Alexandru Popa

Today, precision in supply planning is more critical than ever. Predictive analytics and AI-powered demand forecasting have emerged as transformative tools, enabling organizations to anticipate supply needs with unprecedented accuracy. However, integrating AI-driven solutions into legacy systems, ensuring data quality and accuracy, and overcoming resistance to technological change are significant hurdles. Moreover, the highly variable and often unpredictable nature of clinical trials adds layers of complexity to demand forecasting. Join this session where Alexandru Popa shares real world case studies of MSD’s use of AI to test small scale anomaly detection in clinical trial innovation projects.

Key Topics to be Addressed:

• Integrating Predictive Analytics into Supply Chains: Practical steps for aligning AI tools with existing systems and processes.
• Improving Data Quality for AI Models: Techniques for collecting, cleaning, and maintaining high-quality data inputs.
• Overcoming Adoption Barriers: Strategies for building organizational buy-in and addressing resistance to new technologies