Forecasting in Action: Use Cases in Clinical Supply Management
As oncology trials grow increasingly complex and costly, Randomization and Trial Supply Management (RTSM) systems must evolve to meet the diverse needs of sponsors ranging from small biotechs to large pharmaceutical companies. This document presents three real-world use cases that illustrate how different forecasting algorithms within Prancer® RTSM can be matched to a trial's scale, complexity, and team experience level. From simple Min/Max buffer management for first-in-human studies to fully dynamic unpredictable and predictable demand forecasting for large phase 3 trials, the right algorithm balances supply security with cost efficiency. These advancements in RTSM technology are helping clinical teams streamline operations, reduce investigational medicinal product scrap, and optimize outcomes across the full spectrum of trial complexity.
In this guide, you'll explore
- Oncology trials dominate global clinical research, driving demand for more sophisticated RTSM forecasting strategies.
- A Min/Max buffer algorithm is the optimal choice for small biotechs running first-in-human studies with limited RTSM experience and scarce IMP availability.
- Medium-sized pharma companies benefit from a hybrid Min/Max plus Predictable Demand Forecasting approach that balances simplicity with dynamic supply management.
- Large pharma companies running phase 3 trials should leverage fully dynamic Unpredictable and Predictable Demand Forecasting to automate buffer calculations across thousands of sites.
- Advances in RTSM technology, including enhanced self-service capabilities and versatile forecasting logic, are streamlining trial operations and optimizing clinical supply outcomes.