Today’s industrial automation continues to evolve at a blistering speed, which means traditional distributed control systems (DCS) have to keep up to ensure continuous integration into modern, digital infrastructure.
Their very nature is to multitask, controlling and coordinating complex processes within industrial settings. But what if the growth of industrial automation becomes an insurmountable challenge?
This multitasking can only go that far; it needs to modernise in order to juggle protocol compatibility, legacy infrastructure, cybersecurity and human factors. The solution is a hybrid approach that allows DCS to evolve as the industrial landscape grows.
In essence, a hybrid DCS combines traditional industrial automation technologies with modern digital tools like cloud connectivity, edge computing and Ethernet-based protocols to manage and optimise complex industrial processes. It allows legacy systems to coexist with newer technologies, improving flexibility, visibility and operational efficiency without full system replacement.
Challenges abound
In order to realise a world, or rather an industrial world built on hybrid DCS, it important to take a realistic look at some of the most pertinent challenges it faces:
• Protocol complexity and latency risk: Integrating legacy fieldbus protocols like Foundation Fieldbus, PROFIBUS and Modbus with modern Ethernet-based standards such as EtherNet/IP and PROFINET introduces latency through gateway solutions. In time-sensitive environments like chemical processing, even a 10 to 15 millisecond delay can jeopardise control loops and compromise product integrity.
• Legacy infrastructure: Long-standing systems continue to deliver dependable performance and reflect years of process refinement. Wholesale replacement poses significant risks, particularly in safety-critical sectors. Digital twins offer a simulation bridge but depend on detailed documentation, often missing in older setups.
• Cybersecurity: The move from isolated networks to cloud-connected systems has expanded the cybersecurity threat landscape. Remote access and edge computing introduce new vulnerabilities, requiring layered defence strategies.
• Adaptation: Traditional intrusion detection must evolve to recognise industrial traffic, while zero-trust models need adaptation for real-time control.
• Regulatory and compliance pressure: Hybrid architectures must comply with both legacy safety standards and emerging cybersecurity mandates like the IEC 61508 NERC CIP must coexist with newer regulations such as the EU’s NIS Directive.
• Timing and data harmonisation challenges: Precise timing is vital in industrial control, yet hybrid systems introduce latency and jitter that threaten synchronisation. Applications like power protection and motion control require microsecond-level coordination, which cloud and edge integration can disrupt.
• Harmonising data across systems with differing formats and algorithms requires robust mapping and metadata strategies.
• Edge computing and incremental integration: Edge devices enable local optimisation and reduce network load, but complicate failover and resource planning. Integration must account for network topology and reliability.
Establishing effective hybrid DCS
Considering the above, how can DCS becoming truly hybrid without tripping over the above, very pertinent challenges? For one, the most effective hybrid DCS deployments typically adopt incremental integration strategies rather than attempting wholesale system replacements. This allows organisations to address specific integration challenges systematically while maintaining operational continuity and preserving safety-critical functions. Successful projects also often begin with pilot implementations that demonstrate value while identifying potential issues.
Safety is also paramount particularly as systems become more complex. Here, organisations must demonstrate that hybrid architectures maintain or improve safety performance compared to legacy systems. This, for example, requires comprehensive hazard analysis that considers both technological and human factors.
Future technical debt can also be avoided by proper architectural planning, mitigating complicated future expansion efforts. This means organisations must balance immediate integration needs with long-term strategic objectives. This could mean accepting some redundancy in the short term to preserve flexibility for future enhancements.
Technologies that help
There is no doubt emerging technologies offer potential pathways toward more manageable hybrid architectures:
• Software-defined networking (SDN) capabilities could simplify network management by providing centralised control over distributed networking resources.
• Advances in industrial IoT devices may reduce protocol translation requirements through improved native connectivity options.
• Container-based deployment models might enable more flexible application deployment across hybrid infrastructure. However, these technologies must mature sufficiently to meet industrial reliability and security requirements.
• AI and ML capabilities will likely play increasing roles in managing hybrid system complexity. Predictive maintenance algorithms could identify potential integration issues before they impact operations.
Yet, these advanced capabilities must be implemented with careful attention to human factors and safety considerations.
Digital twin technologies will likely become essential tools for managing hybrid system complexity. These virtual representations enable simulation and validation of proposed changes before implementation, reducing risks associated with system modifications.
Advanced digital twins might eventually enable ‘what-if’ analysis that helps operators understand the potential consequences of different response strategies during emergency situations.
Ultimately, the integration challenges faced by hybrid DCS architectures reflect a broader industry transformation toward digitally enabled manufacturing operations. While these challenges are significant, they represent the necessary steps toward more flexible and capable industrial control systems.
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