The more fragmented that processes become, the more critical it is that they can be assessed and managed for consistent quality. During the peak of the pandemic, as drug and medical device production continued at pace, manufacturers experienced a deepening challenge of how to monitor, analyse, report and act on quality performance and any emerging deviations. Siniša Belina, a senior life sciences consultant at Amplexor, delves deeper into the potential of modern tools and holds up examples of emerging best practices.

Extract:

‘The Case for Smart, Real-time Quality Monitoring in Life Sciences Manufacturing‘

Across a whole swathe of industries, the Covid-19 pandemic has shone a light on restrictive business processes, information silos and poor supply-chain visibility. In life sciences manufacturing, for instance, a range of challenges linked to quality management have been exposed and starkly felt.

On the one hand, public safety measures over the last 18+ months have put physical distance between team members – hampering the usual form-filling, manual signoffs and Excel-based record-keeping associated with monitoring traditional manufacturing processes. And informal discussions at the watercooler, in which patterns of emerging problems might have surfaced, have simply not happened.

Anticipation is Everything

Increased practical barriers to quality assurance, added to the missed opportunities to spot and pre-empt issues along the supply chain using data analytics, have helped drive a renewed business case for intelligent, joined-up quality monitoring based on a single, global, real-time graphical view of all aspects of production.

In the meantime, other parts of pharma organisations have seen first-hand the benefit of pre-emptive signal detection. This is most visible in pharmacovigilance, where use of smart systems present a department’s best chance of accurately processing reams of incoming adverse event data, and meeting deadlines – with the confidence that nothing critical will be missed.

Proactively monitoring and establishing alerts for potential manufacturing quality issues/product deviations, or process non-conformance, would be another logical use case for the same kind of software solution.

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