Automation can help comply with ALCOA
Data integrity is a top concern for pharmaceutical manufacturers. Ensuring that the data collected during production is accurate and accessible is critical but can be challenging when using legacy equipment from multiple vendors. Here, Giuseppe Menin, Life Sciences & Process Industry Manager at automation supplier COPA-DATA, explains how an automation integration layer can help manage the data life cycle more efficiently and effectively.
Pharmaceutical companies today must keep up with soaring demand for a broader range of medicines. According to analysts IQVIA, global medicine use will grow by 12 per cent by 2028, driven by innovative therapeutics and the availability of less expensive generics and biosimilars.
Growing product volumes and varieties will inevitably lead to more data to collect and process throughout the production line. Frequent product changeovers mean manufacturers must rapidly switch from one recipe to another. Traditional paper-based compliance processes are no longer fit for purpose.
The ALCOA principle
This is particularly true of large global businesses. Given that many industrial companies grow through acquisitions, a common hurdle for data capture is the communication barriers of legacy systems and infrastructure. Consider AB InBev as an example.
Integrity relies on attributable, legible, contemporaneous, original and accurate (ALCOA) data. This principle recently expanded to ALCOA+ to include further requirements: complete, consistent, enduring and available.
Regulatory agencies like the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have issued guidance on data integrity based on ALCOA+.
In the US, companies that fail to comply with these requirements face the FDA’s warning letters. In Europe, data integrity failures can result in Non-Compliance Reports published in the EudraGMDP database. If they cannot rectify data integrity issues, companies can face fines or even criminal prosecutions, not to mention the costs due to reputational damage.
Data integrity challenges
Most pharmaceutical manufacturers are aware of the importance of data integrity. Many are investing in digitalization and automation to streamline the process. Yet, gathering, analyzing, sharing and storing data accurately and consistently continues to present a challenge.
Despite the trend toward digitalization, some processes throughout the data life cycle remain paper-based. Human errors are the primary risk associated with manual data entry. In addition, paper-based records may become illegible over time and are easier to tamper with than digital entries.
But paper isn’t the only issue. A Manufacturing Execution System (MES) alone may not be enough to solve data integrity problems. If processes are still predominantly manual, the result is a practice known as “paper-on-glass”. This process involves transcribing data from paper records to a digital platform or from one digital platform to another. As it relies on manual entries, the paper-on-glass approach still remains susceptible to human errors.
Another potential obstacle comes with using machinery from multiple vendors, some of which may be decades old. Each device may operate on a different protocol, which makes machine-to-machine and machine-to-cloud data communication challenging. The lack of standards complicates this situation.
Whereas the OPC Unified Architecture (OPC UA) can help standardize machine-to-machine communication, there is no equivalent standard for sharing data between shop floor equipment and an overarching system.
Automated layer integration is the answer
Automation layer integration means connecting all machines across a pharmaceutical facility to a single automation system, from the shop floor to the MES and the IT infrastructure. This is where the zenon Automation Integration Layer (AIL) software can help.
Thanks to more than 300 connection options, COPA-DATA’s zenon software platform can connect to machines from multiple vendors, including legacy equipment.
zenon automatically contextualizes and archives any data gathered from any machine in the system. This process eliminates the risk of inconsistent data gathering or data loss.
Preventing human errors
A core strength of automation software like zenon is its ability to automatically detect incomplete, missing and incorrect entries. zenon can even record data automatically when connected with sensors.
Another benefit is that data becomes accessible in real time. The system makes information on a specific batch immediately available during production while generating a quality documentation report when the batch is complete. This means that operators can detect and rectify faults while batches are still in progress an alarm notifies the user as soon as zenon detects a problem. This automated approach significantly accelerates time to market and reduces costs due to having to rectify faults further in the process.
Accurate recipe life cycle
In pharmaceutical manufacturing, a recipe contains critical information about a specific product, including instructions on raw materials and processes. Entering recipes correctly into production equipment is essential to prevent quality or safety issues downstream.
zenon allows operators to choose from pre-set recipes, eliminating human errors when setting machines up for a new production cycle.
Users can also record data digitally using mobile devices or the machine HMI, eliminating the need for paper checklists. In addition, a data entry application guides users through every step of the process. Operators cannot move to the next step until the information is complete and the figures entered are within specific tolerance ranges.
Towards automated data integrity
As pharmaceutical manufacturers continue to expand and diversify their product offerings, keeping on top of compliance is paramount. Managing large data sets efficiently and effectively is critical to ensuring medicines are safe and compliant before hitting the market.
Automated layer integration empowers pharmaceutical companies to comply with data integrity requirements while reducing both their costs and time to market.
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