The COVID-19 pandemic has brought to light a series of challenges related to quality management in the manufacturing of life sciences. On the one hand, public safety measures over the past 18+ months have put a physical distance between team members, hampering routine form filling, manual approvals, and record keeping in Excel. associated with the monitoring of traditional manufacturing processes. And the informal talks at the water cooler, during which emerging issues could have arisen, simply did not take place.
Increased practical barriers to quality assurance, added to the missed opportunities to detect and anticipate issues along the supply chain using data analytics, helped re-launch a business case for intelligent and joint quality monitoring based on a single, comprehensive, real-time graphical view of all aspects of production.
In the meantime, other sectors of pharmaceutical organizations have seen firsthand the benefits of preemptive signal detection. This is particularly visible in pharmacovigilance, where the use of intelligent systems presents the best chance for a service to accurately process quantities of incoming adverse event data and meet deadlines, with the confidence that nothing critical will be lack.
Proactive monitoring and alerting for potential manufacturing quality issues / product deviations or process nonconformities would be another logical use case for the same type of software solution.
The case for leveraging intelligent, real-time quality analytics is strong and growing. Particularly when artificial intelligence / machine learning is involved, it’s about being able to detect emerging patterns early on, at the first sign of deviation / non-compliance. Problems can range from recurring problems with the equipment to varying levels of impurities / product instability, the cause of which requires further investigation.
The need for continuous, real-time quality monitoring
Until now, the tendency has been to view quality monitoring as a compliance activity, linked to the regulatory requirement for a periodic review of product quality. Yet this approach does not necessarily invite continuous real-time quality monitoring, nor with it the ability to avoid production line problems before avoidable risks and costs are incurred. If any issues arise during the preparation for a review, they are likely to be properly established and now require retrospective investigation to determine what went wrong, the likely root cause, what the impact may have been, and what corrective actions are now needed.
This is a lost opportunity, especially since much of the data needed for more continuous and faster quality monitoring is being gathered anyway – in order to create this annual review report at a later date.
The problem is that this data is not amalgamated, compared or processed immediately – to produce actionable insights and / or to trigger alerts.
Switching to a situation allowing continuous and active quality monitoring does not require a major upheaval. The main criterion is that the systems can draw on data from all functional or departmental silos, so that deviation details, environmental data, complaint information and CAPA records can be combined and cross-checked. stolen. Best of all, analysis and reporting tools should also be able to draw on historical data, allowing for live comparisons and allowing immediate detection of smart signals whenever incoming data deviates from current parameters and past models.
All life science manufacturers strive to be more efficient and efficient with their resources, thereby improving quality without expanding their internal resources, and intelligent, real-time quality monitoring and reporting directly responds to this. requirement.
Leverage existing data for immediate feedback
In addition, it is much easier to implement such capabilities in today’s “cloud-first” and “platform-based” enterprise IT environment. Here, adding new features and new use cases is often just a matter of activating additional features or user groups that can draw on already centralized and pre-integrated data, for display and application. personalized for their own purposes.
Since, as already stated, much of the data exists or is already captured, the addition of intelligent analysis and reporting capability can generate immediate return by saving resources / reducing waste, and ultimately preventing a batch of substandard products from leaving the production line.
Rather than performing quality reviews and in-depth investigations in retrospect, intelligent on-the-fly reports give manufacturing teams the ability to explore emerging issues and perform real-time root cause analysis. If the levels of impurities exceed accepted standards, for example, teams can act quickly to determine if the problem may be due to a change in air humidity. This in turn could be attributed to a change in the heating / ventilation / air conditioning (HVAC) system.
Merging data sources
The starting point for the shift in focus towards continuous quality monitoring should be a merger of data sources, ideally into a centralized, cloud-based repository that underpins multiple use cases. An integral core data source will support the ability to configure the analytics, visualization, and reporting experience to meet specific user needs, as well as the ability to set thresholds or parameters to trigger push notifications or notifications. alerts to the people concerned. This will deliver efficiency, cost and risk reduction benefits that will be a compelling business case for intelligent real-time quality analysis in life science manufacturing.