By: Caleb Eastman, Field CTO for the Americas, Siemens
Legacy Distributed Control System (DCS) automation has been the backbone of verified process control in pharmaceutical manufacturing for decades. A DCS is generally optimized to maximize uptime and control extensive, ongoing processes. These deployments are typically expensive to maintain and upgrade but offer redundancy and capabilities that enable tight control of Critical Process Parameters (CPPs), such as temperature, pressure, pH and flow, which directly affect Critical Quality Attributes (CQAs), and help meet compliance regulations. Control functions are distributed across dedicated user interfaces near the process itself. A centralized operator interface and engineering system can monitor and coordinate the distributed systems.
Legacy DCS solutions provided advantages over contemporary PLC-only controls, delivering built-in redundancy, alarm management, operator visibility and deterministic control. The enhancements to uptime and safety were substantial, and the highly regulated market demanded the capabilities of a DCS for regulatory confidence. The DCS helped manufacturers meet FDA expectations by providing predictable behavior, well-defined change management, and an audit trail, which reduces regulatory risk over long plant lifecycles.
Designed as comprehensive proprietary systems, legacy DCS deployments have limitations for manufacturers who need to upgrade, scale or even simply maintain their current production systems. DCS data is typically exposed through proprietary systems or limited APIs, making high-frequency, contextualized, enterprise-level data difficult to access. Manufacturers can’t afford to shut down existing processes, and the time and costs to revalidate wholesale replacement of production equipment are prohibitive to upgrading at all.The proprietary nature of these systems has traditionally made it difficult or impossible to integrate third-party equipment. It creates challenges in replacing critical components as needed, driving manufacturers to source replacements from an ever-shrinking pool of original components available on the secondary market. Moving to new production systems, integrating modular/agile processes, or simply scaling up, presents high costs and risks. This “vendor lock-in” is a substantial single-source risk for manufacturers.
Modern automation and AI models offer substantial opportunities for pharmaceutical manufacturers to improve processes, scale more quickly from lab to production, and gain the agility needed to meet shifting market demands. Modular, standards-based hardware and a unified data architecture enable real-time analysis at the process level and high-level insight for decision-makers. The ability to reconfigure lines or directly scale control schemas from lab processes through to pilot and production, reduces costs and streamlines process changes. Most critically, standards-based hardware eliminates vendor lock-in by allowing processes to be vendor-agnostic. How can pharma manufacturers leverage modern automation systems without starting from scratch by replacing their legacy DCS?
Leverage an Abstracted Controls Layer
An abstracted control layer is a virtualized, programmable interface for managing equipment and processes. By abstracting operational application assets from underlying hardware through virtualization, businesses can decouple hardware management from the equipment itself, achieving the best of both worlds. Modern, modular equipment that uses the Module-Type-Package (MTP) standard fits easily into this architecture. What many manufacturers don’t know is that a DCS can also run on top of a PLC/SCADA system by leveraging an abstracted controls layer and the MTP standard, bringing the flexibility and modularity of PLCs while retaining the production advantages of the legacy DCS.
To compete, manufacturers need process agility and the ability to scale production dynamically with demand. Standards-based, open-architecture automation supports low-cost process changes while reducing single-source vendor risk. Automation engineers can build control strategies using clean, contextualized data from R&D. Because the logic becomes portable, teams are no longer forced to delay automation until processes are nearly locked.
MTP is an industry standard for equipment integration with higher-level control systems that provides a standardized description of functions and elements. It eliminates the need for machine-specific custom coding. Software-abstracted controls enable rapid process changes, dynamic scaling, and flexible automation. A unified data architecture facilitates robust machine and process analysis and process portability, providing a clear integration path from initial development to production. Machines designed for MTP communication easily integrate into existing systems and communicate with the controls layer. That’s why standards-based hardware is promoted as “plug-and-produce.”
The enhanced portability of controls, enabled by abstracting module behavior, streamlines the introduction of new components or the augmentation of processes, something frequently needed in a legacy DCS that was previously impossible or extremely expensive. By enabling real-time translation at the edge that connects the legacy DCS to the abstracted MTP layer, pharma manufacturers can leverage the flexibility of modular manufacturing while maintaining their legacy DCS. The MTP standard provides a vendor-agnostic description of equipment behavior to the control layer, standardizes the control-programming architecture, and allows equipment to be introduced, removed or reconfigured without rebuilding automation or orchestration controls. In pharmaceutical manufacturing, the plug-and-produce nature of MTP hardware enables early automation introduction by eliminating the need for extensive device-level programming and configuration.
Standards-based controls can also accelerate R&D. Iterative recipe refinement for automation can be incorporated into the experimental cycle rather than treated as a separate engineering phase. A standards-based controls package means manufacturers can port the same control logic from a 2-liter R&D bench through the pilot and into a 2,000-liter production system. This helps processes mature faster by integrating automation into development rather than treating it as a late-stage augmentation.
Data is a Strategic Asset
The basis of modern, agile automation is the ability to share and use data effectively. Data from legacy systems is often siloed, hard to access and cannot be effectively reported on in high-level analysis because it lacks context. Process visibility is supported by having a data orchestration layer: a unified repository of data that is tagged, standardized and contextualized across OT processes and IT systems. This data layer is a crucial tool for systems with varied standards and sources to work efficiently together.
As pharmaceutical companies demand increasingly complex manufacturing solutions, the data orchestration layer can be leveraged both for real-time optimization and process simulation. This data is a key tool for running simulations that support everything from machine design to facility planning. A unified data architecture supports scaling and agility by enabling new equipment to be reliably optimized and integrated quickly.
Pharmaceutical manufacturing processes involve many variables (e.g., process parameters, equipment states, environmental conditions). And traditional, siloed, univariate controls and monitoring can make it challenging to understand correlations and interactions among these variables, leading to an incomplete understanding of process behavior.
A unified data architecture empowers analytics that span the entire process and is a foundational requirement for deploying machine learning (ML) models to detect anomalies or optimize methods. AI models, such as Model-based Design of Experiments (MB-DoE), can predict process behavior and reduce the need for empirical experiments. Predictive and prescriptive maintenance models can minimize unexpected downtime and optimize planned downtime. At the facility level, production decisions can be augmented by models trained to balance supply chain data with production needs to optimize scheduling.
The tight control of CPPs for pharmaceutical production requires data structures that feed centralized monitoring for optimization. The data orchestration layer facilitates the integration of information from new instruments or skids quickly for analysis, minimizing the challenges of siloed data when scaling or making necessary process changes. This enables monitoring of CPPs and Critical Material Attributes (CMAs) as if they had been part of the original DCS. A unified data orchestration layer empowers pharmaceutical manufacturers to employ automation and AI more effectively in an increasingly complex and dynamic marketplace.
Software Abstraction Minimizes Risk
The combination of a data orchestration layer and a software-abstracted controls architecture that supports standards-based hardware provides the insight and flexibility to ensure pharmaceutical manufacturers aren’t fighting obsolescence with vendor-locked deployments. By employing edge hardware to dynamically decouple the DCS control logic within the software abstraction layer, manufacturers create options previously unavailable to them.
New vendors, parts and equipment can be integrated into legacy systems at a lower commissioning cost by standardizing module interfaces. This means a third-party OEM can quickly and cleanly integrate into the existing DCS while employing their own preferred PLCs, controllers or motion systems. The data orchestration layer ensures process, batch and state information is portable across historians, MES and other platforms, empowering analysis and decision-making. Unlike legacy DCS, where control, data and HMI changes are often inseparable, this also minimizes validation risk when making changes, as the primary impact of new hardware or vendors is on the software-abstracted layers, not the broader system.
Meeting regulatory guidelines is a key concern for all aspects of pharmaceutical manufacturing. To meet the requirements of Continuous Process Verification (CPV) and Real-Time Release (RTR) processes, manufacturers depend on the ability to continuously collect, contextualize and analyze process and quality data. Retrospective analysis is not sufficient. Throughout the production process, these requirements impose stringent requirements on data completeness, traceability and integrity. Implementing a unified data architecture alongside a universal or abstracted control layer streamlines regulatory compliance. It reduces regulatory risk by ensuring consistent access to relevant quality data, regardless of underlying equipment or process changes.
Standards-based hardware and controls deliver flexibility and interoperability. A data orchestration layer and software-abstracted controls maintain continuity in how critical parameters, events and states are captured and analyzed when commissioning new machines or obsolescence forces machine replacement.
Manufacturers need to scale validated processes across different equipment types, vendors and facilities to compete in the modern business environment. Facilities can share process data and coordinate production across facilities. Unified data supports consistent quality outcomes from the individual process level to organizational scale. Unified data architectures and standards-based automation together can enable pharmaceutical manufacturers to integrate support for modern automation and machine learning while reducing single-sourcing risk.
Working for Siemens since 2024, Caleb Eastman is Siemens’ Field CTO for the Americas region. Formerly head of product for Kelvin and CTO of LevelOps, Caleb co-founded WinterWinds Robotics in 2018, leveraging his industrial automation and AI expertise to architect life-saving robotic solutions.
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