In a novel therapeutic area study, the client opted forACCMED's Depot One warehousing services while delegating drug supply management to a third-party vendor. This operational separation has resulted in multiple unresolved challenges, including: demand forecasting ("how much"), timeline planning ("when"), drug scale-up strategies, and dynamic supply adjustments.

As the study progressed, the drug supply management vendor's non-adaptive predictive algorithms led to excessive drug scaling, resulting in overproduction and waste, while the randomization-driven supply protocols lacked flexibility for adjustments, causing warehouse and transportation costs to significantly exceed projections. The sponsor was compelled to reallocate project budgets and revise supply plans, not only placing immense strain on research funding but also exacerbating supply chain complexities and amplifying risks of site stockouts due to drug expiration.

1. The Challenge

Predictive Algorithm Challenges: Drug production and procurement forecasting have long been critical challenges in clinical research, requiring consideration of factors spanning production cycles, manufacturing processes, raw material supply chains, and integrated operations covering procurement, production, warehousing, and distribution. Addressing questions like "how to avoid unnecessary waste," "why adopt a 20% production scale-up," and "the discrepancy between blinded vs. unblinded scaling volumes" demands a combination of service providers' domain expertise, technical know-how, comprehensive service coverage, and specialized professional capabilities.

Supply Strategy Adjustments: Inadequate randomization system functionalities hinder flexible adaptation of drug supply strategies, leading to supply chain inefficiencies and operational breakdown. These deficiencies manifest as medication supply-demand imbalances, inventory management complications, escalating costs, and compromised research budget liquidity. Meeting demands for "multi-option procurement plans," "drug contingency response protocols," and "batch-specific consignment expiration management" necessitates increasingly granular service capabilities.

2. The Solution

ACCMED's drug forecasting model—Supply AI,enables precise demand prediction, supporting monthly/per-drug monitoring and intelligent cost balancing between medication expenses and logistics/transportation outlays.Therefore, it can ensure rational drug allocation, minimizes waste, and reduces per-study drug costs and logistics expenses by millions to tens of millions RMB.

ACCMED'srandomization and drug management system —ACCMED-IRT— facilitates customizable workflows for clinical trial design, randomization algorithms, and subject management, adapting to diverse complex study scenarios from implementation to execution. Its flexible drug supply strategies streamline processes from design through execution, fully supporting increasingly intricate trial protocols.

ACCMED's integrated solution directly addresses root causes of hidden inefficiencies in clinical research, delivering cost optimization through end-to-end resource utilization. By maximizing drug utilization rates up to 90%, this approach creates tangible, impactful value across the entire pharmaceutical R&D lifecycle.