Integrating legacy data in the pharmaceutical sector

We are in the AI and ChatGPT era, but if you look inside any pharmaceutical manufacturing plant, you’ll likely find legacy equipment. Look inside any pharmaceutical manufacturing plant and you’ll likely find legacy equipment. These machines may still perform optimally and generate valuable data, but they are often disconnected from the plant’s IT systems.

Without this connectivity, which is essential to keep accurate electronic batch records (EBRs), ensuring data integrity becomes costly and complex. Yet, integrating legacy equipment into a pharmaceutical company’s IT architecture can be challenging.

Compliance is a top priority for pharmaceutical manufacturers. In the US market, data integrity shortcomings will result in warning letters from the FDA; in Europe, the equivalent disciplinary procedures are non-compliance reports published in the EudraGMDP database. Companies that fail to rectify issues can face fines or even criminal prosecutions, leading to reputational damage.

Therefore, they must keep accurate batch records containing detailed information on production quality and traceability. However, recent regulations are now compelling companies to switch to electronic batch records (EBR) typically based on a manufacturing execution system (MES) to maximise accuracy and traceability.

Recent regulations are now compelling companies to switch to electronic batch records 

Yet a survey recently found that around seven in ten pharmaceutical companies were still using paper-based records. The primary obstacle preventing transition is the high cost of integration and validation of legacy equipment due to limited interoperability.

Integration challenges  

When pharmaceutical companies purchase equipment, they often prioritise performance, quality, and security over data integration and interoperability. Consequently, they may inadvertently end up with machines incompatible with the pharmaceutical plant’s MES.

A typical pharmaceutical manufacturing company operates multiple software platforms that feed data to the overarching enterprise resource planning (ERP) to guarantee compliance and process optimisation

If the machine can’t communicate with the MES, operators often resort to the manufacturing equipment monitor (MEM) to connect the last mile — the so-called “paper-on-glass” approach. While this method eliminates paper-based processes, operators still manually transcribe the data from one HMI into another. Therefore, this approach doesn’t entirely solve the data integrity challenges associated with paper-based batch records. Furthermore, without a direct integration between manufacturing lines and MES, we can’t implement the so-called ‘review by exception’ (RbE), which is key to speeding up the batch release.

Integrating green and brownfield

Several industry bodies have taken initiatives to promote vendor-agnostic data integration architectures in life science manufacturing. This is great news for new machines — the so-called 'greenfield'. However, these initiatives don’t solve the issue of integrating 'brownfield' legacy equipment. This limitation calls for a new modular approach to integration.

A typical pharmaceutical manufacturing company operates multiple software platforms that feed data to the overarching enterprise resource planning (ERP) to guarantee compliance and process optimisation. Beside MES and Historian, typically involved in GMP related functionality, other platforms include energy data management (EDMS), and Overall Equipment Effectiveness (OEE). All need to connect seamlessly with the manufacturing facility’s hardware.

Key steps to AIL

The best approach to overcome data integration complexities in brownfield applications is to create a middleware between OT and IT – an AIL (automation integration layer). This can contextualise and aggregate data while enabling two-way communication between each machine, whether legacy or new, and the software platforms.

The first step is the adoption of “low code/no code” software platforms. This approach enables users to gain extended connectivity with machines and devices from multiple vendors, including legacy equipment, without having to write custom code for each machine.

The second step is applying modular engineering to software and hardware. With platforms like zenon, operators can create a data integration concept for a specific machine that can be easily applied to other equipment in the same category, building a central equipment software library. This concept also enables manufacturers to expand and integrate new equipment seamlessly if they need to scale up production.

An AIL approach also helps democratise data, as teams across the company can access and analyse data to constantly improve processes, quality, and efficiency

The same modular approach applies to hardware. Edge components close to the machines are essential to ensure data integration and two-way communication with the IT system, with data securely transferred from the shop floor to the cloud.

The combination of scalable hardware solutions and software libraries allows pharmaceutical manufacturers to integrate machines faster and more affordably. A modular approach based upon an AIL can also benefit large multinational organisations that can utilise the same software libraries across different sites across different countries.

A modular engineering approach based on an AIL can benefit pharmaceutical manufacturers beyond data integrity. Ease of data integration and extended connectivity enables companies to realise the efficiency and productivity gains that new technologies like AI can yield.

Better data integrity leads to greater productivity thanks to faster batch releases. An AIL approach also helps democratise data, as teams across the company can access and analyse data to constantly improve processes, quality, and efficiency. With an AIL, pharmaceutical manufacturers can ensure that all equipment, including legacy and new machines, plays a role in this transformation.

Giuseppe Menin is life sciences & process industry manager at COPA-DATA