Navigating Digital Transformation in the Pharmaceutical Industry: Challenges and Solutions
The pandemic may have fast-tracked digitalisation in the pharmaceutical industry by more than five years, but much remains for companies to accomplish, according to Kelly Doering, Senior Director at Aspen Technology.
“44% of 400 pharma industry professionals in Europe and the U.S. participating in the latest research by AspenTech and FT Longitude pointed to increased agility and operational efficiency as their foremost digital transformation goals,” says Doering.
Yet many barriers to progress still persist, with cultural immaturity one of the most prominent. “Nearly 23% of pharma companies agreed C-suite changeovers complicate their organisation’s digital transformation. In addition, more than one-in-three pharma businesses (35%) experience an element of risk aversion when it comes to digital transformation.”
Siloed data directly impedes cross-functional collaboration for 48% of respondents, with this being more pronounced for the largest pharma businesses surveyed ($1bn+ USD).
“53% of that group claim data silos negatively impact internal collaboration,” Doering continued. “Regulation and its complexity is widely seen as another major challenge. In line with this, a third of the survey sample recognised ‘improving regulatory compliance’ as the third most important goal for digital transformation.”
Navigating forward in pharmaceutical manufacturing
Given the above challenges, it is clear that pharmaceutical manufacturers need to find a way forward that enables them to become more agile and digitally mature so they make better use of data.
“They must build the capabilities to ensure regulatory compliance. Within manufacturing and its broader supply chain, they must drive operational efficiencies end-to-end.
“The first step towards achieving these aims should include an assessment of the organisation’s digital maturity with emphasis on the most significant opportunities for improvement. It is key here to gain a holistic view of all the data generated inside and outside the plant associated with manufacturing and delivery. An overarching digital reference architecture will unify and integrate data from existing systems to give operations complete transparency. Connecting suppliers is a critical part of the process with data governance, access and security protocols in place.”
The second action needed is to transform paper processes to aggregate data for a complete view while eliminating costly manual involvement, suggests Doering.
“Advanced software applications for planning, scheduling and batch records can help fill gaps in valuable data that can be collected, analysed and used to improve outcomes.”
Step three is to determine cloud strategies early on.
“Moving to the cloud can be accomplished in stages. Cloud solutions can augment validated on-premises solutions, and organisations can extract data to feed machine learning. They also help with implementation of advanced digital solutions in remote locations lacking adequate IT support.”
Finally, businesses should look to leverage industrial AI capabilities to empower industrial systems to operate semi-autonomously to improve patient care and increase profitability.
These steps will unlock the full potential of Industrial AI to drive greater efficiency, resilience, quality and performance.
“At the operational level, workers are supported with knowledge, insights and recommendations to guide their workflow and drive excellence,” says Doering. “Complete automation of processes can occur when closed-loop systems are implemented.”
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