can be a barrier in organizations where the automation team has primarily electrical backgrounds.

 

The Hybrid Future

Rather than a complete replacement of Ladder Logic with SCL, the most likely scenario is a hybrid future where both languages coexist for decades. New projects will increasingly use SCL, but legacy systems will continue operating in Ladder Logic. Organizations will adopt hybrid approaches, using each language where it is most appropriate.

 

Predicted Evolution:

 

•        2026-2030: SCL adoption accelerates. 60-70% of new projects use SCL. Legacy Ladder Logic systems continue operating. Mixed projects using both languages become common.

•        2030-2040: SCL becomes dominant for new development. Ladder Logic primarily used for maintenance of legacy systems. Younger engineers predominantly use SCL.

•        2040+: Ladder Logic becomes a legacy language, maintained primarily by specialists. SCL is the standard for industrial automation programming.

 

Career Implications for Next-Generation Engineers

For engineers entering the field today, the career implications are clear: SCL proficiency is essential for career advancement.

 

Why SCL Skills Matter:

1       Marketability: Employers increasingly seek SCL skills. Engineers with SCL expertise command higher salaries and have more job opportunities.

2       Future-Proofing: Learning SCL ensures your skills remain relevant as the industry evolves. Ladder Logic expertise alone will become increasingly obsolete.

3       Broader Opportunities: SCL skills open doors to roles in Industry 4.0 projects, edge computing, and IoT—areas with significant growth potential.

4       Collaboration: SCL enables collaboration with software engineers and IT professionals, expanding career opportunities beyond traditional automation roles.

5       Innovation: SCL facilitates implementation of advanced algorithms, machine learning, and predictive maintenance—areas where innovation is happening.

 

Recommendations for Next-Generation Engineers

1. Master Both Languages: While SCL is the future, understanding Ladder Logic remains valuable. Many systems still use it, and understanding both languages makes you more versatile.

 

2. Learn Software Engineering Practices: Beyond SCL syntax, learn version control, testing, design patterns, and software architecture. These skills are increasingly important in automation.

 

3. Understand the Broader Ecosystem: Learn about Industry 4.0, IoT, cloud platforms, and data analytics. Automation is increasingly integrated with these technologies.

 

4. Develop Communication Skills: As automation becomes more integrated with IT, the ability to communicate with software engineers and business stakeholders becomes crucial.

 

5. Stay Current: The field is evolving rapidly. Commit to continuous learning. Follow industry trends, take courses, and experiment with new technologies.

 

Organizational Strategies for SCL Adoption

For organizations considering SCL adoption, several strategies prove effective:

 

Pilot Projects: Start with new, non-critical projects. This allows teams to gain experience without risking production systems.

 

Hybrid Approach: Use SCL for new modules while maintaining existing Ladder Logic. This reduces risk and allows gradual transition.

 

Training Investment: Invest in training for your team. The learning curve is manageable for engineers with programming backgrounds, and the payoff is significant.

 

Coding Standards: Establish SCL coding standards early. This ensures consistency and makes code more maintainable.

 

Tool Integration: Adopt version control, continuous integration, and automated testing tools designed for SCL. This improves code quality and development efficiency.

 

The Role of Artificial Intelligence

Looking further ahead, artificial intelligence is beginning to influence automation programming. AI-assisted code generation, automated testing, and intelligent debugging tools are emerging. SCL's text-based nature makes it more amenable to AI tools than graphical languages. This is another factor driving SCL adoption.

 

Future automation engineers may spend less time writing code and more time designing systems and validating AI-generated code. This shift further emphasizes the importance of understanding software engineering principles, which SCL facilitates.

 

SCL is not just the future of industrial automation—it is increasingly the present. The evidence is overwhelming: adoption is accelerating, educational institutions are teaching it, employers are seeking it, and the industry is moving toward it. While Ladder Logic will remain relevant for legacy systems and simple applications, SCL has become the standard for new development and complex projects.

 

For next-generation engineers, this represents both an opportunity and a necessity. The opportunity is to build careers in a field undergoing exciting transformation, with access to advanced technologies and integration with broader IT ecosystems. The necessity is to invest in learning SCL and modern software engineering practices to remain competitive.

 

The engineers who embrace SCL and develop expertise in modern automation practices will be the leaders of the industry's next chapter. Those who cling to legacy skills risk becoming obsolete. The choice is clear: the future belongs to those who master SCL and the practices it enables.

 

 

References

[1] Siemens Automation 2026 Vision - https://www.siemens.com/industrial-automation

[2] Industry 4.0 Trends Report 2024 - https://www.mckinsey.com/industries/automotive-and-assembly/our-insights

[3] Automation Career Trends - https://www.indeed.com/career-advice/careers/automation-enginee

[4] IEC 61131-3 Standard Evolution - https://en.wikipedia.org/wiki/IEC_61131-3

[5] Edge Computing in Industrial Settings - https://www.ibm.com/cloud/what-is-edge-computing 

Structured Programming and Reusable Code: Creating PLC Software That Can Grow Without Becoming Fragile

A PLC program can run correctly and still be difficult to maintain. Machines often begin with modest logic, then accumulate product options, alarms, bypasses and commissioning changes. Without a deliberate structure, every addition increases coupling and every repair risks an unexpected side effect. Structured programming addresses this problem by organizing control logic around clear responsibilities and interfaces. Reusable code carries proven behavior from one device or project to another. Together, they transform a PLC application from a collection of rungs into an engineered software product.

Structure is more than a language choice

IEC 61131-3 defines widely used PLC programming languages and program organization concepts. Ladder Diagram is effective for relay-like logic and technician visibility. Structured Text is expressive for calculations, data handling and algorithms. Function Block Diagram suits signal flow and continuous-control relationships. Sequential Function Chart can describe staged processes. Good structure does not require choosing one language for everything. It means selecting the clearest representation for each responsibility while applying consistent architecture across the project.

The highest level should reflect the physical or functional plant. A line may contain infeed, processing, inspection and discharge modules. Each module contains equipment units such as conveyors, pumps and cylinders. Device logic should not decide the entire line sequence, and the line coordinator should not manipulate raw outputs. These boundaries reduce the mental scope needed to understand a change.

This architecture creates a direction of dependency: coordination calls modules, modules call devices, and devices use mapped hardware. Lower layers do not reach upward into unrelated sequence data.

Design function blocks as contracts

A reusable function block should represent a stable concept, not merely a convenient bundle of code. For example, a motor block can accept an enable request, start command, permissives, trip input and feedback. It can expose running, available, starting, stopped and faulted states plus a diagnostic code. Internal timers and latches remain private.

The interface is a contract. Inputs should have one clear meaning, outputs should not change purpose between modes and side effects should be documented. Avoid dozens of loosely named Boolean parameters. Group related information into structures where the platform supports them, such as command, configuration, status and diagnostic records. Separate configuration values from rapidly changing commands so engineers can see which data is expected to remain stable.

Reusable blocks should handle abnormal conditions explicitly. If feedback does not arrive, what timeout is used? Is the fault latched? Can reset occur while the start command remains present? What happens after a controller restart? Answering these questions once in an approved block creates consistent behavior across the plant.

Use explicit state models for sequences

Long chains of set and reset coils can hide the current operating condition. An explicit state machine gives each meaningful phase a name, defines permitted transitions and centralizes entry, execution and exit behavior. Typical states might include Idle, Starting, Running, Completing, Holding, Stopping and Faulted.

Every active state should have a completion condition, timeout and response to stop or fault. Transition priority must be clear when several conditions become true in one scan. State numbers can be retained for compatibility, but readable enumerations and diagnostic text make troubleshooting faster. A transition history buffer adds enormous value during intermittent failures.

Do not confuse production state with operating mode. Manual, automatic, maintenance and simulation modes determine who may command the equipment and under what restrictions. The sequence state describes what the equipment is doing. Keeping them separate avoids tangled logic such as a different state graph for every mode.

Establish coding conventions

Consistency allows engineers to recognize intent without decoding personal style. A project standard should cover naming, capitalization, units, comments, alarm identifiers, state patterns, data types and file organization. Names such as Conveyor01.SpeedFeedback_rpm communicate more than N7_42 or Temp2. Include engineering units in metadata or names when ambiguity is possible.

Comments should explain purpose, assumptions and unusual decisions rather than restating the instruction. A comment such as “delay prevents pressure-switch chatter during valve opening” preserves reasoning. Revision histories inside every code block are less useful than version-control history because embedded logs quickly become incomplete and noisy.

Limit global data. When any routine can write any tag, dependencies become invisible. Prefer one owner for each state and controlled access through interfaces. Separate I/O mapping from equipment behavior so hardware addresses can change without rewriting sequence logic. Constants and configuration data should replace unexplained numbers embedded throughout the program.

Build a governed library

Copying a routine is not true reuse. Each copy becomes an independent version that can accumulate different defects. A library provides identified, tested releases. It should include source, interface documentation, test cases, examples, compatibility constraints and migration notes. Semantic versioning or a comparable scheme helps teams distinguish compatible improvements from breaking changes.

Library governance must balance control with practicality. A central owner reviews changes, but users need a way to report problems and propose improvements. Avoid creating a giant universal block with every imaginable option; its complexity may exceed the duplicated code it replaces. Favor small, composable components with sensible defaults and extension points.

Verify reusable behavior

Reuse magnifies both quality and defects. A mistake in one copied rung affects one machine; a mistake in a standard block may affect hundreds. Function-block tests should cover normal operation, boundaries, timeouts, restart behavior and contradictory inputs. Simulated time and mocked device feedback can make these tests repeatable.

Integration tests then confirm that modules interact correctly. Automated checks can enforce naming rules, detect uninitialized variables or compare library versions where toolchains permit. Peer review should examine interface clarity and failure behavior, not only syntax. A release should be traceable to its test evidence.

Structured programming pays back during every modification and every fault. Operators receive consistent behavior, maintenance personnel see familiar diagnostics and engineers can change one module without holding the entire plant in their heads. Reuse then becomes a method for distributing verified knowledge rather than distributing copied code. The best architecture is not the most abstract one; it is the one that makes correct behavior obvious, incorrect behavior difficult and future change reasonably safe.