As traditional control systems evolve to meet modern challenges, a new paradigm is emerging that promises to revolutionize industrial operations — software-defined automation
Today the process industries operate in an increasingly complex environment where an aging infrastructure is being confronted with modern digitalization demands. Many plants maintain systems that have been in service for over three decades, often resulting in data silos and operational inefficiencies. Organizations frequently manage varying levels of automation across different locations, even within the same company, while facing pressure to integrate modern digital capabilities. The system landscape in process plants ranges from simple applications to complex, highly integrated safety solutions and precisely validated plants. Data silos are increasingly common in industries that rely on plants that have existed for years. Despite these challenges, many technological innovations have been developed to help upgrade older process-control technologies. However, this requires an understanding of the installed system base of process industry manufacturers.
The process industries are experiencing a fundamental transformation in how they control and optimize their operations
Decoupling of hardware and software enables a flexible modification of operations
Software-defined automation (SDA) addresses the aforementioned issues by providing the ability to integrate data flows and dynamically control systems with no need for extensive hardware modifications. Decoupling hardware and software components to enable more flexible and efficient industrial operations represents a significant evolution in process control systems. This approach addresses current challenges in the process industries while preparing organizations for future technological advances. However, the true potential of SDA lies not just in its technical capabilities but also in how it aligns with and supports broader business strategies.
Software-defined automation fundamentally changes how process control systems operate. According to this new concept, the traditional automation pyramid is being altered to a more dynamic network architecture that enables a real-time data flow between field devices and higher-level systems through Industrial Internet of Things (IIoT) technologies, digital twins and edge computing. Perhaps most significantly, this new architecture enables the breakdown of traditional data silos by combining information from various sources to create comprehensive digital systems that support real-time, data-driven decision-making.
Smart IIoT sensors support data-driven production optimization
Software-defined automation is supported by a confluence of advanced technologies that collectively drive its transformative capabilities. At the heart of this approach is edge computing, which brings computation and data storage closer to the data source. This proximity minimizes latency and bandwidth use, while facilitating the real-time processing and immediate responsiveness that is essential for industrial operations. Coupled with IIoT, SDA leverages smart sensors to collect vast amounts of data from diverse sources across the production environment. These sensors feed into robust data networks that support a seamless interaction between operational technology (OT) and information technology (IT), breaking down the barriers of traditional data silos. The cloud infrastructure further amplifies SDA’s potential by offering scalable computing resources for comprehensive data analysis and long-term storage solutions. This blend of technologies ensures that process industries can do more than enhance their operational efficiency — they can also harness valuable insights through predictive analytics and smart decision-making processes. The strategic deployment of these technologies lays the cornerstone for successful SDA, enabling a new level of flexibility and operational optimization.
Virtual controllers offer a new approach to operational continuity
The recent emergence of virtual controllers signifies another paradigm shift in process automation by offering new possibilities for flexible and scalable control architectures. While hardware-based control systems have been the backbone of the process industries for decades, virtual controllers are now being developed that promise to enable more dynamic and adaptable automation solutions. These software-based control entities are intended to emulate traditional hardware controllers, while adding capabilities for rapid reconfiguration, remote management and enhanced integration with other digital systems. However, their implementation follows a nuanced approach in process manufacturing. In critical processes, for instance in oil and gas operations, robust hardware-based components with established safety standards remain essential. The future lies in hybrid architectures where virtual controllers complement rather than completely replace traditional hardware systems.
The transition to software-defined automation doesn’t require a complete system overhaul, which makes it accessible even for plants with decades-old infrastructure. With a strategic approach to brownfield modernization, process manufacturers can implement software-defined solutions while preserving their existing hardware investments. This “evolution-over-revolution” strategy employs minimally-invasive upgrades and modular concepts that allow plants to maintain operational continuity, while gradually breaking down traditional data silos. For example, companies can selectively upgrade specific plant areas that promise the greatest immediate value, using open interfaces to integrate legacy systems with modern software solutions. The key lies in choosing modular solutions that can interface with existing systems, while providing a clear pathway to future capabilities. This ensures that each modernization step adds value without disrupting critical operations.
Modifying automation for new business strategies
Software-defined automation offers a compelling value proposition: it enables process industries to dynamically configure their automation infrastructure to align with their strategic business objectives. This means that the architecture of production systems should be determined by an organization’s core business strategy rather than technical constraints alone. This turns automation into a service that covers the entire user experience, from engineering to operation. Users can decide for themselves when, where and how they want to use the services — at the edge, in the cloud, or on-site — and who will use them. This allows automation to be integrated much more deeply into the extended ecosystem of a production facility, regardless of its location or organizational affiliation. For instance, in the pharmaceutical industry, contract manufacturers might view plant optimization as their core competency, while large integrated pharmaceutical companies may prefer to focus on product development rather than optimizing plant operations. The latter could choose to outsource plant optimization to specialists like system integrators and maintain oversight through key performance indicators.
This flexibility in implementation reflects a fundamental principle: let the experts be experts, but maintain strategic control over business decisions. Organizations can purchase the necessary services based on their strategic needs, though this approach requires finding the right partners with the appropriate domain expertise. For contract manufacturers, the key difference lies in their ability to optimize plant operations and deeply understand the processes. This encompasses critical process parameters and quality attributes, especially in industries like pharmaceuticals, where translating this kind of knowledge into automation language and continuous optimization is crucial.
Cybersecurity becomes indispensable
As systems have become more connected, cybersecurity has emerged as a critical component of software-defined automation implementation. SDA requires robust security protocols in order to maintain the desired level of connectivity and data sharing. In the connected industrial landscape, cybersecurity is essential for the digital transformation. For instance, many organizations rely on the Defense-in-Depth concept, which follows the recommendations of IEC 62443 and provides protection at all levels. The convergence of IT and OT demands a holistic approach to security that takes into account the specific requirements of both areas. Industrial companies are advised to consider network and automation components with integrated security features — along with the corresponding security services for implementing multi-layered security concepts — when transforming their production into a digital enterprise.
Open integration of cloud technologies enables AI-based co-pilots
The evolution of process control technology continues to accelerate, with an increasing integration of artificial intelligence (AI) and cloud-based solutions. Recent developments include AI-based co-pilots for automated sequence function creation, cloud-based engineering environments that enable global collaboration, enhanced security features, and Good Manufacturing Practices (GMP) capabilities. Organizations that are considering software-defined automation should assess their current automation infrastructure, identify specific operational challenges, develop a phased implementation strategy, consider both immediate needs and future scalability requirements and evaluate the cybersecurity implications — all while ensuring alignment with their overall business strategy.
Simulation and digital twins for improved collaboration and faster time-to-market
The evolution of software-defined automation is being driven by technological advances and changing industry demands. Artificial intelligence is becoming increasingly democratized in industrial settings, with AI-powered co-pilots emerging as valuable tools for a variety of use cases in the engineering and operations space. AI assistants are helping engineers and planners with tasks ranging from basic sequence control to complex process simulations, which is making advanced automation capabilities accessible to a broader workforce. Cloud-based engineering environments are facilitating unprecedented levels of global collaboration by allowing teams to work seamlessly across different locations while maintaining consistent standards. The integration of simulation tools is moving toward allowing rapid testing and validation of process changes without risking physical assets. This development is particularly significant because it permits organizations to continuously optimize their processes while minimizing downtime and risks.
Cloud-based engineering environments are facilitating unprecedented levels of global collaboration by allowing teams to work seamlessly across different locations
IT/OT convergence will help drive sustainability
Looking ahead, the convergence of IT and OT will continue to deepen, with software-defined automation becoming the cornerstone of sustainable and efficient industrial operations. Advanced technologies like digital twins, edge computing, and AI-driven analytics will become standard components of automation infrastructure, and that will enable more predictive and prescriptive operational models. As these technologies mature, we can expect to see an increased focus on sustainability initiatives, with automation systems playing a crucial role in optimizing resource usage and reducing environmental impacts.
As the industrial landscape continues to evolve, organizations need to take proactive steps to prepare for a software-defined future. Success in this transformation requires a balanced approach that combines strategic vision with practical implementation. Companies should begin by assessing their current automation infrastructure and identifying areas where software-defined solutions can deliver immediate value, while developing a clear roadmap for a longer-term transformation. The shift to software-defined automation isn’t just a technical upgrade — it’s a fundamental re-imagining of how industrial operations can be more agile, efficient, and sustainable. Those who embrace this change while taking a measured, strategic approach to implementation will be ideally positioned to thrive in an increasingly dynamic and competitive global market. ♦
Edited by Mary Page Bailey
Acknowledgement
Images provided by Siemens
Author
Axel Lorenz is the CEO of Siemens Process Automation. Prior to this leadership role, he has held many different management, engineering and automation roles at Siemens over his more than three-decade career. He holds a B.S. degree in electrical engineering from Berlin University of Applied Sciences.