THANK YOU FOR SUBSCRIBING
Pharma Tech Outlook | Tuesday, April 16, 2024
A vision for transformation should be defined, with domains of impact and use cases prioritized.
Fremont, CA: Biopharma companies are utilizing digital and analytics to optimize manufacturing, reduce supply chain volatility, and accelerate technology transfers. However, over 70% have made little progress, indicating a need for further development and adaptation in the face of the COVID-19 pandemic.
Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.
The Use Of Digital And Analytics In Biopharma Operations Frequently Fails To Meet Three Success Criteria:
โ The Vision For The Business Transformation. Companies often lack a comprehensive vision for transformation, leading to scattered benefits and potential and senior leaders may be unaware of the potential of digital and analytics.
โ The Vision For The Technology Landscape. Companies should comprehend their IT landscape to enhance transformation strategies, simplify system architectures, and utilize dashboards that align with key performance indicators.
โ The Vision For The Organization Model. Companies frequently assign projects to large, unmanageable teams that lack the necessary resources and central governance for efficient business deployment.
The Four Principles Listed Below Can Assist Biopharma Leaders In Identifying Critical Gaps And Leading Successful Manufacturing Transformations.
1. Start with A Leadership-Backed, Impact-Driven Strategy And Road Map
The leadership team must understand the potential of digital and analytics in enhancing pharma operations. Many companies lack C-suite leaders responsible for digital manufacturing implementations, highlighting the need for more training. A vision for transformation should be defined, with domains of impact and use cases prioritized. An actionable roadmap should guide strategy implementation, considering technology and business priorities. An agile funding structure should focus separately on discovery, minimum viable products, and scaling initiatives.
2. Accelerate Transformation with Experienced Leaders, Skilled Staff, And Multifunctional Teams
To transition to digital and analytics, companies should prioritize people, leadership, skills development, and organization. They should seek individuals with the right experience, organizational leadership, digital and analytics expertise, entrepreneurial skills, and business-change knowledge. A "translator" can bridge the gap between teams, while internal training and external sourcing can enhance competitiveness. Full-scale transformation requires reskilling the workforce, investing in capability building, and creating an effective organizational model.
3. Implement a Strategy, Architecture, And Governance For Data
Data is crucial for digital and analytics transformation, especially in operations. Manufacturers should develop a comprehensive data strategy based on a vision and business case to avoid data-backbone or quality issues. A data architecture should be defined using accessible databases for clean and linked-up data. End-to-end data governance should be set up parallel to data development to democratize data use and improve quality. This model includes organizational construct, roles, processes, data standards, and tools.
More in News