Quantifying Biopharma Trend Dynamics
A Segmented Analysis of CMOs, CGTs, and Pharma
Jason Beckwith , PhD; Paul Rooney; Stephen Goldrick, PhD; William Nixon, PhD; Stavros Kourtzidis, PhD.
Corresponding author email: [email protected]
Introduction
The research aims to address critical questions related to the workforce in biomanufacturing, with a focus on talent management and skills deficits in the evolving biopharma landscape. The first question explored trends and dynamics characterizing the biomanufacturing workforce, particularly in the context of talent management. The study conducted a quantitative comparison of talent supply to talent demand at both macro and micro levels, focusing on Biopharma 4.0 technology clusters, including Automation, Digitisation, Data Science, Cyber, and Sensors. This analysis revealed a nuanced picture, with mature talent in automation, cybersecurity, and sensors, but a potential skills gap in emerging areas such as agile methods, virtual reality, AI, and digital twins.
In the exploration of the evolutionary trajectory of talent availability in specific expertise domains within the Biopharma industry, an analysis of historic talent frequency was conducted. The study focused on examining the historical trends in talent supply over a period exceeding 20 years. The investigation specifically delved into talent distribution across key Biopharma 4.0 technology clusters, namely Automation, Digitisation, Data Science, Cyber, and Sensors. This comprehensive analysis aimed to shed light on how expertise in these crucial domains has evolved over time within the Biopharma sector.
Figure 1 presented as a circle chart, serves as a visual representation of the talent landscape across the identified Biopharma 4.0 segments. The quadrants of the circle correspond to distinct technology clusters, including Automation, Cyber, Sensors, and Data Science. Within this framework, the inner circle further categorizes talent experience into four brackets: 1-5 years, 6-10 years, 11-15 years, and 16-20 years. The diagram effectively illustrates the frequency of talent distribution based on years of experience within each Biopharma 4.0 quadrant. This visual depiction offers a nuanced understanding of how talent with varying levels of experience is distributed across the specified technology clusters, providing valuable insights into the historical evolution of expertise in Automation, Cyber, Sensors, and Data Science within the Biopharma industry.
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Figure 1 clarifies that talent has matured through developed experience in automation, defined as 11-20 years expertise (from the most recent qualification), compared to other technology clusters with developing expertise. Cyber and sensors seem to be relatively immature in expertise adoption, with 6-10 years’ experience (from the most recent qualification). It also shows that data science is becoming more advanced, following automation, with average experience levels of 10 years (from the most recent qualification). This suggests that automation experience has evolved in correspondence to industry - ‘on the job’ - education, as biopharma technology platforms have been introduced and developed. Figure 1 introduces biomanufacturing applications where ‘automation’ expertise is most required and summarises talent supply at a ‘micro’ level, referring to biomanufacturing employee task application.
The analysis aimed to shed light on how expertise in these crucial domains has evolved over time within the Biopharma sector.
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As global leaders in Biopharma Talent IntelligenceTM, Evolution work with University of Dundee conducting novel research into Biopharma Talent Dynamics, Skills Gaps, Training requirements and cost of recruitment models