With AI becoming more prevalent in the pharma space, Raman Bhatnagar, Pharmaceuticals lead, Aspen Technology, explores the power of machine learning and analytics, and how this helps anticipate failure prediction in pharma manufacturing.
Key insights:
- Digital solutions can help pharma manufacturers prevent equipment breakdowns and ensure process consistency, optimising production and protecting the supply of product to customers.
- Deloitte estimates that predictive maintenance and multivariate analytics tools can improve overall equipment effectiveness (OEE) by up to 20% and deliver as much as a 5% increase in yield.
- GSK is currently creating a future-ready supply chain with predictive and prescriptive maintenance using Aspen Mtell, receiving up to 35 days’ advance warning of potential issues.
One of the biggest challenges the pharmaceuticals industry is facing is to ensure fast time to market. The speed at which companies developed and brought new Covid-19 vaccines on stream during the pandemic was widely celebrated. The fastest any vaccine had previously been developed – from viral sampling to approval – was four years – for mumps in the 1960s. Yet the Pfizer-BioNTech COVID vaccine was approved for use on 2 December 2020, just seven months after the start of clinical trials, with other vaccines following close behind.